Incorporating Research Into Sight (IRIS) Essentia Rural Health Institute Marshfield Clinic Penn State University
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1 Incorporating Research Into Sight (IRIS) Essentia Rural Health Institute Marshfield Clinic Penn State University
2 Aim 1. Develop and validate electronic algorithms for ophthalmic conditions and efficacy of medical therapy for ophthalmic conditions and implement other phenotype algorithms developed across the emerge network.. Marshfield Team Peggy Peissig, Jim Linneman, Richard Berg, Terrie Kitchner, Rob Strenn, Jeff Joyce, Scott Hebbring, Murray Brilliant
3 Ophthalmic Phenotypes Develop and validate electronic algorithms for: Ocular hypertension Glaucoma Age Related Macular Degeneration (AMD) Marshfield Network Case Control PPV NPV Sensi Specif Case Control PPV OHTN 495 1,440 98% 92% 100% 99% 771 7,477 76% - 100% Glau 345 1, % 92% 100% 100% 1,124 4,568 76% - 100% AMD 1,130 1, % 80% 66% 100% 2,363 11,749 93% - 100% Network supplied ~ 50% cases & 80% controls
4 # Phenotypes MC/EIRH/PSU Network Participation Network performance: Adult Peds Extra PGX Dropped MC/EIRH/PSU emerge Total phenotype algorithms run: 32 (validated 24 algorithms) Primary or Secondary site: 16 algorithms
5 Can Machine Learning (ML) approaches be used for phenotyping? Methods: Compared relational learning and traditional ML approaches 9 phenotypes w/ 4 emerge inlcuded Retrospective EHR data (diagnoses, labs, meds, procedures) Gold standard comparison was manually reviewed patients
6 Can Machine Learning (ML) approaches be used for phenotyping? Results: Compared relational learning PPV/ and traditional Marshfield ML emerge approaches Precision emerge Network Acute 9 phenotypes Myocardial Infarction w/ 4 inlcuded from emerge Acute Liver Injury Retrospective EHR data (diagnoses, labs, meds, procedures) Atrial Fibrillation Cataracts Used manually reviewed patients as gold standard for comparison Congestive Heart Failure Dementia Type 2 Diabetes Diabetic Retinopathy Deep Vein Thrombosis 0.954
7 EHR-driven Phenotyping Question: Can Machine Learning (ML) approaches be used for phenotyping?
8 Improve EHR-Phenotyping via Better Data Collection - Ophthalmology Background Minimum coded data available for ophthalmology Most information recorded via pen or image documents not structured
9 Surveyed emerge Network
10 (Manuscript submitted )
11 Aim 2. Leverage GWAS data available for nearly 6000 research subjects aged 50 years and older in Marshfield and an additional 20-25,000 subjects throughout the emerge network to undertake genetic discoveries for ophthalmic conditions and ophthalmic pharmacogenetics. PSU Team Yuki Bradford, Molly Hall, Dokyoon Kim, Shefali Verma, Marylyn Ritchie
12 GWAS and GXG in Primary Open Angle Glaucoma Replication Study (emerge and The NEIGHBOR) - Manuscript in Progress # Cases # Controls emerge (5090 samples) THE NEIGHBOR (4383 samples) Generalization approach on GWAS Gene based interactions
13 Ocular Hypertension GWAS Total #Samples: 7113 (after QC)» #Cases: 720» #Controls: 6393 Data extracted from V3 Imputed dataset GWAS is completed Looking for a set to perform replication
14 AMD GWAS Total # Samples: (after QC) #Cases: 2617 #Controls: Adjusting for sex, decade_birth, site, platform, PC1-6 Data extracted from V3 Imputed dataset
15 CNV Burden analysis QC ed CNV data from CHOP
16 emerge-i phenotypes
17 emerge-i phenotypes
18 Models Adjusted model Phenotype = CNV_Burden + Age + Sex + PC1 + PC2 + PC3 + Genotyping_Center + Site Used logistic regression for binary phenotypes Used linear regression for continuous phenotypes
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20 * Removal of events larger than 1MB
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23 Aim 3: Undertake consultation activities with the general community and clinicians related to the incorporation of GWAS results into electronic health records to inform health care decisions Aim 3a. Conduct focus group discussions with physicians related to genetic-based prescribing for ophthalmic conditions Aim 3b. Meet with the PMRP Community Advisory Group to discuss potential reconsent of study subjects to allow the sharing of genetic data Aim 3c. Communicate with the entire PMRP cohort about return of results and study progress through the study newsletter. Aim 3d. Contact all subjects with GWAS data to obtain consent to share their GWAS data with themselves and/or their health care providers, based on the results of the consultation efforts Aim 3e. Incorporate clinically relevant GWAS data into the EMR for eye care professionals
24 AMD Genomic Medicine Pilot Aims To document why people elect not to enroll in a study to learn their genetic risk of AMD To understand how genetic data can be stored in the EPIC electronic medical record so data are readily accessible and interpretable by health care professionals To document patient response to genetic testing and behavioral changes patients make after receiving information about genetic risk of AMD
25 Methods Recruitment through optometry Patients aged without AMD Initially patients with a family history of AMD, then relaxed inclusion to include people without a known family history of AMD Blood draw and DNA extraction at Essentia Banked DNA for future research Genotyping at ArcticDX, return of genetic risk score that includes 4 genes and smoking status Optometrist returned results Follow-up telephone interviews
26 Results Letters of invitation to 147 eligible individuals 32 could not reach, 18 refusals (15%), 101 participated (85%) Enthusiastic support all would be tested again Most have family history of AMD Most made changes, even if low genetic risk Quit smoking, diet, exercise, hat, cholesterol check Completing analyses in nvivo Targeting manuscript to optometrists/ophthalmologists Test has been added clinically at Essentia Health
27 PhenX Administrative Supplement Supplement to year 1 to collect PhenX measures for participants with GWAS data 34 measures selected (related to cataract and/or being used by other PhenX RISING sites) Alcohol, smoking Birthplace of self and grandparents Characteristics of current residence Age, education, employment, ethnicity, race Height, weight UV exposure Hand dominance Depression
28 Questionnaires mailed to 3344 PMRP participants with GWAS data 29 pages, offered $10 to complete 2 mailings to improve response rate 3246 eligible after excluding unknown addresses, deaths 2271 (70.0%) response rate dbgap deposition Gene/environment analyses
29 Comparison of anthropometric data Variable PMRP Mean (min, max) PhenX Mean (min, max) Age (years) 64.8 (45.7, 93.2) 73.1 (54, 101.7) Weight (pounds) (76.0, 350.0) (58.0, 399.0) Height (inches) 65.8 (48.0, 77.0) 65.8 (38.0, 105.0) BMI (kg/m 2 ) 29.6 (17.4, 61.2) 28.2 (10.3, 75.4) Confirms PhenX Toolkit recommendation to use self-reported weight only when measured weight cannot be attained Note outlier on self-reported height (8 ft, 9 inches, versus 69 inches)
30 PGx Follow Up Alerts fired Questionnaire Results
31 PGx Alerts 2184 Letters of invitation to high risk participants 749 participants (750 enrolled; 1 withdrawal); Implemented PGx decision support 8/2014 # Actinable Variant PGx Implementation Initial Rx given w/ respect to PGx Followed implementation: Recommendation? Clopidogrel 198 After 1 No Simvastatin 219 Before 2 No: Prior dose After 1 Yes Warfarin Yes: After 16 No: 5 Hospital System: 3
32 Questionnaire Results change in responses 1. I know enough about genetic testing 2. I am afraid of genetic testing 3. Want to know my health risks 4. Safety of Genetic testing 5. Genetic testing will increase insurance cost 6. Help doc determine meds 7. Genetic testing can reveal Alzheimer s risk 8. Genetic testing is accurate 9. Genetic testing needs to be private 10. Federal/State laws protect against discrimination Disagree Agree 11. I share genetic traits and risks with parents, sibs and kids
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