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QT Interval Readings With a Smartphone Heart MonitorIntroduction A variety of medications ranging from antiarrhythmics to psychotropics, as well as conditions such as bradycardia, can prolong the QT interval, increasing the risk for life-threatening arrhythmias. Monitoring the corrected QT interval (QTc) is therefore critical for patient safety. The recent development of smart phone heart monitors (SHM) may allow for easier QTc monitoring. We sought to evaluate the accuracy of an SHM for assessing the QTc, as compared to the standard 12-lead ECG. Methods and Results We compared the QTc interval in lead-I and lead-II between an SHM and 12-lead ECG. Healthy volunteers and hospitalized patients in sinus rhythm being loaded on dofetilide or sotalol were included. Manual and automatic measurements were studied. Across 99 healthy volunteers, the SHM QTc demonstrated good agreement (bias = 4 milliseconds, standard deviation of bias = 11 milliseconds) compared to the 12-lead ECG, using the Bland–Altman method of agreement. Across all hospitalized patients, the SHM was capable of demonstrating QTc prolongation. Between the 12-lead ECG and SHM, lead-I measurements had reasonable agreement (bias = 3 milliseconds, standard deviation of bias = 46 milliseconds). A QTc of > 500 milliseconds was associated with a higher likelihood (OR = 12.0; 95% CI 1.5–111.4; P = 0.02) to not achieve perfect agreement. Conclusion The SHM is accurate in measuring QTc interval in sinus rhythm when compared to 12-lead ECG in healthy volunteers. For patients receiving QT prolonging antiarrhythmics, SHM is capable of detecting QTc prolongation, and lead-I of the SHM is most accurate in measuring the QTc if < 500 milliseconds.

A variety of medications ranging from antiarrhythmics to psychotropics, as well as conditions such as bradycardia, can prolong the QT interval, increasing the risk for life-threatening arrhythmias. Monitoring the corrected QT interval (QTc) is therefore critical for patient safety. The recent development of smart phone heart monitors (SHM) may allow for easier QTc monitoring. We sought to evaluate the accuracy of an SHM for assessing the QTc, as compared to the standard 12-lead ECG.We compared the QTc interval in lead-I and lead-II between an SHM and 12-lead ECG. Healthy volunteers and hospitalized patients in sinus rhythm being loaded on dofetilide or sotalol were included. Manual and automatic measurements were studied. Across 99 healthy volunteers, the SHM QTc demonstrated good agreement (bias = 4 milliseconds, standard deviation of bias = 11 milliseconds) compared to the 12-lead ECG, using the Bland-Altman method of agreement. Across all hospitalized patients, the SHM was capable of demonstrating QTc prolongation. Between the 12-lead ECG and SHM, lead-I measurements had reasonable agreement (bias = 3 milliseconds, standard deviation of bias = 46 milliseconds). A QTc of500 milliseconds was associated with a higher likelihood (OR = 12.0; 95% CI 1.5-111.4; P = 0.02) to not achieve perfect agreement.The SHM is accurate in measuring QTc interval in sinus rhythm when compared to 12-lead ECG in healthy volunteers. For patients receiving QT prolonging antiarrhythmics, SHM is capable of detecting QTc prolongation, and lead-I of the SHM is most accurate in measuring the QTc if500 milliseconds.

Background We aimed to develop a novel predictive marker for atrial fibrillation (AF) recurrence in patients with inducible AF after catheter ablation, based on power spectral analysis of baseline and postablation electrocardiograms. Methods Twenty-five patients who had undergone their first AF ablation procedure (pulmonary vein isolation and ganglionated plexi ablation) and had inducible AF after ablation were included. A 30-second interval of AF was chosen for each patient before and after ablation, and a periodogram of the atrial activity was computed. A ratio of the power in the dominant frequency to the power in the remainder of the periodogram (DFR) was calculated. Results Eight (32%) patients had recurrent AF at 1 year. The clinical and echocardiographic characteristics of patients with and without recurrence were similar (P > 0.05). After ablation, there was organization of atrial activity, evidenced by an increase in the DFR (0.28 ± 0.22 vs 0.53 ± 0.29; P = 0.001). The percent change in DFR before and after ablation (median [interquartile range]) was significantly higher in patients without AF recurrence (120% [30% to 344%] vs 3% [−27% to 66%]; P = 0.01). Receiver operating curve (ROC) analysis demonstrated that a less than 16% increase in DFR postablation was able to predict recurrence of AF (area under ROC curve = 0.82; P = 0.03) with 75% sensitivity and 94% specificity. Conclusion AF ablation leads to variable organization of atrial activity. Organization of atrial activity after AF ablation is associated with lower 1-year recurrence rates and may be used intraprocedurally after as a novel end point for AF ablation. Larger prospective studies are warranted.