Frailty combined with nutritional risk for predicting stroke-associated pneumonia: a cohort study based on a nomogram model

Abstract

Introduction Stroke-associated pneumonia (SAP) is a common and serious complication in patients with acute severe stroke, and existing risk assessment tools have limited predictive accuracy in critically ill populations. This study innovatively incorporated frailty and nutritional risk, which reflect stress tolerance and overall physiological reserve, into an early SAP prediction model. Methods A retrospective cohort study was conducted on 293 critically ill stroke patients admitted to the Neurocritical Care Unit of the First Affiliated Hospital of Chongqing Medical University between 2013 and 2024. Collect clinical characteristics and laboratory indicators of patients, assess their frailty status and nutritional risk, and analyze the additive interaction effect between the two on the occurrence of SAP. Independent predictors were identified through multivariate logistic regression and incorporated into a visual nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis, and 10-fold cross-validation. Results A total of 293 patients with severe stroke were included in this study, among whom 126 (43%) developed SAP. The results of the additive interaction analysis showed a positive additive interaction between frailty and nutritional risk in the development of SAP, with an attributable proportion (AP) of 0.711 (95% CI = 0.358 ~ 1.065) and a synergy index (SI) of 3.694 (95% CI = 1.200 ~ 11.364). A SAP risk prediction model incorporating age, nasogastric tube use, neutrophil-to-lymphocyte ratio (NLR), frailty status, and nutritional risk demonstrated good discriminative performance, with an area under the curve (AUC) of 0.848, which was significantly higher than that of the conventional SAP prediction score (ISAN score: AUC = 0.589). Internal validation showed that the model achieved an accuracy of 73.93%, sensitivity of 77.30%, and specificity of 71.95%, indicating good stability. The calibration curve demonstrated good agreement between predicted and observed outcomes. Decision curve analysis (DCA) indicated that the model provided substantially greater clinical net benefit than the ISAN score. Discussion This study is the first to integrate frailty and nutritional risk into an SAP prediction model, significantly improving early risk identification and providing an innovative, practical tool for precision prevention and targeted intervention in critically ill stroke patients.