kensaku kawamoto, md, phd director, knowledge management and mobilization

Clinical Decision Support for Genetically Guided Personalized Medicine: a Systematic Review

JAMIA 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

Predoctoral Fellow, Program in Personalized Health CareUniversity of Utah

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

Background

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

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

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.

CDS as Bridge to Realize the Promise of GPM

Study Objectives

• Characterize research to date on use of CDS to enable GPM

• Identify areas of need for future research

Methods

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)

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

Results

Study Identification and Selection

Cancer (n=22)Other Diseases (n=10)

Pharma-cogenomics

(n=6)

CDS GPM Areas of Focus

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 FHx CDS 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)

Cancer (n=22)Other Dis-

eases (n=10)

Pharma-cogenomics

(n=6)

RAGs: FHx-Driven Cancer Management

Emery J et al. BMJ. 1999;319:32-6.

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.

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.

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)

Hughes RiskApps

Ozanne EM et al. Breast J 2009;15:155e62. http://www.hughesriskapps.net.

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)

REACT

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

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)

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)

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)

Cancer (n=22)

Other Dis-eases (n=10)

Phar-macogenomics (n=6)

Warfarin PGx CDS Tool

26Bon Homme M, Reynolds KK, Valdes R Jr, et al. Dynamic pharmacogenetic modelsin anticoagulation therapy. Clin Lab Med 2008;28:539-52.

Medication Surveillance in the Netherlands

27Swen JJ, Wilting I, de Goede AL, et al. Pharmacogenetics: from bench to byte. Clin Pharmacol Ther 2008;83:781-7.

Other CDS for GPM (Table 6)

• FHx-driven CDS (n = 6)

• Genotype-driven CDS (n = 4)Cancer (n=22)Other

Diseases (n=10)

Pharma-cogenomics

(n=6)

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)

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)

GeneInsight

Aronson SJ et al. Hum Mutat 2011;32:532e6.

Trend Analyses

Publications by Year

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110

1

2

3

4

5

6

7

Integrated vs. Stand-Alone CDS

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110

1

2

3

4

5

6

7

Integrated CDS Stand Alone CDS

FHx vs. Genotype-Driven CDS

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110

1

2

3

4

5

6

7

Genotype FHx; genotype FHx

Discussion

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

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

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

RCT Outcomes

• 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

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

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

Acknowledgements

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

Questions?

Kensaku Kawamoto, 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