Prediction of Lethal Arrhythmic Events Through Remote Monitoring Using Heart Rate Variability Analysis in Patients with an Implantable Cardioverter Defibrillator

Predictors of appropriate therapies delivered by the implantable cardioverter-defibrillator in patients with coronary artery disease during long-term period

This prospective study aimed to investigate the ability of cardiac autonomic nervous system (CANS) activity assessment to predict appropriate implantable cardioverter-defibrillator (ICD) therapy in patients with coronary artery disease (CAD) during long-term follow-up period. We enrolled patients with CAD and ICD implantation indications that included both secondary and primary prevention of sudden cardiac death. Before ICD implantation CANS was assessed by using heart rate variability (HRV), myocardium scintigraphy with 123I-meta-iodobenzylguanidine (123I-MIBG) and erythrocyte membranes β-adrenoreactivity (EMA). The study's primary endpoint was the documentation of appropriate ICD therapy. Of 45 (100.0%) patients, 15 (33.3%) had appropriate ICD therapy during 36 months follow-up period. Patients with appropriate ICD therapy were likely to have a higher summed 123I-MIBG score delayed (p < 0.001) and lower 123I-MIBG washout rate (p = 0.008) indicators. These parameters were independently associated with endpoint in univariable and multivariable logistic regression. We created a logistic equation and calculated a cut-off value. The resulting ROC curve revealed a discriminative ability with AUC of 0.933 (95% confidence interval 0.817-0.986; sensitivity 100.00%; specificity 93.33%). Combined CANS activity assessment is useful in prediction of appropriate ICD therapy in patients with CAD during long-term follow-up period after device implantation.

Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring

BACKGROUND

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

OBJECTIVES

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

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Potential of electronic devices for detection of health problems in older adults at home: A systematic review and meta-analysis

OBJECTIVE

The aim of this review was to evaluate the overall diagnostic performance of e-devices for detection of health problems in older adults at home.

METHODS

A systematic review was conducted following the PRISMA-DTA guidelines.

RESULTS

31 studies were included with 24 studies included in meta-analysis. The included studies were divided into four categories according to the signals detected: physical activity (PA), vital signs (VS), electrocardiography (ECG) and other. The meta-analysis showed the pooled estimates of sensitivity and specificity were 0.94 and 0.98 respectively in the 'VS' group. The pooled sensitivity and specificity were 0.97 and 0.98 respectively in the 'ECG' group.

CONCLUSIONS

All kinds of e-devices perform well in diagnosing the common health problems. While ECG-based health problems detection system is more reliable than VS-based ones. For sole signal detection system has limitation in diagnosing specific health problems, more researches should focus on developing new systems combined of multiple signals.