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.

ESC Heart Failure
|January 22, 2022
PubMed

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.
Abstract

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