Abstract
BACKGROUND: The cardiometabolic index, a composite indicator integrating central obesity and lipid abnormalities, has demonstrated predictive value in several cardiovascular diseases. However, its role in predicting major adverse cardiovascular events among patients with atrial fibrillation remains underexplored. METHODS: In this single-center retrospective cohort study, 192 atrial fibrillation (AF) patients under management at the Jinyang Community Health Service Center in Pudong, Shanghai, from January 2022 to January 2024 were enrolled. Patients were stratified into tertiles based on baseline cardiometabolic index (CMI). The primary endpoint was major adverse cardiovascular events (MACE), comprising cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, hospitalization for worsening heart failure, and coronary revascularization due to unstable angina or ischemic events. Multivariable Cox proportional hazards models were used to assess the independent association between CMI and MACE. Kaplan-Meier curves and Log-rank tests were applied to compare event incidence across groups. Restricted cubic spline analysis examined potential nonlinearity. An extreme gradient boosting model was developed to evaluate predictive performance, with SHapley Additive exPlanations used to assess variable importance. Subgroup analyses were conducted to evaluate the consistency of CMI's predictive value across different clinical populations. The median follow-up duration was 664 days (interquartile range: 384-900 days), estimated using the reverse Kaplan-Meier method. RESULTS: MACE incidence increased significantly with rising CMI levels. Compared to the low CMI group, the high CMI group had a significantly higher risk of MACE (HR = 5.56, 95% CI: 1.48 - 20.90, P = 0.011). Kaplan-Meier analysis showed significant differences in cumulative incidence among the three groups (Log-rank P < 0.001). restricted cubic spline (RCS) modeling revealed a nonlinear positive association, with a sharp increase in MACE risk above a CMI threshold of approximately 0.85 (P for nonlinearity < 0.001). The Extreme Gradient Boosting (XGBoost) model achieved a C-index of 0.737 in the test set, with SHapley Additive exPlanations (SHAP) analysis ranking CMI as the fourth most influential predictor, following age, left atrial diameter, and left ventricular ejection fraction. Subgroup analyses suggested that the predictive value of CMI was particularly evident in patients without chronic kidney disease and those without prior catheter ablation. CONCLUSION: Elevated CMI is independently associated with increased MACE risk in patients with atrial fibrillation and demonstrates a nonlinear dose-response relationship. As a simple, accessible metabolic indicator, CMI shows promise for improving cardiovascular risk identification and guiding personalized management-especially in high-risk AF patients without overt metabolic dysfunction.