An atrial fibrillation prediction model based on quantitative features of electrocardiogram during sinus rhythm in the Chinese population.
10.12122/j.issn.1673-4254.2025.02.02
- Author:
Xiaoqing ZHU
1
;
Yajun SHI
2
;
Juan SHEN
1
;
Qingsong WANG
1
;
Tingting SONG
1
;
Jiancheng XIU
3
;
Tao CHEN
1
;
Jun GUO
1
Author Information
1. Senior Department of Cardiology, Sixth Medical Center of PLA General Hospital, Beijing 100048, China.
2. Department of Cardiology, First Medical Center of PLA General Hospital, Beijing 100853, China.
3. Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
Boruta algorithm;
LASSO regression;
atrial fibrillation;
electrocardiogram
- MeSH:
Humans;
Atrial Fibrillation/epidemiology*;
Electrocardiography;
Middle Aged;
Male;
Female;
China/epidemiology*;
Proportional Hazards Models;
Adult;
Risk Factors;
Risk Assessment;
East Asian People
- From:
Journal of Southern Medical University
2025;45(2):223-228
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:To develop an early atrial fibrillation (AF) risk prediction model based on large-scale electrocardiogram (ECG) data from the Chinese population.
METHODS:The data of multiple ECG records of 30 383 patients admitted in the Chinese PLA General Hospital between 2009 and 2023 were randomly divided into the training set and the internal testing set in a 7:3 ratio. The predictive factors were selected based on the training set using univariate analysis, LASSO regression, and the Boruta algorithm. Cox proportional hazards regression was used to establish the ECG model and the composite model incorporating age, gender, and ECG model score. The discrimination power, calibration, and clinical net benefits of the models were evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curves.
RESULTS:The cohort included 51.1% male patients with a median age of the patients of 51 (36, 62) years and an AF incidence of 4.5% (1370/30 383). In the ECG model, the parameters related to the P wave and QRS complex were identified as significant predictors. In the testing set, the AUROC of the ECG model for predicting 5-year AF risk was 0.77 (95% CI: 0.74-0.80), which was increased to 0.81 (95% CI: 0.78-0.83) after incorporating age and gender, with a net reclassification improvement of 0.123 and an integrated discrimination improvement of 0.04 (P<0.05). The calibration curve of the model was close to the diagonal line. Decision curve analysis showed that the clinical net benefit of the composite model was higher than that of the ECG model across the majority of threshold probability.
CONCLUSIONS:The composite model incorporating quantitative ECG features during sinus rhythm, along with age and gender, can effectively predict AF risk in the Chinese population, thus providing a low-cost screening tool for early AF risk assessment and management.
