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Cited 2 time in webofscience Cited 2 time in scopus
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Development and Validation of 3-Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Modelsopen access

Authors
Yum, YunjinShin, Seung YongYoo, HakjeKim, Yong HyunKim, Eung JuLip, Gregory Y. H.Joo, Hyung Joon
Issue Date
Jun-2022
Publisher
Wiley-Blackwell
Keywords
atrial fibrillation; ECG; electronic health record; risk prediction
Citation
Journal of the American Heart Association, v.11, no.12
Indexed
SCIE
SCOPUS
Journal Title
Journal of the American Heart Association
Volume
11
Number
12
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/61150
DOI
10.1161/JAHA.121.024045
ISSN
2047-9980
2047-9980
Abstract
Background Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF‐related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and computerized ECG interpretation. Methods and Results A total of 671 318 patients were screened from 3 tertiary hospitals. After careful exclusion of cases with missing values and a prior AF diagnosis, AF prediction models were developed from the derivation cohort of 25 584 patients without AF at baseline. In the internal/external validation cohort of 117 523 patients, the model using 6 clinical features and 5 ECG diagnoses showed the highest performance for 3‐year new‐onset AF prediction (C‐statistic, 0.796 [95% CI, 0.785–0.806]). A more simplified model using age, sex, and 5 ECG diagnoses (atrioventricular block, fusion beats, marked sinus arrhythmia, supraventricular premature complex, and wide QRS complex) had comparable predictive power (C‐statistic, 0.777 [95% CI, 0.766–0.788]). The simplified model showed a similar or better predictive performance than the previous models. In the subgroup analysis, the models performed relatively better in patients without risk factors. Specifically, the predictive power was lower in patients with heart failure or decreased renal function. Conclusions Although the 3‐year AF prediction model using both clinical and ECG variables showed the highest performance, the simplified model using age, sex, and 5 ECG diagnoses also had a comparable prediction power with broad applicability for incident AF.
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Ansan Hospital (Department of Cardiology, Ansan Hospital)
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