Modulation of Cardiac Injury by ACE inhibitor/ARB in Patients with Severe COVID-19
Bibliographic record
Abstract
Abstract Introduction: Cardiac injury occurs in 7-22% of patient hospitalized with COVID-19 and an elevation in troponin is associated with a 4.2-fold increase in the risk of mortality. Preliminary data showed ACEi/ARB usage might not increase mortaily in COVID-19 patients. However, it is unknown if cardiac injury in patients with severe COVID-19 can be modulated by ACEi/ARB usage during evolution of the cardiac injury.Methods: In 154 COVID-19 patients with cardiac injury, the effect of ACEi/ARB treatment (17 patients) was compared with 137 patients without ACEi/ARB treatment. Cardiac injury was indicated by cTnI level.Results: In ACEi/ARB treatment group and no ACEi/ARB treatment group, peak cTnI level did not show significant difference (150.5 pg/ml [31.75-1179], vs 207 pg/ml [54.65-989.4], respectively, P = 0.21). Evolution of Cardiac injury (temporal change of cTnI at day 6, 9, 12, 15, 18, 21, 24, 27, 30, and 33) showed no statistical difference. Mortality (ACEi/ARB group vs no ACEi/ARB group; 52.9% vs 69.9%, P = 0.17), atrial arrhythmias (11.7% vs 24.4%, P = 0.36), requirement for invasive ventilatory support (29.4% vs 48.2%, P = 0.14) also showed no significant difference in two groups.Conclusions: ACEi/ARB usage during the COVID-19 was not associated with exacerbation of cardiac injury. These data should be interpreted as essentially hypothesis-generating due to small sample size.Clinical Trial Registration: This retrospective study was registered in Chinese clinical trial registry (ChiCTR 2000031301).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".