ArticleInternational journal of chronic obstructive pulmonary disease2024
Automated Quantification of QT-Intervals by an Algorithm: A Validation Study in Patients with Chronic Obstructive Pulmonary Disease.
Article in International journal of chronic obstructive pulmonary disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.