Automated Quantification of QT-Intervals by an Algorithm: A Validation Study in Patients with Chronic Obstructive Pulmonary Disease

IF 2.8 3区医学 Q1 Medicine

International Journal of Chronic Obstructive Pulmonary Disease Pub Date : 2024-03-12 DOI:10.2147/copd.s445412

Dario Kohlbrenner, Maya Bisang, Sayaka S Aeschbacher, Emanuel Heusser, Silvia Ulrich, Konrad E Bloch, Michael Furian

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引用次数: 0

Abstract

Study Objectives: To assess the diagnostic accuracy of a purpose-designed QTc-scoring algorithm versus the established hand-scoring in patients with chronic obstructive pulmonary disease (COPD) undergoing sleep studies.
Methods: We collected 62 overnight electrocardiogram (ECG) recordings in 28 COPD patients. QT-intervals corrected for heart rate (QTc, Bazett) were averaged over 1-min periods and quantified, both by the algorithm and by cursor-assisted hand-scoring. Hand-scoring was done blinded to the algorithm-derived results. Bland-Altman statistics and confusion matrixes for three thresholds (460, 480, and 500ms) were calculated.
Results: A total of 32944 1-min periods and corresponding mean QTc-intervals were analysed manually and by computer. Mean difference between manual and algorithm-based QTc-intervals was − 1ms, with limits of agreement of − 18 to 16ms. Overall, 2587 (8%), 357 (1%), and 0 QTc-intervals exceeding the threshold 460, 480, and 500ms, respectively, were identified by hand-scoring. Of these, 2516, 357, and 0 were consistently identified by the algorithm. This resulted in a diagnostic classification 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. Sensitivity was 0.97, 1.00, and NA for 460, 480, and 500ms, respectively. Specificity was 0.98, 1.00, and 1.00 for 460, 480, and 500ms, respectively.
Conclusion: Overall, 8% of nocturnal 1-min periods showed clinically relevant QTc prolongations in patients with stable COPD. The automated QTc-algorithm accurately identified clinically relevant QTc-prolongations with a very high sensitivity and specificity. Using this tool, hospital sleep laboratories may identify asymptomatic patients with QTc-prolongations at risk for malignant arrhythmia, allowing them to consult a cardiologist before an eventual cardiac event.

Keywords: QTc, long-QT syndrome, COPD, algorithm, validity, ECG

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通过算法自动量化 QT 间期：慢性阻塞性肺病患者的验证研究

研究目的评估在接受睡眠研究的慢性阻塞性肺病（COPD）患者中，专门设计的 QTc 评分算法与既有的手工评分方法的诊断准确性：我们收集了 28 名慢性阻塞性肺病（COPD）患者的 62 次夜间心电图（ECG）记录。根据心率校正的 QT 间隔（QTc，Bazett）在 1 分钟内取平均值，并通过算法和光标辅助手工评分进行量化。手工评分对算法得出的结果进行盲测。计算了三个阈值（460、480 和 500 毫秒）的布兰-阿尔特曼统计和混淆矩阵：人工和计算机共分析了 32944 个 1 分钟周期和相应的平均 QTc 间隔。人工和基于算法的 QTc 间隔的平均差异为-1 毫秒，差异范围为-18 至 16 毫秒。总体而言，通过人工评分识别出超过阈值 460、480 和 500 毫秒的 QTc 间隔分别为 2587（8%）、357（1%）和 0。其中，有 2516、357 和 0 个 QTc 间隔被算法一致识别。因此，460、480 和 500 毫秒的诊断分类准确率分别为 0.98（95% CI 0.98/0.98）、1.00（1.00/1.00）和 1.00（1.00/1.00）。460、480 和 500 毫秒的灵敏度分别为 0.97、1.00 和 NA。460、480 和 500 毫秒的特异性分别为 0.98、1.00 和 1.00：总的来说，在病情稳定的慢性阻塞性肺病患者中，8% 的夜间 1 分钟时间段出现了临床相关的 QTc 延长。自动 QTc 算法能准确识别与临床相关的 QTc 延长，具有极高的灵敏度和特异性。利用这一工具，医院的睡眠实验室可以识别出QTc延长的无症状患者，使他们能够在最终发生心脏事件之前咨询心脏病专家：QTc、长QT综合征、慢性阻塞性肺病、算法、有效性、心电图

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Chronic Obstructive Pulmonary Disease RESPIRATORY SYSTEM-

CiteScore

5.10

自引率

10.70%

发文量

372

审稿时长

16 weeks

期刊介绍： An international, peer-reviewed journal of therapeutics and pharmacology focusing on concise rapid reporting of clinical studies and reviews in COPD. Special focus will be given to the pathophysiological processes underlying the disease, intervention programs, patient focused education, and self management protocols. This journal is directed at specialists and healthcare professionals

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