In JACC. Asia
BACKGROUND : Atrial fibrillation (AF) increases the risk of heart failure (HF); however, little focus is placed on the risk stratification for, and prevention of, incident HF in patients with AF.
OBJECTIVES : This study aimed to construct and validate a machine learning (ML) prediction model for HF hospitalization in patients with AF.
METHODS : The Fushimi AF Registry is a community-based prospective survey of patients with AF in Fushimi-ku, Kyoto, Japan. We divided the data set of the registry into derivation (n = 2,383) and validation (n = 2,011) cohorts. An ML model was built to predict the incidence of HF hospitalization using the derivation cohort, and predictive ability was examined using the validation cohort.
RESULTS : HF hospitalization occurred in 606 patients (14%) during a median follow-up period of 4.4 years in the entire registry. Data of transthoracic echocardiography and biomarkers were frequently nominated as important predictive variables across all 6 ML models. The ML model based on a random forest algorithm using 7 variables (age, history of HF, creatinine clearance, cardiothoracic ratio on x-ray, left ventricular [LV] ejection fraction, LV end-systolic diameter, and LV asynergy) had high prediction performance (area under the receiver operating characteristics curve [AUC]: 0.75) and was significantly superior to the Framingham HF risk model (AUC: 0.67; P < 0.001). Based on Kaplan-Meier curves, the ML model could stratify the risk of HF hospitalization during the follow-up period (log-rank; P < 0.001).
CONCLUSIONS : The ML model revealed important predictors and helped us to stratify the risk of HF, providing opportunities for the prevention of HF in patients with AF.
Hamatani Yasuhiro, Nishi Hidehisa, Iguchi Moritake, Esato Masahiro, Tsuji Hikari, Wada Hiromichi, Hasegawa Koji, Ogawa Hisashi, Abe Mitsuru, Fukuda Shunichi, Akao Masaharu
2022-Nov
AF, atrial fibrillation, AUC, area under the receiver operating characteristics curve, HF, heart failure, LV, left ventricular, ML, machine learning, SHAP, Shapley Additive exPlanation, atrial fibrillation, heart failure, machine learning, risk prediction
