Fig. 2: Consistent predictive performance and prognostic value of AIHFLevel.

a AI modeling hybrid framework overview. The image was created with a licensed version of bioRender.com. b Schematic illustration of optimal scheme identification. The diagram delineates the comprehensive evaluation process of 132 distinct modeling schemes through an array of lollipop plots, including 10 repeated 10-fold cross-validation, 100 iterations of Monte-Carlo cross-validation (MCCV) with a 70% sampling ratio, and a thorough bootstrap analysis comprising 1000 iterations. c Analysis of non-linear relationship between AIHFLevel and ACM risk using restricted cubic spline regression based on Replication cohort (P_overall <0.0001, and P_non-linear < 0.0001). Non-linear patterns indicating a ‘fast-to-low’ increase in ACM risk associated with rising AIHFLevel. Univariate Cox regression analysis highlighted the AIHFLevel as a significant clinical predictor for ACM, with a hazard ratio (HR) of 1.615 (95% CI = 1.417–1.863). Statistic test: two-sided Wald test: P < 0.0001. Line chart displaying the estimated logarithm HRs represented by blue lines, along with 95% CIs indicated by shading. d Cumulative Kaplan-Meier estimates the delineating time to the survival difference for ACM stratified by AIHFLevel within the Replication cohort. e Time-dependent ROC analysis for predicting ACM within the Replication cohort. AUCs at 6-, 12-, 24-, and 30-months demonstrating strong predictive accuracy: 0.902, 0.932, 0.932, 0.903. f Calibration curves depicting the predicted versus observed probabilities of ACM as evaluated by AIHFLevel within the Replication cohort. g Decision Curve Analysis (DCA) illustrating net benefit curves of AIHFLevel for predicting ACM within the Replication cohort. h Analysis of non-linear relationship between AIHFLevel and ACM risk using restricted cubic spline regression within Meta cohort (P_overall <0.0001, and P_non-linear < 0.0001). Univariate Cox regression analysis highlighted the AIHFLevel as a significant clinical predictor for ACM, with an HR of 1.878 (95% CI = 1.770–1.992). Statistic test: two-sided Wald test: P < 0.0001. Line chart displaying the estimated logarithm HRs represented by blue lines, along with 95% CIs indicated by shading. i Cumulative Kaplan-Meier estimates delineating time to the first adjudicated occurrence of ACM stratified by AIHFLevel within Meta cohort. j Time-dependent ROC analysis for predicting ACM within Meta cohort. AUCs at 6-, 12-, 24-, and 30-months confirming predictive excellence: 0.925, 0.947, 0.965, and 0.960. k Calibration curves depicting the predicted versus observed probabilities of ACM within the Meta cohort. l DCA illustrating net benefit curves of AIHFLevel for predicting ACM within Meta cohort.