A Bedside Coagulation-Platelet Tipping Point for Mortality in Nontraumatic Subarachnoid Hemorrhage

Nontraumatic subarachnoid hemorrhage (ntSAH) is associated with high in-hospital mortality, and current prognostic models often overlook systemic hemostatic disturbances. We aimed to develop a coagulation-platelet index (INR_PLT) for mortality risk, quantify its nonlinear threshold, and construct an externally validated risk model using stacking ensemble learning.

This retrospective study included ntSAH patients from Huizhou Central People's Hospital (n = 287, training cohort) and the MIMIC-IV (n = 621, external validation). INR_PLT was derived by logistic regression: -1.596 + 1.364×INR - 0.008×PLT. Nonlinear relationships and thresholds were identified using restricted cubic splines and segmented regression. Nomograms and machine learning models (LR, SVM, DT, and LGBM) were developed, with a stacking ensemble as the final model. The performance of the models was evaluated by receiver operating characteristic (ROC) curves, calibration plots, and SHAP.

Of the 908 patients, 163 (17.95%) died. The independent predictors included age, GCS score, nimodipine use, and the INR_PLT (OR 2.425). INR_PLT was significantly correlated with antiplatelet therapy (P = 0.006) and mediated 19.8% of diabetes-associated mortality risk. A threshold at INR_PLT = -2.457 markedly increased mortality risk (OR 2.527, P = 0.002). The nomogram (C-index 0.847) and stacking model (AUC 0.80, F1 = 0.763) demonstrated strong performance.

The INR_PLT is a bedside index for identifying a significant mortality threshold in patients with ntSAH, supporting precise risk stratification with external validation.