Interpretable machine learning for multiclass trajectory prediction in cardiovascular-kidney-metabolic syndrome stage: development and external validation.

Background: Cardiovascular-kidney-metabolic (CKM) syndrome imposes a substantial global burden, yet most studies reduce stage change to a binary outcome that cannot distinguish clinically meaningful trajectories. This study developed and externally validated a multiclass machine learning framework for predicting CKM stage trajectories. Methods: A longitudinal cohort of 2,971 adults with baseline CKM stages 0-3 was used for development, with trajectories classified as Improvement, Stable, Mild Progression and Rapid Progression. Six algorithms were compared by Macro-AUC and decision curve analysis. SHAP values identified consensus predictors for a parsimonious model, externally validated in an independent hospital-based cohort (N = 291). Results: XGBoost achieved the highest Macro-AUC (0.786, 95% CI 0.757-0.813), with the six algorithms performing comparably. For rapid-progression screening, the primary model reached a sensitivity of 0.767, a specificity of 0.739 and a negative predictive value of 0.949 at a threshold of 0.23, and identified 41.9% of rapid progressors within the highest-risk 10%. Baseline CKM stage was the dominant prognostic determinant, with a stage-only benchmark reaching 0.689. Six consensus predictors were identified: baseline CKM stage, age, fasting glucose, TyG-BMI index, systolic blood pressure and triglycerides. The parsimonious model retained over 98% of full-model discrimination and reached a Macro-AUC of 0.752 externally without retraining. Conclusion: This framework distinguishes CKM trajectories and is intended for screening rapid progression rather than assigning individuals to a single trajectory. Baseline CKM stage is the principal prognostic determinant, and the six-predictor model preserves discrimination using routinely available measurements, supporting stratified follow-up pending confirmation in larger cohorts.