A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network

Abstract

Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burden of this disease. The use of electronic medical record (EMR) data for this purpose is challenging given the syndromic nature of HF and the need to distinguish HF with preserved or reduced ejection fraction. Using a gold standard cohort of manually abstracted cases, an EMR-driven phenotype algorithm based on structured and unstructured data was developed to identify all the cases. The resulting algorithm was executed in two cohorts from the Electronic Medical Records and Genomics (eMERGE) Network with a positive predictive value of > 95%. The algorithm was expanded to include three hierarchical definitions of HF (i.e., Definite, Probable, Possible) based on the degree of confidence of the classification to capture HF cases in a whole population whereby increasing the algorithm utility for use in e-Epidemiologic research.

A comparison between physicians and computer algorithms for form CMS‐2728 data reporting

“…Although manual review by experts is considered the gold standard for validation, there is currently no consensus in the medical informatics community on standard approaches for the design of expert review panels and the number of records to be reviewed . Because of this lack of gold standard, we chose to examine nine available algorithms that have been used locally use for research and quality purposes by the Regenstrief Institute; moving forward would require design and validation (through iterative expert manual review) of algorithms for all of the comorbidities across other ESRD cohorts. In addition to algorithms, implementation would also require the use of NLP as not all comorbidities are assessable by algorithms.…”

Improvement in Cardiovascular Risk Prediction with Electronic Health Records

“…However, electronic health record (EHR) systems are becoming ubiquitous due to the Meaningful Use Standards implemented as part of the Health Information Technology for Economic and Clinical Health (HITECH) Act [10]. Secondary use of EHR data has shown to be a robust and cost-effective strategy for epidemiologic, genomic, and translational research [11-15]. Specifically, leveraging EHR data for disease risk scores, allows for additional risk factors to be incorporated and the range of outcomes increased [16].…”

Validity of Cardiovascular Data From Electronic Sources

“…This need is particularly relevant to the success of studies such as the PMI All of Us Research Program, 10 other national research initiatives, 27,28 the future of CVD epidemiology, 3–5 and the development of learning healthcare systems. 7 Although several studies have evaluated the different dimensions of EHR data quality and developed EHR phenotyping algorithms, 16,29–32 they frequently are from a single healthcare institution and use data from clinical care as a gold standard reference, including paper charts, patient and physician interviews, standardized patient encounters, registry data, and claims data. 23 …”

“…In recent years, national research consortiums, such as eMERGE (electronic medical records and genomics) Network, the NIH Collaboratory, and other research groups, have developed, tested, and validated EHR detection algorithms for various cardiovascular disease risk factors and events, including diabetes, HTN, heart failure, and coronary heart disease. 16,31,32 Many of these algorithms are developed at a single center, tested at other centers, and adapted if needed. 30 Consistent with prior studies, our results underscore the additive value of clinical data to ICD codes in case detection, especially for obesity, which has previously been reported as under-detected in administrative databases.…”