A machine learning model utilizing 10 baseline clinical variables accurately predicted paroxysmal and persistent atrial fibrillation subtypes with an AUC of 0.870.
Observational (n=1,600)
No
Can a machine learning model using baseline EHR data accurately predict paroxysmal versus persistent atrial fibrillation?
A machine learning model using 10 baseline variables, notably left atrial diameter and NT-proBNP, can accurately predict paroxysmal versus persistent atrial fibrillation at admission.
Effect estimate: AUC 0.870 (95% CI 0.858-0.882)
BACKGROUND: There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm for identification of paroxysmal and persistent AF, and investigate the influencing factors. METHODS: We collected demographic data, medication use, serological indicators, and baseline cardiac ultrasound data of all included subjects, totaling 50 variables. The diagnosis of AF subtypes is confirmed by ECG observation for at least more than 7 days. Variable selection was performed by spearman correlation analysis, recursive feature elimination, and least absolute shrinkage and selection operator regression. We built a prediction model for AF using three machine learning methods. Finally, the significance of each variable was analyzed by Shapley additive explanations method. RESULTS: After screening, we found the optimal variable set consisting of 10 variables. The model we built achieved good predictive performance (AUC = 0.870, 95%CI 0.858 to 0.882), and had specificity of 0.851 (95%CI 0.844 to 0.858) and sensitivity of 0.716 (95%CI 0.676 to 0.755). Good predictive performance was stably achieved in different age subgroups and different gender subgroups. LA and NT-proBNP were the two most important variables for predicting paroxysmal and persistent AF in all models, except for the female subgroup aged less than 60 years. CONCLUSIONS: Our model makes it possible to predict paroxysmal and persistent AF based on baseline data at admission. Early and individualized intervention strategies based on our model may help to improve clinical outcomes in AF patients.
Zhang et al. (Mon,) conducted a observational in Atrial fibrillation (n=1,600). Machine learning prediction model (GradientBoost) was evaluated on Prediction of paroxysmal and persistent atrial fibrillation (AUC) (AUC 0.870, 95% CI 0.858-0.882). A machine learning model utilizing 10 baseline clinical variables accurately predicted paroxysmal and persistent atrial fibrillation subtypes with an AUC of 0.870.