ECG Machine QTc Intervals Are Inaccurate in Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: Nephrologists need effective screening tools to identify hemodialysis patients at elevated risk for sudden cardiac death, the leading cause of death in this population. QTc intervals longer than 450 ms in males and 470 ms in females, measured by the gold standard tangent method (trueQTc), are prolonged and increase sudden cardiac death in healthy populations and patients with long QT syndrome. METHODS: We performed a retrospective ECG and chart review of hemodialysis patients. Our first objective was to determine if machine-measured QTc intervals (macQTc) could be used to identify dialysis patients with prolonged trueQTc. Our second objective was to determine at what macQTc could prolonged trueQTc be confidently diagnosed. RESULTS: macQTc differed from the trueQTc by an average of 16.54 ms, and by at least 20 ms in 46.8, 36.1, 53.6, 50.0 and 57.1% of all, short-hours daily hemodialysis, intermittent conventional hemodialysis, frequent nocturnal hemodialysis and intermittent nocturnal hemodialysis patients, respectively. The positive predictive value, negative predictive value, sensitivity and specificity of prolonged macQTc predicting prolonged trueQTc was 57.6, 92.6, 79.1 and 81.8%, respectively. Thus, macQTc is inaccurate at predicting the gold standard trueQTc in hemodialysis patients. macQTc greater than 480 ms in hemodialysis patients predicts trueQTc prolongation with a positive predictive value of 95.2%, but with a low sensitivity of 32.3%. CONCLUSION: In hemodialysis patients, ECG macQTc intervals are insufficiently sensitive or specific to predict prolonged trueQTc intervals, unless >480 ms.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".