A Nomogram to Predict the Risk of Acute Ischemic Stroke in Patients with Maintenance Hemodialysis: A Retrospective Cohort Study

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.

Keywords: Acute ischemic stroke; Maintenance hemodialysis; Nomogram; Predict review; Retrospective cohort study.

Fig. 1.

Flowchart of patient selection from the surveillance, epidemiology and end results (SEER) database.

Fig. 2.

Demographic and clinical feature selection using LASSO binary logistic regression model. a Optimal parameter (lambda) selection in the LASSO model was determined through fivefold cross-validation using the minimum criteria. The partial likelihood deviance (binomial deviance) curve was plotted against log(lambda). Dotted vertical lines were drawn at the optimal values based on the minimum criteria and the 1 SE of the minimum criteria (the 1-SE criteria). b LASSO coefficient profiles of the 30 features. A coefficient profile plot was generated against the log(lambda) sequence. A vertical line was drawn at the value selected using fivefold cross-validation, resulting in optimal lambda, which retained five features with nonzero coefficients. LASSO, least absolute shrinkage and selection operator; SE, standard error.

Fig. 3.

Nomogram for estimating the risk of ischemic stroke. The corresponding score for each indicator can be found by moving vertically down, and the total score for each patient can be obtained on the total points scale.

Fig. 4.

Calibration plots of the ischemic stroke nomogram prediction for the training cohort (a) and validation cohort (b). The x-axis represents the predicted ischemic stroke risk, while the y-axis represents the actual ischemic stroke rate. The diagonal dotted line signifies a perfect prediction by an ideal model, while the solid line represents the performance of the nomogram. A closer alignment of the solid line with the diagonal dotted line indicates a better predictive performance of the nomogram.

Fig. 5.

ROC curves in the training cohort (a) and validation cohort (b).

Fig. 6.

DCA curves for the nomogram in the training cohort (a) and validation cohort (b).