Addressing gaps-in-clinically-useful-evidence-on-dd is-nlm-training-2014


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An overview of the research aims for the National Library of Medicine funded research project titled "Addressing gaps in clinically useful evidence on drug-drug interactions" (1R01LM011838-01)

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  • I am very privileged to have been invited to speak at your seminar. I would like to thank Dr. Lang Li for inviting me and all those who have helped arrange my visit.

    The title of my talk is….
  • One of the things about PDDIs that makes them hard to study from an epidemiologic perspective is that there are many reasons why a PDDI might not result in harm….drugs are designed to be safe, random effects like patient non-compliance…

    Well designed prospective and retrospective studies have found evidence of the role of PDDIs in causing patient harm…

    Gurwitz et al, in their cohort study of ADEs among older Americans receiving ambulatory care, found that 13.3% of preventable errors leading to an ADE involved the co-prescription of drugs for which a “...well established, clinically important interaction” was known.2

    Nearly 7% (23/338) of the ADEs experienced by residents of two academic NHs over a nine-month period were attributable to PDDIs.3

    Sixteen cohort and case-control studies reported an elevated risk of hospitalization in patients who were exposed to PDDIs.4

  • First there are many defenses in place that help reduce risk…..


    Indeed, health care providers often have inadequate knowledge of what drug interactions can occur, patient specific factors that can increase the risk of harm from an interaction, and how to properly manage an interaction when patient exposure cannot be avoided
  • A hypothetical illustration of how incomplete PDDI knowledge could lead to a harmful medication error.

    A clinician considers risperidone for treating an HIV patient taking ritonavir.

    Only one of two different drug information systems lists the PDDI.

    How system PDDI knowledge might influence probable clinician actions and patient outcomes.
  • There is plenty of evidence on how drug information systems disagree about PDDIs

    Contraindications are generally considered to be cases where two drugs should almost never be prescribed together…yet…
  • Addressing gaps-in-clinically-useful-evidence-on-dd is-nlm-training-2014

    1. 1. Biomedical Informatics1 Addressing Gaps in Clinically Useful Evidence on Drug-drug Interactions – overview of aims Richard Boyce, PhD University of Pittsburgh Department of Biomedical Informatics NLM Training Conference June 18th 2014
    2. 2. Biomedical Informatics2 My Lab - Translational Informatics Applied to Drug Safety (TRIADS)
    3. 3. Biomedical Informatics3 The focus of todays “Show Case” Improving drug-drug interaction knowledge representation and information retrieval National Library of Medicine (1R01LM011838-01)
    4. 4. Biomedical Informatics4 What is a drug-drug interaction • Drug-drug interaction: – a clinically meaningful alteration of the effect of a drug (object drug) occurs as a result of coadministration of another drug (precipitant drug) [1] • Potential drug-drug interaction (PDDI): – two drugs known to interact are prescribed whether or not harm ensues [1] 1.Hines LE, Malone DC, Murphy JE. Recommendations for Generating, Evaluating, and Implementing Drug-Drug Interaction Evidence. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. 2012;32(4):304–313.
    5. 5. Biomedical Informatics5 The clinical importance of PDDIs • Exposure to PDDIs is a significant source of preventable drug-related harm [2,3] • Studies of drug-drug interactions – Harm 1.9 to 5 million inpatients per year – Cause 2,600 to 220,000 emergency department visits per year 2. Magro L, Moretti U, Leone R. Epidemiology and characteristics of adverse drug reactions caused by drug-drug interactions. Expert Opin Drug Saf. 2012 Jan;11(1):83-94. doi: 10.1517/14740338.2012.631910. Epub 2011 Oct 25. Review. PubMed PMID: 22022824. 3., Last Accessed 12/06/2013
    6. 6. Biomedical Informatics6 Knowledge is important Borrowed from Phil Hansten and John Horn
    7. 7. Biomedical Informatics7 Key point • Many drug information systems disagree about PDDIs – the specific ones that exist – their potential to cause harm • This leads to – confusion and frustration for clinicians – greater risks of harm to patients
    8. 8. Biomedical Informatics8 The danger of incomplete drug- drug interaction knowledge
    9. 9. Biomedical Informatics9 Evidence of drug compendia problems • Three PDDI information sources agreed upon only 25% of 59 contraindicated drug pairs found in black box warnings [28] • 18 (28%) of 64 pharmacy information and clinical decisions support systems correctly identified 13 clinically significant DDIs [29] • Four sources agreed on only 2.2% of 406 PDDIs considered to be “major” by at least one source [30]
    10. 10. Evidence from the drug compendium perspective Pre-market studies Post-market studies Product labeling Reported in Clinical experience Scientific literature Rarely reported in Rarely reported in Reported in Rarely reported in Drug Compendia synthesize PDDI evidence into knowledge but • May fail to include important PDDIs • Often disagree about PDDI evidence and seriousness ranking • May include numerous PDDIs with little evidence for liability reasons Source for Source for
    11. 11. Biomedical Informatics11 There is a need for a new PDDI knowledge representation paradigm This paradigm should do for PDDIs what the Pharmacogenomics Knowledge Base (PharmGKB) and Pharmacogenomics Research Network (PGRN) have done for clinical pharmacogenomics
    12. 12. Biomedical Informatics12 PharmGKB as inspiration for a new drug interaction knowledge base (DIKB) • PharmGKB – A single point of entry to nearly all relevant pharmacogenomics research – A network of researchers and stakeholders – A growing set of clinical pharmacogenomics guidelines • Future DIKB – A single point of entry to nearly all relevant DDI research and case reports – A network of researchers and stakeholders – A growing set of clinical guidelines for PDDI exposure
    13. 13. Biomedical Informatics13 Informatics foundations for a new DIKB: Aim 1 1. Derive a new PDDI meta-data standard that can meet the information needs of drug compendia editors and pharmacist working in different care settings – the best thinking of drug information system designers and the biomedical ontology community – extends existing national drug terminology efforts – will have a high likelihood of widespread adoption Pre-market studies Post-market studies Clinical experience A framework for representing PDDI assertions and evidence as interoperable Linked Data available for community annotation Semantic annotation pipeline
    14. 14. Biomedical Informatics14 Aim 1 – Highlights of the approach • …the best thinking of drug information system designers and the biomedical ontology community • a new OBO ontology for PDDIs and evidence • grounded competency questions – qualitative analysis of interviews with clinical pharmacists, drug compendia editors, and the results of a systematic search of the literature • …extends existing national drug terminology efforts • interoperability with RxNorm and the NDF-RT • …will have a high likelihood of widespread adoption • stakeholders from FDA, NLM, W3C, Pharma, and Cochrane Collaboration
    15. 15. Biomedical Informatics15
    16. 16. Biomedical Informatics16 Informatics foundations for a new DIKB: Aim 2 2. Apply a novel evidence synthesis process to enhance drug product label PDDI information – implement a pipeline for extracting PDDI mentions from product labeling and integrating them with other public sources – annotations can be “curated” by a distributed group of drug experts and non-experts – dynamically enhance product label content A framework for representing PDDI assertions and evidence as interoperable Linked Data available for community annotation Data driven: • Synthesis of public PDDI sources Expert: • Web-based scientific discourse Knowledge curationAim 1 Aim 2
    17. 17. Aim 2 - a step toward the next generation of drug product labeling PDDI Extraction algorithm Lovastatin product label Human curation Semantic tags Linking to other relevant sources
    18. 18. Biomedical Informatics18 Take the drug interactions section of a drug product label…
    19. 19. Biomedical Informatics19 Make it simple for the reader to see claims that could expand or update the information in this section…
    20. 20. Biomedical Informatics20 Example: an interaction affecting venlafaxine that may not be in this section…
    21. 21. Biomedical Informatics21 Aim 2 – Highlights of the approach
    22. 22. Aim 2 – Highlights of the approach continued • PDDI information interlinking • Drug name mapping across sources [1] • Identification and merging of PDDI public information sources [2] • Advancing PDDI evidence reviews • A “Micropublication” model for drug-drug interaction evidence [3] 1. Hassanzadeh O, Zhu Q, Freimuth R, Boyce R. Extending the "Web of Drug Identity" with Knowledge Extracted from United States Product Labels. AMIA Summits Transl Sci Proc. 2013 Mar 18;2013:64-68. PubMed PMID: 24303301; PMCID: PMC3814463 2. Ayvaz S., Zhu Q., Hochheiser H., Brochhausen M., Horn, J., Dumontier, M., Samwald M., Boyce, RD. “Drug-Drug Interaction Data Source Survey and Linking.” Abstract and Poster presentation to appear in AMIA Summits Transl Sci Proc. 2014. 3. Schneider, J., Collins, C., Hines, L., Horn, JR, Boyce, R. “Modeling Arguments in Scientific Papers.” at the 12th Annual ArgDiaP Conference: From Real Data to Argument Mining. Warsaw, Poland, May 23-24 2014.
    23. 23. SPL/DailyMed Jamboree Workshop Using DailyMed Drug Product Label Data September 18, 9:30 AM to 4:15 PM Lister Hill Auditorium, National Library of Medicine Topics include: • extracting indication and drug interaction data from structured product labels using natural language processing • Linked Data and structured product labels
    24. 24. Informatics foundations for a new DIKB: Aim 3 3. Pilot test new methods for PDDI information retrieval supporting drug information experts • Develop a high performance PDDI information retrieval algorithm • Develop and iteratively refine multiple initial prototypes based on feedback from end users • Report on a single end-user validated design implemented for public demonstration
    25. 25. Informatics foundations for a new DIKB Product labeling Scientific literature A framework for representing PDDI assertions and evidence as interoperable Linked Data available for community annotation Semantic annotation pipeline Reduced risk of a PDDI medication error! More efficient synthesis of PDDI evidence, easier identification of gaps Expected benefits: • More complete and accurate PDDI evidence • Better informed pharmacists and other clinicians • More effective PDDI alerting and decisions support systems Data driven: • Synthesis of public PDDI sources Expert: • Web-based scientific discourse Knowledge curation Dynamic enhancements
    26. 26. Biomedical Informatics26 Acknowledgements - People • Co-investigators: Harry Hochheiser, Phil Empey, Carol Collins (UW Seattle), John Horn (UW Seattle), Dan Malone (U of A), Lisa Hines (U of A), William Hogan (UAMS), Mathias Brochhausen (UAMS) • Programmer: Yifan Ning • Students and Research assistants: Katrina Romagnoli, Andres Hernandez Camacho, Jeremy Jao, Serkan Ayvaz (Kent State), Majid Rastegar-Mojarad (Mayo) • Advisors: Rebecca Crowley, Steven Handler, Chip Reynolds, Jordan Karp, Wendy Chapman (U of Utah), Tim Clark and Paulo Ciccarese (Harvard), Robert Freimuth (Mayo, PGRN), Qian Zhu (U of Maryland) • Additional stakeholders: FDA, Cochrane, W3C Health Care and Life Sciences Interest Group, ASHP, IBM Research
    27. 27. Biomedical Informatics27 Acknowledgements - Funding • The American taxpayers via: – NLM (1R01LM011838-01 and T15 LM007059-24) – NIH/NIA (K01AG044433-01, K07AG033174) – Agency for Healthcare Research and Quality (K12HS019461 and R01HS018721) – NIH/NCATS (KL2TR000146) – NIH/NIGMS (U19 GM61388; the Pharmacogenomic Research Network)
    28. 28. Biomedical Informatics28 Discussion