Introduction

Patients with cardiopulmonary disorders may be at risk of malignant arrhythmia due to long-QT syndrome. A large proportion of these patients undergo sleep studies including overnight ECG. The purpose of this study was to compare an automated algorithm versus manual scoring of QT-intervals in patients with COPD undergoing sleep studies.

Methods

We collected 62 overnight ECG recordings in 28 patients with COPD. All one-minute QT-intervals corrected for heart rate (QTc) were quantified, both by the automated algorithm and by manual cursor-assisted measurements of a mean ECG curve computed for each 1-min epoch. Manual scoring was done blinded for the results from the algorithm. Agreement of the two methods was calculated using Bland-Altman statistics. To quantify the accuracy for clinically relevant QT prolongations, we used confusion matrixes for 3 thresholds (460, 480, and 500ms).

Results

32944 one-minute intervals were analysed. Mean difference between manual and algorithm-based QTc-intervals was -1.4ms, with limits of agreement of -18.3, 15.5ms. A total of 2587, 357 and 0 QTc-intervals exceeding the threshold 460, 480, and 500ms, respectively, were identified by manual scoring. Using the automated algorithm, diagnostic classification revealed an accuracy of 0.98 (95%CI 0.98/0.98), 1.00 (1.00/1.00), and 1.00 (1.00/1.00) for 460, 480, and 500ms, respectively.

Conclusion

Clinically relevant QTc-prolongations were accurately identified by the automated algorithm. The implementation of this tool in hospital sleep laboratories may identify asymptomatic patients with long-QT at risk for malignant arrhythmia, allowing them to consult a cardiologist before an eventual cardiac event.