Repolarization abnormalities on admission predict 1-year outcome in COVID-19 patients
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: ECG abnormalities in COVID-19 have been widely reported, however data after discharge is limited. The aim was to describe ECG abnormalities on admission and following recovery of COVID-19, and their associated mortality. METHODS: All patients hospitalized in a tertiary care hospital between March 7th and July 1st 2020 with COVID-19 were included in a retrospective registry. The first ECG on admission was collected, together with an ECG after hospital discharge in the absence of acute pathology. Automated measures and clinical ECG interpretations were collected. Multivariate Cox regression analysis was performed to predict 1-year all-cause mortality. RESULTS: In total 420 patients were included, of which 83 patients (19.8%) died during the 1-year follow-up period. Repolarization abnormalities were present in 189 patients (45.0%). The extent of repolarization abnormalities was an independent predictor of 1-year all-cause mortality (HR per region 1.30, 95%CI 1.04-1.64) together with age (/year HR 1.06, 95%CI 1.04-1.08), heart rate (/bpm HR 1.02, 95%CI 1.01-1.03), neurological disorders (HR 2.41, 95%CI 1.47-3.93), active cancer (HR 2.75, 95%CI 1.57-4.82), CRP (per 10 mg/L HR 1.05, 95%CI 1.02-1.08) and eGFR (per 10 mg/L HR 0.90, 95%CI 0.83-0.98).In 245 patients (68.1%) an ECG post discharge was available. New repolarization abnormalities were more frequent in patients who died after discharge (4.7% versus 41.7%, p < 0.001) and 8 (3.3%) had new ventricular conduction defects, none of whom died during follow-up. CONCLUSIONS: The presence and extent of repolarization abnormalities predicted outcome in patients with COVID-19. New repolarization abnormalities after discharge were associated with post-discharge mortality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it