Prospective electrocardiographic and cardiovascular magnetic resonance alterations in the UK Biobank coronavirus disease 2019 repeat imaging study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular magnetic resonance (CMR) and electrocardiographic (ECG) abnormalities after coronavirus disease 2019 (COVID-19) are widely reported. However, the absence of pre-infection assessments limits causal inference from these studies. This study aims to compare interval change in CMR and ECG measures in participants with incident COVID-19 and matched uninfected controls in UK Biobank. METHODS: UK Biobank participants with documented COVID-19 who had CMR and ECG performed before the pandemic were invited for repeat assessment, along with uninfected participants matched on age, sex, ethnicity, location, and date of baseline imaging. Automated pipelines were used to extract ECG phenotypes and CMR measures of cardiac structure and function, aortic distensibility, aortic flow, and myocardial native T1. Logistic regression was used to examine associations of baseline metrics with incident COVID-19. Standardized residual approach was used to compare the degree of interval change in CMR and ECG metrics between cases and controls. RESULTS: We analyzed 2092 participants (1079 cases and 1013 controls) with average age of 60 ± 7 years. 47.1% were male. There was 3.2 ± 1.5 years between pre- and post-infection assessments. 3.6% of cases were hospitalized. Lower baseline left ventricular ejection fraction and worse longitudinal, circumferential, and radial strain were associated with higher risk of incident COVID-19. There were no significant differences in interval change of any CMR or ECG metric between cases and controls. CONCLUSION: While pre-existing cardiovascular abnormalities are linked to higher risk of COVID-19, exposure to infection does not alter interval change of highly sensitive CMR and ECG indicators of cardiovascular health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".