Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention

Abstract

Background: Practice guidelines recommend anticoagulation therapy for patients with atrial fibrillation (AF) who have other risk factors putting them at an elevated risk of stroke. These patients remain undertreated, but, with increasing use of electronic healthcare records (EHRs), it may be possible to identify candidates for treatment.

Objective: To test algorithms for identifying AF patients who also have known risk factors for stroke and major bleeding using EHR data.

Materials and methods: We evaluated the performance of algorithms using EHR data from the Partners Healthcare System at identifying AF patients and 16 additional conditions that are risk factors in the CHA 2 DS 2 -VASc and HAS-BLED risk scores for stroke and major bleeding. Algorithms were based on information contained in problem lists, billing codes, laboratory data, prescription data, vital status, and clinical notes. The performance of candidate algorithms in 1000 bootstrap resamples was compared to a gold standard of manual chart review by experienced resident physicians.

Results: : Physicians reviewed 480 patient charts. For 11 conditions, the median positive predictive value (PPV) of the EHR-derived algorithms was greater than 0.90. Although the PPV for some risk factors was poor, the median PPV for identifying patients with a CHA 2 DS 2 -VASc score ≥2 or a HAS-BLED score ≥3 was 1.00 and 0.92, respectively.

Discussion: We developed and tested a set of algorithms to identify AF patients and known risk factors for stroke and major bleeding using EHR data. Algorithms such as these can be built into EHR systems to facilitate informed decision making and help shift population health management efforts towards patients with the greatest need.

Keywords: algorithms; anticoagulation; chronic disease; natural language processing; outcomes; quality improvement; stroke.

Figure 1.

Concordance between chart reviewers. All reviewer pairs including reviewer 1, independently reviewed 50 of the same charts. Reviewer pairs that did not include reviewer 1 independently reviewed 40 of the same charts. GI, gastrointestinal; Kappa, measure of concordance for different raters after accounting for chance agreement; NSAID, non-steroidal anti-inflammatory drug; PABAK, prevalence and bias-adjusted kappa.

Figure 2.

Sensitivity, specificity, PPV, and NPV of selected algorithms. GI, gastrointestinal; PPV, positive predictive value; NPV, negative predictive value; NSAID, non-steroidal anti-inflammatory drug; Spe, Specificity; Sen, Sensitivity.

Figure 3.

Distribution of selected algorithm versus gold-standard calculated risk scores. (A) CHA₂DS₂-VASc scores, and (B) HAS-BLED scores. CHA₂DS₂-VASc score includes: female gender, age 65–74, age ≥75, prior ischemic stroke/transient ischemic attack/systemic embolism, congestive heart failure, hypertension, diabetes, vascular disease (myocardial infarction/peripheral artery disease). HAS-BLED score includes: age >65, hypertension, renal disease, liver disease, prior ischemic stroke/transient ischemic attack, prior bleeds (intracranial or gastrointestinal), NSAID/antiplatelet use, alcohol use disorder. NSAID, non-steroidal anti-inflammatory drug.