Phenotyping to Facilitate Accrual for a Cardiovascular Intervention

Abstract

Background: The conventional approach for clinical studies is to identify a cohort of potentially eligible patients and then screen for enrollment. In an effort to reduce the cost and manual effort involved in the screening process, several studies have leveraged electronic health records (EHR) to refine cohorts to better match the eligibility criteria, which is referred to as phenotyping. We extend this approach to dynamically identify a cohort by repeating phenotyping in alternation with manual screening.

Methods: Our approach consists of multiple screen cycles. At the start of each cycle, the phenotyping algorithm is used to identify eligible patients from the EHR, creating an ordered list such that patients that are most likely eligible are listed first. This list is then manually screened, and the results are analyzed to improve the phenotyping for the next cycle. We describe the preliminary results and challenges in the implementation of this approach for an intervention study on heart failure.

Results: A total of 1,022 patients were screened, with 223 (23%) of patients being found eligible for enrollment into the intervention study. The iterative approach improved the phenotyping in each screening cycle. Without an iterative approach, the positive screening rate (PSR) was expected to dip below the 20% measured in the first cycle; however, the cyclical approach increased the PSR to 23%.

Conclusions: Our study demonstrates that dynamic phenotyping can facilitate recruitment for prospective clinical study. Future directions include improved informatics infrastructure and governance policies to enable real-time updates to research repositories, tooling for EHR annotation, and methodologies to reduce human annotation.

Keywords: Cohort identification; Electronic health records; Intervention; Phenotyping.

Figure 1

DP consists of multiple screen cycles. At the start of each cycle, phenotyping is used to identify eligible patients from the EHR, creating an ordered list in which the most eligible patients are listed first. This list is manually screened, and the results are analyzed to improve phenotyping for the next cycle. DP: dynamic phenotyping; EHR: electronic health record.

Figure 2

Composition of manual screening performed in each cycle. 1) No HF and EF accounted for 25% of false positives. HF was detected by machine learning and EF was extracted using a simple regular expression from clinical notes. 2) Optimization of EF parser and inferring HF from low EF eliminated many false positives. But failure to exclude patients not primarily managed by the outpatient cardiology clinic at BWH emerged as a challenge. This was because the cardiologist was inferred using total number of EHRs entries authored for the patient. 3) Cardiologist was inferred from number of EHR entries limited to the outpatient setting. But this did not significantly reduce false positives. 4) Use of machine learning to infer the primary cardiologist significantly excluded patients that are not managed at BWH. HF: heart failure; EF: ejection fraction; BWH: Brigham and Women’s Hospital; EHR: electronic health record.