Abstract MP21: Feasibility of Electronic Health Records-based community surveillance of cardiovascular disease: Findings from the Atherosclerosis Risk in Communities Study

• Published in: Circulation (New York, N.Y.) Vol. 137; no. suppl_1
• Main Authors: Bogle, Brittany M, Rosamond, Wayne D, Folsom, Aaron R, Sorlie, Paul, Soliman, Elsayed Z, Wagenknecht, Lynne, Kucharska-Newton, Anna, Moore, Carlton, Haas, Stephanie W, Heiss, Gerardo
• Format: Journal Article
• Language: English
• Published: 20.03.2018
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/circ.137.suppl_1.mp21

Abstract only Background: Accurate community surveillance of cardiovascular disease requires hospital record abstraction, which is typically a manual process. The costly and time-intensive nature of manual abstraction precludes its use on a regional or national scale in the US. Whether an efficient system can accurately reproduce traditional community surveillance methods by processing electronic health records (EHRs) has not been established. Objective: We sought to develop and test an EHR-based system to reproduce abstraction and classification procedures for acute myocardial infarction (MI) as defined by the Atherosclerosis Risk in Communities (ARIC) Study. Methods: Records from hospitalizations in 2014 within ARIC community surveillance areas were sampled using a broad set of ICD discharge codes likely to harbor MI. These records were manually abstracted by ARIC study personnel and used to classify MI according to ARIC protocols. We requested EHRs in a unified data structure for the same hospitalizations at 6 hospitals and built programs to convert free text and structured data into the ARIC criteria elements necessary for MI classification. Per ARIC protocol, MI was classified based on cardiac biomarkers, cardiac pain, and Minnesota-coded electrocardiogram abnormalities. We compared MI classified from manually abstracted data to (1) EHR-based classification and (2) final ICD-9 coded discharge diagnoses (410-414). Results: These preliminary results are based on hospitalizations from 1 hospital. Of 684 hospitalizations, 355 qualified for full manual abstraction; 83 (23%) of these were classified as definite MI and 78 (22%) as probable MI. Our EHR-based abstraction is sensitive (>75%) and highly specific (>83%) in classifying ARIC-defined definite MI and definite or probable MI (Table). Conclusions: Our results support the potential of a process to extract comprehensive sets of data elements from EHR from different hospitals, with completeness and accuracy sufficient for a standardized definition of hospitalized MI.