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Updated: Feb 20, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Nomogram for Predicting Cardiovascular Mortality in Incident Peritoneal Dialysis Patients: An Observational Study
Xi Xia1,2, Chen Zhao1,2,3, Qimei Luo1,2
1The First Affiliated Hospital of Sun Yat-sen University, Department of Nephrology, Guangzhou, 510080, People's Republic of China.
Abstract:
Cardiovascular mortality risk is high for peritoneal dialysis (PD) patients but it varies considerably among individuals. There is no clinical tool to predict cardiovascular mortality for PD patients yet. Therefore, we developed a cardiovascular mortality risk nomogram in a PD patient cohort. We derived and internally validated the nomogram in incident adult PD patients randomly assigned to a training (N = 918) or a validation (N = 460) dataset. The nomogram was built using the LASSO Cox regression model. Increasing age, history of cardiovascular disease or diabetes were consistent predictors of cardiovascular mortality. Low hemoglobin and serum albumin, high hypersensitive C-reactive protein and decreasing 24 hours urine output were identified as non-traditional cardiovascular risk predictors. In the validation dataset, the above nomogram performed good discrimination (1 year c-statistic = 0.83; 3 year c-statistic = 0.78) and calibration. This tool can classify patients between those at high risk of cardiovascular mortality (high-risk group) and those of low risk (low-risk group). Cardiovascular mortality was significantly different in the internal validation set of patients for the high-risk group compared to the low-risk group (HR 3.77, 2.14-6.64; p < 0.001). This novel nomogram can accurately predict cardiovascular mortality risk in incident PD patients.
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