Subclinical myocardial injury, coagulopathy, and inflammation in COVID-19: A meta-analysis of 41,013 hospitalized patients
Bibliographic record
Abstract
Infection with the SARS-CoV-2 virus can lead to myocardial injury, evidenced by increases in specific biomarkers and imaging. To quantify the association between biomarkers of myocardial injury, coagulation, and severe COVID-19 and death in hospitalized patients. Studies were identified through a systematic search of indexed articles in PubMed, Embase, CINAHL, Cochrane, Web of Science, and Scopus, published between December 2019 to August 2021. Effect estimates from individual studies for association between markers of myocardial injury (Troponin), myocardial stretch (N-terminal-pro hormone BNP, NT-proBNP), and coagulopathy (D-Dimer) and death or severe/critical COVID-19 were pooled using inverse variance weighted random-effects model. Odds Ratios (OR), Hazard Ratios (HR), and 95% Confidence Intervals (CI) were pooled separately and reported by outcomes of critical/severe COVID-19 and death. A meta-analysis of proportions was also performed to summarize the pooled prevalence of co-morbidities in patients hospitalized with COVID-19. We included 62 articles, with a total of 41,013 patients. The pooled proportion of patients with history of hypertension was 39% (95% CI: 34–44%); diabetes, 21% (95% CI: 18%–24%); coronary artery disease, 13% (95% CI: 10–16%); chronic obstructive pulmonary disease, 7% (95% CI: 5–8%); and history of cancer, 5% (95% CI: 4–7%). Elevated troponin was associated with higher pooled odds of critical/severe COVID-19 and death [Odds Ratio (OR: 1.76, 95% CI: 1.42–2.16)]; and also separately for death (OR: 1.72, 95% CI: 1.32–2.25), and critical/severe COVID-1919 (OR: 1.93, 95% CI: 1.45–2.40). Elevations in NT-proBNP were also associated with higher severe COVID-19 and death (OR: 3.00, 95% CI: 1.58–5.70). Increases in D-dimer levels was also significantly associated with critical/severe COVID-19 and death (pooled OR: 1.38, 95% CI: 1.07–1.79). This meta-analysis synthesizes existing evidence showing that myocardial injury, and coagulopathy are complications of COVID-19. The durability of these complications and their contributions to long-term cardiac implications of the disease is still being investigated. Patients who have recovered from COVID-19 may benefit from minimally invasive assessment for markers of myocardial injury, stretch and coagulopathy for early risk stratification purposes.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".