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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram predicting all-cause mortality for patients with an implantable cardioverter
Bin Zhou1, Lei Ding2, Xuerong Sun3
1Department of Cardiology, Fuwai Shenzhen Hospital, Chinese Academy of Medical Sciences, Shenzhen, Guangdong, China.
Insights
A new nomogram predicts mortality risk in implantable cardioverter defibrillator (ICD) patients using physical activity and clinical data. This tool offers personalized risk assessment for those at high risk of sudden cardiac death.
Area of Science:
- Cardiology
- Medical Informatics
- Predictive Analytics
Background:
- Patients with implantable cardioverter defibrillators (ICDs) still face high mortality risk.
- Identifying high-risk individuals is crucial for improving outcomes.
Purpose of the Study:
- To develop and validate a nomogram for predicting all-cause mortality in ICD patients.
- To integrate physical activity data from ICDs with clinical factors for risk prediction.
Main Methods:
- Retrospective analysis of a multicenter ICD registry (617 patients for development, 196 for validation).
- Utilized Cox regression to identify mortality predictors (physical activity, diabetes, left ventricular end-diastolic diameter).
- Nomogram performance assessed using C-index and calibration curves, with subgroup and sensitivity analyses.
Main Results:
- The nomogram, incorporating physical activity, diabetes, and LVEDD, showed good predictive accuracy (C-index 0.80 in development, 0.74 in validation).
- Calibration curves demonstrated strong agreement between predicted and actual survival probabilities.
- The model maintained discriminative capacity across different patient subgroups and outcomes.
Conclusions:
- A validated nomogram integrating ICD-monitored physical activity and clinical data accurately predicts all-cause mortality in ICD patients.
- This tool enables personalized death risk assessment, aiding clinical decision-making.
Background:
For patients with a high risk of sudden cardiac death, despite the benefits of an implantable cardioverter defibrillator (ICD), some patients are still at high risk of death.
Aim:
The purpose of this study was to develop and validate a nomogram predicting all-cause mortality for patients with an ICD.
Methods:
We retrospectively analysed the data of multicentre ICD registration study from 2010 to 2014 in China. A total of 617 ICD patients formed a development cohort. The physical activity monitored by ICD and clinical data was collected. Univariate and multivariate Cox regression analyses were used to screen mortality predictors and construct the nomogram. The performance of the nomogram was evaluated by the consistency index (C-index) and the calibration curve. Additionally, extensive subgroup and sensitivity analyses were conducted to evaluate the model's robustness. A total of 196 ICD patients formed a validation cohort.
Results:
In the development cohort, physical activity, diabetes and left ventricular end-diastolic diameter were selected as independent prognostic factors. The nomogram was constructed by these three factors. The C-index of the nomogram was 0.80 (95% CI 0.75 to 0.84). The calibration curve showed that the predicted survival probability of the nomogram was in good agreement with the actual survival probability. In the validation cohort, the C-index of the nomogram was 0.74 (95% CI 0.64 to 0.84), and the calibration curve still maintained good consistency. Crucially, the nomogram maintained stable and excellent discriminative capacity across primary and secondary prevention subgroups, as well as for predicting specific cardiac and sudden cardiac death.
Conclusions:
Our study develops and validates a nomogram predicting all-cause mortality for patients with an ICD by integrating the physical activity monitored by ICD and clinical data. The nomogram performs well and can provide personalised death risk assessment for ICD patients.
Trial Registration Number:
ChiCTR-ONRC-13003695.
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