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Kensaku Kawamoto, MD, PhD Director, Knowledge Management and Mobilization

Clinical Decision Support for Genetically Guided Personalized Medicine: a Systematic Review JAMIA Journal Club February 7, 2013. Kensaku Kawamoto, MD, PhD Director, Knowledge Management and Mobilization Assistant Professor, Department of Biomedical Informatics University of Utah

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Kensaku Kawamoto, MD, PhD Director, Knowledge Management and Mobilization

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  1. Clinical Decision Support for Genetically Guided Personalized Medicine: a Systematic ReviewJAMIA Journal ClubFebruary 7, 2013 Kensaku Kawamoto, MD, PhDDirector, Knowledge Management and Mobilization Assistant Professor, Department of Biomedical Informatics University of Utah Brandon Welch, MSPh.D. Candidate, Department of Biomedical Informatics PredoctoralFellow, Program in Personalized Health Care University of Utah

  2. Disclaimers • KK is, or has been in the recent past, a consultant on clinical decision support to the following entities: • Office of the National Coordinator for Health IT (ONC) • Partners HealthCare • RAND Corporation • ARUP Laboratories • Clinica Software, Inc. • Religent, Inc. • Inflexxion, Inc. • Intelligent Automation, Inc. • BW is the founder and owner of SGgenomics, Inc., which developed ItRunsInMyFamily.com, a patient-centered family health history tool

  3. Background

  4. Definitions • Clinical decision support (CDS) • Provision of pertinent knowledge and/or person-specific information to clinical decision makers to enhance health and health care1 • Genetically guided personalized medicine (GPM) • Delivery of individually tailored medical care that leverages information about each person’s unique genetic characteristics • Includes use of genotype, gene expression profile, and/or family health history (FHx) Ref 1. Osheroff et al., JAMIA, 2006

  5. The Promise of GPM • Fueled by rapid advances in genetics & genomics • E.g., cost of full genome sequencing = thousands of dollars today vs. billions of dollars ~10 years ago (Human Genome Project) • Anticipated benefits: • Improved prevention through better risk identification • Enhanced diagnosis of diseases and their molecular sub-types • Improved treatment tailored to individual genetic profiles • Ultimately, improved outcomes at a lower cost

  6. Why CDS for GPM? • Even for “traditional” medicine, it can take 15+ years to translate research from bench to bedside1 • GPM faces unique challenges to clinical translation • Limited genetic proficiency of clinicians • Limited availability of genetics experts • Breadth and growth of genetic knowledge base • CDS is a proven mechanism for translating evidence into practice2 Ref 1. Balas et al., Yearbook of Medical Informatics, 2000. Ref 2. Kawamoto et al., BMJ, 2005.

  7. CDS as Bridge to Realize the Promise of GPM

  8. Study Objectives • Characterize research to date on use of CDS to enable GPM • Identify areas of need for future research

  9. Methods

  10. Literature Search • Data Sources • MEDLINE + Embase, 1990-2011 (last searched 6/2012) • Search Strategy • Adapted from previous systematic reviews of CDS, genetic health services, and FHx • Inclusion Criteria • English, human focus, peer-reviewed primary article • Intervention study evaluating impact of CDS for GPM in an actual patient care setting, OR • Methodology article focused on how CDS systems should be designed to support GPM (includes system description articles)

  11. Study Selection and Data Abstraction • Initial screening: title + index terms + abstract • Final screening: full text articles • Data abstraction • Users and study location • CDS purpose and clinical application area • CDS type – stand-alone vs. integrated • Genetic information used (FHx, genotype, or both) • Manuscript type (e.g., RCT, system description) • Manuscript summary and trial details (if applicable) • Notable informatics aspects

  12. Results

  13. Study Identification and Selection

  14. CDS GPM Areas of Focus

  15. CDS for Genetically Guided Cancer Mgmt. • Risk Assessment in Genetics (RAGs) system for providing FHx-driven CDS for breast, ovarian, and colorectal cancer (n = 6) (Table 1) • Other FHxCDS tools for breast cancer (n = 6) (Table 2) • Genotype-driven CDS tools for breast cancer (n = 4) (Table 3) • CDS tools for other cancers, primarily colorectal cancer (n = 6) (Table 4)

  16. RAGs: FHx-Driven Cancer Management Emery J et al. BMJ. 1999;319:32-6.

  17. GRAIDS Pedigree Editor Emery J. The GRAIDS Trial: the development and evaluation of computer decision support for cancer genetic risk assessment in primary care. Ann Hum Biol 2005;32:218-27.

  18. GRAIDS Trial, 2007 • Study design: cluster RCT across 45 general practitioner teams in UK • Intervention: GRAIDS (RAGs successor) used by designated clinician at each site • Results: Significantly increased referrals to regional genetics clinic (p = 0.001), with referrals being significantly more consistent with referral guidelines (p = 0.006) Emery J et al. Br J Cancer 2007;97:486-93.

  19. Other FHx CDS Tools for Breast Cancer • FHx-based risk assessment tools for breast cancer and BRCA mutation risk (Tsouskas, 1997; Berry, 2002) • RCT of stand-alone breast cancer CDS tool  limited impact due to lack of awareness and use by GPs (Wilson, 2006) • RCT of stand-alone CDS tool calculating breast cancer, heart disease, osteoporosis, and endometrial cancer risk  increased genetic counselor effectiveness (Matloff, 2005 and 2006)

  20. Hughes RiskApps Ozanne EM et al. Breast J2009;15:155e62. http://www.hughesriskapps.net.

  21. Genotype-Driven CDS for Breast Cancer • Focused on decision making after BRCA mutation status known • 2 RCTs of patient-facing decision aids found them to be effective for risk assessment and decision making (Schwartz, 2009; Hooker, 2011) • Affirmative qualitative evaluation of REACT, a system for providing a graphical assessment of lifetime risk based on alternative risk-reduction interventions (Glasspool, 2007 and 2010)

  22. REACT Glasspool DW et al. J Cancer Educ 2010;25:312-16.

  23. CDS for Other Cancers • Strong focus on colorectal cancer, and in particular Lynch syndrome • CRCAPRO – use of FHx to identify patients with Lynch syndrome (Bianchi, 2007) • FHx CDS system for Dr. Lynch’s hereditary cancer consulting service  significant reduction in time spent on cases (Evans, 1995) • RCT of electronic reminders to consider Lynch syndrome genetic testing based on FHx  significantly increased risk identification and genetic testing (Overbeek, 2010)

  24. CDS for Other Cancers (cont’d) • Stand-alone, Web-based CDS for other cancers • Oral cavity squamous cell carcinoma: tool for predicting reoccurrence based on medical images, genetic markers, and other data(Picone, 2011) • Alcohol-related cancer: tool for assessing alcohol-related cancer risk based on genotype; RCT with college students found significant reductions in drinking(Hendershot, 2010) • Prostate cancer: tool for providing personalized risk assessment and management recommendations based on age and FHx(Wakefield, 2011)

  25. CDS for Pharmacogenomics (PGx) (Table 5) • HIV PGx(n = 2) • System description (Pazzani, 1997) • RCT  improved therapy outcomes vs. SOC (Tural, 2002) • CDS integration into primary clinical information systems (n = 3) • Integration of PGx knowledge base for national use (Swen, 2008) • Impact of alternate SNP models in EHR for CDS (Deshmukh, 2009) • Availability of patient data required for PGx within EHR (Overby, 2010) • Warfarin PGx tool that estimated plasma warfarin levels over time (Bon Homme, 2008)

  26. Warfarin PGx CDS Tool Bon Homme M, Reynolds KK, Valdes R Jr, et al. Dynamic pharmacogenetic models in anticoagulation therapy. Clin Lab Med2008;28:539-52.

  27. Medication Surveillance in the Netherlands Swen JJ, Wilting I, de Goede AL, et al. Pharmacogenetics: from bench to byte. ClinPharmacolTher2008;83:781-7.

  28. Other CDS for GPM (Table 6) • FHx-driven CDS (n = 6) • Genotype-driven CDS (n = 4)

  29. FHx-Driven CDS Systems • GenInfer – use of FHx to calculate genetic risks and probability of inheritance (Harris, 1990) • System used by Russian federal genetics center for genetics care (Kobrinskii, 1997 and Kobrinsky, 1998) • MeTree – a primary care FHx tool for various conditions (Orlando, 2011) • RCT of CDC Family Healthware  no difference with control group (Rubinstein, 2011) • EHR-based cardiovascular risk assessment, including use of FHx(Wells, 2007)

  30. Genotype-Driven CDS Systems • @neurIST – use of genetics, radiology results, and clinical data from CISs to provide guidance on intracranial aneurism care (Iavindrasana, 2008) • Portable medical device for diagnosing rheumatoid arthritis and multiple sclerosis using clinical data + miniature genetic analysis device (Kalatzis, 2009) • Survey finding clinicians felt EHRs could do much more to meet their GPM needs (Scheuner, 2009) • GeneInsight – system for patient-specific genetic testing reports + notifications regarding updates to interpretations (Aronson, 2011)

  31. GeneInsight Aronson SJ et al. Hum Mutat2011;32:532e6.

  32. Trend Analyses

  33. Publications by Year

  34. Integrated vs. Stand-Alone CDS

  35. FHx vs. Genotype-Driven CDS

  36. Discussion

  37. Summary • Systematic review of CDS for GPM, 1990-2011 • 38 primary research articles, majority 2007-2011 • Focal areas: cancer, FHx, PGx • Increasing trend to genotype-driven, integrated CDS • 9 RCTs

  38. Strengths • First systematic review on CDS for GPM • Search strategy based on previous systematic reviews on related topics • Used Embase in addition to MEDLINE • Insights and trend analyses show how field has developed and where it is headed

  39. Limitations • Does not provide a quantitative meta-analysis of the impact of CDS interventions • Not possible due to limited number of outcomes studies in the field • Included manuscripts only in English • Some relevant articles in 2011 may not have been indexed yet • Potential publication bias with 77% (7/9) RCTs reporting positive results (vs. 60%) • Potentially due to system use being required by many study protocols

  40. RCTOutcomes • Automatic provision of CDS not essential in CDS for GPM? (Kawamoto, 2005) • 6 positive RCTs without automatic provision • 5/6 mandated CDS use by study protocol • GRAIDS RCT did not mandate use, but designated clinicians extensively trained and managed all relevant patients  may not be feasible outside study • CDS for GPM not exception to the requirement for automatic CDS

  41. Future Directions • Need more research and development • Need more RCTs • Need more integration with primary clinical information systems • Need more use of standards • Need more use of genotype data, in particular whole genome sequence data

  42. Next Frontier: Whole Genome Sequence CDS Low-cost, one-time storage of whole genome data could overcome significant barrier to GPM (need for near real-time, low-cost genetic testing) Still many challenges Genome data management – How and where should WGSs be stored? Genome knowledge management – How do we build and maintain an accurate and comprehensive knowledge base? Clinical genome application – How do we bring it all together to make a practical impact on patient care? Fertile area for future research

  43. Acknowledgements • Financial support • NHGRI K01 HG004645 (PI: K. Kawamoto) • University of Utah Dept. of Biomedical Informatics • University of Utah Program in Personalized Health Care

  44. Questions? KensakuKawamoto, MD, PhDDirector, Knowledge Management and Mobilization Assistant Professor, Department of Biomedical Informatics University of Utah kensaku.kawamoto@utah.edu Brandon Welch, MSPh.D. Candidate, Department of Biomedical Informatics Predoctoral Fellow, Program in Personalized Health Care University of Utah brandon.welch@utah.edu

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