AUTHOR=Cong Xuhui , Zou Xuli , Zhu Ruilou , Li Yubao , Liu Lu , Zhang Jiaqiang 

TITLE=Development and validation of an interpretable risk prediction model for perioperative ischemic stroke in noncardiac, nonvascular, and nonneurosurgical patients: a retrospective study

JOURNAL=Frontiers in Physiology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2025.1628475

DOI=10.3389/fphys.2025.1628475

ISSN=1664-042X

ABSTRACT=BackgroundPerioperative stroke is a severe complication for patients undergoing non-cardiac, non-vascular, and non-neurosurgical surgeries, resulting in significant morbidity and mortality. Despite its clinical relevance, effective predictive models for stroke risk in this population are scarce. This study seeks to develop and validate an interpretable predictive model that incorporates essential perioperative variables to assess stroke risk. The goal is to enhance risk stratification and support more informed clinical decision-making.MethodsA retrospective cohort study included 106,328 patients aged 18 years or older who underwent non-cardiac, non-vascular, and non-neurosurgical surgeries at our institution. The development cohort comprised 74,429 patients, with 140 perioperative stroke incidents, while the validation cohort consisted of 31,899 patients, with 59 incidents. Risk factors for perioperative stroke were identified using univariable logistic regression analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was applied to select variables, followed by the development, validation, and performance evaluation of the predictive model using multivariate logistic regression analysis.ResultsThe prediction model, developed using nine variables including demographic information, medical history, and pre- and post-operative data, demonstrated strong discriminatory power in predicting perioperative stroke (AUC = 0.869; 95% CI, 0.827–0.910). It also exhibited an excellent fit with the validation cohort (Hosmer–Lemeshow test, χ2 = 6.877, P = 0.650). Additionally, the SHAP (Shapley Additive Explanations) interpretability model was integrated to enhance the model’s transparency, allowing clinicians to better understand the contribution of each predictor. Decision curve analysis confirmed the model’s significant net benefit, further validating its clinical utility.ConclusionThis study developed and validated a robust predictive model for perioperative stroke risk in patients undergoing non-cardiac, non-vascular, and non-neurosurgical procedures. Despite its retrospective design, the model exhibited strong performance and clinical relevance. It provides a solid foundation for future multi-center studies aimed at refining and expanding its applicability.