ArticleJMIR medical informatics2021
Electronic Health Record-Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding Algorithm Development and Validation Study.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Real-world risk stratification for coronary heart disease: a one-year prediction model using health information exchange data.BMC public health · 2025Article
- How to use digital devices to detect and manage arrhythmias: an EHRA practical guide.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2022Article
- Recent Updates and Technological Developments in Evaluating Cardiac Syncope in the Emergency Department.Current cardiology reviews · 2022Article
- Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
23 authors at 12 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiac 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.
objectiveThe aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia.
methodsRetrospective (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).
resultsThe 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.
conclusionsOur 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.
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