Electronic Health Record–Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study

Abstract

Background Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. Objective The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. Methods Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. Conclusions Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.

Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan

“…We have explored to apply an algorithmic approach to the bedside electronic health record (EHR) datasets, developing predictive analytics ( 7 – 29 ) to drive translational medicine for improved diagnosis of high impact diseases and prediction of clinical resource utilization. Regarding KD, in 2013, we tested the hypothesis whether statistical learning on clinical and laboratory test patterns can lead to a single-step algorithm for KD diagnosis ( 9 ).…”

Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Asia

“…We have explored to apply an algorithmic approach to the bedside electronic health record (EHR) datasets, developing predictive analytics [8-37] to drive translational medicine for improved diagnosis of high impact diseases and prediction of clinical resource utilization. Regarding KD, in 2013, we tested the hypothesis whether statistical learning on clinical and laboratory test patterns can lead to a single-step algorithm for KD diagnosis [10].…”