Abstract
INTRODUCTION: Acute ischemic stroke (AIS) stands as a leading cause of death and disability globally. This study aimed to investigate the risk factors and relevance linked with AIS in patients undergoing maintenance hemodialysis (MHD) and to create and validate nomogram models. METHODS: We examined the medical records of 314 patients with stage 5 chronic kidney disease (CKD 5) undergoing MHD, who sought neurology outpatient department consultation for suspected AIS symptoms between January 2018 and December 2023. These 314 patients were randomly divided into the training cohort (n = 222) and validation cohort (n = 92). The least absolute shrinkage selection operator (LASSO) regression model was employed for optimal feature selection in the AIS risk model. Subsequently, multivariable logistic regression analysis was used to construct a predictive model incorporating the features selected through LASSO. This predictive model's performance was assessed using the C-index and the area under the receiver operating characteristic curve (AUC). Additionally, calibration and clinical utility were evaluated through calibration plots and decision curve analysis (DCA). The model's internal validation was conducted using the validation cohort. RESULTS: Predictors integrated into the prediction nomogram encompassed cardiovascular disease (CVD) (odds ratio [OR] 7.95, 95% confidence interval [CI] 2.400-29.979), smoking (OR 5.7, 95% CI: 1.661-21.955), dialysis time (OR: 5.91, 95% CI: 5.866-29.979), low-density lipoprotein (OR: 2.99, 95% CI: 0.751-13.007), and fibrin degradation products (OR: 5.47, 95% CI: 1.563-23.162). The model exhibited robust discrimination, with a C-index of 0.877 and 0.915 in the internal training and validation cohorts, respectively. The AUC for the training set was 0.857, and a similar AUC of 0.905 was achieved in the validation cohort. DCA demonstrated a positive net benefit within a threshold risk range of 2-96%. CONCLUSION: The proposed nomogram effectively identifies MHD patients at high risk of AIS at an early stage. This model holds the potential to aid clinicians in making preventive recommendations.