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

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 patie...

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Bibliographic Details
Published in:Scientific reports 2024-12, Vol.14 (1), p.30162-11, Article 30162
Main Authors: Wen, Jie, Rong, Yi, Kang, Yinbo, Lv, Dingyang, Cui, Fan, Zhou, Huiyu, Jia, Mohan, Wang, Qiwei, Shuang, Weibing
Format: Article
Language:English
Subjects:
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Adult
Aged
Carcinoma, Renal Cell - complications
Clear cell renal cell carcinoma
Clear cell-type renal cell carcinoma
Diabetes mellitus
Female
Global health
Health risks
Humanities and Social Sciences
Humans
Hypertension
Ischemia
Ischemic stroke
Ischemic Stroke - etiology
Kidney cancer
Kidney Neoplasms - complications
Low density lipoprotein
Male
Middle Aged
multidisciplinary
Nephrectomy
Nomogram
Nomograms
Patients
Public health
Regression analysis
Retrospective Studies
Risk Assessment - methods
Risk Factors
ROC Curve
Science
Science (multidisciplinary)
Stroke
Training
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Description
Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-82072-9