Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts

Abstract

Background and aims: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation.

Methods: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis.

Results: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor.

Conclusions: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.

Keywords: Implantable cardioverter-defibrillator; Myocardial infarction; Primary prevention; Sudden cardiac death.

Structured Graphical Abstract

Brief summary of the background, methodology, and results of the PROFID data analysis. ICD patients, patients with left ventricular ejection fraction ≤ 35% who had received a cardioverter-defibrillator implantation for primary prevention of sudden cardiac death; non-ICD patients ≤35%, patients who did not carry a cardioverter-defibrillator and had a left ventricular ejection fraction ≤ 35%; and non-ICD patients >35%, patients who did not carry a cardioverter-defibrillator and had a left ventricular ejection fraction > 35%. Detailed inclusion and exclusion criteria are provided in the text. CMR, cardiac magnetic resonance; ICD, implantable cardioverter-defibrillator; LVEF, left ventricular ejection fraction; MI, myocardial infarction.

Figure 1

Forest plot of the c-statistic for the prediction of risk for the primary endpoint at 36 months across the three subgroups in the Phase 1 analysis, i.e. the analysis excluding cardiac magnetic resonance imaging parameters. ICD patients, patients with left ventricular ejection fraction ≤ 35% who had received a cardioverter-defibrillator implantation for primary prevention of sudden cardiac death; non-ICD patients ≤35%: patients who did not carry a cardioverter-defibrillator and had a left ventricular ejection fraction ≤ 35%; and non-ICD patients >35%, patients who did not carry a cardioverter-defibrillator and had a left ventricular ejection fraction > 35%. In ICD patients, endpoint was first appropriate therapy, and in non-ICD patients ≤35% and non-ICD patients >35%, endpoint was sudden cardiac death. In two data sets with non-ICD patients, the primary endpoint included additionally life-threatening ventricular arrhythmias (ventricular fibrillation or ventricular tachycardia). Please note that the leave-one-data set-out cross-validation was applied meaning that each time one data set was left out, a model was built in all remaining data sets and the model was then applied in the data set that had been left out. This cycle was then repeated for every data set. The resulted estimates of predictive performance for the primary endpoint, one per data set, were then combined by random effects meta-analysis providing the overall estimate of the predictive performance of ejection fraction across all data sets as well as the associated prediction interval, which gives the expected performance in a new data set that is similar to the analysed ones. A wide prediction interval indicates limited generalizability to a new data set. To select the candidate predictors for the multivariable models, only those predictors were considered that were present in ≥75% of observations and recorded in the majority of data sets. For the multivariable flexible parametric survival models, backwards selection under Bayesian information criteria stopping rule was applied. The named data sets on the y-axis denote the data set left-out for model development and then used to validate the subsequent model to produce the corresponding performance estimates shown. For abbreviations of the individual data sets, please see the ‘Description of data sets’ in the Supplementary data online, Material

Figure 2

Fine and Gray cumulative incidence of the primary endpoint stratified by predicted risk categories obtained from each model for the three subgroups in the Phase 1 analysis. The figure depicts Fine and Gray cumulative incidence plots of the primary endpoint stratified by predicted risk, for the three cohorts in the Phase 1 analysis. In each iteration of the leave-one-data set cross-validation loop, patients in the validation data set were split into three equally sized groups (low, medium, and high) based on 36-month risk, predicted by the model that was developed in the remaining data sets. After completing the leave-one-data set cross-validation process, all patients in the low-risk group were pooled to produce the cumulative incidence plot, and the same happened for the medium-risk and high-risk groups. ICD patients, patients with left ventricular ejection fraction ≤ 35% who had received a cardioverter-defibrillator implantation for primary prevention of sudden cardiac death; non-ICD patients ≤35%, patients who did not carry a cardioverter-defibrillator and had a left ventricular ejection fraction ≤ 35%; and non-ICD patients >35%, patients who did not carry a cardioverter-defibrillator and had a left ventricular ejection fraction > 35%. In ICD patients, endpoint was first appropriate therapy, and in non-ICD patients ≤35% and non-ICD patients >35%, endpoint was sudden cardiac death. In two data sets with non-ICD patients, the primary endpoint included additionally life-threatening ventricular arrhythmias (ventricular fibrillation or ventricular tachycardia). To select the candidate predictors for the multivariable models, only those predictors were considered that were present in ≥75% of observations and recorded in the majority of data sets. For the multivariable flexible parametric survival models, backwards selection under Bayesian information criteria stopping rule was applied