Abstract
INTRODUCTION: Stroke-associated pneumonia (SAP) is a major cause of mortality during the acute phase of stroke. The A(2)DS(2) score is widely used to predict SAP risk but does not include 24-h non-contrast computed tomography-Alberta Stroke Program Early CT Score (NCCT-ASPECTS) or red cell distribution width (RDW). We aim to evaluate the added prognostic value of incorporating 24-h NCCT-ASPECTS and RDW into the A(2)DS(2) score and to develop a novel prediction model for SAP following thrombolysis. METHODS: This retrospective cohort study included thrombolyzed AIS patients at Saraburi Hospital, Thailand. The combined A(2)DS(2)-MFP model incorporated 24-h NCCT-ASPECTS and RDW, along with non-linear continuous predictors, using multivariable fractional polynomial (MFP) regression. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AuROC), calibration plots, and decision curve analysis (DCA), comparing it with the traditional A(2)DS(2) model and a model with continuous predictors. The goodness of fit for logistic regression models in relation to the observed data was determined through the Hosmer-Lemeshow method, and the accuracy of the probability predictions was examined using a calibration curve. Internal validation was performed using a bootstrapping approach. The predicted probability equation obtained from the final model after optimism correction was developed into a web-based application for predicting the risk of SAP, using PHP and JavaScript. RESULTS: Of 345 AIS patients, 20.3% developed SAP. The combined A(2)DS(2)-MFP model demonstrated excellent discriminative performance (AuROC: 0.917) compared to the traditional A(2)DS(2) model (AuROC: 0.868) and the model with continuous predictors (AuROC: 0.888). Both the calibration curve and the Hosmer-Lemeshow test indicated that the predicted probabilities and observed frequencies were in acceptable agreement. Incorporating 24-h NCCT-ASPECTS and RDW significantly improved risk prediction and clinical utility, as shown by improved reclassification indices and DCA. The model was internally validated with a C-statistic of 0.912, confirming its robustness. CONCLUSIONS: The combined A(2)DS(2)-MFP calculation showed superior performance, enabling early SAP detection and improving survival outcomes. This novel model offers a practical tool for resource-limited settings, supporting better SAP risk stratification and clinical management.