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Updated: Oct 5, 2025

Pre-clinical Model of Cardiac Donation after Circulatory Death
Published on: August 2, 2019
Nomogram predicting death and heart transplantation before appropriate ICD shock in dilated cardiomyopathy
Yu Deng1, Nixiao Zhang1, Wei Hua1
1Cardiac Arrhythmia Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 Bei Li Shi Road, Xicheng District, Beijing, 100037, China.
Insights
This study developed a nomogram to predict mortality and heart transplantation in dilated cardiomyopathy patients receiving implantable cardioverter-defibrillators (ICDs). The tool aids in selecting appropriate ICD recipients and shared decision-making.
Area of Science:
- Cardiology
- Medical Statistics
- Biomedical Engineering
Background:
- Non-ischaemic dilated cardiomyopathy (DCM) patients often receive implantable cardioverter-defibrillators (ICDs).
- Predicting outcomes like mortality and heart transplantation (HT) is crucial for optimizing ICD therapy.
- Current risk stratification tools may not fully account for competing risks in DCM patients.
Purpose of the Study:
- To develop and validate a competing risk nomogram for predicting all-cause mortality and HT before the first appropriate shock in DCM patients with ICDs.
- To identify key predictors of adverse outcomes in this population.
- To provide a tool for improved patient selection and risk stratification.
Main Methods:
- Retrospective analysis of 218 DCM patients implanted with ICDs.
- Development of a Fine-Gray competing risk model incorporating implantation indication, left ventricular end-diastolic diameter, N-terminal pro-brain natriuretic peptide, ACE inhibitor/ARB use, and amiodarone treatment.
- Internal validation using ROC curves, Harrell's C-index, calibration curves, and decision curve analysis.
Main Results:
- The nomogram demonstrated good discriminative ability with 1-, 3-, and 5-year AUCs of 0.83, 0.84, and 0.85, respectively.
- Harrell's C-index was 0.788, indicating strong predictive performance.
- Five variables were identified as independent predictors, and the model was found to be clinically useful across various treatment thresholds.
Conclusions:
- The developed nomogram is a simple and effective tool for risk stratification in DCM patients undergoing ICD implantation.
- It can aid clinicians in shared decision-making regarding ICD therapy selection.
- The tool helps identify patients who may not benefit from ICDs, potentially avoiding unnecessary interventions.
Aims:
This study aimed to develop and validate a competing risk nomogram for predicting all-cause mortality and heart transplantation (HT) before first appropriate shock in non-ischaemic dilated cardiomyopathy (DCM) patients receiving implantable cardioverter-defibrillators (ICD).
Methods And Results:
A total of 218 consecutive DCM patients implanted with ICD between 2010 and 2019 at our institution were retrospectively enrolled. Cox proportional hazards model was primarily built to identify variables associated with death and HT. Then, a Fine-Gray model, accounting for the appropriate shock as a competing risk, was constructed using these selected variables along with implantation indication (primary vs. secondary). Finally, a nomogram based on the Fine-Gray model was established to predict 1-, 3-, and 5-year probabilities of all-cause mortality and HT before first appropriate shock. The area under the receiver operating characteristic (ROC) curve (AUC), Harrell's C-index, and calibration curves were used to evaluate and internally validate the performance of this model. The decision curve analysis was applied to assess its clinical utility. The 1-, 3-, and 5-year cumulative incidence of all-cause mortality and HT without former appropriate shock were 5.3% [95% confidence interval (CI) 2.9-9.9%], 16.6% (95% CI 11-25.0%), and 25.3% (95% CI 17.2-37.1%), respectively. Five variables including implantation indication, left ventricular end-diastolic diameter, N-terminal pro-brain natriuretic peptide, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, and amiodarone treatment were independently associated with it (all P < 0.05) and were used for constructing the nomogram. The 1-, 3-, and 5-year AUC of the nomogram were 0.83 (95% CI 0.73-0.94, P < 0.001), 0.84 (95% CI 0.75-0.93, P < 0.001), and 0.85 (95% CI 0.77-0.94, P < 0.001), respectively. The Harrell's C-index was 0.788 (95% CI 0.697-0.877, P < 0.001; 0.762 for the optimism-corrected C-index), showing the good discriminative ability of the model. The calibration was acceptable (optimism-corrected slope 0.896). Decision curve analysis identified our model was clinically useful within the entire range of potential treatment thresholds for ICD implantation. Three risk groups stratified by scores were significantly different between cumulative incidence curves (P < 0.001). The identified high-risk group composed 17.9% of our population and did not derive long-term benefit from ICD.
Conclusions:
The proposed nomogram is a simple, useful risk stratification tool for selecting potential ICD recipients in DCM patients. It might facilitate the shared decision-making between patients and clinicians.
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