Predicting appropriate shocks in patients with heart failure: Patient level meta-analysis from SCD-HeFT and MADIT II

Abstract

Background: No precise tools exist to predict appropriate shocks in patients with a primary prevention ICD. We sought to identify characteristics predictive of appropriate shocks in patients with a primary prevention implantable cardioverter defibrillator (ICD).

Methods: Using patient-level data from the Multicenter Automatic Defibrillator Implantation Trial II (MADIT II) and the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT), we identified patients with any appropriate shock. Clinical and demographic variables were included in a logistic regression model to predict appropriate shocks.

Results: There were 1,463 patients randomized to an ICD, and 285 (19%) had ≥1 appropriate shock over a median follow-up of 2.59 years. Compared with patients without appropriate ICD shocks, patients who received any appropriate shock tended to have more severe heart failure. In a multiple logistic regression model, predictors of appropriate shocks included NYHA class (NYHA II vs. I: OR 1.65, 95% CI 1.07-2.55; NYHA III vs. I: OR 1.74, 95% CI 1.10-2.76), lower LVEF (per 1% change) (OR 1.04, 95% CI 1.02-1.06), absence of beta-blocker therapy (OR 1.61, 95% CI 1.23-2.12), and single chamber ICD (OR 1.67, 95% CI 1.13-2.45).

Conclusion: In this meta-analysis of patient level data from MADIT-II and SCD-HeFT, higher NYHA class, lower LVEF, no beta-blocker therapy, and single chamber ICD (vs. dual chamber) were significant predictors of appropriate shocks.

Keywords: implantable cardioverter defibrillator; meta-analysis; primary prevention.

© 2017 Wiley Periodicals, Inc.

Figures

Figure 1. Receiver operator curves

T he dataset was randomly split into halves: training and validation sets. The logistic regression model was re-fit in each of the training sets, and then actual values from the validation sets were compared against the corresponding predicted values with the receiver operating characteristics curves (ROC) and the area under the curve (AUC). This process was repeated 100 times to obtain the median and interquartile ranges of the derived AUCs.