Global burden of acute myocardial injury associated with COVID-19: A systematic review, meta-analysis, and meta-regression
Bibliographic record
Abstract
BACKGROUND: The body of evidence showed that there is a strong correlation between acute myocardial Injury and COVID-19 infection. However, the link between acute myocardial infection and COVID-19, the prevalence, reliability of diagnostic modalities, independent predictors, and clinical outcomes are still uncertain and a topic of debate. The current study was designed to determine the prevalence, determinants, and outcomes of acute myocardial injury based on a systematic review and meta-analysis the global published peer-reviewed works of literature. METHODS: A comprehensive search was conducted in PubMed/Medline; Science direct, CINHAL, and LILACS from December 2019 to May 2021. All observational studies reporting the prevalence of AMI were included while case reports and reviews were excluded. The data were extracted with two independent authors in a customized format. The methodological quality of included studies was evaluated using the Newcastle-Ottawa appraisal tool. RESULTS: A total of 397 articles were identified from different databases. Thirty-seven Articles with 21, 204 participants were included while seven studies were excluded. The meta-analysis revealed that the pooled prevalence of myocardial injury during the COVID-19 pandemic was 22.33 % (95 % CI: 17.86 to 26.81, 37). CONCLUSION: Our meta-analysis showed that mortality among patients with an acute myocardial injury during COVID-19 was more than four times more likely as compared to those without AMI. This necessitates a mitigating strategy to prevent and manage before its clinical outcomes getting worse.
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How this classification was reachedexpand
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.033 | 0.005 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".