The Clinical Utility of Continuous QT Interval Monitoring in Patients Admitted With COVID-19 Compared With Standard of Care: A Prospective Cohort Study
Bibliographic record
Abstract
Background QT interval monitoring has gained much interest during the COVID-19 pandemic because of the use of QT-prolonging medications and the concern about viral transmission with serial electrocardiograms (ECGs). We hypothesized that continuous telemetry-based QT monitoring is associated with better detection of prolonged QT episodes. Methods We introduced continuous cardiac telemetry (CCT) with an algorithm for automated QT interval monitoring to our designated COVID-19 units. The daily maximum automated heart rate-corrected QT (Auto-QTc) measurements were recorded. We compared the proportion of marked QTc prolongation (Long-QTc) episodes, defined as QTc ≥ 500 ms, in patients with suspected or confirmed COVID-19 who were admitted before and after CCT was implemented (control group vs CCT group, respectively). Manual QTc measurement by electrophysiologists was used to verify Auto-QTc. Charts were reviewed to describe the clinical response to Long-QTc episodes. Results We included 33 consecutive patients (total of 451 monitoring days). Long-QTc episodes were detected more frequently in the CCT group (69/206 [34%] vs 26/245 [11%]; P < 0.0001) and ECGs were performed less frequently (32/206 [16%] vs 78/245 [32%]; P < 0.0001). Auto-QTc correlated well with QTc measurement by electrophysiologists with an excellent agreement in detecting Long-QTc (κ = 0.8; P < 0.008). Only 28% of patients with Long-QTc episodes were treated with recommended therapies. There was 1 episode of torsade de pointes in the control group and none in the CCT group. Conclusions Continuous QT interval monitoring is superior to standard of care in detecting episodes of Long-QTc with minimal need for ECGs. The clinical response to Long-QTc episodes is suboptimal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".