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Updated: Sep 9, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram for predicting hyperphosphatemia in non-dialysis patients with chronic
Xianhui Zhao1, Caiyun Zheng2, Qitong Su1
1Nanping First Hospital Affiliated to Fujian Medical University, Nanping, Fujian, China.
Insights
This study developed a predictive model to identify patients with chronic kidney disease (CKD) at high risk for hyperphosphatemia. The model accurately predicts elevated serum phosphate levels, aiding in targeted interventions for better patient outcomes.
Area of Science:
- Nephrology
- Biochemistry
- Medical Informatics
Background:
- Elevated serum phosphate is a significant mortality risk factor in chronic kidney disease (CKD).
- Hyperphosphatemia is a common complication in non-dialysis CKD patients.
- Identifying risk factors is crucial for managing CKD complications.
Purpose of the Study:
- To identify independent risk factors for hyperphosphatemia in non-dialysis CKD patients.
- To develop and validate a predictive model for hyperphosphatemia risk assessment.
- To improve clinical management and patient prognosis in CKD.
Main Methods:
- Retrospective analysis of 216 non-dialysis CKD patients' data.
- Least absolute shrinkage and selection operator (LASSO) regression for predictor screening.
- Multivariate logistic regression and nomogram construction for risk prediction.
- Internal validation using C-index, ROC curves, calibration, and decision curve analysis.
Main Results:
- Hyperphosphatemia was observed in 62.04% of the study cohort.
- Independent predictors identified: hemoglobin, blood urea nitrogen, serum creatinine, and parathyroid hormone.
- The developed nomogram demonstrated high predictive accuracy (C-index=0.916) and discriminative ability (AUC=0.953 in validation set).
Conclusions:
- A validated nomogram accurately identifies CKD patients at high risk for hyperphosphatemia.
- The model facilitates prospective monitoring and targeted preventive interventions.
- Individualized risk assessment can lead to customized treatment strategies and improved long-term prognosis.
Background:
Elevated serum phosphate levels are strongly associated with an increased risk of all-cause mortality in patients with chronic kidney disease (CKD). The aim of this study was to identify independent risk factors for hyperphosphatemia in patients with non-dialysis CKD and use the findings to develop and validate a predictive model for assessing hyperphosphatemia risk.
Methods:
Data of patients with CKD discharged from the Department of Nephrology between January 2021 and December 2023 were retrospectively analyzed. Potential predictors were screened from an array of clinical variables using least absolute shrinkage and selection operator regression in conjunction with 10-fold cross-validation. A multivariate logistic regression model was constructed to identify independent risk factors for predicting hyperphosphatemia. The C-index, receiver operating characteristic curve, calibration curve, and decision curve analysis were used to evaluate model predictive power, discriminability, accuracy, and clinical utility. Internal validation was implemented through a comparison of results from a validation set and the entire dataset.
Results:
This study included 216 patients, with 134 (62.04%) individuals who developed hyperphosphatemia. Logistic regression revealed that hemoglobin, blood urea nitrogen, serum creatinine, and parathyroid hormone were independently correlated with hyperphosphatemia. The nomogram C-index was 0.916 (95% confidence interval [CI]: 0.872-0.961). The model demonstrated excellent discriminative ability in the independent validation set (area under the curve [AUC] = 0.953, 95% CI: 0.909-0.998), with the full dataset analysis showing concordant results (AUC = 0.923, 95% CI: 0.889-0.958). The decision and clinical impact curves showed the clinical value of our nomogram for patients with CKD and hyperphosphatemia.
Conclusions:
The nomogram model was highly accurate in identifying CKD subpopulations at an elevated risk of serum phosphorus metabolic disorders. Our model can be utilized for prospective monitoring and preventive intervention. Furthermore, through individualized risk assessments, the model can contribute to the development of customized treatment strategies that have the potential to markedly improve long-term prognosis.
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