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Development and internal validation of cardiovascular disease risk prediction model for patients with chronic kidney disease stage 3-5 within 5 years

cardiology
epidemiology
nephrology

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: 301 patients with CKD stage 3-5 were recruited from January 2010 to January 2022 and followed up un til July 2022. 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 the c-statistic and P-value of the Hosmer-Lemeshow test. Decision curve analysis was performed to assess clinical effectiveness.

Results: During follow-up, 169 (56.1%) developed first CVD events within 5 years. The median time of occurrence was 10 months. Of 29 clinical parameters, 11 variables were finally identified as significant predictors and included in the prediction model. 4 prediction models were created in a derivation cohort: original, inflammation, imaging, and full model. The full model had the lowest AIC of 311.531 and a P-value of 0.3319 of the Hosmer-Lemeshow test.

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 5 years.

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