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Electronic Health Record-Based Prediction of 1-Year Risk of Incident Cardiac Dysrhythmia: Prospective Case-Finding
Yaqi Zhang1,2, Yongxia Han1,2, Peng Gao2,3
1School of Electrical Power Engineering, South China University of Technology, Guangzhou, China.
Insights
This study developed a machine learning algorithm to predict cardiac dysrhythmia, offering an early warning system for potential arrhythmias and improving population-level cardiac care.
Area of Science:
- Cardiology
- Machine Learning
- Public Health
Background:
- Cardiac dysrhythmia is a prevalent condition with severe complications like heart failure, stroke, and sudden death.
- Early prediction of incident arrhythmia is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a predictive model for incident cardiac dysrhythmia within a 1-year timeframe.
- To establish an early warning system for potential arrhythmias in the general population.
Main Methods:
- Utilized retrospective and prospective cohorts (over 2 million individuals) from integrated electronic health records.
- Developed an ensemble machine learning workflow incorporating diverse health and socioeconomic determinants.
- Validated the predictive model using isotonic regression and assessed performance via AUROC, achieving 0.854 (retrospective) and 0.827 (prospective).
Main Results:
- The cardiac dysrhythmia case-finding algorithm stratified the population into 5 risk groups.
- The very high-risk group (0.003% of the population) had a 51.85% confirmed incidence of cardiac dysrhythmia within 1 year.
- The algorithm demonstrated strong predictive performance in both retrospective and prospective validation.
Conclusions:
- The developed case-finding algorithm shows promise for prospectively predicting 1-year incident cardiac dysrhythmias.
- This algorithm can function as an early warning system for statewide, population-level screening and surveillance.
- Implementation can lead to improved cardiac dysrhythmia care through early detection and intervention.
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.
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