ArticlePeerJ2026
A retrospective study on development and internal validation of cardiovascular disease risk prediction model for patients with chronic kidney disease stage 3-5 within 5 years.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Cardiovascular disease (CVD) is the leading cause of mortality in chronic kidney disease (CKD) patients. Traditional CVD risk factors exhibit diminished predictive utility in advanced CKD, necessitating integration of non-traditional biomarkers. Previous prediction models based only on traditional CVD risk show limitations and inaccuracies. This study aimed to develop and validate a 5-year CVD risk prediction model combining clinical, laboratory, and imaging parameters for CKD stages 3-5 patients. Methods: Three hundred and one patients with CKD stage 3-5 were recruited from January 2010 to January 2022 and followed up until July 2022. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression were used to identify baseline predictors for model development including clinical data, medication history and laboratory parameters, regression modeling was performed using logistic regression and internally validated using tenfold cross-validation. Discrimination and calibration of resulting prediction models were assessed using c-statistic and Results: During follow-up, 169 (56.1%) experienced a first CVD event within 5 years. The median time of occurrence was 10 months. From 29 candidate variables, 11 independent predictors were identified. Through nested model comparisons, we demonstrated that adding inflammatory marker C-reactive protein (CRP) and echocardiographic marker interventricular septum thickness (IVS) markers to traditional risk factors progressively improved predictive performance. The full model-integrating clinical, inflammatory, and imaging parameters-achieved the highest discrimination (area under the curve (AUC), 0.845, 95% confidence interval (CI), [0.802-0.888]) and best fit (Akaike information criterion (AIC), 311.531), with excellent calibration (Hosmer-Lemeshow Conclusions: This study established and validated a clinical risk prediction model based on readily available variables in clinical practice, aiming to predict the risk of CVD events in patients with CKD stages 3-5 over a 5-year period.
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