Building the Partners HealthCare Biobank at Partners Personalized Medicine: Informed Consent, Return of Research Results, Recruitment Lessons and Operational Considerations

Karlson, Elizabeth W.; Boutin, Natalie T.; Hoffnagle, Alison G.; Allen, Nicole L.

doi:10.3390/jpm6010002

Cited by 165 publications

(152 citation statements)

References 15 publications

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“…The Partners Biobank is an ongoing virtual cohort study of patients across the Partners HealthCare hospital system (including Brigham and Women's Hospital, Massachusetts General Hospital, and other affiliated hospitals), which provides a large-scale resource of linked longitudinal EHR data, genomic data, and self-reported survey data (Karlson, Boutin, Hoffnagle, & Allen, 2016). All patients provided informed consent before enrollment, and all study procedures were approved by the Partners HealthCare Institutional Review Board.…”

Section: Sample and Proceduresmentioning

confidence: 99%

Physical activity offsets genetic risk for incident depression assessed via electronic health records in a biobank cohort study

et al. 2019

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Background: Physical activity is increasingly recognized as an important modifiable factor for depression. However, the extent to which individuals with stable risk factors for depression, such as high genetic vulnerability, can benefit from the protective effects of physical activity, remains unknown. Using a longitudinal biobank cohort integrating genomic data from 7,968 individuals of European ancestry with high-dimensional electronic health records and lifestyle survey responses, we examined whether physical activity was prospectively associated with reduced risk for incident depression in the context of genetic vulnerability.Methods: We identified individuals with incident episodes of depression, based on two or more diagnostic billing codes for a depressive disorder within 2 years following their lifestyle survey, and no such codes in the year prior. Polygenic risk scores were derived based on large-scale genome-wide association results for major depression. We tested main effects of physical activity and polygenic risk scores on incident depression, and effects of physical activity within stratified groups of polygenic risk.Results: Polygenic risk was associated with increased odds of incident depression, and physical activity showed a protective effect of similar but opposite magnitude, even after adjusting for BMI, employment status, educational attainment, and prior depression. Higher levels of physical activity were associated with reduced odds of incident depression across all levels of genetic vulnerability, even among individuals at highest polygenic risk.Conclusions: Real-world data from a large healthcare system suggest that individuals with high genetic vulnerability are more likely to avoid incident episodes of depression if they are physically active.

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Section: Sample and Proceduresmentioning

confidence: 99%

Physical activity offsets genetic risk for incident depression assessed via electronic health records in a biobank cohort study

et al. 2019

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“…For example, the Partners Healthcare Biobank is a repository of patient blood samples linked to the EHR and supplemented by survey data capturing data not readily available in the EHR (e.g. sun exposure, family history, and sleep habits) [Karlson and others 2016]. At this writing more than 60,000 patients have enrolled using face-to-face and electronic web-based informed consent [Boutin and others 2016] with approximately 2000 added monthly at Partners hospitals including Massachusetts General Hospital, Brigham and Women’s Hospital, and McLean Hospital.…”

Section: Combining Ehr Data and Genomic Datamentioning

confidence: 99%

The use of electronic health records for psychiatric phenotyping and genomics

Smoller

2017

American J of Med Genetics Pt B

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The widespread adoption of electronic health record (EHRs) in healthcare systems has created a vast and continuously growing resource of clinical data and provides new opportunities for population-based research. In particular, the linking of EHRs to biospecimens and genomic data in biobanks may help address what has become a rate-limiting study for genetic research: the need for large sample sizes. The principal roadblock to capitalizing on these resources is the need to establish the validity of phenotypes extracted from the EHR. For psychiatric genetic research, this represents a particular challenge given that diagnosis is based on patient reports and clinician observations that may not be well-captured in billing codes or narrative records. This review addresses the opportunities and pitfalls in EHR-based phenotyping with a focus on their application to psychiatric genetic research. A growing number of studies have demonstrated that diagnostic algorithms with high positive predictive value can be derived from EHRs, especially when structured data are supplemented by text mining approaches. Such algorithms enable semi-automated phenotyping for large-scale case-control studies. In addition, the scale and scope of EHR databases have been used successfully to identify phenotypic subgroups and derive algorithms for longitudinal risk prediction. EHR-based genomics are particularly well-suited to rapid look-up replication of putative risk genes, studies of pleiotropy (phenomewide association studies or PheWAS), investigations of genetic networks and overlap across the phenome, and pharmacogenomic research. EHR phenotyping has been relatively under-utilized in psychiatric genomic research but may become a key component of efforts to advance precision psychiatry.

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“…Previous studies have found that disease specific ICD-codes are registered for patients even if they do not have that disease (54,55). To find the optimal number of ICD codes for patient selection, we explored the ICD code performance in previously reviewed medical records for rheumatoid arthritis in the Partners Biobank (56). Here, one rheumatologist screened the medical records, and assigned case status both by the ACR2010 criteria (14) as well as her expert opinion.…”

Section: Validation Through Simulationmentioning

confidence: 99%

Using genetics to differentiate patients with similar symptoms: application to inflammatory arthritis in the rheumatology outpatient clinic

Knevel

et al. 2019

Preprint

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Slow developing complex diseases are a clinical diagnostic challenge. Since genetic information is increasingly available prior to a patient's first visit to a clinic, it might improve diagnostic accuracy. We aimed to devise a method to convert genetic information into simple probabilities discriminating between multiple diagnoses in patients presenting with inflammatory arthritis.We developed G-Prob, which calculates for each patient the genetic probability for each of multiple possible diseases. We tested this for inflammatory arthritis-causing diseases (rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis and gout). After validating in simulated data, we tested G-Prob in biobank cohorts in which genetic data were linked to electronic medical records: -1,200 patients identified by ICD-codes within the eMERGE database (n= 52,623); -245 patients identified through ICD codes and review of medical records within the Partners Biobank (n=12,604); -243 patients selected prospectively with final diagnoses by medical record review within the Partners Biobank (n=12,604). The calibration of G-Prob with the disease status was high (with regression coefficients ranging from 0.90-1.08 (ideal would be 1.00) in all cohorts. G-Prob's discriminative ability was high in all cohorts with pooled Area Under the Curve (AUC)=0.69 [95%CI 0.67-0.71], 0.81 [95%CI 0.76-0.84] and 0.84 [95%CI 0.81-0.86]. For all patients, at least one disease could be ruled out, and in 45% of patients a most likely diagnosis could be identified with an overall 64% positive predictive value. In 35% of instances the clinician's initial diagnosis was incorrect. Initial clinical diagnosis explained 39% of the variance in final disease prediction which improved to 51% (P<0.0001) by adding G-Prob genetic data.

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Building the Partners HealthCare Biobank at Partners Personalized Medicine: Informed Consent, Return of Research Results, Recruitment Lessons and Operational Considerations

Cited by 165 publications

References 15 publications

Physical activity offsets genetic risk for incident depression assessed via electronic health records in a biobank cohort study

Physical activity offsets genetic risk for incident depression assessed via electronic health records in a biobank cohort study

The use of electronic health records for psychiatric phenotyping and genomics

Using genetics to differentiate patients with similar symptoms: application to inflammatory arthritis in the rheumatology outpatient clinic

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