Predictive nomogram for ischemic stroke risk in clear cell renal cell carcinoma patients

Abstract

Clear cell renal cell carcinoma (ccRCC) and ischemic stroke are critical global health challenges with a notable association. This study explores the correlation between tumor-related factors and ischemic stroke risk, aiming to construct a predictive nomogram model for ischemic stroke in ccRCC patients. We retrospectively analyzed data from ccRCC patients who underwent nephrectomy at the First Hospital of Shanxi Medical University between January 1, 2013, and May 31, 2022. The data were randomly divided into a training cohort (70%) and a validation cohort (30%). Predictive factors were identified using univariate logistic regression, least absolute shrinkage and selection operator regression, and multivariate logistic regression. A nomogram and a Shiny local calculator were developed using these predictors. We identified six predictors for the nomogram: WHO/ISUP grade, diabetes, hypertension, LDL-C, age, and D-dimer. The nomogram showed good discrimination, with an area under the ROC curve of 0.816 in the training cohort and 0.775 in the validation cohort. The optimal cutoff value was 53.7%. The model demonstrated excellent calibration and clinical applicability. WHO/ISUP grade correlates with ischemic stroke risk, offering insights into cancer-related ischemic stroke mechanisms. This nomogram aids in identifying high-risk individuals among ccRCC patients, facilitating early management and improved outcomes.