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Published on: September 16, 2022
Development and Validation of a Cardiovascular Disease Risk Prediction Model for Patients with Non-Dialysis-Dependent
Ning Li1, Zhao Wang1, Xue Yang1
1Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Insights
This study developed a novel nomogram to predict cardiovascular events in chronic kidney disease (CKD) patients. The model accurately assesses risk, aiding clinicians in personalized treatment strategies for non-dialysis-dependent CKD (NDD-CKD) patients.
Area of Science:
- Nephrology
- Cardiology
- Biostatistics
Background:
- Chronic kidney disease (CKD) patients frequently experience cardiovascular complications prior to renal replacement therapy.
- Accurate cardiovascular prognostication is crucial for individualized patient management in CKD.
- Predictive models can guide therapeutic strategies for non-dialysis-dependent CKD (NDD-CKD) patients.
Purpose of the Study:
- To develop and validate a predictive model for cardiovascular events in NDD-CKD patients.
- To identify key predictors of cardiovascular events in this population.
- To create a clinical tool for assessing cardiovascular risk.
Main Methods:
- A cohort of 1,138 NDD-CKD patients was randomly assigned to training and validation sets (7:3 ratio).
- Least Absolute Shrinkage and Selection Operator (Lasso) regression screened predictors.
- Model performance was evaluated using Area Under the Receiver Operating Characteristic Curve (AUC), calibration curves, and Kaplan-Meier (KM) curves.
Main Results:
- Five predictors were identified: albumin, estimated glomerular filtration rate, CKD etiology, cardiovascular disease history, and age.
- The nomogram achieved 1-year, 2-year, and 3-year AUCs of 0.81, 0.80, and 0.80 in the training set.
- External validation demonstrated good performance with AUCs of 0.77, 0.84, and 0.82 for 1, 2, and 3 years, respectively.
Conclusions:
- A novel nomogram was successfully developed for predicting cardiovascular events in NDD-CKD patients.
- The nomogram demonstrates robust predictive performance and good calibration.
- This tool holds significant potential for clinical application in managing cardiovascular risk in NDD-CKD.
Introduction:
Most chronic kidney disease (CKD) patients experience cardiovascular issues before commencing renal replacement therapy. An accuracy prediction model is helpful for physicians to assess cardiovascular prognoses in each individual and to provide insights on how to outline individualized lines of therapy.
Method:
This study enrolled 1,138 participants with non-dialysis-dependent chronic kidney disease (NDD-CKD). Following a proportion of 7:3, patients were randomly assigned to training and validation cohorts. The relevant predictors of cardiovascular events were screened using the least absolute shrinkage and selection operator (Lasso) regression. The area under the receiver operating characteristic curve (AUC) and the calibration curve with 1,000 bootstrap resamples were used to assess the nomogram's performance. Tests on the discrimination of the prediction model used Kaplan-Meier (KM) curve.
Results:
After screening all the predictors by lasso regression, the five remaining ones (albumin, estimated glomerular filtration rate, etiology of CKD, cardiovascular disease history, and age) were used to construct the prediction model. The AUCs of 1 year, 2 years, and 3 years were 0.81 (95% CI = 0.75-0.87), 0.80 (95% CI = 0.75-0.86), and 0.80 (95% CI = 0.73-0.86), respectively. The calibration curve and the KM curve showed good prediction features, and the external validation also had a good prediction performance (AUCs of 1, 2, and 3 years were 0.77, 0.84, and 0.82, respectively).
Conclusion:
We successfully developed a novel nomogram that has decent prediction performance and can be used for assessing the probability of cardiovascular events in patients with NDD-CKD, displaying valuable potential for clinical application.
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