A Minimum Representation of Potential Drug-Drug Interaction Knowledge and Evidence - Technical and User-centered Foundation

Final Community Group Report

Latest editor's draft:
https://w3id.org/hclscg/pddi
Editors:
Richard D. Boyce (Department of Biomedical Informatics, University of Pittsburgh, USA)
Elizabeth A. Garcia (Pharmacy Consulting International (PCI), USA)
Harry Hochheiser (Department of Biomedical Informatics, University of Pittsburgh, USA)
Serkan Ayvaz (Department of Software Engineering, Bahcesehir University, Istanbul, Turkey)
Ratnesh Sahay (Insight Center for Data Analytics, NUI Galway, Ireland)
Michel Dumontier (Institute of Data Science, Maastricht University, Netherlands)
Authors:
Juan M. Banda (Stanford University, USA)
Oya Beyan (Fraunhofer FIT, Germany)
Mathias Brochhausen (University of Arkansas for Medical Sciences, USA)
Evan Draper (Pharmacy Services, Mayo Clinic, USA)
Sam Habiel (Open Source Electronic Health Records Alliance (OSEHRA), USA)
Oktie Hassanzadeh (IBM Research, USA)
Maria Herrero-Zazo (King’s College London, United Kingdom )
Brian Hocum (Genelex, Seattle, USA)
John Horn (School of Pharmacy, University of Washington, USA)
Xia Jing (Ohio University, USA)
Brian LeBaron (Southeast Louisiana Veterans Health Care System, USA)
Daniel C. Malone (College of Pharmacy, University of Arizona, USA)
Gerald McEvoy (USA)
Øystein Nytrø (Norwegian University of Science and Technology, Norway)
Thomas Reese (University of Utah, USA)
Katrina Romagnoli (University of Pittsburgh Medical Center, USA)
Jodi Schneider (University of Illinois at Urbana-Champaign, USA)
Lorne Walker (University of Pittsburgh Medical Center, USA)
Louisa (Yu) Zhang (University of Pittsburgh, USA)
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Abstract

The purpose of this Community Group Report is to provide a technical and user-centered foundation for a minimum information model for information about potential drug-drug interactions. The Report provides non-ambiguous definitions for 10 core information items. It also provides 8 detailed best practice recommendations related to the 10 core information items. The definitions and recommendations are shown as both narrative and prototype JSON artifacts using 2 exemplar potential drug-drug interactions. Twelve user stories with related goals illustrate use cases for the information model. Adoption of the recommendations by developers of PDDI knowledge artifacts will improve the usefulness of the artifacts within clinical workflows. Intended downstream applications include clinical decision support, drug product label enhancement, cohort identification, and other activities relevant to protecting patients from harmful drug interactions.

Status of This Document

This specification was published by the Semantic Web in Health Care and Life Sciences Community Group. It is not a W3C Standard nor is it on the W3C Standards Track. Please note that under the W3C Community Final Specification Agreement (FSA) other conditions apply. Learn more about W3C Community and Business Groups.

This document is governed by the W3C requirements of the Community and Business Group Process related to deliverables.

If you wish to make comments regarding this document, please send them to public-hclscg@w3.org (subscribe, archives).

1. Introduction

This section is non-normative.

Ensuring medication therapy occurs safely and to the maximum benefit for any given patient is of great interest to clinicians [institute-medicine-2006]. One possible threat to patient safety comes from exposure to two or more drugs that may interact (i.e., potential drug-drug interactions or PDDIs), and could therefore lead to a clinically observable effect on the patient (i.e., an actual drug-drug interaction). While the effects that may occur due to exposure to some PDDIs can benefit patients (e.g., some HIV therapies use a low-dose of ritonavir to increase plasma concentrations of co-administered protease inhibitors by inhibiting their metabolism), PDDIs are more often a patient safety concern. Clinically important events attributable to PDDI exposure have been identified in multiple studies, with rates ranging from 5.3% - 14.3% of inpatients, and up to 231,000 emergency department visits each year in the United States alone [magro-2012][cdc-faststats]. A recent systematic review and meta-analysis of 13 studies conducted on 3 continents found the median rate of PDDI associated hospital admissions to be 22.2% (interquartile range 16.6 - 36.0%) [dechanont-2014]. The potential for harm from PDDIs is an international concern reflected in guidance documents of regulatory agencies around the world [rekic-2017][european-medicines-2012][usdhhs-2017]. Moreover, in the United States, PDDI alerting is a criteria included in the so-called Meaningful Use criteria for Electronic Health Records [cms-2013][ridgely-2012]), and population-based strategies for tracking exposure are promoted by organizations such as the Pharmacy Quality Alliance [ahrq-drug-drug].

Clinicians often face barriers to the effective and appropriate management of PDDI exposures [nabovati-2017]. Barriers include PDDI alerts with poor specificity and incomplete clinician knowledge about PDDIs [abarca-2004][van-der-sijs-2006]. An awareness of the need for PDDI decision support prompts clinicians to use various drug knowledge resources including print or online drug information references, drug interaction checking tools, and alerting systems. Unfortunately, poor specificity leads clinicians to be overwhelmed by PDDI information that is “difficult to retrieve, sort and digest into clinical decision making” [bottiger-2009]. PDDI alerts are often criticized for “over-alerting” that obfuscates the most important information, hinders the usability of the decision support system, and leads to alert fatigue and clinician dissatisfaction [bottiger-2009][payne-2015]. Moreover, while many compilations of PDDI evidence exist to help improve prescriber and pharmacist knowledge, they are not concordant in their coverage, accuracy, and agreement [wang-2010][saverno-2011][ayvaz-2015][fung-2017]. Together, these shortcomings suggest the need for harmonized approaches for documenting and sharing PDDI information. The purpose of this Community Group Report is to provide a technical and user-centered foundation for a minimum information model for information about potential drug-drug interactions.

1.1 Need, envisioned workflow, and high-level requirements

New information regarding PDDIs is published every day in primary sources such as drug product labeling and the scientific literature. A PubMed search for publications indexed with the Medical Subject Headings keyword “Drug interactions” shows growth from a little more than 6,000 publications in the year 2000 to more than 9,000 publications in 2018. This suggests that the body of evidence about PDDIs is overwhelming and dynamic. As it is impossible for clinicians to keep up with the PDDI evidence base, drug experts generate summaries of PDDI evidence from primary sources. These summaries bring PDDI knowledge to clinicians in the form of published drug information compendium, clinical decision support rules, and interaction checking applications. However, there are currently no broadly accepted standards to guide these experts in the organization, content, and presentation of PDDI information.

Figure 1 provides an overview of the roles envisioned for a PDDI minimal information model. Drug experts would generate summaries of PDDI evidence from primary sources using the information elements from the PDDI minimal information model. The information elements would cover the minimum set of information required for the effective clinical management of PDDI exposure. The resulting common representation of PDDI summaries would facilitate curation and information exchange. The annotated knowledge artifacts could be shared in a knowledgebase and applied to a variety of use cases.

Downstream applications would process these representations into forms amenable for clinical decision support (see Section § 7.1 Shareable PDDI Clinical Decision Support Using HL7 Standards ), drug product label enhancement (see Section § 7.2 Enhancing the Drug-drug Interaction Section of Structured Product Labeling), to inform cohort descriptions useful for generating evidence from retrospective health records (see Section § 7.3 Highlighting and Filling Gaps in Evidence Needed to Develop PDDI Decision Support), and other medication safety activities.

Figure 1 An overview of the role envisioned for a PDDI minimal information model. Starting from the top of the figure, drug experts synthesize drug-drug interaction evidence from pre-clinical studies, clinical studies, and clinical observation into knowledge artifacts annotated using information categories in the PDDI minimal information model. The annotated knowledge artifacts can be shared in a knowledgebase, and applied to a variety of use cases including clinical decision support, structuring PDDI information in drug product labeling, and to inform cohort descriptions useful for generating evidence from retrospective health records.

To achieve the envisioned roles, the minimal information model must be grounded in non-trivial real world use cases. Where possible, model elements should draw upon accepted biomedical taxonomies and ontologies to represent medications, diagnoses, and descriptions of potential adverse reactions. Being a minimum information model, the goal is not to represent every aspect of a PDDI using formal logic and ontologies. Rather, the model should 1) specify the key information that is needed for effective clinical decision making, and 2) provide recommendations on how to minimize ambiguity common to free-text descriptions about PDDIs

The purpose of this Community Group Report is to provide a technical and user-centered foundation for a minimum information model for PDDI information. The principal contributions include:

  1. Definitions for the model's 10 core information items, examples of using these definitions to represent two PDDIs, and a set of additional PDDIs selected as case studies for future work on the information model.
  2. Eight detailed best practice recommendations for developers of PDDI knowledge artifacts related to the 10 core information items.
  3. A statement on the appropriate scope of knowledge representation for the information model.
  4. Clarification on the intended users of the information model in the form of 12 use stories with specific information needs.
  5. Prototype representations of 2 exemplar PDDIs as narrative and JSON.

2. The Minimum Information Model

2.1 Definitions of Core Information Items

The minimum information model contains a total of 10 core information items. Definitions are given below; descriptions of considerations informing the definitions of these term can be found in the user-centered definition working documents on github.

This section summarizes the Community Group's recommendations related to the minimum information model. Figure 2 shows an overview of the recommendations and how they related to one another. A brief summary of each recommendation is provided below the figure. Each recommendation summary uses capitalized keywords (MUST, MUST NOT, REQUIRED, SHALL, SHALL NOT, SHOULD, SHOULD NOT, RECOMMENDED, MAY, AND OPTIONAL) defined in RFC 2119 [RFC2119]. Two examples of applying the recommendations to PDDIs are provided in § 5. Examples of the use of the minimal information model.

R1 - Explicitly state the drugs involved: The drugs involved in a PDDI MUST be explicitly stated. To reduce ambiguity and support a computable representation of the PDDI, the drugs involved SHOULD be listed as sets of terms from a terminology such as RxNorm or the Anatomical Therapeutic Chemical Classification System (ATC). Such so called value sets MAY be referenced by a drug concept set identifier (http://purl.obolibrary.org/obo/DIDEO_00000128) such as a URI to a public repository such as the Value Set Authority Center that is maintained by the United States National Library of Medicine. The implementer MAY choose any terminology they think appropriate. To promote broader accessibility to PDDI knowledge, the implementer SHOULD use a terminology that is actively maintained and freely accessible to the public.
R2 - Report a mechanism if known: The mechanism of a PDDI MUST be reported if known. If the mechanism is not known, that MUST be explicitly stated. The description SHOULD be written for a clinician audience and include details that help the clinician decide what course of management action to take. To reduce ambiguity, the description MAY refer to specific drugs or health conditions using codes from terminologies.
R3 - State the frequency of harm relative to frequency of exposure if known: Information about the frequency of harm of exposure to a PDDI relative to frequency of exposure and is rarely available. If such information is available, it can help a clinician assess the risk/benefit trade-off of exposure to PDDI. Therefore, if such information is not available, an explicit statement declaring this fact MUST be provided. However, if such information is available and considered reliable, it MUST be provided. Wherever possible, each frequency of harm and frequency of exposure datum SHOULD refer to a specific source in the form of a citation.
R4 - Explicitly state clinical consequences: The clinical consequences associated with a PDDI MUST be reported if known. Clinical consequences MUST refer health outcomes as specifically as possible. To reduce ambiguity and support a computable representation of the PDDI, clinical consequences SHOULD be represented as one or more sets of terms from a terminology such as ICD-10 or SNOMED-CT. Such so called value sets MAY be referenced by a URI to a public repository such as the Value Set Authority Center that is maintained by the United States National Library of Medicine. The implementer MAY choose any terminology they think appropriate. To promote broader accessibility to PDDI knowledge, the implementer SHOULD use a terminology that is actively maintained and freely accessible to the public.
R5 - Note if a clinical consequence is serious: A clinical consequence associated with a PDDI MUST be noted as serious if it can result in death, life-threatening hospitalization, congenital anomaly, disability, or if it requires intervention to prevent permanent impairment or damage.
R6 - Include an operational classification statement: A PDDI MUST report at least one operational classification statement. The description MAY include more than one operational classification statements as long as each statement depends on a single set of contextual information/modifying factors. If there is more than one operational classification statement, each operational classification statement MUST be mentioned along with the specific contextual information/modifying factor that applies to the situation. While implementers MAY use any system that provides clear operational classification statements, the use of the OpeRational ClassificAtion of Drug Interactions [hansten-2001] is RECOMMENDED.
R7 - State each known risk modifying factor or patient context: Contextual information/modifying factors are necessary for alerts that are both sensitive and specific. Like clinical consequences, each known factor MUST be stated as specifically as possible. The factors SHOULD be amenable to implementation as executable logic using value sets from clinical terminologies such as RxNorm, the Anatomical Therapeutic Chemical Classification System (ATC), ICD-10, and SNOMED-CT. Each factor SHOULD be related to a specific operational classification statement that is supported by the evidence about a suspected drug-drug interaction. Wherever possible, each evidence statement SHOULD refer to a specific source in the form of a citation.

4. Knowledge representation for the minimal information model

The intended audience for this section includes persons interested in recommendations related to information model knowledge representation. Persons interested in the clinically-oriented application of the information model can skip this section.

The wide range of potential use cases for the information model requires flexibility in the knowledge representation. Moreover, the number of pre-existing ontologies relevant to this domain clearly demonstrates the richness of the domain [herrero-zazo-2015]. To keep the minimum information model lean and ensure its maintainability and usability, it was necessary to develop a clear scope for the knowledge representation including issues such as:

The following strategies are suggested for the representation of PDDI minimal information (see § A.3 Definition of guidelines for knowledge representation for a description of the process used to develop these recommendations):
  1. The Minimum PDDI Information Ontology (MPIO) should be used for the core information items relevant to the PDDI domain (see Section § 2.1 Definitions of Core Information Items). The ontology is limited in its semantic richness by design, but remains compatible with semantically rich models such as the Drug-drug Interaction and Drug-drug Interaction Evidence Ontology (DIDEO) and the Drug-Drugs Interaction Ontology (DINTO).
  2. The MPIO is written in the Web Ontology Language (OWL 2) [owl2-overview]. The individual classes in the MPIO are sub-classes of classes provided by the Basic Formal Ontology (BFO) upper ontology. This enables integration with numerous other ontologies written in OWL 2 that use the same upper ontology. This approach fosters integration with a number of existing formal ontology efforts including DIDEO, DINTO, Ontology for Biomedical Investigations, and the Ontology of Adverse Events.
  3. The core PDDI information items in the MPIO are all information content entities. This approach is chosen to reflect the hypothetical nature of the information represented. As abstract descriptions of events and relationships that might occur, instances of the PDDI model do not make any claims that these interactions must actually exist. However, as OWL models are generally interpreted to discuss objects that exist in the world, knowledge representations must take care to avoid making these claims of any of the PDDI information. To address this problem, MPIO classifies items in the minimal information model as information content entities, essentially saying that they are "about something" Thus, each element of the model essentially exists as a statement about the PDDI. As a result, the OWL 2 entities do not refer to the actual material entities or processes. Rather, all properties of the core information items are terminological in nature and refer to relations between the term and other terms.
  4. An important requirement of the PDDI minimum information model is that it allow for integration of terms from existing biomedical terminologies such as ICD-10, SNOMED-CT, RxNorm, and LOINC. This presents a challenge since these terminologies represent similar items at different levels of semantic richness, methodological rigor, and semantic commitments. While it is highly unlikely that it would be possible to unify all external terms semantically at an upper level with the core PDDI information items (see Section § 2.1 Definitions of Core Information Items), terms for which a human understandable, non-circular, definition exist, MAY be re-used in the MPIO. Re-use SHALL be accomplished by representing an information content entity about the object referenced by the term. For example, the diagnosis referred to by a diagnostic code from ICD 10 would be reused in the MPIO by representing an information content entity corresponding to the ICD 10 code. Practically speaking, concepts from any of the terminologies might be used, but they should be represented whenever possible by unambiguous identifiers for both the source terminology and the specific concept.

5. Examples of the use of the minimal information model

This section is non-normative.

In this section, we provide limited discussion of the information model's definitions and recommendations in the form of two example PDDIs. These examples are in narrative form and were chosen from a larger set exemplar PDDIs examined during the development of the minimal information model (see Appendix § A.1.1.2 Decision Trees Created for the Minimum Information Model Domain Analysis). Note that while the examples shown here are realistic, they are not intended for direct integration into a clinical applications. The same 2 PDDIs examples shown below are also represented as prototype JSON documents in Appendix § A.5 JSON Examples

5.1 Applying the Minimal Information Model definitions to the PDDI between warfarin and non-steroidal anti-inflammatory drugs (NSAIDs)

We illustrate here the application of the Minimum Information Model definitions to representing the PDDI involving warfarin and systemic non-steroidal anti-inflammatory drugs (NSAIDs). Details of the development process can be found in Appendix § A.1.1.2.1 Example PDDI:Warfarin + NSAIDs.

Drugs involved: Warfarin and systemic non-steroidal anti-inflammatory drugs (NSAIDs)
Comment: Per the recommendation R1 - Explicitly state the drugs involved, value sets for warfarin and NSAIDs are explicitly enumerated. This example states systemic NSAIDs generally and then refers to more specific NSAID formulations in Contextual information/modifying factors (since the interaction is only relevant for bioavailable forms). We note that the drugs involved can be described at various levels of abstraction, from drug classes at one level to more closely defined medicinal product concepts at the other. The information carried by the other attributes of the minimum information model MUST remain clinically accurate for the particular level of abstraction chosen for the interactants. Also, we note that this example explicitly lists the drugs involved. As Appendix § A.4.1 Some Background on Concept Identification and 'Value Sets' mentions, this extensional definition is one approach to declaring value sets. It is also possible to define an algorithm that, when executed by a machine (or interpreted by a human), yields a set of such elements. Such a intensional definition is also an acceptable approach to declaring the drugs involved.
Mechanism of Interaction: Systemic non-steroidal anti-inflammatory drugs (NSAIDs) have antiplatelet effects which increase the bleeding risk when combined with oral anticoagulants such as warfarin. The antiplatelet effect of systemic NSAIDs lasts only as long as the NSAID is present in the circulation, unlike aspirin’s antiplatelet effect, which lasts for up to 2 weeks after aspirin is discontinued. NSAIDs also can cause peptic ulcers and most of the evidence about increased bleeding risk with NSAIDs plus warfarin is due to upper gastrointestinal bleeding (UGIB).
Comment: Per recommendation R2 - Report a mechanism if known, there is a clear statement of the mechanism of interaction written for a clinician audience.
Frequency of Harm Relative to Frequency of Exposure to the PDDI: Unknown
Comment: Per recommendation R3 - State the frequency of harm relative to frequency of exposure if known, an explicit statement is made declaring that information on frequency of harm relative to frequency of exposure to the PDDI is not available.
Clinical Consequences: Increased risk of bleeding including gastrointestinal bleeding, intracranial hemorrhage, and cerebral hemorrhage
Comment: Per the recommendation R4 - Explicitly state clinical consequences, value sets for the clinical consequences are explicitly enumerated. As Appendix § A.4.1 Some Background on Concept Identification and 'Value Sets' mentions, this extensional definition is one approach to declaring value sets. It is also possible to define an algorithm that, when executed by a machine (or interpreted by a human), yields a set of such elements. Such a intensional definition is also an acceptable approach to declaring clinical conseqences.
Seriousness: Bleeding is a serious potential clinical consequence because it can result in death, life-threatening hospitalization, and disability.
Comment: Per the recommendation R5 - Note if a clinical consequence is serious, there is a clear statement that the potential clinical consequence of bleeding serious.
Operational Classification Statement: Depending on the patient context, there are different operational classification statements for the warfarin-NSAIDs PDDI. See the contextual information/modifying factors for this interaction.
Comment: Per recommendation R6 - Include an operational classification statement, the PDDI description includes more than one operational classification statements. Because there is multiple contextual information/modifying factors, no single general operational classification statement can be mentioned. Rather, an operational classification statement is provided for each factor.
Contextual information/modifying factors:
  1. The NSAID is topical or ophthalmic diclofenac
    • Operational Classification Statement: No special precautions
    • Evidence About a Suspected Drug-Drug Interaction: Both topical and ophthalmic formulations of diclofenac have relatively low systemic absorption; in one study a topical gel (16 g/day) produced about 6% of the absorption seen with systemic administration of 150 mg/day. A higher than recommended dose of topical gel (48 g/day) produced 20% of a systemic dose of diclofenac. The UK Summary of Product Characteristics for Voltarol Ophtha Multidose Eye Drops states, "No measurable levels of diclofenac could be found in humans after ocular application of diclofenac sodium eye drops". The FDA-approved SPL for Diclofenac Sodium Ophthalmic Solution 0.1% states, "Results from a bioavailability study established that plasma levels of diclofenac following ocular instillation of two drops of Diclofenac sodium ophthalmic solution, 0.1% to each eye were below the limit of quantification (10 ng/mL) over a 4-hour period. This study suggests that limited, if any, systemic absorption occurs with Diclofenac sodium ophthalmic solution".
  2. The NSAID is NEITHER topical nor ophthalmic diclofenac but the patient is concomitantly taking a proton pump inhibitor or misoprostol
    • Operational Classification Statement: Assess risk and take action if necessary
    • Evidence About a Suspected Drug-Drug Interaction: Proton pump inhibitors and misoprostol may reduce the risk of UGIB in patients receiving NSAIDs and warfarin.
  3. The NSAID is NEITHER topical nor ophthalmic diclofenac, the patient is NOT concomitantly taking a proton pump inhibitor or misoprostol, and the patient has one or more of the following risk factors:
    • History of upper gastrointestinal bleeding (UGIB) or peptic ulcer or age > 65 years old
      • Operational Classification Statement: Use only if benefit outweighs risk
      • Evidence About a Suspected Drug-Drug Interaction: Patients with a history of UGIB or peptic ulcer may have an increased risk of UGIB from this interaction. The extent to which older age is an independent risk factor for UGIB due to these interactions is not firmly established, but UGIB in general is known to increase with age.
    • Concomitantly taking systemic corticosteroids, aldosterone antagonists, or high dose or multiple systemic NSAIDs
      • Operational Classification Statement: Use only if benefit outweighs risk
      • Evidence About a Suspected Drug-Drug Interaction: Both corticosteroids and aldosterone antagonists have been shown to substantially increase the risk of UGIB in patients on systemic NSAIDs, with relative risks of 12.8 and 11 respectively compared to a risk of 4.3 with NSAIDs alone [masclee-2014]
Comment: Per recommendation R6 - Include an operational classification statement, the PDDI description includes one or more operational classification statements. Because there is multiple contextual information/modifying factors, no single general operational classification statement can be mentioned. Rather, an operational classification statement is provided for each factor. Per recommendation R7 - each known risk modifying factor or patient context, each known contextual information/modifying factor is stated specifically. Value sets for the clinical consequences are explicitly enumerated. As Appendix § A.4.1 Some Background on Concept Identification and 'Value Sets' mentions, this extensional definition is one approach to declaring value sets. It is also possible to define an algorithm that, when executed by a machine (or interpreted by a human), yields a set of such elements. Such a intensional definition is also an acceptable approach to declaring clinical conseqences. An operational classification statement is provided for each contextual information/modifying factor and each evidence statement refers to a specific source in the form of a citation.
Recommended Action: If the systemic NSAID is being used as an analgesic or antipyretic, it would be prudent to use an alternative such as acetaminophen. In some people, acetaminophen can increase the anticoagulant effect of warfarin, so monitor the INR if acetaminophen is used in doses over 2 g/day for a few days. For more severe pain consider short-term opioids in place of the NSAID.
Comment: Per recommendation R8 - State a recommended action if one is known, a recommended action is stated using clear and concise language. Unfortunately, the statement provides no specific citation to evidence in the form of a citation.

5.2 Applying the Minimal Information Model definitions to the PDDI between BCR-ABL Tyrosine Kinase Inhibitors (TKI) + Proton Pump Inhibitors (PPI)

Minimal information Model definitions and discussion comments for the PDDI involving BCR-ABL Tyrosine Kinase Inhibitors (TKIs) + Proton Pump Inhibitors (PPIs).

Drugs involved: Tyrosine Kinase Inhibitors (TKIs) and Proton Pump Inhibitors (PPIs)
Comment: Per the recommendation R1 - Explicitly state the drugs involved, value sets for TKIs and PPIs are provided. Note that in the warfarin-NSAIDs example the drugs involved are specified using RxNorm while this example uses ATC. This is to emphasize that, while the drugs involved SHOULD be listed as sets of terms from a terminology such as RxNorm or the Anatomical Therapeutic Chemical Classification System (ATC), the implementer MAY choose any terminology they think appropriate. To promote broader accessibility to PDDI knowledge, implementer SHOULD use a terminology that is actively maintained and freely accessible to the public.
Mechanism of Interaction: BCR-ABL Tyrosine Kinase inhibitors demonstrate pH dependent absorption for oral administration which may result in decreased efficacy when given concomitantly with medications that increase gastric pH.
Comment: Per recommendation R2 - Report a mechanism if known, there is a clear statement of the mechanism of interaction written for a clinician audience. A limitation of this example is that it refers to 'medications that increase gastric pH' without reference any specific drugs or codes from a drug terminology.
Frequency of Harm Relative to Frequency of Exposure to the PDDI: Unknown
Comment: Per recommendation R3 - State the frequency of harm relative to frequency of exposure if known, an explicit statement is made declaring that information on frequency of harm relative to frequency of exposure to the PDDI is not available.
Clinical Consequences: Decreased efficacy relative to treatment for chronic myeloid leukemia
Comment: Per the recommendation R4 - Explicitly state clinical consequences, a value set for the clinical consequence is explicitly enumerated. Note that in the warfarin-NSAIDs example, the clinical consequences involved are specified using ICD-10 while this example uses SNOMED-CT. This is to emphasize that, while the clinical consequences SHOULD be listed as sets of terms from a terminology such as ICD-10 or SNOMED-CT, the implementer MAY choose any terminology they think appropriate. To promote broader accessibility to PDDI knowledge, implementers SHOULD use a terminology that is actively maintained and freely accessible to the public.
Seriousness: A decrease in chronic myeloid leukemia treatment efficacy is a serious potential clinical consequence because it can result in death, life-threatening hospitalization, and disability.
Comment: Per the recommendation R5 - Note if a clinical consequence is serious, there is a clear statement that the potential clinical consequence of a decrease in chronic myeloid leukemia treatment efficacy is serious.
Contextual information/modifying factors:
  • The TKI is imatinib or ponatinib
    • Operational Classification Statement: No special precautions
    • Evidence About a Suspected Drug-Drug Interaction: Imatinib and ponatinib AUCs are not appreciably decreased by PPI co-administration
      • Iclusig [package insert]. Cambridge, MA: ARIAD Pharmaceuticals, Inc. 2016., and
      • Egorin MJ, Shah DD, Christner SM, et al. Effect of a proton pump inhibitor on the pharmacokinetics of imatinib. Br J Clin Pharmacol. 2009;68(3):370-374.)
  • The TKI is nilotinib
    • Operational Classification Statement: Assess risk and take action if necessary
    • Evidence About a Suspected Drug-Drug Interaction:Bosutinib and nilotinib AUCs are decreased with concomitant PPIs but antacids and H2 antagonists may be considered if TKI is given 2 hours before the antacid/H2 antagonist. However, for nilotinib a retrospective study has shown no difference in cytogenetic response rates for patients taking PPIs.
      • Yin OQ, Giles FJ, Baccarani M, et al. Concurrent use of proton pump inhibitors or H2 blockers did not adversely affect nilotinib efficacy in patients with chronic myeloid leukemia. Cancer Chemother Pharmacol. 2012;70(2):345-350.
  • The TKI is bosutinib or dasatinib
    • Operational Classification Statement: Use only if benefit outweighs risk
    • Evidence About a Suspected Drug-Drug Interaction:Bosutinib and nilotinib AUCs are decreased with concomitant PPIs but antacids and H2 antagonists may be considered if TKI is given 2 hours before the antacid/H2 antagonist.
      • Sprycel [package insert]. Princeton, NJ: Bristol-Myers Squibb Company; 2015
      • Bosulif [package insert]. New York, NY: Pfizer Labs; 2015.
      • Tasigna [package insert]. East Hanover, NJ: Novartis; 2015.
Comment: Per recommendation R7 - each known risk modifying factor or patient context, each known contextual information/modifying factor is stated specifically. Value sets for the clinical consequences are explicitly enumerated. Notice that there is no recommended action statement to complement the operational classification statements. Such a statement would be most useful for the recommendation to "assess risk and take action if necessary." In this case, there is not sufficient evidence for a recommended action statement to provide more information than the operational classification statement already provides. Notice that this example could be improved because it references coded terms for TKIs but not for antacids and H2 antagonists.

6. Background and use cases

This section is non-normative.

6.1 The overarching use case for the minimum information model

The overarching use case for a minimum information model for representing PDDI information in a clinical context is to provide a technical foundation for effective PDDI clinical decision support. Unfortunately, existing drug information sources generally organize interaction information into a more or less narrative format and include only some of the core PDDI information elements. To illustrate, consider the PDDI between oral anticoagulants and non-steroidal anti-inflammatory drugs (NSAIDs) reported in the well-curated French Thesaurus des interactions médicamenteuses PDDI dataset [ansm-2016] shown in Figure 3.

Figure 3 PDDI example from a French compendium

The PDDI narrative shown is structured into short and easy-to-read description and management sections. However, the narrative does not satisfy the proposed minimum information model:

As is evident from the listing above, there are four minimum information items that are not provided in the narrative (contextual information/modifying factors, frequency of exposure to the PDDI, frequency of harm for persons who have been exposed to the PDDI, and Evidence About a Suspected Drug-Drug Interaction). Contextual information would include drug and patient characteristics factors that might increase or mitigate the risk of harm from exposure to the interaction drug pair. Such information often complements, and sometimes is based on, information on the frequency information items (frequency of exposure to the PDDI and frequency of harm for exposed persons who have been exposed). Together, these information items help to inform the clinician about the risk-benefit trade-off of PDDI exposure. In fact, it has been shown that effective clinical decision support that improves patient outcomes can be built using such information. For example, Tamblyn et al. found that a novel medication clinical decision support system that provided patient-specific risk estimates of injury due to falls reduced fall-related injury by 1.7 injuries per 1000 patients (95% CI 0.2/1000 to 3.2/1000 p=0.02) [tamblyn-2012]. Conversely, when a PDDI summary provides no context about risk and no frequency information, only clinical decision support alerts based on simple exposure to the drug combination can be built. This leads to highly sensitive but unspecific alerts and is a primary cause of alert fatigue and clinician dissatisfaction [van-der-sijs-2006].

Further, the PDDI narrative in Figure 3 provides information without citing supporting evidence. Previous community efforts concluded that “providing access to the evidence is a critical component of weighing the risks and benefits of co-prescribing drugs that have the potential to result in a drug-drug interaction” [tilson-2016]. Evidence in supporting PDDIs includes physiological and pharmacological observations from clinical studies; mechanistic knowledge derived from pre-clinical and clinical studies; and observational data including case reports and various non-randomized studies [utecht-2017][brochhausen-2014]. Evidence may be useful for establishing the existence of an interaction without providing information about the potential clinical effect. Other evidence can help to answer questions about the associated clinical effects and their magnitude, variability, and estimated frequency [scheife-2015]. A PDDI representation should cite specific supporting evidence items and provide some acceptable appraisal of the total body of evidence [tilson-2016].

The narrative in Figure 3 also fails to provide critical information in computable form suitable for creation of personalized decision support presentation. The interaction described above notes that the mechanism of the interaction involves gastroduodenal irritation by the NSAID, suggesting gastrointestinal hemorrhage as a possible consequence. Such an occurrence would seem unlikely to occur for NSAIDs administered topically rather than orally. However, this constraint on the applicability of this PDDI is not stated: the formulation of the NSAID being described is ambiguous, and the importance of the means of administration is implied, but not stated directly. Describing PDDI evidence in terms of drugs in established drug terminologies, such as RxNorm (https://www.nlm.nih.gov/research/umls/rxnorm/), will reduce ambiguity and enable computation through rules and inference used to turn the PDDI descriptions into actionable content for clinical decision support.

By comparing the narrative in Figure 3 with the core elements of the minimum information model, we see that, despite being readable, the information provided lacks both the structure and semantics necessary for effective decision support. Problems like these are not unique to the French Thesaurus des interactions médicamenteuses. For example, a search for the same Oral Anticoagulant / NSAID interaction executed at the drug ingredient level in the freely accessible database DrugBank returns a single statement that vaguely describes the clinical effect but with no other information from the core items mentioned above:

“Ibuprofen may increase the anticoagulant activities of Warfarin.”

As Figure 4 shows, slightly more information is provided in United States drug product labeling than in DrugBank but there are still many information gaps relative to the core PDDI information items suggested by conference series attendees.[bristol-myers-warfarin])

Figure 4 An oral Anticoagulant / NSAID PDDI shown at the drug ingredient level from the United States drug product label for COUMADIN- warfarin sodium tablet.[bristol-myers-warfarin]

Although missing information is the primary concern for the examples discussed, the minimum information model would also increase the utility of narratives that are abundant with information. For example, a search for oral anticoagulant / NSAID in the online interaction checking tool provided by Drugs.com returns a very detailed narrative that includes mention of clinical effect, mechanism, management options, some contextualized risk information, and specific citations of evidence.[drugs-dot-com-interaction] In this case, the minimum information model would be useful for suggesting how to provide structure and semantics to the description to best enable clinical decision support systems through the use of coded drugs names, clinical consequences, and contextual information and modifying factors. The provision of these details in a standardized, computable form will facilitate integration of PDDI information with data in a patient's electronic health record, thus enabling patient-specific alerting and decision support.

6.2 End-users of the minimum information model

Twelve user stories with related goals were developed to clarify specific use cases of the minimum information model's core information elements (see Appendix § A.2.2.2 A set of user stories focused on medication reconciliation). These stories are associated with seven types of users (physicians, pharmacists, nurses, drug compendia editors, health science librarians, systems analysts, and content specialists.

Three user stories focus on the critical process of medication reconciliation for PDDIS: See § A.2.2.2 A set of user stories focused on medication reconciliation

Three user stories focus on physicians conducting treatment planning: See § A.2.2.3 Treatment Planning, Physician

The remaining six stories cover a variety of other use cases:

Additional user types considered “out of scope” are listed in Appendix § A.2.2.10 Out of scope user stories.

Each user story was color coded for mention of core information items in the minimum information model (see § A.2.2.1 Color codes used in user stories).

7. Discussion

This section is non-normative.

This Community Group Report provides a concrete, user centered, basis for design and implementation activities using the PDDI minimum information model. The non-ambiguous definitions for core information items are consistent with recently published consensus recommendations [scheife-2015][payne-2015] and the results of a separate international Delphi study on how to improve the delivery of medication alerts within computerized physician order entry systems [riedmann-2011]. The eight minimum information model recommendations are similarly based on recently published consensus recommendations [scheife-2015][payne-2015] and an analysis of a wide variety of PDDIs and real-world use cases.

Detailed descriptions of the PDDIs that informed the model, including specific clinical contexts, are available in the Appendix (see § A.1.1.2 Decision Trees Created for the Minimum Information Model Domain Analysis). Two example PDDIs were annotated with the core information items along with detailed recommendations (see Sections § 5.1 Applying the Minimal Information Model definitions to the PDDI between warfarin and non-steroidal anti-inflammatory drugs (NSAIDs) and § 5.2 Applying the Minimal Information Model definitions to the PDDI between BCR-ABL Tyrosine Kinase Inhibitors (TKI) + Proton Pump Inhibitors (PPI). Twelve user stories with related goals show how the PDDI minimum information model would support various users (see § 6.2 End-users of the minimum information model). A subset of the user stories and selected PDDIs were integrated into three medication reconciliation use cases (see Section § A.2.2.2 A set of user stories focused on medication reconciliation).

The next sub-sections provide a discussion of three potential applications of the minimum information that are shown in Figure 1

7.1 Shareable PDDI Clinical Decision Support Using HL7 Standards

The creation and maintenance of PDDI clinical decision support (CDS) generally requires considerable time and energy from highly trained domain experts. An additional need is to standardize the electronic health records context that is used to trigger CDS services. This can include context parameters that currently might not available but that, if present, would be useful for increasing the specificity of the PDDI CDS alerts. Achieving this sort of standardization is important to ensure that PDDI decision support can be implemented across a variety of systems.

Fast Health Interoperability Resources (FHIR) provides a possible solution to the challenge of seamless information exchange and data interoperability of information resources in health care related environments [bender-2013][mandel-2016]. FHIR provides a collection of specifications for resources in healthcare settings for the purpose of overcoming the data exchange challenges in healthcare. FHIR defines a set of standardized data models for health care covering key areas including Clinical information, Diagnostics, and Medications. For instance, the Medication resource contains medication codes, ingredient details, packaging information and related information. FHIR resources are API-based, making it possible for third party systems to access electronic health records data for the purpose of supporting new use cases.

FHIR encodings of PDDI information could form the basis of information exchange for more standardized PDDI clinical decision support. Indeed, a new HL7 project related to PDDI decision support has been recently approved by the HL7 Clinical Decision Support and Pharmacy Workgroups. The project seeks to develop an implementation guide for PDDI CDS that will specify both a knowledge representation format for PDDI logic and CDS services for PDDI with electronic health record (EHR) systems. Specifically, the implementation guide will include specifications for:

  1. How to represent PDDI logic in the HL7 Clinical Quality Language (CQL) using the FHIR Clinical Reasoning module.
  2. How to use the emerging CDS Hooks standard as a mechanism for electronic health records to request PDDI CDS from CDS services.

As is stated in the project's scope statement, the HL7 PDDI CDS project will draw from the technical foundation, PDDI information, and user centered design artifacts reported in this Community Group Report. It is anticipated that representation of PDDI minimal information may require the creation of new FHIR resource(s) (e.g., a resource to represent drug interactions) and/or FHIR profile(s) (e,g, for PDDI context representation). At the time of this writing, participation in the PDDI CDS project is open to public participation. Person interested in participation can find more information on the HL7 PDDI CDS project wiki.

7.2 Enhancing the Drug-drug Interaction Section of Structured Product Labeling

Serious consequences for patients could result if information about known drug interactions is missing from a drug’s product labeling. To address this potential risk, the United States Food and Drug Administration (FDA) mandated in 2006 that all product labels for FDA-approved prescription drugs include clinically significant interactions (c.f., CFR 21 201.57(c)(8)), as well as the results of pharmacokinetic studies that establish the absence of effect (c.f., CFR 21 201.57(c)(13)(C)) [fda-cfr-21-4]. Similar requirements have been enacted by the European Medicine's Agency for their equivalent of product labeling called Summary of Product Characteristics.

As mentioned in the section § 6.1 The overarching use case for the minimum information model, drug product labeling can contain many gaps in information relative to the core PDDI information. Other prior work has shown that many known PDDIs are not mentioned in drug labels [boyce-2013]. This problem is not unique to United States labeling. For instance, Pfistermeister et al. reported that critical drug–drug interaction warnings are frequently missing, or are mentioned inconsistently in the United States, United Kingdom, and German labels of the involved drugs [pfistermeister-2014].

As is shown Figure 1, the PDDI minimum information model could help address this issue by making PDDI information reported in product labeling more complete and clinically useful. In the United States and other parts of the world, the content of a drug's product label is written by the company that owns the drug as part of the drug approval process. There is currently no information model to guide product label authors as they write drug interaction information. The PDDI minimum information model could provide authors with clear guidance on the core information items that they should discuss.

This approach might require an annotation tool to help authors tag the core PDDI information items correctly and consistently. Once the PDDI information is authored using the minimum information model, it could be published in a computer readable format. In the United States, this would currently be accomplished using the Structured Product Label (SPL) standard. Other standards, such as the International Organization for Standardization (ISO) for the identification of medicinal products (IDMP) serve a similar function outside of the United States. Focusing on the United States as case study, SPLs are XML documents written using the HL7 SPL specification that the FDA requires industry to use when submitting drug product label content (FDA 2005). The HL7 Biomedical Research and Regulation developed and maintains the SPL standard. Currently, the SPLs for all drug products marketed in the United States are available for download from the National Library of Medicine's DailyMed resource [dailymed-about]. At the time of this writing, DailyMed provides access to drug product labeling for more than 30,000 prescription products.

PDDI information annotated using the minimum information model could be published in a computer readable format using what is called an SPL indexing file. Indexing files provide additional useful information augmenting official FDA drug SPLs. Currently, indexing files are used to specify pharmacologic classes, billing units, warning letter alerts, and other information [dailymed-indexing]. Supplemental indexing files including the elements of the PDDI minimum information model might be stored in XML and, through the use of XSL and XSLT, be used to render the data in various formats including HTML, and PDF, and character-delimited tables.

7.3 Highlighting and Filling Gaps in Evidence Needed to Develop PDDI Decision Support

Existing PDDI knowledge is heterogeneous with respect to coverage of the core information elements. For example, frequency of exposure and frequency of harm evidence was not available for either of the two PDDI examples detailed in this Report (see Sections § 5.1 Applying the Minimal Information Model definitions to the PDDI between warfarin and non-steroidal anti-inflammatory drugs (NSAIDs) and § 5.2 Applying the Minimal Information Model definitions to the PDDI between BCR-ABL Tyrosine Kinase Inhibitors (TKI) + Proton Pump Inhibitors (PPI)). Other examples of knowledge gaps are mentioned in Appendix § A.1.1.2 Decision Trees Created for the Minimum Information Model Domain Analysis, such as the poorly understood mechanism of the PDDI between warfarin and Ifosfamide/Etoposide.

Part of the reason that there are many knowledge gaps is that many drug interactions are identified in case reports or observational studies that provide little or no indication of causal mechanisms. Other interactions, especially pharmacokinetic interactions, are established based on small clinical studies that rarely suggest a clinical consequence. Still other interactions might be inferred from the pharmacodynamic properties of two drugs, leaving unanswered questions about contextual factors that might increase or mitigate patient risks. Moreover, gaps in knowledge can exist about the risk factors or appropriate management options for a given interaction, even when solid evidence is available for its existence, the mechanism of its occurrence, and the likely clinical consequence from exposure.

The information model can also act as a template for drug experts to use while synthesizing PDDI evidence. As the experts compile evidence for each of the core information categories, critical gaps in knowledge will become apparent. These gaps might be used to develop prioritized research agendas aimed at providing evidence for clinical recommendations.

Similarly, the PDDI model might be used to address knowledge gaps through the creation of computable cohort descriptions — serialized queries that combine concept sets with logical operations to extract specific patient sub-populations from a clinical data repository. Computable cohort descriptions are a cornerstone for research on PDDIs using real world data. A recent project showed the feasibility of converting PDDI descriptions created as part of this Report to computable cohort definitions [rosko-2017]. The target for translation was the Atlas clinical research tool created by the Observational Data Health and Informatics collaborative. Atlas has a powerful interface for creating, running, and sharing cohort descriptions [ohdsi-atlas]. Once created, the cohort descriptions were executed over a clinical dataset for a simulated population stored in the OHDSI common data model. The code, concept sets, and cohort descriptions for the demonstration are stored in a GitHub repository and Docker container.

8. Conclusion

This section is non-normative.

This Community Group Report provides motivation and a detailed domain analysis for a minimum information model for PDDI information. The Report also suggests potential applications of the minimum information model that could lead to improvements in patient safety with respect to PDDIs. The overarching goal of these contributions is to provide a technical foundation for effective PDDI clinical decision support. A reference implementation of the information model is the subject of future work.

9. Acknowledgments

This section is non-normative.

Development of this Community Group Report was partially supported by a grant from the United States National Library of Medicine (R01LM011838) and Agency for Healthcare Research and Quality (R21-HS023826). The opinions expressed in this note do not necessarily reflect the opinions of the funding agencies.

A. Appendices

This section is non-normative.

A.1 Development Process

Toward the goal of developing such a model, a volunteer-based Task Force force was formed by the Health Care and Life Sciences Interest Group, an interest group that operates publicly through the World Wide Web Consortium (W3C). This Task Force has taken a user-centered design approach to designing the minimal information model through the a series of complementary activities:

  1. Development of decision trees of more than a dozen PDDIs to represent using the new information model.
  2. Creation of user stories and use cases that define the requirements for the minimum information model.
  3. Definition of guidelines for knowledge representation

A.1.1 Decision Trees

A.1.1.1 Selecting PDDIs to implement using the minimum information model

Prior work by some members of the Task Force has sought to develop evidence-based clinical algorithms that consider a patient’s electronic health record information to provide a clinician with actionable information tailored to the patient’s specific context. 1The algorithms are formulated as decision trees to provide concise information including the interaction description, the purported mechanism and possible effects, the evidence supporting the mechanism and effects along with citations listed in the footnotes. Two sample decision trees illustrating the management options can be found in the appendix (see Appendix DECISION TREES).

1The initial decision trees were developed through the “Individualized Drug Interaction Alerts” AHRQ grant by Task Force members Dan Malone and John Horn, as well as Phil Hansten (AHRQ Project: R21-HS023826-01; Title: Individualized Drug Interaction Alerts; Authors: Daniel C. Malone, University of Arizona; John Horn, Philip Hansten, University of Washington).

The Task Force built on this prior work by selecting PDDIs to demonstrate the new minimum information model and then creating decision trees for each of the PDDIs that they selected. Task Force drug experts selected the PDDIs and identified contextual information/modifying factors that would warrant any of three different recommended actions - No special precautions, Assess risk and take action if necessary, and Use only if benefit outweighs risk. Draft decision trees were presented during sub-team monthly meetings for thorough discussion. Revisions were made iteratively until the group reached consensus on the presented drafts and finalized the decision trees.

The Task Force began with a discussion of how to select the PDDIs for developing decision trees. One option was to select the most serious PDDIs. However, it was noted that the seriousness of a PDDI depends a great deal on the patient characteristics context. This meant that it would be difficult to identify PDDIs that were considered the most serious in all clinical settings and for all patients. An alternative approach was to choose PDDIs that would allow the Task Force to demonstrate how the information model should be used when facing known issues with PDDI evidence and knowledge. Toward that aim, participants were requested to provide suggestions of PDDIs meeting at least one or more of the following criteria which follow from the aforementioned information categories suggested by attendees of the multi-stakeholder conference meetings/series mentioned in Section § 6. Background and use cases:

  1. The interaction could (and should) be contextualized for specific patients or clinical circumstances.
  2. The interaction applies at the drug class level.
  3. The interaction does not apply at the drug class level.
  4. The mechanism is known and is pharmacokinetic.
  5. The mechanism is known and is pharmacodynamic.
  6. The mechanism is poorly described, not well elucidated.
  7. The evidence supporting the interaction is strong.
  8. The evidence supporting the interaction is weak.
  9. The frequency of exposure data is available.
  10. The frequency of exposure data is not available.
  11. The frequency of adverse event data is available.
  12. The frequency of adverse event data is not available.
  13. The recommended action is “monitor” or “take note”.
  14. The recommended action is “avoid”.
  15. The recommended action is “a clear alternative drug and dose”.
A.1.1.2 Decision Trees Created for the Minimum Information Model Domain Analysis

The Task Force force developed 14 PDDI decision trees detailing the steps taken in using the the minimal information model to describe PDDIS. These exemplars were chosen to represent questions identified by the Task Force force as potentially affecting the search and syntheses of PDDI information. The potential interactions and the information situations they were selected for are listed in Table 1. Value sets defining relevant drug classes and clinical consequences are given in § A.4 Value Sets to Support PDDI Examples.

Table 1

Exemplar potential drug-drug interactions Drug or Drug Class 1 Drug or Drug Class 2 Explanation/Justification
Can (and should) be contextualized for specific patients or clinical circumstances Tamoxifen Paroxetine Patients with extensive CYP2D6 status on paroxetine will derive no benefit from tamoxifen. (Status: completed, view at Zenodo).
Potassium (KCL) Potassium-sparing Diuretics Combination has known patient-specific risk factors. (Status: completed, email HCLS CG to request)
Applies at the class level Monoamine Oxidase Inhibitors (MAOIs) Indirect Sympathomimetics A class interaction involving all drugs in the class. (Status: completed, view at Zenodo).
Does not apply at the class level Tyrosine Kinase Inhibitors Proton Pump Inhibitors Not all kinase inhibitors have pH dependent absorption. Imatinib, nilotinib, dasatinib, bosutinib, and ponatinib are BCR‐ABL tyrosine kinase inhibitors. Imatinib and ponatinib do not have a significant interaction due to pH dependent absorption with proton pump inhibitors, whereas nilotinib, dasatinib, and bosutinib do (Lexi‐comp and Micromedex). (Status: completed, view at Zenodo).
The mechanism is known and is pharmacokinetic Warfarin CYP2C9 Inhibitors (ie. Bactrim) A CYP-mediated pharmacokinetic interaction. (Status: completed, view at Zenodo)
Digoxin Cyclosporin A transport protein (p-glycoprotein) mediated interaction. (Status: completed, email HCLS CG to request)
The mechanism is known and is pharmacodynamic Epinephrine Beta-Blockers The interaction is different differentiates between selective and non-selective beta blockers. The clinical outcome is a; hypertensive crisis. (Status: completed, email HCLS CG to request)
The mechanism is not well elucidated/known Warfarin Ifosfamide/Etoposide Drugs for treating cancers of the blood drugs. - Clinical effect is INR change buts no mention of what mechanism could be found. (Status: completed, view at Zenodo)
The evidence supporting the interaction is strong Epinephrine Beta-Blockers Widely known interaction with considerable available evidence. (Status: completed, email HCLS CG to request)
Simvastatin, Atorvastatin, Lovastatin Clarithromycin Widely known interaction with considerable available evidence. (Status: completed, view at Zenodo)
The evidence supporting the interaction is weak Warfarin Antibiotics for which it is uncertain if inhibition of CYP2C9 affects warfarin Hard to find evidence for the interaction. (Status: completed, view at Zenodo)
The frequency of exposure data is available Warfarin Non-steroidal anti-inflammatory drugs (NSAIDs) Paper by Malone et al. - National sample. (Status: completed, view at Zenodo).
The frequency of exposure data is not available Simvastatin Fluconazole Based on literature search. (Status: completed, view at Zenodo).
The frequency of adverse event data is available Spironolactone Potassium supplements Associated with Risk of hospitalization. (Status: completed, email HCLS CG to request)
The frequency of adverse event data is not available Simvastatin Fluconazole Literature search did not find frequency of adverse event data. (Status: completed, view at Zenodo).
The recommended action is “monitor” or “take note” Potassium (KCL) Potassium-sparing Diuretics See explanation above. (Status: completed, email HCLS CG to request)
The recommended action is “avoid” Monoamine Oxidase Inhibitors (MAOIs) Indirect Sympathomimetics See explanation above. (Status: completed, view at Zenodo).
The recommended action is a clear alternative drug and dose Simvastatin Amiodarone The target of a drug safety communication from a regulatory agency [fda-simvastatin-amiodarone]. (Status: completed, view at Zenodo).
A.1.1.2.1 Example PDDI:Warfarin + NSAIDs

Non-steroidal anti-inflammatory drugs (NSAIDs) have antiplatelet effects which increase the bleeding risk when combined with oral anticoagulants such as warfarin. The antiplatelet effect of NSAIDs lasts only as long as the NSAID is present in the circulation, unlike aspirin’s antiplatelet effect, which lasts for up to 2 weeks after aspirin is discontinued. NSAIDs also can cause peptic ulcers and most of the evidence for increased bleeding risk with NSAIDs plus warfarin is due to upper gastrointestinal bleeding (UGIB).

Figure 5 ¢ = No special precautions. n = Assess risk and take action if necessary. u = Use only if benefit outweighs risk. Originally developed through "Individualized Drug Interactions" (AHRQ Project R21-HS023826-01) by Task Force Members Daniel Malone, John Horn, and Philp Hansten.

Footnotes:

  1. Topical diclofenac has relatively low systemic absorption; in one study a topical gel (16 g/day) produced about 6% of the absorption seen with systemic administration of 150 mg/day. A higher than recommended dose of topical gel (48 g/day) produced 20% of a systemic dose of diclofenac. The UK Summary of Product Characteristics for Voltarol Ophtha Multidose Eye Drops states, "No measurable levels of diclofenac could be found in humans after ocular application of diclofenac sodium eye drops". The FDA-approved SPL for Diclofenac Sodium Ophthalmic Solution 0.1% states, "Results from a bioavailability study established that plasma levels of diclofenac following ocular instillation of two drops of Diclofenac sodium ophthalmic solution, 0.1% to each eye were below the limit of quantification (10 ng/mL) over a 4-hour period. This study suggests that limited, if any, systemic absorption occurs with Diclofenac sodium ophthalmic solution".
  2. If the NSAID is being used as an analgesic or antipyretic, it would be prudent to use an alternative such as acetaminophen. In some people, acetaminophen can increase the anticoagulant effect of warfarin, so monitor the INR if acetaminophen is used in doses over 2 g/day for a few days. For more severe pain consider short-term opioids in place of the NSAID.
  3. Proton pump inhibitors and misoprostol may reduce the risk of UGIB in patients receiving NSAIDs and warfarin.
  4. Patients with a history of UGIB or peptic ulcer may have an increased risk of UGIB from this interaction. The extent to which older age is an independent risk factor for UGIB due to these interactions is not firmly established, but UGIB in general is known to increase with age.
  5. Both corticosteroids and aldosterone antagonists have been shown to substantially increase the risk of UGIB in patients on NSAIDs, with relative risks of 12.8 and 11 respectively compared to a risk of 4.3 with NSAIDs alone [masclee-2014]
A.1.1.2.2 Example PDDI: BCR-ABL Tyrosine Kinase Inhibitors (TKI) + Proton Pump Inhibitors (PPI)

BCR-ABL Tyrosine Kinase inhibitors bosutinib, dasatinib, imatinib, nilotinib, and ponatinib are indicated for Philadelphia chromosome-positive chronic myeloid leukemia. Ponatinib is only approved in T315I-positive patients. These TKIs demonstrate pH dependent absorption for oral administration which may result in decreased efficacy when given concomitantly with medications that increase gastric pH. Dasatinib area under the curve (AUC) is decreased when co-administered with antacids, H2 antagonists, and PPIs.2 Bosutinib and nilotinib AUCs are decreased with concomitant PPIs but antacids and H2 antagonists may be considered if TKI is given 2 hours before the antacid/H2 antagonist.3,4 However, for nilotinib a retrospective study has shown no difference in cytogenetic response rates for patients taking PPIs.5 Imatinib and ponatinib AUCs are not appreciably decreased by PPI co-administration.6,7

Figure 6 ¢ = No special precautions. n = Assess risk and take action if necessary. u = Use only if benefit outweighs risk. Originally developed through "Addressing gaps in clinically useful evidence on drug-drug interactions" (R01LM011838) NIH Grant by Task Force Members Evan Draper, Daniel C. Malone, John Horn, and Philip Hansten.

Footnotes:

  1. [sprycel-2015]
  2. [bosulif-2015]
  3. [tasigna-2015]
  4. [yin-2012]
  5. [iclusig-2016]
  6. [egorin-2009]

A.2 User Stories

This section is non-normative.

A.2.1 Workflow for arriving at stories and goals

User stories and goals were developed in order to showcase how the PDDI minimum information model will support users. The Task Force began developing the Stakeholder Description document and the PDDI Minimum Information Model User Scenarios document in order to identify key users. These stakeholder descriptions and user scenarios were used as the basis for further brainstorming with the assistance of a user experience expert to develop a master list of tasks, users, information needs, information values, and barriers to drug-drug interaction based decision-making in a variety of situations. A core set of user types was selected for development of user stories based on the scope of the minimum information model.

To develop the user stories for the core user types, Task Force members created an initial information needs list and then was supplemented it with user interviews, interview transcripts collected as a part of a recently published manuscript on PDDI information needs of drug information compendium editors [romagnoli-2017], and the published literature. Where possible, user stories were based on PDDIs suggested by the Task Force’s PDDI experts. All user stories were reviewed during team meetings to solicit feedback and comments. Based on Task Force member suggestions, the user stories were edited to make them more clinically relevant, accurate and appropriate. Information model items were highlighted based on a color-coded key to indicate the minimum information model information item in question.

A.2.2 User stories for the minimum information model

The following user stories use the color-coding described in to represent core information items defined for the minimum information model. A table summarizing the information needs exposed by these use cases is also presented in Appendix § A.2.2.2 A set of user stories focused on medication reconciliation.

A.2.2.1 Color codes used in user stories

This section is non-normative.

The following table illustrates the colors used to describe user stories. Note that frequency of exposure is not addressed in any of the user stories.

Clinical Consequences
Evidence
Recommended Actions
Mechanism of Interaction
Contextual Evidence/Modifying Factors
Seriousness Rating
Frequency of Harm
Frequency of Exposure
A.2.2.2 A set of user stories focused on medication reconciliation

This section is non-normative.

A.2.2.2.1 Use Story 1: Hospital Pharmacist, Medication Reconciliation upon Admission
  • A PDDI with unknown modifying factors: Linezolid + SSRIs (sertraline)

Beth is a hospital pharmacist who is reviewing the medications in the physician admission order for Bill, an 85-year-old male dementia patient who was transferred from a skilled nursing facility to the hospital after being diagnosed with a vancomycin-resistant Enterococcus faecium (VRE) infection. At the nursing home, Bill was prescribed sertraline to treat depression. Beth receives an alert that linezolid, which is being considered to treat the VRE infection, has a potential interaction with the sertraline that Bill is currently taking. Linezolid is a weak monoamine oxidase inhibitor, and has been shown to increase the risk of serotonin syndrome when taken concurrently with an SSRI such as sertraline. Beth would like to know the risks and benefits of continuing the sertraline and adding on the linezolid, the potential seriousness of the interaction’s clinical consequence, and recommended management options, such as selecting an alternative medication or discontinuing the sertraline. She would like to see the current evidence behind the interaction, so that she can determine if Bill has an increased risk of serotonin syndrome. In order to gather this information, she reviews Bill’s history, lab results, and allergies from the health records faxed by his skilled nursing facility, as well as his medication list upon admission. She reviews Lexicomp™ and the hospital’s intranet resources for additional information, but is having trouble finding information that is relevant to Bill’s situation. She does a literature search using PubMed in order to try to locate information about the frequency of adverse events in due to this potential interaction in other patients like Bill, but she does not have access to all of the articles in the search results.

A.2.2.2.2 User Story 2: Hospital Pharmacist, Medication Reconciliation upon Discharge
  • A PDDI that can (and should) be contextualized for specific patients or clinical circumstances: KCL (potassium chloride) + K-sparing Diuretics (spironolactone)

Beth is reviewing the physician’s discharge order for Maria. Maria is a 72-year old woman who was admitted to the hospital with acute decompensated heart failure. While reviewing Maria’s medications, Beth sees that Maria is being discharged with spironolactone, a potassium-sparing diuretic that could potentially interact with the potassium chloride that Maria had been taking to treat low potassium levels. Spironolactone may increase potassium levels in Maria’s blood, leading to hyperkalemia. Beth reviews Maria’s electronic health record in order to view her lab results and her other medications. She sees that Maria is also taking the ACE inhibitor lisinopril for heart failure, and since ACE inhibitors can also increase potassium levels, Beth would like to know how much this modifying factor might increase Maria’s risk of hyperkalemia due to the interaction between potassium chloride and spironolactone. Beth would like to know how likely it is that Maria will experience hyperkalemia, how serious hyperkalemia may be, and how to manage the interaction, such as by discontinuing one of Maria’s medications. Beth reviews the hospital’s intranet, as well as Micromedex™, for recommendations. She would also like more information about the potassium chloride that Maria was taking as one of her home medications, so she will need to contact Maria’s community pharmacy in order to find out the strength of the medication and if the prescription was still current.

A.2.2.2.3 User Story 3: Consultant Pharmacist, Medication Reconciliation upon Readmission
  • A PDDI with a known, pharmacokinetic mechanism: Warfarin + 2C9 inhibitors (metronidazole)

Patrick is a nursing home consultant pharmacist who is reviewing the medications of a readmitted patient, Nancy. Nancy is a 78 year-old woman who is being transferred back to her skilled nursing facility after a hospital admission for a Clostridium difficile (C. diff) infection; prior to the hospital admission, she was prescribed warfarin at the skilled nursing facility for deep vein thrombosis (DVT) treatment. Based on the hospital discharge summary, it appears that Nancy was taken off of the warfarin at the hospital due to an increased INR, and returned to the skilled nursing facility without an order for warfarin. Patrick sees that a potential interaction may occur with the warfarin that Nancy had been prescribed prior to her hospitalization, and the metronidazole now used to treat her infection, since metronidazole is a CYP2C9 inhibitor and may increase the plasma concentration of warfarin. A clinical consequence of this interaction would be an increased INR leading to an increased risk of bleeding. Patrick would like to gather management recommendations for this interaction prior to contacting Nancy’s physician. He is interested in Nancy’s duration of therapy for both the warfarin and the metronidazole, her current risk factors for a DVT, and if she is indicated for prophylactic therapy. Patrick also wants to know if and when warfarin should be restarted, and at what dose, in order to reduce the risk of bleeding due to the interaction. He would also like to know if metronidazole is the best option to continue treating Nancy’s C. diff infection, or if there is an alternative option that may not interact with warfarin. To gather this information, he reviews Nancy’s previous INR values, medication list, and history. He also contacts the hospital to determine whether warfarin had been given at any point during Nancy’s stay, if the dosage had been adjusted, what other medications she was given, and if any of her other medications were discontinued. He reviews his company’s intranet resources for additional information about the interaction and possible evidence-based recommendations. Finally, Patrick is interested in the frequency of serious bleeding events in geriatric patients co-prescribed warfarin and metronidazole, and the literature surrounding the interaction.

A.2.2.3 Treatment Planning, Physician

This section is non-normative.

Simvastatin + Amiodarone

  • Kathleen is a physician who is treating a patient who has a ventricular arrhythmia. Kathleen would normally prescribe amiodarone for this particular patient, but he is being treated with simvastatin for dyslipidemia, and she knows that a potentially serious interaction may occur leading to rhabdomyolysis. Kathleen wants to know what the patient’s risk factors are for rhabdomyolysis, what the benefits and risks would be to switching him to an alternative statin, and if amiodarone is not the best option for this patient, what alternatives to amiodarone exist for this patient, and what the available evidence shows in terms of ventricular arrhythmia patient outcomes.

(Pediatrics) Fluoxetine + Ondansetron

  • Evelyn is a pediatric emergency medicine physician caring for an adolescent with a history of major depressive disorder treated with fluoxetine, who presents with acute onset of vomiting and diarrhea. Evelyn’s usual first-line antiemetic for acute gastroenteritis is ondansetron, but Evelyn knows that both fluoxetine and ondansetron are listed as QTc-prolonging medications. Evelyn would like to know the likelihood of clinically significant QTc prolongation due to a brief course of co-administration of fluoxetine and ondansetron, and if there is a recommendation for dose adjustment or an alternate antiemetic.

(Pediatrics) Azole antifungals + Tacrolimus

  • William is a pediatric hospitalist caring for a child with a history of liver transplant due to congenital liver disease, treated with tacrolimus to prevent organ rejection. The patient is admitted with a fever and starts broad anti-infective therapy, including vancomycin, piperacillin-tazobactam and fluconazole. William knows that azole antifungals can increase tacrolimus levels and wants to know if there is evidence to guide a decrease the patient’s tacrolimus dose to prevent tacrolimus toxicity. He additionally wants to know the mechanism of interaction to avoid further interacting medications.
A.2.2.4 Evaluation of Management Options for Drug-Drug Interactions, Physician

This section is non-normative.

Warfarin + Naproxen

  • Melissa is a family physician whose patient called because he is experiencing noticeable bruising. Melissa knows that the patient is taking warfarin, but he has not experienced bruising before. She asks if the patient has taken any new medications recently, and he mentions that he visited a pain clinic for his chronic back pain and they prescribed the NSAID naproxen. Melissa knows that NSAIDs can increase the risk of bleeding when taken with warfarin, and she wants to know the best way to manage this interaction.
A.2.2.5 Evaluation of Management Options for Drug-Drug Interactions, Pharmacist

This section is non-normative.

Atorvastatin + Clarithromycin

  • James is a community pharmacist reviewing an electronic prescription that just came in for clarithromycin; an alert in his pharmacy’s information system indicates that there is a potential interaction between the clarithromycin and the atorvastatin that the patient was prescribed a year ago by different physician. James calls the patient in order to discuss her medications; she tells him that she is taking the atorvastatin as prescribed, and cannot remember if she has ever taken clarithromycin in the past. In preparation for following up with the patient’s physician, James would like to know the likelihood of an adverse drug event such as rhabdomyolysis occurring due to a potential interaction and how serious the interaction could be. He would also like to know if monitoring would be appropriate for this patient, or if a dose adjustment or temporary discontinuation of one of the drugs would be best.
A.2.2.6 Screening for Drug-Drug Interactions, Nurse

This section is non-normative.

Glipizide + Lisinopril (Sulfonylureas + ACE Inhibitors)

  • Nancy is a licensed practice nurse who works in a skilled nursing facility. She has noticed that her patient is experiencing symptoms of hypoglycemia. She sees that the patient was recently prescribed lisinopril, and is wondering if it interacts with one of the five medications that she is taking. Nancy remembers reading about a potential interaction with the glipizide that the patient is currently taking. She would like to know if the patient’s symptoms are a possible consequence of an interaction between the glipizide and the lisinopril, or the lisinopril and one of the other medications that the patient is taking, and if so, what information she should pass along to the registered nurse in charge in order to help treat the patient.
A.2.2.7 Synthesis for Dissemination, Drug Compendium Editor

This section is non-normative.

Tyrosine Kinase Inhibitors + Proton Pump Inhibitors

  • Olivia is a drug compendium editor who is reviewing the available literature for the potential interaction between tyrosine kinase inhibitors and proton pump inhibitors. She would like to review the most recent literature available surrounding the interaction, and would like to compare it against the existing entry in her drug compendium. She would like to understand more about the mechanism of the interaction, whether it applies to all drugs within the classes, whether certain populations are at greater risk, and the types and strength of the evidence available. She would also like to learn more about recommended management options.
A.2.2.8 Synthesis for Dissemination, Librarian

This section is non-normative.

  • Michael is a librarian who works for the medication safety unit in a regulatory agency. He has graduate training in library and information science, and has a good understanding of medical reference sources. When he is asked to locate information about a potential drug-drug interaction, he wants to understand more about the terms used to describe the drugs so that he can develop search strategies to run daily, weekly, and monthly searches. He would like to find terms used to describe the specific drugs involved in the interaction, drug class concepts, clinical consequences of the interaction, and existing types of evidence of the interaction.
A.2.2.9 Synthesis for Dissemination, Clinical Decision Support Team - Systems Analyst & Content Specialist

This section is non-normative.

  • Richard is a systems analyst who is working with Joe, a content specialist, in order to design a new clinical information system which can provide personalized clinical knowledge and patient information for clinicians to improve healthcare quality. Richard is professionally trained in algorithms, databases, and programming. He also has some knowledge about electronic medical records. In order to help Richard design and implement the system, Joe would like to know about the evidence, clinical consequences, and mechanisms of interactions of potential drug-drug interactions so that he can develop rules for the most clinically relevant interactions. With that information, he can help Richard create linkages and designs algorithms based on electronic medical records. Joe can also help Richard prioritize what to display and how to display information or alerts for clinicians.
A.2.2.10 Out of scope user stories

This section is non-normative.

The following user stories were considered as outside of scope of the task of developing the PDDI minimum information model, since the goals of the user stories were to determine how healthcare providers use PDDI information in patient care, what PDDI resources healthcare providers use when making patient care decisions, and how those PDDI resources are developed.

Synthesis for Dissemination, Clinical Decision Support User Interface Designer
Synthesis for Dissemination, Data Scientist
Population Management, Pharmacoepidemiologist
Population Management, Insurance Companies
Population Management, P&T Committee (Formulary Development)
Population Management, Med Safety Department (Pharmaceutical Industry)
Passive Role, Patient

A.3 Definition of guidelines for knowledge representation

This section is non-normative.

A sub-team of the task force focused on setting the scope of knowledge representation for the minimum information model. A series of teleconferences bringing together domain experts, biomedical informatics specialists, and knowledge representation experts across the task force. Starting with a core set of questions, topics were discussed, arguments for different approaches were laid out and, agreement among the participants was sought. Besides the conversation during the teleconferences, participants had the opportunity to add comments and voice their opinion to the statements in the document. When the task force arrived at a consolidated version, the group voted by teleconference and through e-mail. The result of that vote was written into a draft document. The final version of the document was revised and written as Section § 4. Knowledge representation for the minimal information model of this document.

A.4 Value Sets to Support PDDI Examples

This section is non-normative.

A.4.1 Some Background on Concept Identification and 'Value Sets'

According to the National Library of Medicine, value sets are lists of codes and corresponding terms from standard clinical vocabularies (such as SNOMED CT®, RxNorm, LOINC® and others), that define clinical concepts to support effective and interoperable health information exchange.[NLM-vsac-2019] Value sets are also referred to as "concept sets". Figure 7 shows some of the terminology sources that provide codes useful for constructing values sets for information items in the PDDI information model. Value sets may be explicitly defined by containing a terminology code for each term in the value set concepts. They can also be intensionally defined using an algorithm that, when executed by a machine (or interpreted by a human), yields a set of such elements. An HL7 project related to PDDI decision support effort is currently underway within HL7. The work has produced a draft implementation guide that includes a specification for constructing value sets for the drugs included in a PDDI.[HL7-cds-PDDI-draft]

Figure 7 Sources of value sets for two of the core information items of PDDI. Identifiers may come from above mentioned code systems or other relevant terminologies. As is typical for value sets, version, original code system and the code system version are required. The National Library of Medicine's Value Set Authority Center is a public site commonly used to store individual value sets.

A.4.2 aldosterone antagonists and products containing an aldosterone receptor antagonist (RxNorm)

298869 - eplerenone
9997 - Spironolactone
351256 - eplerenone 25 MG Oral Tablet
351257 - eplerenone 50 MG Oral Tablet
198224 - Hydrochlorothiazide 25 MG / Spironolactone 25 MG Oral Tablet
198225 - Hydrochlorothiazide 50 MG / Spironolactone 50 MG Oral Tablet
198222 - Spironolactone 100 MG Oral Tablet
313096 - Spironolactone 25 MG Oral Tablet
198223 - Spironolactone 50 MG Oral Tablet

A.4.3 bosutinib (ATC)

L01XE14 - bosutinib

A.4.4 cerebral hemorrhage (ICD-10-CM)

I61.0 - Nontraumatic intracerebral hemorrhage in hemisphere, subcortical
I61.1 - Nontraumatic intracerebral hemorrhage in hemisphere, cortical
I61.2 - Nontraumatic intracerebral hemorrhage in hemisphere, unspecified
I61.3 - Nontraumatic intracerebral hemorrhage in brain stem
I61.4 - Nontraumatic intracerebral hemorrhage in cerebellum
I61.5 - Nontraumatic intracerebral hemorrhage, intraventricular
I61.6 - Nontraumatic intracerebral hemorrhage, multiple localized
I61.9 - Nontraumatic intracerebral hemorrhage, unspecified
P10.2 - Intraventricular hemorrhage due to birth injury
P10.4 - Tentorial tear due to birth injury
P52.0 - Intraventricular (nontraumatic) hemorrhage, grade 1, of newborn
P52.1 - Intraventricular (nontraumatic) hemorrhage, grade 2, of newborn
P52.2 - Intraventricular (nontraumatic) hemorrhage, grade 3 and grade 4, of newborn
P52.3 - Unspecified intraventricular (nontraumatic) hemorrhage of newborn
P52.4 - Intracerebral (nontraumatic) hemorrhage of newborn
P52.9 - Intracranial (nontraumatic) hemorrhage of newborn, unspecified

A.4.5 chronic myeloid leukemia (SNOMED-CT)

415287001 - Relapsing chronic myeloid leukemia
277589003 - Atypical chronic myeloid leukemia
277587001 - Juvenile chronic myeloid leukemia

A.4.6 dasatinib (ATC)

L01XE06 - dasatinib

A.4.7 upper gastrointestinal bleeding or peptic ulcer (ICD-10-CM)

k92.2Gastrointestinal hemorrhage, unspecified
k25.0Acute gastric ulcer with hemorrhage
k25.2Acute gastric ulcer with hemorrhage and perforation
k25.4Chronic or unspecified gastric ulcer with hemorrhage
k25.6Chronic or unspecified gastric ulcer with both hemorrhage and perforation
k26.0Acute duodenal ulcer with hemorrhage
k26.2 - Acute duodenal ulcer with hemorrhage and perforation
k26.4Chronic duodenal ulcer with hemorrhage
k26.6Chronic duodenal ulcer with hemorrhage and perforation
k27.0Acute peptic ulcer, site unspecified, with hemorrhage
k27.2 - Acute peptic ulcer, site unspecified, with hemorrhage and perforation
k27.4Chronic peptic ulcer, site unspecified, with hemorrhage
k27.6Chronic peptic ulcer, site unspecified, with hemorrhage and perforation
k28.0Acute gastrojejunel ulcer with hemorrhage
k28.2 - Acute gastrojejunel ulcer with hemorrhage and perforation
k28.4Chronic gastrojejunel ulcer with hemorrhage
k28.6Chronic Acute gastrojejunel ulcer with hemorrhage and perforation
k29.01Acute gastritis with bleeding
k29.31Chronic gastritis with bleeding
k29.41Chronic atrophic gastritis with bleeding
k29.51Unspecified chronic gastritis with bleeding
k29.61Other gastritis with bleeding
k29.71Gastritis, unspecified, with bleeding
k29.81Duodenitis with bleeding
k29.91Gastroduodenitis, unspecified, with bleeding
k31.811Angiodysplasiaof stomach and duodenum with bleeding
k31.82Dieulafoy lesion (hemorrhagic) of stomach and duodenum

A.4.8 imatinib (ATC)

L01XE01 - imatinib

A.4.9 intracranial hemorrhage (ICD-10-CM)

I62.0 - Nontraumatic subdural hemorrhage
I62.1 - Nontraumatic extradural hemorrhage
I62.9 - Nontraumatic intracranial hemorrhage, unspecified
I69.2 - Sequelae of other nontraumatic intracranial hemorrhage
P10.0 - Subdural hemorrhage due to birth injury
P10.1 - Cerebral hemorrhage due to birth injury
P10.2 - Intraventricular hemorrhage due to birth injury
P10.3 - Subarachnoid hemorrhage due to birth injury
P10.4 - Tentorial tear due to birth injury
P10.8 - Other intracranial lacerations and hemorrhages due to birth injury
P10.9 - Unspecified intracranial laceration and hemorrhage due to birth injury
P52.0 - Intraventricular (nontraumatic) hemorrhage, grade 1, of newborn
P52.1 - Intraventricular (nontraumatic) hemorrhage, grade 2, of newborn
P52.2 - Intraventricular (nontraumatic) hemorrhage, grade 3 and grade 4, of newborn
P52.3 - Unspecified intraventricular (nontraumatic) hemorrhage of newborn
P52.4 - Intracerebral (nontraumatic) hemorrhage of newborn
P52.5 - Subarachnoid (nontraumatic) hemorrhage of newborn
P52.6 - Cerebellar (nontraumatic) and posterior fossa hemorrhage of newborn
P52.8 - Other intracranial (nontraumatic) hemorrhages of newborn
P52.9 - Intracranial (nontraumatic) hemorrhage of newborn, unspecified
S06.5 - Traumatic subdural hemorrhage
S06.8 - Other specified intracranial injuries

A.4.10 misoprostol (RxNorm)

42331 -  Misoprostol

A.4.11 nilotinib (ATC)

L01XE08 - nilotinib

A.4.12 Systemic non-steroidal anti-inflammatory drugs (NSAIDs) (RxNorm)

1665675 - 1 ML Ketorolac Tromethamine 15 MG/ML Cartridge
860092 - 1 ML Ketorolac Tromethamine 15 MG/ML Injection
860113 - 1 ML Ketorolac Tromethamine 15 MG/ML Prefilled Syringe
1665679 - 1 ML Ketorolac Tromethamine 30 MG/ML Cartridge
1665461 - 1 ML Ketorolac Tromethamine 30 MG/ML Injection
860114 - 1 ML Ketorolac Tromethamine 30 MG/ML Prefilled Syringe
1367426 - 12 HR Naproxen sodium 220 MG / Pseudoephedrine Hydrochloride 120 MG Extended Release Oral Tablet
1665682 - 2 ML Ketorolac Tromethamine 30 MG/ML Cartridge
1665459 - 2 ML Ketorolac Tromethamine 30 MG/ML Injection
860115 - 2 ML Ketorolac Tromethamine 30 MG/ML Prefilled Syringe
855657 - 24 HR Diclofenac Sodium 100 MG Extended Release Oral Tablet
310245 - 24 HR Etodolac 400 MG Extended Release Oral Tablet
359500 - 24 HR Etodolac 500 MG Extended Release Oral Tablet
310247 - 24 HR Etodolac 600 MG Extended Release Oral Tablet
314059 - 24 HR Ketoprofen 100 MG Extended Release Oral Capsule
311230 - 24 HR Ketoprofen 150 MG Extended Release Oral Capsule
359697 - 24 HR Ketoprofen 200 MG Extended Release Oral Capsule
433845 - 24 HR Naproxen 1000 MG Extended Release Oral Tablet
1116320 - 24 HR Naproxen 375 MG Extended Release Oral Tablet
1116339 - 24 HR Naproxen 500 MG Extended Release Oral Tablet
1116349 - 24 HR Naproxen 750 MG Extended Release Oral Tablet
992420 - 8 ACTUAT Ketorolac Tromethamine 15.8 MG/ACTUAT Nasal Inhaler
205322 - celecoxib 100 MG Oral Capsule
205323 - celecoxib 200 MG Oral Capsule
349514 - celecoxib 400 MG Oral Capsule
686379 - celecoxib 50 MG Oral Capsule
1297390 - Chlorpheniramine Maleate 2 MG / Ibuprofen 200 MG / Pseudoephedrine Hydrochloride 30 MG Oral Tablet
1310503 - Chlorpheniramine Maleate 4 MG / Ibuprofen 200 MG / Phenylephrine Hydrochloride 10 MG Oral Tablet
1442116 - Diclofenac 18 MG Oral Capsule
1442128 - Diclofenac 35 MG Oral Capsule
859063 - Diclofenac Potassium 1.67 MG/ML Oral Solution
858342 - Diclofenac Potassium 25 MG Oral Capsule
857702 - Diclofenac Potassium 25 MG Oral Tablet
855942 - Diclofenac Potassium 50 MG Oral Tablet
1234493 - Diclofenac Sodium 100 MG Oral Capsule
857696 - Diclofenac Sodium 100 MG Rectal Suppository
857698 - Diclofenac Sodium 12.5 MG Rectal Suppository
855664 - Diclofenac Sodium 25 MG Delayed Release Oral Tablet
857703 - Diclofenac Sodium 25 MG Rectal Suppository
1599787 - Diclofenac Sodium 37.5 MG/ML Injectable Solution
857706 - Diclofenac Sodium 50 MG / Misoprostol 0.2 MG Oral Tablet
855906 - Diclofenac Sodium 50 MG Delayed Release Oral Tablet
857709 - Diclofenac Sodium 50 MG Rectal Suppository
1359105 - Diclofenac Sodium 75 MG / Misoprostol 0.2 MG Oral Tablet
855926 - Diclofenac Sodium 75 MG Delayed Release Oral Tablet
895664 - Diphenhydramine Citrate 38 MG / Ibuprofen 200 MG Oral Tablet
901814 - Diphenhydramine Hydrochloride 25 MG / Ibuprofen 200 MG Oral Capsule
1550957 - Diphenhydramine Hydrochloride 25 MG / Naproxen sodium 220 MG Oral Tablet
994005 - Esomeprazole 20 MG / Naproxen 375 MG Delayed Release Oral Tablet
994008 - Esomeprazole 20 MG / Naproxen 500 MG Delayed Release Oral Tablet
197684 - Etodolac 200 MG Oral Capsule
197685 - Etodolac 300 MG Oral Capsule
197686 - Etodolac 400 MG Oral Tablet
199390 - Etodolac 500 MG Oral Tablet
1100066 - Famotidine 26.6 MG / Ibuprofen 800 MG Oral Tablet
199740 - fenbufen 450 MG Oral Tablet
197694 - Fenoprofen 200 MG Oral Capsule
858116 - Fenoprofen 400 MG Oral Capsule
310291 - Fenoprofen 600 MG Oral Tablet
197724 - Flurbiprofen 100 MG Oral Tablet
199749 - Flurbiprofen 200 MG Extended Release Oral Capsule
197725 - Flurbiprofen 50 MG Oral Tablet
859315 - Hydrocodone Bitartrate 10 MG / Ibuprofen 200 MG Oral Tablet
858770 - Hydrocodone Bitartrate 2.5 MG / Ibuprofen 200 MG Oral Tablet
858778 - Hydrocodone Bitartrate 5 MG / Ibuprofen 200 MG Oral Tablet
858798 - Hydrocodone Bitartrate 7.5 MG / Ibuprofen 200 MG Oral Tablet
310963 - Ibuprofen 100 MG Chewable Tablet
198405 - Ibuprofen 100 MG Oral Tablet
854183 - Ibuprofen 100 MG/ML Injectable Solution
1310487 - Ibuprofen 20 MG/ML / Pseudoephedrine Hydrochloride 3 MG/ML Oral Suspension
197803 - Ibuprofen 20 MG/ML Oral Suspension
1369775 - Ibuprofen 200 MG / Phenylephrine Hydrochloride 10 MG Oral Tablet
1299018 - Ibuprofen 200 MG / Pseudoephedrine Hydrochloride 30 MG Oral Capsule
1299021 - Ibuprofen 200 MG / Pseudoephedrine Hydrochloride 30 MG Oral Tablet
310964 - Ibuprofen 200 MG Oral Capsule
310965 - Ibuprofen 200 MG Oral Tablet
204442 - Ibuprofen 40 MG/ML Oral Suspension
1049589 - Ibuprofen 400 MG / Oxycodone Hydrochloride 5 MG Oral Tablet
197805 - Ibuprofen 400 MG Oral Tablet
197806 - Ibuprofen 600 MG Oral Tablet
197807 - Ibuprofen 800 MG Oral Tablet
346508 - Indomethacin 1 MG/ML Injectable Solution
199549 - Indomethacin 100 MG Rectal Suppository
1490727 - Indomethacin 20 MG Oral Capsule
197817 - Indomethacin 25 MG Oral Capsule
1491529 - Indomethacin 40 MG Oral Capsule
310991 - Indomethacin 5 MG/ML Oral Suspension
197818 - Indomethacin 50 MG Oral Capsule
197819 - Indomethacin 50 MG Rectal Suppository
310992 - Indomethacin 75 MG Extended Release Oral Capsule
199321 - Ketoprofen 100 MG Oral Capsule
249482 - Ketoprofen 100 MG Oral Tablet
199553 - Ketoprofen 100 MG Rectal Suppository
431823 - Ketoprofen 150 MG Extended Release Oral Tablet
429192 - Ketoprofen 25 MG Oral Tablet
197855 - Ketoprofen 50 MG Oral Capsule
247630 - Ketoprofen 50 MG Rectal Suppository
197856 - Ketoprofen 75 MG Oral Capsule
834022 - Ketorolac Tromethamine 10 MG Oral Tablet
860096 - Ketorolac Tromethamine 30 MG/ML Injectable Solution
618552 - Meclofenamate 100 MG Oral Capsule
618557 - Meclofenamate 50 MG Oral Capsule
597406 - meloxicam 1.5 MG/ML Oral Suspension
152695 - meloxicam 15 MG Oral Tablet
311486 - meloxicam 7.5 MG Oral Tablet
311892 - nabumetone 500 MG Oral Tablet
311893 - nabumetone 750 MG Oral Tablet
245420 - Naproxen 125 MG Oral Tablet
311913 - Naproxen 25 MG/ML Oral Suspension
198013 - Naproxen 250 MG Oral Tablet
603103 - Naproxen 375 MG Delayed Release Oral Tablet
198012 - Naproxen 375 MG Oral Tablet
311915 - Naproxen 500 MG Delayed Release Oral Tablet
198014 - Naproxen 500 MG Oral Tablet
199490 - Naproxen 500 MG Rectal Suppository
1112231 - Naproxen sodium 220 MG Oral Capsule
849574 - Naproxen sodium 220 MG Oral Tablet
849398 - Naproxen sodium 275 MG Oral Tablet
849450 - Naproxen sodium 500 MG / Sumatriptan 85 MG Oral Tablet
849431 - Naproxen sodium 550 MG Oral Tablet
1653765 - Naproxen sodium 60 MG / Sumatriptan 10 MG Oral Tablet
312132 - oxaprozin 600 MG Oral Tablet
198107 - Piroxicam 10 MG Oral Capsule
199559 - Piroxicam 10 MG Oral Tablet
247066 - Piroxicam 10 MG Rectal Suppository
198108 - Piroxicam 20 MG Oral Capsule
199560 - Piroxicam 20 MG Oral Tablet
105942 - Piroxicam 20 MG Rectal Suppository
199279 - Sulindac 100 MG Oral Tablet
198238 - Sulindac 150 MG Oral Tablet
198239 - Sulindac 200 MG Oral Tablet
199516 - tenoxicam 10 MG Oral Tablet
105954 - tenoxicam 20 MG Oral Tablet
250197 - tenoxicam 20 MG Rectal Suppository
198295 - Tolmetin 200 MG Oral Tablet
198296 - Tolmetin 400 MG Oral Capsule
198297 - Tolmetin 600 MG Oral Tablet
349319 - valdecoxib 10 MG Oral Tablet
349321 - valdecoxib 20 MG Oral Tablet

A.4.13 ponatinib (ATC)

L01XE24 - ponatinib

A.4.14 proton pump inhibitor (ATC)

A02BC06 - dexlansoprazole
A02BC05 - esomeprazole
A02BC03 - lansoprazole
A02BC01 - omeprazole
A02BC02 - pantoprazole
A02BC04 - rabeprazole

A.4.15 proton pump inhibitor (RxNorm)

816346 -  Dexlansoprazole
283742 -   Esomeprazole
17128 -  Lansoprazole
7646 -  Omeprazole
40790 -  Pantoprazole
114979 -  Rabeprazole

A.4.16 systemic corticosteroids (RxNorm)

1514 - Betamethasone
2878 - Cortisone
3264 - Dexamethasone
4452 - Fludrocortisone
5492 - Hydrocortisone
6902 - Methylprednisolone
8638 - prednisolone
8640 - Prednisone
10759 - Triamcinolone
1085795 - 100 ACTUAT Triamcinolone Acetonide 0.055 MG/ACTUAT Nasal Inhaler
833245 - 120 ACTUAT Triamcinolone Acetonide 0.05 MG/ACTUAT Nasal Inhaler
1085798 - 120 ACTUAT Triamcinolone Acetonide 0.055 MG/ACTUAT Nasal Inhaler
1493473 - 60 ACTUAT Triamcinolone Acetonide 0.055 MG/ACTUAT Nasal Inhaler
670084 - Betamethasone 0.12 MG/ML Oral Solution
308709 - Betamethasone 0.6 MG Oral Tablet
578803 - Betamethasone 3 MG/ML / Betamethasone acetate 3 MG/ML Injectable Suspension
197543 - Cortisone 10 MG Oral Tablet
197545 - Cortisone 5 MG Oral Tablet
828248 - cortisone acetate 25 MG Oral Tablet
309686 - Dexamethasone 0.1 MG/ML Oral Solution
197577 - Dexamethasone 0.5 MG Oral Tablet
854177 - Dexamethasone 0.7 MG Drug Implant
343033 - Dexamethasone 0.75 MG Oral Tablet
197579 - Dexamethasone 1 MG Oral Tablet
309684 - Dexamethasone 1 MG/ML Oral Solution
197580 - Dexamethasone 1.5 MG Oral Tablet
309696 - Dexamethasone 10 MG/ML Injectable Solution
197581 - Dexamethasone 2 MG Oral Tablet
197582 - Dexamethasone 4 MG Oral Tablet
197583 - Dexamethasone 6 MG Oral Tablet
1116927 - Dexamethasone phosphate 4 MG/ML Injectable Solution
313979 - Fludrocortisone 0.1 MG Oral Tablet
260192 - Hydrocortisone 0.01 MG/MG Rectal Ointment
310878 - Hydrocortisone 1.67 MG/ML Enema
197782 - Hydrocortisone 10 MG Oral Tablet
310868 - Hydrocortisone 10 MG/ML Rectal Cream
1494032 - Hydrocortisone 10 MG/ML Vaginal Cream
310899 - Hydrocortisone 2 MG/ML Oral Suspension
197783 - Hydrocortisone 20 MG Oral Tablet
1246528 - Hydrocortisone 25 MG/ML / Pramoxine hydrochloride 10 MG/ML Rectal Cream
310879 - Hydrocortisone 25 MG/ML Rectal Cream
199320 - Hydrocortisone 4 MG Oral Tablet
197787 - Hydrocortisone 5 MG Oral Tablet
238755 - Hydrocortisone 50 MG/ML Injectable Solution
1012221 - hydrocortisone acetate 0.0055 MG/MG / Lidocaine Hydrochloride 0.028 MG/MG Rectal Gel
1012223 - hydrocortisone acetate 0.025 MG/MG / Lidocaine Hydrochloride 0.03 MG/MG Rectal Gel
1012229 - hydrocortisone acetate 10 MG/ML / Lidocaine Hydrochloride 30 MG/ML Rectal Cream
1235049 - hydrocortisone acetate 10 MG/ML / Pramoxine hydrochloride 10 MG/ML Rectal Cream
828362 - hydrocortisone acetate 10 MG/ML / Pramoxine hydrochloride 10 MG/ML Rectal Foam
1545172 - hydrocortisone acetate 100 MG/ML Rectal Foam
1114854 - hydrocortisone acetate 18.5 MG/ML / Pramoxine hydrochloride 11.5 MG/ML Rectal Cream
1012233 - hydrocortisone acetate 20 MG/ML / Lidocaine Hydrochloride 20 MG/ML Rectal Cream
1094443 - hydrocortisone acetate 23.5 MG/ML / Pramoxine hydrochloride 10 MG/ML Rectal Cream
1291082 - hydrocortisone acetate 25 MG Rectal Suppository
1294025 - hydrocortisone acetate 25 MG/ML / Pramoxine hydrochloride 10 MG/ML Rectal Cream
1291085 - hydrocortisone acetate 30 MG Rectal Suppository
1012235 - hydrocortisone acetate 5 MG/ML / Lidocaine Hydrochloride 30 MG/ML Rectal Cream
199771 - Methylprednisolone 100 MG Oral Tablet
328161 - Methylprednisolone 16 MG Oral Tablet
197969 - Methylprednisolone 2 MG Oral Tablet
197970 - Methylprednisolone 24 MG Oral Tablet
197971 - Methylprednisolone 32 MG Oral Tablet
259966 - Methylprednisolone 4 MG Oral Tablet
311659 - Methylprednisolone 40 MG/ML Injectable Solution
314099 - Methylprednisolone 62.5 MG/ML Injectable Solution
1357886 - Methylprednisolone 65.4 MG/ML Injectable Solution
197973 - Methylprednisolone 8 MG Oral Tablet
1358510 - methylprednisolone acetate 20 MG/ML Injectable Suspension
1358610 - methylprednisolone acetate 40 MG/ML Injectable Suspension
1358617 - methylprednisolone acetate 80 MG/ML Injectable Suspension
199343 - prednisolone 1 MG Oral Tablet
312614 - prednisolone 1 MG/ML Oral Solution
643123 - prednisolone 10 MG Disintegrating Oral Tablet
643125 - prednisolone 15 MG Disintegrating Oral Tablet
794979 - prednisolone 2 MG/ML Oral Solution
429199 - prednisolone 20 MG Oral Tablet
199967 - prednisolone 25 MG Oral Tablet
283077 - prednisolone 3 MG/ML Oral Solution
793099 - prednisolone 3 MG/ML Oral Suspension
643127 - prednisolone 30 MG Disintegrating Oral Tablet
702306 - prednisolone 4 MG/ML Oral Solution
198142 - prednisolone 5 MG Oral Tablet
249066 - prednisolone 5 MG/ML Oral Solution
1303125 - Prednisone 1 MG Delayed Release Oral Tablet
198144 - Prednisone 1 MG Oral Tablet
315187 - Prednisone 1 MG/ML Oral Solution
198145 - Prednisone 10 MG Oral Tablet
1303132 - Prednisone 2 MG Delayed Release Oral Tablet
198146 - Prednisone 2.5 MG Oral Tablet
312615 - Prednisone 20 MG Oral Tablet
1303135 - Prednisone 5 MG Delayed Release Oral Tablet
312617 - Prednisone 5 MG Oral Tablet
205301 - Prednisone 5 MG/ML Oral Solution
198148 - Prednisone 50 MG Oral Tablet
198301 - Triamcinolone 1 MG Oral Tablet
1085728 - Triamcinolone Acetonide 0.001 MG/MG Oral Paste
1085750 - Triamcinolone Acetonide 10 MG/ML Injectable Suspension
1085754 - Triamcinolone Acetonide 40 MG/ML Injectable Suspension
1085996 - triamcinolone hexacetonide 20 MG/ML Injectable Suspension
1085992 - triamcinolone hexacetonide 5 MG/ML Injectable Suspension

A.4.17 topical or opthalamic diclofenac (RxNorm)

1234735 - diclofenac epolamine 0.0129 MG/MG Topical Gel
854801 - Diclofenac Sodium Ophthalmic Solution

A.4.18 tyrosine kinase inhibitors (TKIs) (ATC)

L01XE04 - sunitinib
L01XC03 - trastuzumab
L01XC14 - ado-trastuzumab emtansine

A.4.19 warfarin (RxNorm)

855288 - Warfarin Sodium 1 MG Oral Tablet
855296 - Warfarin Sodium 10 MG Oral Tablet
855302 - Warfarin Sodium 2 MG Oral Tablet
855308 - Warfarin Sodium 2 MG/ML Injectable Solution
855312 - Warfarin Sodium 2.5 MG Oral Tablet
855318 - Warfarin Sodium 3 MG Oral Tablet
855324 - Warfarin Sodium 4 MG Oral Tablet
855332 - Warfarin Sodium 5 MG Oral Tablet
855338 - Warfarin Sodium 6 MG Oral Tablet
855344 - Warfarin Sodium 7.5 MG Oral Tablet
11289 - Warfarin

A.5 JSON Examples

This section is non-normative.

Here, we present prototype JSON documents for the two example PDDIs described in Section § 5. Examples of the use of the minimal information model. These examples should be considered informative (non-normative) and subject to revision.

Both of the examples below follow the recommendations provided in this Community Group Report. The examples are intended to show a simple approach to applying the recommendations to PDDIs using JSON, the Web Annotation Data Model [web-annotation-model], and BibJSON [bib-json].

An entirely different approach that is also compliant with the recommendations in this Report is the HL7 CDS Workgroup's draft Implementation Guide for PDDI CDS as a service [hl7-pddi-cds-ig-git]. The Implentation Guide integrates the PDDI minimum information model into PDDI CDS using the health information technology standards FHIR, Clinical Quality Language, and CDS Hooks.

The following non-normative conventions apply to the examples shown below:

  1. The core information items are represented as key-value pairs.
  2. The string labels of the core information items (e.g., "drugs involved", "clinical consequences", etc.) are the keys.
  3. An identifier for the core information item class is provided in the "type" field of the value assocated with each infomation item. For example, the type of any value assigned to "clinical consequences" is "MPIO_0000003". A persistent URL can be constructed from each identifier by concatenating "http://purl.obolibrary.org/obo/" to the beginning of the identifier string (e.g., http://purl.obolibrary.org/obo/MPIO_0000003.
  4. A unique URN identifier is used to identify each information item instance in the "id" field of the value assocated with each infomation item.
  5. The "display" field of the value assocated with each infomation item shows human readable content relevant to the associated information item
  6. There are many cases the unstructured text in a "display" field refers to an entity that is more clearly defined using a value set. In such cases, the value object has a "concept set references" field that holds a list of objects that can be used to unambiguously associate text mentions with concept sets. These objects use the Web Annotation Data Model [web-annotation-model] as follows:
    1. The type of each object is a Web Annotation Data Model "TextQuoteSelector".
    2. The value assigned to the Web Annotation Data Model "exact" field is used to indicate the specific text mention that is being mapped
    3. The value assigned to the Web Annotation Data Model "body" field is an object that links the text mention to a concept set object provided within the JSON document. The concept set object is present in a list value assigned to the "concept sets" key within the same JSON document as the concept set reference. The the "value" field of the "body" of the concept set reference object holds the URN that uniquely identifies the specific concept set.
      • As an example, the following "clinical consequences" value has three concept set references for entities mentioned in the description. One for the mention of "gastrointestinal bleeding", one for the mention of "intracranial hemorrhage", and one for the mention of "cerebral hemorrhage". Each concept set reference object points to the exact value set using the URN assigned to the "value" field of the "body".
      • "clinical consequences": {
        "type": "MPIO_0000003",
        "id": "5476c5b5-b52b-4750-882d-cc47f4fa52f4",
        "display": "Increased risk of bleeding including gastrointestinal bleeding, intracranial hemorrhage, and cerebral hemorrhage",
        "concept set references": [
        {
               "type": "TextQuoteSelector",
               "exact": "gastrointestinal bleeding",
               "body": {
               "type": "SemanticTag",
               "value": "af3209ab-082f-40e6-9c37-6f2945afa850"
               }
        },
        {
               "type": "TextQuoteSelector",
               "exact": "intracranial hemorrhage",
               "body": {
               "type": "SemanticTag",
               "value": "2b083e55-755c-432a-9d78-ac5f55787737"
               }
        },
        {
               "type": "TextQuoteSelector",
               "exact": "cerebral hemorrhage",
               "body": {
               "type": "SemanticTag",
               "value": "5d5cf201-0a37-4c10-8397-da1aa774811e"
               }
        }
        ]}
    4. Whenever "evidence about a suspected drug-drug interaction" refers to a citable evidence item, a reference to that item is provided as BibJSON (a JSON version of BibTex) [bib-json]

A.5.1 Example 1: warfarin and systemic non-steroidal anti-inflammatory drugs (NSAIDs)

Example 2: Warfarin and systemic non-steroidal anti-inflammatory drugs (NSAIDs)
{
  "drugs involved": [
    {
      "type": "DIDEO_00000128",
      "id": "13d483d8-a955-49d1-af34-a762c6ad182c"
    },
    {
      "type": "DIDEO_00000128",
      "id": "7eecfe1a-073c-4af0-a14d-54b06d64455d"
    }
  ],
  "clinical consequences": {
    "type": "MPIO_0000003",
    "id": "5476c5b5-b52b-4750-882d-cc47f4fa52f4",
    "display": "Increased risk of bleeding including gastrointestinal bleeding, intracranial hemorrhage, and cerebral hemorrhage",
    "concept set references": [
      {
        "type": "TextQuoteSelector",
        "exact": "upper gastrointestinal bleeding",
        "body": {
          "type": "SemanticTag",
          "value": "af3209ab-082f-40e6-9c37-6f2945afa850"
        }
      },
      {
        "type": "TextQuoteSelector",
        "exact": "intracranial hemorrhage",
        "body": {
          "type": "SemanticTag",
          "value": "2b083e55-755c-432a-9d78-ac5f55787737"
        }
      },
      {
        "type": "TextQuoteSelector",
        "exact": "cerebral hemorrhage",
        "body": {
          "type": "SemanticTag",
          "value": "5d5cf201-0a37-4c10-8397-da1aa774811e"
        }
      }
    ]
  },
  "seriousness": {
    "type": "MPIO_0000009",
    "id": "06dea7b0-5cef-4587-86a2-54052a8d0457",
    "value": "serious",
    "display": "Bleeding is a serious potential clinical consequence because it can result in death, life-threatening hospitalization, and disability."
  },
  "mechanism of interaction information": {
    "type": "MPIO_0000005",
    "id": "8a8ce6c0-c78e-4d38-bda2-835118f976ea",
    "display": "Systemic non-steroidal anti-inflammatory drugs (NSAIDs) have antiplatelet effects which increase the bleeding risk when combined with oral anticoagulants such as warfarin. The antiplatelet effect of NSAIDs lasts only as long as the NSAID is present in the circulation, unlike aspirin's antiplatelet effect, which lasts for up to 2 weeks after aspirin is discontinued. NSAIDs also can cause peptic ulcers and most of the evidence for increased bleeding risk with NSAIDs plus warfarin is due to upper gastrointestinal bleeding (UGIB).",
    "concept set references": [
      {
        "type": "TextQuoteSelector",
        "attribute": "display",
        "exact": "Non-steroidal anti-inflammatory drugs (NSAIDs)",
        "body": {
          "type": "SemanticTag",
          "value": "7eecfe1a-073c-4af0-a14d-54b06d64455d"
        }
      },
      {
        "type": "TextQuoteSelector",
        "attribute": "display",
        "exact": "NSAIDs",
        "body": {
          "type": "SemanticTag",
          "value": "7eecfe1a-073c-4af0-a14d-54b06d64455d"
        }
      },
      {
        "type": "TextQuoteSelector",
        "attribute": "display",
        "exact": "warfarin",
        "body": {
          "type": "SemanticTag",
          "value": "13d483d8-a955-49d1-af34-a762c6ad182c"
        }
      }
    ]
  },
  "recommended action": {
    "type": "MPIO_0000008",
    "id": "b05ddcfe-c72a-4009-ad7e-c89221bb1ec7",
    "display": "If the NSAID is being used as an analgesic or antipyretic, it would be prudent to use an alternative such as acetaminophen. In some people, acetaminophen can increase the anticoagulant effect of warfarin, so monitor the INR if acetaminophen is used in doses over 2 g/day for a few days. For more severe pain consider short-term opioids in place of the NSAID."
  },
  "frequency of exposure to the PDDI": {
    "type": "MPIO_0000007",
    "id": "f8885290-1507-40a5-85cb-247756cb0064",
    "display": "Unknown"
  },
  "frequency of harm for persons who have been exposed to the PDDI": {
    "type": "MPIO_0000006",
    "id": "03853fb2-e398-42a0-a07b-a93141931bd4",
    "display": "Unknown"
  },
  "modifying factors information": {
    "type": "MPIO_0000000",
    "id": "56a7ba39-467b-42c7-b117-485fecacb91d",
    "expressionLogic": "human readable",
    "members": [
      {
        "display": "The NSAID is topical or ophthalmic diclofenac",
        "operational classification statement": {
          "type": "MPIO_0000111",
          "display": "No special precautions."
        },
        "evidence about a suspected drug-drug interaction": {
          "type": "MPIO_0000004",
          "display": "Topical diclofenac has relatively low systemic absorption; in one study a topical gel (16 g/day) produced about 6% of the absorption seen with systemic administration of 150 mg/day. A higher than recommended dose of topical gel (48 g/day) produced 20% of a systemic dose of diclofenac."
        },
        "concept set references": [
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "topical or ophthalmic diclofenac",
            "body": {
              "type": "SemanticTag",
              "value": "484ef188-5765-408d-b8c3-f2f38ae8066c"
            }
          }
        ]
      },
      {
        "display": "The NSAID is NOT topical diclofenac but the patient is concomitantly taking a proton pump inhibitor or misoprostol",
        "operational classification statement": {
          "type": "MPIO_0000111",
          "display": "Assess risk and take action if necessary."
        },
        "evidence about a suspected drug-drug interaction": {
          "type": "MPIO_0000004",
          "display": "Proton pump inhibitors and misoprostol may reduce the risk of UGIB in patients receiving NSAIDs and warfarin."
        },
        "concept set references": [
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "topical or ophthalmic diclofenac",
            "body": {
              "type": "SemanticTag",
              "value": "484ef188-5765-408d-b8c3-f2f38ae8066c"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "proton pump inhibitor",
            "body": {
              "type": "SemanticTag",
              "value": "818e9fed-e18d-45de-b900-1edbedee8c89"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "misoprostol",
            "body": {
              "type": "SemanticTag",
              "value": "ab5731f3-a127-4ce7-bfe8-cfcafd6e3b86"
            }
          }
        ]
      },
      {
        "display": "The NSAID is NOT topical diclofenac, the patient is NOT concomitantly taking a proton pump inhibitor or misoprostol, and the patient 1) has a history of upper gastrointestinal bleeding (UGIB) or peptic ulcer or 2) age > 65 years old",
        "type": "MPIO_0000111",
        "operational classification statement": {
          "type": "MPIO_0000111",
          "display": "Use only if benefit outweighs risk."
        },
        "evidence about a suspected drug-drug interaction": {
          "type": "MPIO_0000004",
          "display": "Patients with a history of UGIB or peptic ulcer may have an increased risk of UGIB from this interaction. The extent to which older age is an independent risk factor for UGIB due to these interactions is not firmly established, but UGIB in general is known to increase with age."
        },
        "concept set references": [
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "topical or ophthalmic diclofenac",
            "body": {
              "type": "SemanticTag",
              "value": "484ef188-5765-408d-b8c3-f2f38ae8066c"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "proton pump inhibitor",
            "body": {
              "type": "SemanticTag",
              "value": "818e9fed-e18d-45de-b900-1edbedee8c89"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "misoprostol",
            "body": {
              "type": "SemanticTag",
              "value": "ab5731f3-a127-4ce7-bfe8-cfcafd6e3b86"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "upper gastrointestinal bleeding (UGIB) or peptic ulcer",
            "body": {
              "type": "SemanticTag",
              "value": "6a085888-7bc2-4b6a-8547-95d635cbcb3a"
            }
          }
        ]
      },
      {
        "display": "The NSAID is NOT topical diclofenac, the patient is NOT concomitantly taking a proton pump inhibitor or misoprostol, and the patient is concomitantly taking systemic corticosteroids, aldosterone antagonists, or high dose or multiple NSAIDs",
        "operational classification statement": {
          "type": "MPIO_0000111",
          "display": "Use only if benefit outweighs risk."
        },
        "evidence about a suspected drug-drug interaction": {
          "type": "MPIO_0000004",
          "display": "Both corticosteroids and aldosterone antagonists have been shown to substantially increase the risk of UGIB in patients on NSAIDs, with relative risks of 12.8 and 11 respectively compared to a risk of 4.3 with NSAIDs alone [masclee-2014].",
          "References": [
            {
              "title": "Risk of upper gastrointestinal bleeding from different drug combinations",
              "author": [
                {
                  "name": "Gwen MC Masclee "
                },
                {
                  "name": "Vera E Valkhoff"
                },
                {
                  "name": "Preciosa M Coloma"
                },
                {
                  "name": "Maria de Ridder"
                },
                {
                  "name": "Silvana Romio"
                },
                {
                  "name": "Martijn J Schuemie"
                },
                {
                  "name": "Ron Herings"
                },
                {
                  "name": "Giampiero Mazzaglia"
                },
                {
                  "name": "Gino Picelli"
                },
                {
                  "name": "L Scotti"
                }
              ],
              "type": "article",
              "year": "2014",
              "journal": {
                "name": "Gastroenterology",
                "volume": "147",
                "number": "4",
                "pages": "784--792"
              },
              "link": [
                {
                  "url": "https://www.gastrojournal.org/article/S0016-5085%2814%2900768-9/fulltext"
                }
              ],
              "identifier": [
                {
                  "type": "doi",
                  "id": "10.1053/j.gastro.2014.06.007"
                }
              ]
            }
          ]
        },
        "concept set references": [
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "topical or ophthalmic diclofenac",
            "body": {
              "type": "SemanticTag",
              "value": "484ef188-5765-408d-b8c3-f2f38ae8066c"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "proton pump inhibitor",
            "body": {
              "type": "SemanticTag",
              "value": "818e9fed-e18d-45de-b900-1edbedee8c89"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "misoprostol",
            "body": {
              "type": "SemanticTag",
              "value": "ab5731f3-a127-4ce7-bfe8-cfcafd6e3b86"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "corticosteroids",
            "body": {
              "type": "SemanticTag",
              "value": "13d50375-4ad3-4d19-af8c-e13870b06907"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "aldosterone antagonists",
            "body": {
              "type": "SemanticTag",
              "value": "a32b4ed0-1aff-4520-a973-879f705a0366"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "systemic NSAIDs",
            "body": {
              "type": "SemanticTag",
              "value": "7eecfe1a-073c-4af0-a14d-54b06d64455d"
            }
          }
        ]
      }
    ]
  },
  "concept sets": [
    {
      "id": "13d483d8-a955-49d1-af34-a762c6ad182c",
      "display": "warfarin",
      "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
      "code": "11289",
      "members": [
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855288",
          "display": "Warfarin Sodium 1 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855296",
          "display": "Warfarin Sodium 10 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855302",
          "display": "Warfarin Sodium 2 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855308",
          "display": "Warfarin Sodium 2 MG/ML Injectable Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855312",
          "display": "Warfarin Sodium 2.5 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855318",
          "display": "Warfarin Sodium 3 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855324",
          "display": "Warfarin Sodium 4 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855332",
          "display": "Warfarin Sodium 5 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855338",
          "display": "Warfarin Sodium 6 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "855344",
          "display": "Warfarin Sodium 7.5 MG Oral Tablet"
        }
      ]
    },
    {
      "id": "7eecfe1a-073c-4af0-a14d-54b06d64455d",
      "display": "Systemic anti-inflammatory agents, Non-Steroidal",
      "system": "http://www.nlm.nih.gov/research/umls/snomedct",
      "code": "C0003211",
      "members": [
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1665675",
          "display": "1 ML Ketorolac Tromethamine 15 MG/ML Cartridge"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "860092",
          "display": "1 ML Ketorolac Tromethamine 15 MG/ML Injection"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "860113",
          "display": "1 ML Ketorolac Tromethamine 15 MG/ML Prefilled Syringe"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1665679",
          "display": "1 ML Ketorolac Tromethamine 30 MG/ML Cartridge"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1665461",
          "display": "1 ML Ketorolac Tromethamine 30 MG/ML Injection"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "860114",
          "display": "1 ML Ketorolac Tromethamine 30 MG/ML Prefilled Syringe"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1367426",
          "display": "12 HR Naproxen sodium 220 MG / Pseudoephedrine Hydrochloride 120 MG Extended Release Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1665682",
          "display": "2 ML Ketorolac Tromethamine 30 MG/ML Cartridge"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1665459",
          "display": "2 ML Ketorolac Tromethamine 30 MG/ML Injection"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "860115",
          "display": "2 ML Ketorolac Tromethamine 30 MG/ML Prefilled Syringe"
        },
        {
          "type": "APOLLO_SV_00000461",
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          "code": "855657",
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          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1294025",
          "display": "hydrocortisone acetate 25 MG/ML / Pramoxine hydrochloride 10 MG/ML Rectal Cream"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1291085",
          "display": "hydrocortisone acetate 30 MG Rectal Suppository"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1012235",
          "display": "hydrocortisone acetate 5 MG/ML / Lidocaine Hydrochloride 30 MG/ML Rectal Cream"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "199771",
          "display": "Methylprednisolone 100 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "328161",
          "display": "Methylprednisolone 16 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "197969",
          "display": "Methylprednisolone 2 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "197970",
          "display": "Methylprednisolone 24 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "197971",
          "display": "Methylprednisolone 32 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "259966",
          "display": "Methylprednisolone 4 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "311659",
          "display": "Methylprednisolone 40 MG/ML Injectable Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "314099",
          "display": "Methylprednisolone 62.5 MG/ML Injectable Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1357886",
          "display": "Methylprednisolone 65.4 MG/ML Injectable Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "197973",
          "display": "Methylprednisolone 8 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1358510",
          "display": "methylprednisolone acetate 20 MG/ML Injectable Suspension"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1358610",
          "display": "methylprednisolone acetate 40 MG/ML Injectable Suspension"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1358617",
          "display": "methylprednisolone acetate 80 MG/ML Injectable Suspension"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "199343",
          "display": "prednisolone 1 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "312614",
          "display": "prednisolone 1 MG/ML Oral Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "643123",
          "display": "prednisolone 10 MG Disintegrating Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "643125",
          "display": "prednisolone 15 MG Disintegrating Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "794979",
          "display": "prednisolone 2 MG/ML Oral Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "429199",
          "display": "prednisolone 20 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "199967",
          "display": "prednisolone 25 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "283077",
          "display": "prednisolone 3 MG/ML Oral Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "793099",
          "display": "prednisolone 3 MG/ML Oral Suspension"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "643127",
          "display": "prednisolone 30 MG Disintegrating Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "702306",
          "display": "prednisolone 4 MG/ML Oral Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198142",
          "display": "prednisolone 5 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "249066",
          "display": "prednisolone 5 MG/ML Oral Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1303125",
          "display": "Prednisone 1 MG Delayed Release Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198144",
          "display": "Prednisone 1 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "315187",
          "display": "Prednisone 1 MG/ML Oral Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198145",
          "display": "Prednisone 10 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1303132",
          "display": "Prednisone 2 MG Delayed Release Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198146",
          "display": "Prednisone 2.5 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "312615",
          "display": "Prednisone 20 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1303135",
          "display": "Prednisone 5 MG Delayed Release Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "312617",
          "display": "Prednisone 5 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "205301",
          "display": "Prednisone 5 MG/ML Oral Solution"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198148",
          "display": "Prednisone 50 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198301",
          "display": "Triamcinolone 1 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1085728",
          "display": "Triamcinolone Acetonide 0.001 MG/MG Oral Paste"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1085750",
          "display": "Triamcinolone Acetonide 10 MG/ML Injectable Suspension"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1085754",
          "display": "Triamcinolone Acetonide 40 MG/ML Injectable Suspension"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1085996",
          "display": "triamcinolone hexacetonide 20 MG/ML Injectable Suspension"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "1085992",
          "display": "triamcinolone hexacetonide 5 MG/ML Injectable Suspension"
        }
      ]
    },
    {
      "id": "a32b4ed0-1aff-4520-a973-879f705a0366",
      "display": "aldosterone antagonists and products containing an aldosterone receptor antagonist",
      "type": "DIDEO_00000128",
      "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
      "members": [
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "298869",
          "display": "eplerenone"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "9997",
          "display": "Spironolactone"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "351256",
          "display": "eplerenone 25 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "351257",
          "display": "eplerenone 50 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198224",
          "display": "Hydrochlorothiazide 25 MG / Spironolactone 25 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198225",
          "display": "Hydrochlorothiazide 50 MG / Spironolactone 50 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198222",
          "display": "Spironolactone 100 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "313096",
          "display": "Spironolactone 25 MG Oral Tablet"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "198223",
          "display": "Spironolactone 50 MG Oral Tablet"
        }
      ]
    }
  ]
}

A.5.2 Example 2: Tyrosine Kinase inhibitors and medications that increase gastric pH

Example 3: Tyrosine Kinase inhibitors and medications that increase gastric pH
{
  "drugs involved": [
    {
      "type": "DIDEO_00000128",
      "id": "48695d2d-9545-4a31-80d1-201d7d7e2cdb"
    },
    {
      "type": "DIDEO_00000128",
      "id": "05b06c16-a763-4ef4-90d8-899abb34abd8"
    }
  ],
  "clinical consequences": {
    "type": "MPIO_0000003",
    "id": "5476c5b5-b52b-4750-882d-cc47f4fa52f4",
    "display": "Decreased efficacy relative to treatment for chronic myeloid leukemia.",
    "concept set references": [
      {
        "type": "TextQuoteSelector",
        "exact": "chronic myeloid leukemia",
        "body": {
          "type": "SemanticTag",
          "value": "2e1bca79-953c-4ea3-9c40-4c16da2dde31"
        }
      }
    ]
  },
  "seriousness": {
    "type": "MPIO_0000009",
    "id": "d8ea3c5c-bc25-4cca-b9a8-ed742742e741",
    "value": "serious",
    "display": "A decrease in chronic myeloid leukemia treatment efficacy is a serious potential clinical consequence because it can result in death, life-threatening hospitalization, and disability."
  },
  "mechanism of interaction information": {
    "type": "MPIO_0000005",
    "id": "8a8ce6c0-c78e-4d38-bda2-835118f976ea",
    "display": "These TKIs demonstrate pH dependent absorption for oral administration which may result in decreased efficacy when given concomitantly with medications that increase gastric pH."
  },
  "frequency of exposure to the PDDI": {
    "type": "MPIO_0000007",
    "id": "56ebf54f-a148-4f2d-9924-b44f174ea72e",
    "display": "Unknown"
  },
  "frequency of harm for persons who have been exposed to the PDDI": {
    "type": "MPIO_0000006",
    "id": "207ae890-3098-436d-b248-578279c65d9c",
    "display": "Unknown"
  },
  "modifying factors information": {
    "type": "MPIO_0000000",
    "id": "b4566e23-3884-495f-93e1-04a0b5c9a008",
    "expressionLogic": "human readable",
    "members": [
      {
        "display": "The TKI is imatinib or ponatinib",
        "operational classification statement": {
          "type": "MPIO_0000111",
          "display": "No special precautions."
        },
        "evidence about a suspected drug-drug interaction": {
          "type": "MPIO_0000004",
          "display": "Imatinib and ponatinib AUCs are not appreciably decreased by PPI co-administration.",
          "References": [
            {
              "title": "Iclusig",
              "author": [
                {
                  "name": "ARIAD Pharmaceuticals, Inc."
                }
              ],
              "type": "package insert",
              "year": "2016",
              "link": [
                {
                  "url": "https://specialtydrug.magellanprovider.com/media/32235/iclusig.pdf"
                }
              ]
            },
            {
              "title": "Effect of a proton pump inhibitor on the pharmacokinetics of imatinib",
              "author": [
                {
                  "name": "Merrill J Egorin"
                },
                {
                  "name": "Dhvani D Shah"
                },
                {
                  "name": "Susan M Christner"
                },
                {
                  "name": "Mara A Yerk"
                },
                {
                  "name": "Kristin A Komazec"
                },
                {
                  "name": "Leonard R Appleman"
                },
                {
                  "name": "Robert L Redner"
                },
                {
                  "name": "Brian M  Miller"
                },
                {
                  "name": "Jan H Beumer"
                }
              ],
              "type": "article",
              "year": "2009",
              "journal": {
                "name": "British journal of clinical pharmacology",
                "volume": "68",
                "number": "3",
                "pages": "370--374"
              },
              "link": [
                {
                  "url": "https://bpspubs.onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2125.2009.03466.x"
                }
              ],
              "identifier": [
                {
                  "type": "doi",
                  "id": "10.1111/j.1365-2125.2009.03466.x"
                }
              ]
            }
          ]
        },
        "concept set references": [
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "Imatinib",
            "body": {
              "type": "SemanticTag",
              "value": "bd1da3d0-0543-42b6-9698-62f672d20ea4"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "ponatinib",
            "body": {
              "type": "SemanticTag",
              "value": "901791fd-5575-49fd-b043-a865743432b6"
            }
          }
        ]
      },
      {
        "display": "The TKI is nilotinib",
        "operational classification statement": {
          "type": "MPIO_0000111",
          "display": "Assess risk and take action if necessary."
        },
        "evidence about a suspected drug-drug interaction": {
          "type": "MPIO_0000004",
          "display": "Bosutinib and nilotinib AUCs are decreased with concomitant PPIs but antacids and H2 antagonists may be considered if TKI is given 2 hours before the antacid/H2 antagonist. However, for nilotinib a retrospective study has shown no difference in cytogenetic response rates for patients taking PPIs.",
          "References": [
            {
              "title": "Concurrent use of proton pump inhibitors or H2 blockers did not adversely affect nilotinib efficacy in patients with chronic myeloid leukemia",
              "author": [
                {
                  "name": "Ophelia QP Yin"
                },
                {
                  "name": "Frank J Giles "
                },
                {
                  "name": "Michele Baccarani"
                },
                {
                  "name": "Philipp Le Coutre"
                },
                {
                  "name": "Ovidiu Chiparus"
                },
                {
                  "name": "Neil Gallagher"
                },
                {
                  "name": "Giuseppe Saglio"
                },
                {
                  "name": "Timothy P Hughes"
                },
                {
                  "name": "Andreas Hochhaus"
                },
                {
                  "name": "Hagop M Kantarjian"
                },
                {
                  "name": "RA Larson"
                }
              ],
              "type": "article",
              "year": "2012",
              "journal": {
                "name": "Cancer chemotherapy and pharmacology",
                "volume": "70",
                "number": "2",
                "pages": "345--350"
              },
              "link": [
                {
                  "url": "https://link.springer.com/article/10.1007/s00280-012-1881-3"
                }
              ],
              "identifier": [
                {
                  "type": "doi",
                  "id": "10.1007/s00280-012-1881-3"
                }
              ]
            }
          ]
        },
        "concept set references": [
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "nilotinib",
            "body": {
              "type": "SemanticTag",
              "value": "d8dc65de-aefc-45a1-a0b5-d576838bcb1b"
            }
          }
        ]
      },
      {
        "display": "The TKI is bosutinib or dasatinib",
        "type": "MPIO_0000111",
        "operational classification statement": {
          "type": "MPIO_0000111",
          "display": "Use only if benefit outweighs risk."
        },
        "evidence about a suspected drug-drug interaction": {
          "type": "MPIO_0000004",
          "display": "Bosutinib and nilotinib AUCs are decreased with concomitant PPIs but antacids and H2 antagonists may be considered if TKI is given 2 hours before the antacid/H2 antagonist.",
          "References": [
            {
              "title": "Sprycel",
              "author": [
                {
                  "name": "Bristol-Myers Squibb Company"
                }
              ],
              "type": "package insert",
              "year": "2015",
              "link": [
                {
                  "url": "https://packageinserts.bms.com/pi/pi_sprycel.pdf"
                }
              ]
            },
            {
              "title": "Bosulif",
              "author": [
                {
                  "name": "Pfizer Labs"
                }
              ],
              "type": "package insert",
              "year": "2015",
              "link": [
                {
                  "url": "https://www.accessdata.fda.gov/drugsatfda_docs/label/2012/203341lbl.pdf"
                }
              ]
            },
            {
              "title": "Tasigna",
              "author": [
                {
                  "name": "Novartis"
                }
              ],
              "type": "package insert",
              "year": "2015",
              "link": [
                {
                  "url": "https://www.accessdata.fda.gov/drugsatfda_docs/label/2007/022068lbl.pdf"
                }
              ]
            }
          ]
        },
        "concept set references": [
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "bosutinib",
            "body": {
              "type": "SemanticTag",
              "value": "3b4fd6a6-15a8-4520-8ec4-ffb62898bc33"
            }
          },
          {
            "type": "TextQuoteSelector",
            "attribute": "display",
            "exact": "dasatinib",
            "body": {
              "type": "SemanticTag",
              "value": "e57501f1-5af2-4479-99c6-dc971e1dec35"
            }
          }
        ]
      }
    ]
  },
  "concept sets": [
    {
      "id": "2e1bca79-953c-4ea3-9c40-4c16da2dde31",
      "display": "chronic myeloid leukemia",
      "type": "SNOMED-CT",
      "system": "https://www.nlm.nih.gov/healthit/snomedct/",
      "members": [
        {
          "type": "IAO_0020000",
          "system": "https://www.nlm.nih.gov/healthit/snomedct/",
          "code": "415287001",
          "display": "Relapsing chronic myeloid leukemia"
        },
        {
          "type": "IAO_0020000",
          "system": "https://www.nlm.nih.gov/healthit/snomedct/",
          "code": "277589003",
          "display": "Atypical chronic myeloid leukemia"
        },
        {
          "type": "IAO_0020000",
          "system": "https://www.nlm.nih.gov/healthit/snomedct/",
          "code": "277587001",
          "display": "Juvenile chronic myeloid leukemia"
        },
        {
          "type": "IAO_0020000",
          "system": "https://www.nlm.nih.gov/healthit/snomedct/",
          "code": "128826001",
          "display": "Atypical chronic myeloid leukemia, BCR/ABL negative"
        }
      ]
    },
    {
      "id": "48695d2d-9545-4a31-80d1-201d7d7e2cdb",
      "display": " tyrosine kinase inhibitors (TKIs)",
      "type": "DIDEO_00000128",
      "system": "https://www.whocc.no/atc_ddd_index",
      "members": [
        {
          "type": "ATC",
          "system": "https://www.whocc.no/atc_ddd_index",
          "code": "L01XE04",
          "display": "sunitinib"
        },
        {
          "type": "ATC",
          "system": "https://www.whocc.no/atc_ddd_index",
          "code": "L01XC03",
          "display": "trastuzumab"
        },
        {
          "type": "ATC",
          "system": "https://www.whocc.no/atc_ddd_index",
          "code": "L01XC14",
          "display": "ado-trastuzumab emtansine"
        }
      ]
    },
    {
      "id": "05b06c16-a763-4ef4-90d8-899abb34abd8",
      "display": "proton pump inhibitors",
      "type": "DIDEO_00000128",
      "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
      "members": [
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "816346",
          "display": " Dexlansoprazole"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "283742",
          "display": "  Esomeprazole"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "17128",
          "display": " Lansoprazole"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "7646",
          "display": " Omeprazole"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "40790",
          "display": " Pantoprazole"
        },
        {
          "type": "APOLLO_SV_00000461",
          "system": "http://www.nlm.nih.gov/research/umls/rxnorm",
          "code": "114979",
          "display": " Rabeprazole"
        }
      ]
    },
    {
      "id": "bd1da3d0-0543-42b6-9698-62f672d20ea4",
      "display": "Imatinib",
      "type": "DIDEO_00000128",
      "system": "https://www.whocc.no/atc_ddd_index",
      "members": [
        {
          "type": "ATC",
          "system": "https://www.whocc.no/atc_ddd_index",
          "code": "L01XE01",
          "display": "imatinib"
        }
      ]
    },
    {
      "id": "901791fd-5575-49fd-b043-a865743432b6",
      "display": "ponatinib",
      "type": "DIDEO_00000128",
      "system": "https://www.whocc.no/atc_ddd_index",
      "members": [
        {
          "type": "ATC",
          "system": "https://www.whocc.no/atc_ddd_index",
          "code": "L01XE08",
          "display": "ponatinib"
        }
      ]
    },
    {
      "id": "d8dc65de-aefc-45a1-a0b5-d576838bcb1b",
      "display": "nilotinib",
      "type": "DIDEO_00000128",
      "system": "https://www.whocc.no/atc_ddd_index",
      "members": [
        {
          "type": "ATC",
          "system": "https://www.whocc.no/atc_ddd_index",
          "code": "L01XE08",
          "display": "nilotinib"
        }
      ]
    },
    {
      "id": "3b4fd6a6-15a8-4520-8ec4-ffb62898bc33",
      "display": "bosutinib",
      "type": "DIDEO_00000128",
      "system": "https://www.whocc.no/atc_ddd_index",
      "members": [
        {
          "type": "ATC",
          "system": "https://www.whocc.no/atc_ddd_index",
          "code": "L01XE14",
          "display": "bosutinib"
        }
      ]
    },
    {
      "id": "e57501f1-5af2-4479-99c6-dc971e1dec35",
      "display": "dasatinib",
      "type": "DIDEO_00000128",
      "system": "https://www.whocc.no/atc_ddd_index",
      "members": [
        {
          "type": "ATC",
          "system": "https://www.whocc.no/atc_ddd_index",
          "code": "L01XE06",
          "display": "dasatinib"
        }
      ]
    }
  ]
}

B. References

B.1 Normative references

[hansten-2001]
ORCA:OpeRational ClassificAtion of Drug Interactions.. Philip Hansten; John R. Horn; Thomas K. Hazlet. Journal of the American Pharmaceutical Association. March/April 2001.
[herrero-zazo-2015]
DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug-Drug Interactions and Their Mechanisms. Maria Herrero-Zazo; Isabel Segura-Bedmar; Janna Hastings; Paloma Martinez. Journal of Chemical Information and Modeling. Aug 24, 2015. URL: https://www.ncbi.nlm.nih.gov/pubmed/26147071
[owl2-overview]
OWL 2 Web Ontology Language Document Overview (Second Edition). W3C OWL Working Group. W3C. 11 December 2012. W3C Recommendation. URL: https://www.w3.org/TR/owl2-overview/
[RFC2119]
Key words for use in RFCs to Indicate Requirement Levels. S. Bradner. IETF. March 1997. Best Current Practice. URL: https://tools.ietf.org/html/rfc2119
[usdhhs-2011]
Guidance for Industry - Providing Regulatory Submissions in Electronic Format - Content of Labeling. U.S. Department of Health and Human Services. 2011. URL: https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072331.pdf
[who-umc-glossary]
UMC - Glossary. World Health Organization. 2017. URL: https://www.who-umc.org/global-pharmacovigilance/global-pharmacovigilance/glossary/

B.2 Informative references

[abarca-2004]
Concordance of severity ratings provided in four drug interaction compendia. Jacob Abarca; Daniel Malone; Edward Armstrong; Amy Grizzle; Philip Hansten; Robin Van, Bergen; Richard Lipton. Journal of the American Pharmacists Association: JAPhA. April 2004. URL: https://www.ncbi.nlm.nih.gov/pubmed/15098847
[ahrq-drug-drug]
Drug-drug interactions: percentage of patients who received a prescription for a target medication during the measurement period and who were dispensed a concurrent prescription for a precipitant medication. National Quality Measures Clearinghouse. AHRQ. URL: https://qualitymeasures.ahrq.gov/summaries/summary/47511/drugdrug-interactions-percentage-of-patients-who-received-a-prescription-for-a-target-medication-during-the-measurement-period-and-who-were-dispensed-a-concurrent-prescription-for-a-precipitant-medication?q=drugdrug+interactions
[ansm-2016]
Thesaurus des interactions medicamenteuses. ANSM. 2016. URL: http://ansm.sante.fr/var/ansm_site/storage/original/application/de444ea9eb4bc084905c917c902a805f.pdf
[ayvaz-2015]
Toward a complete dataset of drug-drug interaction information from publicly available sources. Serkan Ayvaz; John Horn; Oktie Hassanzadeh; Qian Zhu; Johann Stan; Nicholas Tatonetti; Santiago Vilar; Mathias Brochhausen; Matthias Samwald; Majid Rastegar-Mojarad; Michel Dumontier; Richard Boyce. Journal of Biomedical Informatics. Jun 2015. URL: https://www.ncbi.nlm.nih.gov/pubmed/25917055
[bender-2013]
HL7 FHIR: An Agile and RESTful approach to healthcare information exchange. D. Bender; K. Sartipi. June 2013. URL: http://ieeexplore.ieee.org/document/6627810/?reload=true
[bib-json]
How to do BibJSON. Open Knowledge Foundatoin. 2016. URL: http://okfnlabs.org/bibjson/
[bosulif-2015]
Bosulif. Pfizer Labs, New York, NY. 2015.
[bottiger-2009]
{SFINX}-a drug-drug interaction database designed for clinical decision support systems. Ylva Bottiger; Kari Laine; Marine Andersson; Tuomas Korhonen; Bjorn Molin; Marie-Louise Ovesjo; Tuire Tirkkonen; Anders Rane; Lars Gustafsson; Birgit Eiermann. European Journal of Clinical Pharmacology. Jun 2009. URL: https://www.ncbi.nlm.nih.gov/pubmed/19205683
[boyce-2013]
Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness. Richard Boyce; John Horn; Oktie Hassanzadeh; Anita De Waard; Jodi Schneider; Joanne Luciano; Majid Rastegar-Mojarad; Maria Liakata. Journal of Biomedical Semantics. URL: https://jbiomedsem.biomedcentral.com/track/pdf/10.1186/2041-1480-4-5?site=jbiomedsem.biomedcentral.com
[bristol-myers-warfarin]
{DailyMed} - {COUMADIN}- warfarin sodium tablet. Bristol-Myers Squibb. 2017. URL: https://dailymed.nlm.nih.gov/dailymed/drugInfo.cfm?setid=d91934a0-902e-c26c-23ca-d5accc4151b6
[brochhausen-2014]
Towards a foundational representation of potential drug- drug interaction knowledge. Mathias Brochhausen; Jodi Schneider; Daniel Malone; Philip Empey; R. Hogan; Richard Boyce. URL: http://ceur-ws.org/Vol-1309/paper2.pdf
[cdc-faststats]
FastStats. CDC. FASTSTATS. URL: https://www.cdc.gov/nchs/fastats/emergency-department.htm
[cms-2013]
Eligible Professional Meaningful Use Core Measures, Measure 2 of 15. CMS. 2013. URL: http://www.opendental.com/manual/EHR%202011/2%20Drug%20Interaction%20Checks%202012-07%2027.pdf
[dailymed-about]
DailyMed - About. DailyMed. 2019. URL: https://dailymed.nlm.nih.gov/dailymed/about-dailymed.cfm
[dailymed-indexing]
SPL RESOURCES: Download All Indexing & REMS Files. DailyMed. 2019. URL: https://dailymed.nlm.nih.gov/dailymed/spl-resources-all-indexing-files.cfm
[dechanont-2014]
Hospital admissions/visits associated with drug-drug interactions: a systematic review and meta-analysis. Supinya Dechanont; Sirada Maphanta; Bodin Butthum; Chuenjid Kongkaew. Pharmacoepidemiology and Drug Safety. May 2014. URL: http://www.ncbi.nlm.nih.gov/pubmed/24616171
[drugs-dot-com-interaction]
Drug Interaction Report. Drugs.com. 1/28/2019. URL: https://www.drugs.com/interactions-check.php?drug_list=1310-0%2C2311-0&professional=1&types%5B%5D=major
[egorin-2009]
Effect of a proton pump inhibitor on the pharmacokinetics of imatinib. Merrill J. Egorin; Dhvani D. Shah; Susan M. Christner; Mara A. Yerk; Kristin A. Komazec; Leonard R. Appleman; Robert L. Redner; Brian M. Miller; Jan H. Beumer. British Journal of Clinical Pharmacology. 2009. URL: https://www.ncbi.nlm.nih.gov/pubmed/19740393
[european-medicines-2012]
Guideline on the investigation of drug interactions. European Medicines Agency. Jun 2012. URL: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf
[fda-cfr-21-4]
CFR - Code of Federal Regulations Title 21, Volume 4. FDA. URL: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=201.57
[fda-simvastatin-amiodarone]
FDA Drug Safety Communication: Revised dose limitation for Zocor (simvastatin) when taken with amiodarone. FDA. 2011. URL: https://www.fda.gov/Drugs/DrugSafety/ucm283137.htm
[fung-2017]
Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. Kin Fung; Joan Kapusnik-Uner; Jean Cunningham; Stefanie Higby-Baker; Olivier Bodenreider. Journal of the American Medical Informatics Association: JAMIA. Jul 01, 2017. URL: https://www.ncbi.nlm.nih.gov/pubmed/28339701
[HL7-cds-PDDI-draft]
Potential Drug-Drug Interaction (PDDI) CDS IG : Ballot for Comment 2 - Terminology page. HL7 Clinical Decision Support Workgroup. 2019. URL: http://build.fhir.org/ig/HL7/PDDI-CDS/terminology.html
[hl7-pddi-cds-ig-git]
Potential Drug-Drug Interaction (PDDI) CDS Implementation Guide project. HL7 Clinical Decision Support Workgroup. 2019. URL: https://github.com/HL7/PDDI-CDS
[iclusig-2016]
Iclusig. ARIAD Pharmaceuticals, Inc., Cambridge, MA. 2016.
[institute-medicine-2006]
Preventing Medication Errors. Institute of Medicine. July 2006. URL: https://www.nap.edu/catalog/11623/preventing-medication-errors
[magro-2012]
Epidemiology and characteristics of adverse drug reactions caused by drug-drug interactions. Lara Magro; Ugo Moretti; Roberto Leone. Expert Opinion on Drug Safety. Jan 2012. URL: https://www.ncbi.nlm.nih.gov/pubmed/22022824
[mandel-2016]
SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. Joshua Mandel; David Kreda; Kenneth Mandl; Isaac Kohane; Rachel Ramoni. Journal of the American Medical Informatics Association: JAMIA. Sep 2016. URL: https://www.ncbi.nlm.nih.gov/pubmed/26911829
[masclee-2014]
Risk of upper gastrointestinal bleeding from different drug combinations.. Preciosa Coloma; Gini Rosa; Ron Herings; Ernst Kuipers; Gwen Masclee; Giampiero Mazzaglia; Lars Pedersen; Gino Picelli; Silvana Romio; Martijn Schuemie; Lorenza Scotti; Miriam Sturkenboom; Vera Valkhoff; Maria de Ridder; Johan van der Lei. Gastroenterology. October 2014. URL: https://www.ncbi.nlm.nih.gov/pubmed/24937265
[nabovati-2017]
A survey of attitudes, practices, and knowledge regarding drug-drug interactions among medical residents in Iran. Ehsan Nabovati; Hasan Vakili-Arki; Zhila Taherzadeh; Mohammad Saberi; Ameen Abu-Hanna; Saeid Eslami. International Journal of Clinical Pharmacy. Jun 2017. URL: www.ncbi.nlm.nih.gov/pubmed/28382584
[NLM-vsac-2019]
Value Set Authority Center - U.S. National Library of Medicine. NLM. 2019. URL: https://vsac.nlm.nih.gov/
[ohdsi-atlas]
OHDSI - Atlas: is an open source software tool for researchers to conduct scientific analyses on standardized observational data. OHDSI. Observational Health Data Sciences and Informatics. URL: https://github.com/OHDSI/Atlas
[payne-2015]
Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. Thomas Payne; Lisa Hines; Raymond Chan; Seth Hartman; Joan Kapusnik-Uner; Alissa Russ; Bruce Chaffee; Christian Hartman; Victoria Tamis; Brian Galbreth; Peter Glassman; Shobha Phansalkar; Heleen Sijs; Sheila Gephart; Gordon Mann; Howard Strasberg; Amy Grizzle; Mary Brown; Gilad Kuperman; Chris Steiner; Amanda Sullins; Hugh Ryan; Michael Wittie; Daniel Malone. Journal of the American Medical Informatics Association: JAMIA. Nov 2015. URL: https://www.ncbi.nlm.nih.gov/pubmed/25829460
[pfistermeister-2014]
Inconsistencies and misleading information in officially approved prescribing information from three major drug markets. B. Pfistermeister; A. SaB; M. Criegee-Rieck; T. Burkle; M. Fromm; R. Maas. Clinical Pharmacology and Therapeutics. Nov 2014. URL: https://www.ncbi.nlm.nih.gov/pubmed/25062063
[rekic-2017]
Clinical Drug-Drug Interaction Evaluations to Inform Drug Use and Enable Drug Access. Dinko Rekic; Kellie Reynolds; Ping Zhao; Lei Zhang; Kenta Yoshida; Madhav Sachar; Micheline Piquette', u'Miller; Shiew-Mei Huang; Issam Zineh. Journal of Pharmaceutical Sciences. Sep 2017. URL: https://www.ncbi.nlm.nih.gov/pubmed/28435142
[ridgely-2012]
Too Many Alerts, Too Much Liability: Sorting through the Malpractice Implications of Drug-Drug Interaction Clinical Decision Support Clinical Support Systems for Drug-Drug Interactions: Implementing Effective Systems, Limiting Malpractice Liability. M. Ridgely; Michael Greenberg. Saint Louis University Journal of Health Law & Policy. 2011-2012. URL: http://heinonline.org/HOL/Page?handle=hein.journals/sljhlp5&collection=journals&id=271&startid=&endid=310
[riedmann-2011]
How to improve the delivery of medication alerts within computerized physician order entry systems: an international Delphi study. Daniel Riedmann; Martin Jung; Werner Hackl; Elske Ammenwerth. Journal of the American Medical Informatics Association: JAMIA. 2011 Nov-Dec. URL: https://www.ncbi.nlm.nih.gov/pubmed/21697293
[romagnoli-2017]
Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews. Katrina Romagnoli; Scott Nelson; Lisa Hines; Philip Empey; Richard Boyce; Harry Hochheiser. BMC medical informatics and decision making. February 2017. URL: https://www.ncbi.nlm.nih.gov/pubmed/28228132
[rosko-2017]
Toward shareable individualized drug interaction alerts.. Samuel C. Rosko; Philip Hansten; John R. Horn; Daniel C. Malone; Andrew Romero; Richard D. Boyce. AMIA Summit on Clinical Research Informatics. Mar 2017.
[saverno-2011]
Ability of pharmacy clinical decision-support software to alert users about clinically important drug-drug interactions. Kim Saverno; Lisa Hines; Terri Warholak; Amy Grizzle; Lauren Babits; Courtney Clark; Ann Taylor; Daniel Malone. Journal of the American Medical Informatics Association: JAMIA. 2011 Jan-Feb. URL: https://www.ncbi.nlm.nih.gov/pubmed/21131607
[scheife-2015]
Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support. Richard Scheife; Lisa Hines; Richard Boyce; Sophie Chung; Jeremiah Momper; Christine Sommer; Darrell Abernethy; John Horn; Stephen Sklar; Samantha Wong; Gretchen Jones; Mary Brown; Amy Grizzle; Susan Comes; Tricia Wilkins; Clarissa Borst; Michael Wittie; Daniel Malone. Drug Safety. Feb 2015. URL: https://www.ncbi.nlm.nih.gov/pubmed/25556085
[sprycel-2015]
Sprycel. Bristol-Myers Squibb Company, Princeton, NJ. 2015.
[tamblyn-2012]
The effectiveness of a new generation of computerized drug alerts in reducing the risk of injury from drug side effects: a cluster randomized trial. Robyn Tamblyn; Tewodros Eguale; David Buckeridge; Allen Huang; James Hanley; Kristen Reidel; Sherry Shi; Nancy Winslade. Journal of the American Medical Informatics Association: JAMIA. 2012 Jul-Aug. URL: https://www.ncbi.nlm.nih.gov/pubmed/22246963
[tasigna-2015]
Tasigna. Novartis, East Hanover, NJ. 2015.
[tilson-2016]
Recommendations for selecting drug-drug interactions for clinical decision support. Hugh Tilson; Lisa Hines; Gerald McEvoy; David Weinstein; Philip Hansten; Karl Matuszewski; Marianne Comte; Stefanie Higby-Baker; Joseph Hanlon; Lynn Pezzullo; Kathleen Vieson; Amy Helwig; Shiew-Mei Huang; Anthony Perre; David Bates; John Poikonen; Michael Wittie; Amy Grizzle; Mary Brown; Daniel Malone. American journal of health-system pharmacy: AJHP: official journal of the American Society of Health-System Pharmacists. Apr 15, 2016. URL: http://www.ncbi.nlm.nih.gov/pubmed/27045070
[usdhhs-2017]
Clinical Drug Interaction Studies — Study Design, Data Analysis, and Clinical Implications. U.S. Department of Health and Human Services; Food and Drug Administration; Center for Drug Evaluation and Research (CDER). Oct 2017. URL: https://www.fda.gov/downloads/drugs/guidances/ucm292362.pdf
[utecht-2017]
Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation. Joseph Utecht; Mathias Brochhausen; John Judkins; Jodi Schneider; Richard Boyce. Studies in Health Technology and Informatics. 2017. URL: https://www.ncbi.nlm.nih.gov/pubmed/29295242
[van-der-sijs-2006]
Overriding of drug safety alerts in computerized physician order entry. Heleen Van Der Sijs; Jos Aarts; Arnold Vulto; Marc Berg. Journal of the American Medical Informatics Association: JAMIA. 2006 Mar-Apr. URL: https://www.ncbi.nlm.nih.gov/pubmed/16357358
[wang-2010]
Black box warning contraindicated comedications: concordance among three major drug interaction screening programs. Lorraine Wang; Maple Wong; James Lightwood; Christine Cheng. The Annals of Pharmacotherapy. Jan 2010. URL: https://www.ncbi.nlm.nih.gov/pubmed/20040698
[web-annotation-model]
Web Annotation Data Model. Robert Sanderson; Paolo Ciccarese; Benjamin Young. 2/23/2017. URL: https://www.w3.org/TR/annotation-model/
[yin-2012]
Concurrent use of proton pump inhibitors or H2 blockers did not adversely affect nilotinib efficacy in patients with chronic myeloid leukemia. Ophelia Q.P. Yin; Frank J. Giles; Michele Baccarani; Philipp Le Coutre; Ovidiu Chiparus; Neil Gallagher; Giuseppe Saglio; Timothy P. Hughes; Andreas Hochhaus; Hagop M. Kantarjian; Richard A. Larson. Cancer Chemotherapy and Pharmacology. 2012. URL: https://www.ncbi.nlm.nih.gov/pubmed/22623211