Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring

Although implantable cardioverter-defibrillator (ICD) therapies are associated with increased morbidity and mortality, the prediction of malignant ventricular arrhythmias has remained elusive.

The purpose of this study was to evaluate whether daily remote-monitoring data may predict appropriate ICD therapies for ventricular tachycardia or ventricular fibrillation.

This was a post hoc analysis of IMPACT (Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices), a multicenter, randomized, controlled trial of 2,718 patients evaluating atrial tachyarrhythmias and anticoagulation for patients with heart failure and ICD or cardiac resynchronization therapy with defibrillator devices. All device therapies were adjudicated as either appropriate (to treat ventricular tachycardia or ventricular fibrillation) or inappropriate (all others). Remote monitoring data in the 30 days before device therapy were utilized to develop separate multivariable logistic regression and neural network models to predict appropriate device therapies.

A total of 59,807 device transmissions were available for 2,413 patients (age 64 ± 11 years, 26% women, 64% ICD). Appropriate device therapies (141 shocks, 10 antitachycardia pacing) were delivered to 151 patients. Logistic regression identified shock lead impedance and ventricular ectopy as significantly associated with increased risk of appropriate device therapy (sensitivity 39%, specificity 91%, AUC: 0.72). Neural network modeling yielded significantly better (P < 0.01 for comparison) predictive performance (sensitivity 54%, specificity 96%, AUC: 0.90), and also identified patterns of change in atrial lead impedance, mean heart rate, and patient activity as predictors of appropriate therapies.

Daily remote monitoring data may be utilized to predict malignant ventricular arrhythmias in the 30 days before device therapies. Neural networks complement and enhance conventional approaches to risk stratification.