Baseline and Dynamic Risk Predictors of Appropriate Implantable Cardioverter Defibrillator Therapy

Abstract

Background Current approaches fail to separate patients at high versus low risk for ventricular arrhythmias owing to overreliance on a snapshot left ventricular ejection fraction measure. We used statistical machine learning to identify important cardiac imaging and time-varying risk predictors. Methods and Results Three hundred eighty-two cardiomyopathy patients (left ventricular ejection fraction ≤35%) underwent cardiac magnetic resonance before primary prevention implantable cardioverter defibrillator insertion. The primary end point was appropriate implantable cardioverter defibrillator discharge or sudden death. Patient characteristics; serum biomarkers of inflammation, neurohormonal status, and injury; and cardiac magnetic resonance-measured left ventricle and left atrial indices and myocardial scar burden were assessed at baseline. Time-varying covariates comprised interval heart failure hospitalizations and left ventricular ejection fractions. A random forest statistical method for survival, longitudinal, and multivariable outcomes incorporating baseline and time-varying variables was compared with (1) Seattle Heart Failure model scores and (2) random forest survival and Cox regression models incorporating baseline characteristics with and without imaging variables. Age averaged 57±13 years with 28% women, 66% white, 51% ischemic, and follow-up time of 5.9±2.3 years. The primary end point (n=75) occurred at 3.3±2.4 years. Random forest statistical method for survival, longitudinal, and multivariable outcomes with baseline and time-varying predictors had the highest area under the receiver operating curve, median 0.88 (95% CI, 0.75-0.96). Top predictors comprised heart failure hospitalization, left ventricle scar, left ventricle and left atrial volumes, left atrial function, and interleukin-6 level; heart failure accounted for 67% of the variation explained by the prediction, imaging 27%, and interleukin-6 2%. Serial left ventricular ejection fraction was not a significant predictor. Conclusions Hospitalization for heart failure and baseline cardiac metrics substantially improve ventricular arrhythmic risk prediction.

Keywords: cardiac magnetic resonance imaging; heart failure; risk stratification; sudden cardiac death; ventricular arrhythmia.

Figure 1. Median area under the curve (AUC) performances for predicting the primary end point for each of the 8 models.

Models incorporating only baseline covariates are shown as dotted or dashed lines. The 95% CIs for the AUCs over time for random forest statistical method for survival, longitudinal, and multivariable outcomes (RF‐SLAM) incorporating time‐varying covariates with (pink shaded area) and without (gray shaded area) imaging are also shown. RF‐SLAM with both imaging and time‐varying covariates (dark red solid line) had the highest AUC. RFS indicates random forest survival method.

Figure 2. Summary tree of RF‐SLAM depicting the top 7 predictors for the primary end point at 5 years of follow‐up that accounted for > 95% of the prediction.

Decision rules at each tree node are shown in bold italics and the number of cohort patients meeting criteria at each node is noted. The annual predicted ventricular arrhythmic (VA) risk is shown at the bottom of the decision tree. The VA risk boxes are color coded according to the magnitude of the annual risk, with white corresponding to the lowest risk subgroup and dark red corresponding to the highest risk subgroup. EF indicates ejection fraction; HF, heart failure; IL, interleukin; LA, left atrium; LV, left ventricle; and RF‐SLAM, random forest statistical method for survival, longitudinal, and multivariable outcomes.

Figure 3. Variable dependence plots calculated from the RF‐SLAM predicted values, stratified by interim HF hospitalization.

(A) shows an individual’s risk of the primary end point as a function of the number of interim HF hospitalizations. (B) shows the collective effect of all 5 imaging variables, stratified by HF status, holding IL‐6 constant, and plotted against the scale of the imaging variable gray zone mass, selected because it best illustrates the collective effect of all imaging variables and reflects the largest range of effects. (C) is the risk attributable to IL‐6, controlling for all of the other variables. The dependence plots can be used to ascertain a person’s risk given his/her HF status along the gradient of imaging results (here gray zone mass) and ≥1 interval HF hospitalization or by IL‐6 level and HF status. HF indiates heart failure; IL, interleukin; and RF‐SLAM, random forest statistical method for survival, longitudinal, and multivariable outcomes.