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Updated: Jun 18, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and Validation of a Nomogram to Predict the Future Risk of Cardiovascular Disease
Xuechun Shen1, Wei He2, Jinyu Sun3
1Department of Cardiology, The Second Affiliated Hospital of Nanjing Medical University, 210011 Nanjing, Jiangsu, China.
Insights
This study developed a nomogram to predict cardiovascular disease (CVD) risk using cardiac myosin-binding protein-C (cMyBP-C), age, blood pressure, smoking, and family history. The model shows high predictive accuracy for early CVD identification.
Area of Science:
- Cardiology
- Preventive Medicine
- Biomarker Research
Background:
- Early identification of individuals at high risk of cardiovascular disease (CVD) is crucial for timely intervention.
- Cardiovascular diseases remain a leading cause of mortality worldwide, necessitating improved risk prediction strategies.
Purpose of the Study:
- To construct and validate a nomogram for predicting the risk of future cardiovascular disease (CVD) events in the general population.
- To identify key predictors of CVD events for enhanced risk stratification.
Main Methods:
- A retrospective analysis of 537 patients was conducted, with data split into training and validation cohorts (7:3 ratio).
- The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were employed to identify significant CVD risk factors.
- Cardiovascular disease events were defined as sudden cardiac death, myocardial infarction, acute heart failure exacerbation, or stroke.
Main Results:
- The study identified cardiac myosin-binding protein-C (cMyBP-C), age, diastolic blood pressure, smoking frequency, and family history of CVD as significant predictors of future CVD events.
- The developed nomogram demonstrated high predictive ability, with an Area Under the Curve (AUC) of 0.816 in the training cohort and 0.774 in the validation cohort.
- Elevated cMyBP-C levels were significantly associated with a higher incidence of CVD events (p=0.001).
Conclusions:
- A nomogram incorporating cMyBP-C, age, diastolic blood pressure, smoking, and family history provides a valuable tool for CVD risk prediction.
- The nomogram exhibits robust predictive performance, aiding in the early identification of high-risk individuals.
- This tool can support clinical decision-making and the implementation of personalized preventive strategies for cardiovascular disease.
Background:
Early identification of individuals at a high risk of cardiovascular disease (CVD) is crucial. This study aimed to construct a nomogram for CVD risk prediction in the general population.
Methods:
This retrospective study analyzed the data between January 2012 and September 2020 at the Physical Examination Center of the Second Affiliated Hospital of Nanjing Medical University (randomized 7:3 to the training and validation cohorts). The outcome was the occurrence of CVD events, which were defined as sudden cardiac death or any death related to myocardial infarction, acute exacerbation of heart failure, or stroke. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to screen the significant variables related to CVD.
Results:
Among the 537 patients, 54 had CVD (10.1%). The median cardiac myosin-binding protein-C (cMyBP-C) level in the CVD group was higher than in the no-CVD group (42.25 pg/mL VS 25.00 pg/mL, p = 0.001). After LASSO selection and multivariable analysis, cMyBP-C (Odds ratio [OR] = 1.004, 95% CI [CI, confidence interval]: 1.000-1.008, p = 0.035), age (OR = 1.023, 95% CI: 0.999-1.048, p = 0.062), diastolic blood pressure (OR = 1.025, 95% CI: 0.995-1.058, p = 0.103), cigarettes per day (OR = 1.066, 95% CI: 1.021-1.113, p = 0.003), and family history of CVD (OR = 2.219, 95% CI: 1.003-4.893, p = 0.047) were associated with future CVD events (p 0.200). The model, including cMyBP-C, age, diastolic blood pressure, cigarettes per day, and family history of CVD, displayed a high predictive ability with an area under the curve (AUC) of 0.816 (95% CI: 0.714-0.918) in the training cohort (specificity and negative predictive value of 0.92 and 0.96) and 0.774 (95% CI: 0.703-0.845) in the validation cohort.
Conclusions:
A nomogram based on cMyBP-C, age, diastolic blood pressure, cigarettes per day, and family history of CVD was constructed. The model displayed a high predictive ability.
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