Myocardial Involvement Is Just as Common in Patients Hospitalised for Influenza as in Those Hospitalised for Covid-19: Insights From the GEMINI Initiative
Bibliographic record
Abstract
BACKGROUND: The possibility of myocardial involvement is well recognised with Covid-19 infection but less so with influenza. We designed this study to explore the frequency of elevated biomarkers and new clinical cardiac diagnoses in patients hospitalised with influenza or Covid-19. METHODS: This was a retrospective cohort study of all adults hospitalised from April 2015 to March 2023 with either influenza or Covid-19 in 29 hospitals in Ontario. We used multivariable regression with generalised estimating equations to compare troponin and natriuretic peptide levels and new cardiac diagnoses during the index hospitalisations. RESULTS: The 25,200 patients with Covid-19 were younger (67 vs 72 years) and more likely to be men (56% vs 47%), and fewer had prior cardiovascular disease (7% vs 13%) than the 8569 patients with influenza (all P < 0.001). Although more likely to have their troponin (84.2% vs 78.7%; P < 0.001) or natriuretic peptides (21.6% vs 12.7%; P < 0.001) measured, patients with Covid-19 were not more likely to have elevated levels compared with influenza patients: 42.7% vs 39.8% for troponin (adjusted risk ratio [aRR] 1.07, 95% CI 0.99-1.15), and 72.3% vs 81.7% for natriuretic peptides (aRR 0.93, 95% CI 0.87-0.99). The frequency of new clinical diagnoses of heart failure (2.4% vs, 2.6%, aRR 1.18, 95% CI 0.79-1.77) or new atrial fibrillation (3.4% vs 5.2%, aRR 0.89, 95% CI 0.79-1.77) did not differ between those with Covid-19 or influenza. CONCLUSIONS: The frequencies of elevated troponins (two-fifths) and natriuretic peptides (three-fourths) were similar in patients hospitalised with influenza and Covid-19 who had biomarkers measured, but the frequencies of clinically recognised diagnoses were low.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".