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 Models.

Yum, Yunjin ORCID: 0000-0003-3070-3615, Shin, Seung Yong ORCID: 0000-0002-3408-6899, Yoo, Hakje, Kim, Yong Hyun ORCID: 0000-0003-1376-5128, Kim, Eung Ju, Lip, Gregory YH ORCID: 0000-0002-7566-1626 and Joo, Hyung Joon ORCID: 0000-0003-1846-8464 (2022) 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 Models. Journal of the American Heart Association, 11 (12). e024045-.

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Official URL: http://dx.doi.org/10.1161/jaha.121.024045

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.

Item Type:	Article
Uncontrolled Keywords:	Humans, Atrial Fibrillation, Electrocardiography, Incidence, Risk Assessment, Risk Factors, Electronic Health Records
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	26 Oct 2022 09:27
Last Modified:	18 Jan 2023 19:49
DOI:	10.1161/jaha.121.024045
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3165771