Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/36140
Title: Machine learning optimization of an electronic health record audit for heart failure in primary care
Authors: Raat, Willem
Smeets, Miek
Henrard, Severine
Aertgeerts, Bert
PENDERS, Joris 
Droogne, Walter
MULLENS, Wilfried 
Janssens, Stefan
Vaes, Bert
Issue Date: 2022
Publisher: WILEY PERIODICALS, INC
Source: ESC HEART FAILURE, 9(1), p. 39-47
Abstract: Aims The diagnosis of heart failure (HF) is an important problem in primary care. We previously demonstrated a 74% increase in registered HF diagnoses in primary care electronic health records (EHRs) following an extended audit procedure. What remains unclear is the accuracy of registered HF pre-audit and which EHR variables are most important in the extended audit strategy. This study aims to describe the diagnostic HF classification sequence at different stages, assess general practitioner (GP) HF misclassification, and test the predictive performance of an optimized audit. Methods and results This is a secondary analysis of the OSCAR-HF study, a prospective observational trial including 51 participating GPs. OSCAR used an extended audit based on typical HF risk factors, signs, symptoms, and medications in GPs' EHR. This resulted in a list of possible HF patients, which participating GPs had to classify as HF or non-HF. We compared registered HF diagnoses before and after GPs' assessment. For our analysis of audit performance, we used GPs' assessment of HF as primary outcome and audit queries as dichotomous predictor variables for a gradient boosted machine (GBM) decision tree algorithm and logistic regression model. Of the 18 011 patients eligible for the audit intervention, 4678 (26.0%) were identified as possible HF patients and submitted for GPs' assessment in the audit stage. There were 310 patients with registered HF before GP assessment, of whom 146 (47.1%) were judged not to have HF by their GP (over-registration). There were 538 patients with registered HF after GP assessment, of whom 374 (69.5%) did not have registered HF before GP assessment (under-registration). The GBM and logistic regression model had a comparable predictive performance (area under the curve of 0.70 [95% confidence interval 0.65-0.77] and 0.69 [95% confidence interval 0.64-0.75], respectively). This was not significantly impacted by reducing the set of predictor variables to the 10 most important variables identified in the GBM model (free-text and coded cardiomyopathy, ischaemic heart disease and atrial fibrillation, digoxin, mineralocorticoid receptor antagonists, and combinations of renin-angiotensin system inhibitors and beta-blockers with diuretics). This optimized query set was enough to identify 86% (n = 461/538) of GPs' self-assessed HF population with a 33% reduction (n = 1537/4678) in screening caseload. Conclusions Diagnostic coding of HF in primary care health records is inaccurate with a high degree of under-registration and over-registration. An optimized query set enabled identification of more than 80% of GPs' self-assessed HF population.
Notes: Raat, W (corresponding author), Katholieke Univ Leuven, Dept Publ Hlth & Primary Care, Kapucijnenvoer 33,Blok J Bus 7001, B-3000 Leuven, Belgium.
willem.raat@kuleuven.be
Keywords: Chronic heart failure; Primary care; Audit and feedback; Electronic;health records; Screening
Document URI: http://hdl.handle.net/1942/36140
ISSN: 2055-5822
e-ISSN: 2055-5822
DOI: 10.1002/ehf2.13724
ISI #: 000721559000001
Rights: © 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License
Category: A1
Type: Journal Contribution
Validations: ecoom 2022
Appears in Collections:Research publications

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