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Updated: Aug 8, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram to predict mortality risk in patients with ischemic heart disease
Long Yang1, Xia Dong2, Baiheremujiang Abuduaini3
1College of Pediatrics, Xinjiang Medical University, Ürümqi, China.
Insights
This study developed a machine learning nomogram to predict mortality risk in Ischemic Heart Disease (IHD) patients. The model uses six key factors to forecast 1, 3, and 5-year survival, aiding clinical decision-making.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Ischemic Heart Disease (IHD) is a leading cause of cardiovascular mortality.
- Existing research primarily focuses on IHD risk factors and mortality, with limited predictive models for patient outcomes.
- There is a need for effective tools to predict mortality risk in IHD patients.
Purpose of the Study:
- To construct and validate a nomogram prediction model for mortality risk in patients with Ischemic Heart Disease (IHD).
- To identify key clinical variables associated with long-term mortality in IHD patients.
- To provide a tool for clinicians to assess patient prognosis and inform treatment strategies.
Main Methods:
- Retrospective analysis of 1,663 IHD patients, with data split into training and validation sets (3:1 ratio).
- Least Absolute Shrinkage and Selection Operator (LASSO) regression used for variable selection from 31 initial parameters.
- Model performance evaluated using Receiver Operating Characteristic (ROC) curves, C-index, calibration plots, and Decision Curve Analysis (DCA).
Main Results:
- LASSO regression identified six significant predictors of mortality: age, uric acid, serum total bilirubin, albumin, alkaline phosphatase, and left ventricular ejection fraction.
- The nomogram demonstrated reliable predictive accuracy with C-indices ranging from 0.683 to 0.720 across 1, 3, and 5-year prediction intervals in both training and validation sets.
- Calibration plots and DCA confirmed the nomogram's good performance and clinical utility.
Conclusions:
- Age, uric acid, total serum bilirubin, serum albumin, alkaline phosphatase, and left ventricular ejection fraction are significant predictors of mortality in IHD patients.
- A user-friendly nomogram model was developed to predict 1, 3, and 5-year mortality risk in IHD patients.
- This nomogram can assist clinicians in assessing prognosis and optimizing tertiary prevention strategies for IHD.
Background:
Ischemic Heart Disease (IHD) is the leading cause of death from cardiovascular disease. Currently, most studies have focused on factors influencing IDH or mortality risk, while few predictive models have been used for mortality risk in IHD patients. In this study, we constructed an effective nomogram prediction model to predict the risk of death in IHD patients by machine learning.
Methods:
We conducted a retrospective study of 1,663 patients with IHD. The data were divided into training and validation sets in a 3:1 ratio. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen the variables to test the accuracy of the risk prediction model. Data from the training and validation sets were used to calculate receiver operating characteristic (ROC) curves, C-index, calibration plots, and dynamic component analysis (DCA), respectively.
Results:
Using LASSO regression, we selected six representative features, age, uric acid, serum total bilirubin, albumin, alkaline phosphatase, and left ventricular ejection fraction, from 31 variables to predict the risk of death at 1, 3, and 5 years in patients with IHD, and constructed the nomogram model. In the reliability of the validated model, the C-index at 1, 3, and 5 years was 0.705 (0.658-0.751), 0.705 (0.671-0.739), and 0.694 (0.656-0.733) for the training set, respectively; the C-index at 1, 3, and 5 years based on the validation set was 0.720 (0.654-0.786), 0.708 (0.650-0.765), and 0.683 (0.613-0.754), respectively. Both the calibration plot and the DCA curve are well-behaved.
Conclusion:
Age, uric acid, total serum bilirubin, serum albumin, alkaline phosphatase, and left ventricular ejection fraction were significantly associated with the risk of death in patients with IHD. We constructed a simple nomogram model to predict the risk of death at 1, 3, and 5 years for patients with IHD. Clinicians can use this simple model to assess the prognosis of patients at the time of admission to make better clinical decisions in tertiary prevention of the disease.
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