RESUMO
BACKGROUND: Guidelines recommend defibrillation testing (DFT) during subcutaneous implantable cardioverter-defibrillator (S-ICD) implantation. Implant position, patient characteristics and device factors, such as shock impedance, influence defibrillation success. To evaluate the shock impedance, a manual synchronous 10J shock (low energy synchronous shock [LESS]) can be delivered, without the need to induce ventricular fibrillation (VF). OBJECTIVE: To compare LESS and DFT impedance values and to evaluate the diagnostic accuracy of LESS impedance for predicting a successful DFT during S-ICD implantation. METHODS: Consecutive S-ICD implantations were included. Shock impedances were compared by paired t-tests. Univariate analysis was performed to investigate factors associated with successful DFT. A prediction model of successful DFT based on LESS impedance was assessed by logistic regression. Receiver operating characteristic (ROC) curve, area under the ROC curve and the Hosmer-Lemeshow tests were used to evaluate the accuracy of LESS impedance. RESULTS: Sixty patients were included (52 ± 14 years; 69% male). LESS and DFT impedance values were highly correlated (r2 = 0.97, p < .01). Patients with a failed first shock had higher body mass index (BMI) (30 ± 3 vs. 25.7 ± 4.3, p = .014), higher mean LESS (120 ± 35Ω vs. 86. ± 23Ω, p = .0013) and DFT impedance (122 ± 33Ω vs. 87 ± 24Ω, p = .0013). ROC analysis showed that LESS impedance had a good diagnostic performance in predicting a successful conversion test (AUC 84% [95% CI: 0.72-0.92]) with a cutoff value of <94Ω to identify a successful DFT (sensitivity 71%, specificity 73%). CONCLUSION: LESS impedance values without the need to induce VF can intraoperatively predict a successful DFT.
RESUMO
AIMS: Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF. METHODS AND RESULTS: We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0-100% and a specificity of 93.5-99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients. CONCLUSION: Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.
