CDG-MACE score: an interpretable scoring model for risk stratification in emergency chest pain with normal ECG

Objective.Rapid stratification of acute chest pain patients with non-ischemic electrocardiograms (ECGs) remains challenging. We developed and externally validated an interpretable cardiodynamicsgram (CDG)- major adverse cardiovascular events (MACE) Score to predict 30 d MACE.Approach.We proposed a three-step framework: (1) ECG dynamic analysis: using deterministic learning to model the ST-T repolarization process and derive CDG features that capture subtle repolarization abnormalities. We defined the temporal heterogeneity index and spatial heterogeneity index as quantitative CDG. (2) Ensemble model: training an XGBoost classifier on eight pre-specified variables with five-fold cross-validation. Patient-level splits were used; only the first emergency department ECG per patient entered the model. (3) Score derivation: transforming the ensemble into a sparse, globally interpretable score via SHAP-based variable contributions. To mitigate the demographic influence, age and gender were included as explicit covariates, and the study results were prespecified to be reported stratified by age and gender in both cohorts.Main results.Calibration and decision-analytic utility were assessed. Two independent cohorts (n= 2836) were included. In Cohort-1 (n= 2196; 23.27% MACE), the ensemble model achieved AUC 0.8441. Adding CDG dynamics to clinical variables improved discrimination compared with a clinical-only model (AUC 0.7963-0.8221). The derived CDG-MACE Score maintained discrimination (internal AUC 0.8221) and generalized well to Cohort-2 (n= 640; 11.09% MACE; external AUC 0.8219). Using prespecified cutoffs from the training set (low ⩽ 9.52; high > 26.83), the internal low-risk group had negative predictive value (NPV) 99.22% and MACE 0.78%, while the external low-risk group achieved NPV 100%. Ablation analyses confirmed that CDG dynamics contributed independent signals beyond demographics.Significance.The CDG-MACE Score combines dynamic ECG modeling with a SHAP-linearized scoring system to achieve discrimination with global interpretability, enabling safe exclusion of low-risk patients without typical ischemic ECG changes. External validation suggests robustness and clinical utility; additional multicenter prospective studies and fairness monitoring are warranted.