Coronary Risk Estimation Based on Clinical Data in Electronic Health Records

Abstract

Background: Clinical features from electronic health records (EHRs) can be used to build a complementary tool to predict coronary artery disease (CAD) susceptibility.

Objectives: The purpose of this study was to determine whether an EHR score can improve CAD prediction and reclassification 1 year before diagnosis, beyond conventional clinical guidelines as determined by the pooled cohort equations (PCE) and a polygenic risk score for CAD.

Methods: We applied a machine learning framework using clinical features from the EHR in a multiethnic, clinical care cohort (BioMe) comprising 555 CAD cases and 6,349 control subjects and in a population-based cohort (UK Biobank) comprising 3,130 CAD cases and 378,344 control subjects for external validation.

Results: Compared with the PCE, the EHR score improved CAD prediction by 12% in the BioMe Biobank and by 9% in the UK Biobank. The EHR score reclassified 25.8% and 15.2% individuals in each cohort respectively, compared with the PCE score. We observed larger improvements in the EHR score over the PCE in a subgroup of individuals with low CAD risk, with 20% increased discrimination and 34.4% increased reclassification. In all models, the polygenic risk score for CAD did not improve CAD prediction, compared with the PCE or EHR score.

Conclusions: The EHR score resulted in increased prediction and reclassification for CAD, demonstrating its potential use for population health monitoring of short-term CAD risk in large health systems.

Keywords: biobank; coronary artery disease; electronic health record; machine learning; polygenic risk score; pooled cohort equations; prevention.

FIGURE 1

Study Design and Flowchart The BioMe Biobank (A) was used for training and the UK Biobank (B) was used for external validation. Statin indicates cholesterol-lowering medications. AUROC = area under the receiver-operating characteristic curve; GBT = gradient boosted trees; NRI = net reclassification improvement; PCE = pooled cohort equations; RF = random forest; SVM = support vector machines.

FIGURE 2

Receiver-Operating Characteristic Curves for 1-Year CAD Risk Prediction Models The models were tested in BioMe (A), low-risk BioMe (B), UK Biobank (C), and low-risk UK Biobank (D) individuals. The y- and x-axes correspond to averaged true positive and false negative rates, respectively, across 100 iterations. The averaged area under the curve is indicated for every model. AUC = area under the curve; EHR = electronic health record; PC = principal component; PCE = pooled cohort equations; PRS = polygenic risk score.

FIGURE 3

Change in Prediction Score Between PCE model and EHR Model The models were tested in BioMe (A) and UK Biobank (B) individuals. Dot plot shows the averaged prediction of the EHR and PCE models for every tested individual in the BioMe and UK Biobank cohorts. Red and blue indicates actual cases and control subjects, respectively. Top and right histograms show the distribution of averaged PCE and EHR prediction scores, respectively. The secondary y-axes in the histograms show the percentage of control subjects predicted in a 1% wide bin of the prediction score. Dotted vertical line indicates the 85% threshold. Abbreviations as in Figure 2.

CENTRAL ILLUSTRATION

Electronic Health Record Clinical Features Predict Short-Term Coronary Artery Disease Risk Clinical features from the electronic health record can predict 1-year coronary artery disease risk with a discrimination power of 0.94 AUROC and reclassify 25.8% of predictions coming from the Pooled Cohort Equations. AUROC = area under the receiver-operating characteristic curve; CAD = coronary artery disease; EHR = electronic health record; NRI = net reclassification improvement; PCE = Pooled Cohort Equations.