Hospitalization, major complications and mortality in acute myocardial infarction patients during the COVID-19 era: A systematic review and meta-analysis
Bibliographic record
Abstract
Since the SARS-CoV-2 pandemic began, numerous studies have reported a concerning drop in the number of acute myocardial infarction (AMI) admissions. In the present systematic review and meta-analysis, we aimed to compare the rate of AMI admissions and major complication during the pandemic, in comparison with pre-pandemic periods. Three major databases (PubMed, Scopus, and Web of Science Core Collection) were searched. Out of 314 articles, 41 were entered into the study. Patients hospitalized for AMI were 35% less in the COVID-19 era compared with pre-pandemic periods, which was statistically significantly (OR = 0.65; 95% CI: 0.56–0.74; I2 = 99%; p < 0.001; 28 studies). Patients hospitalized for STEMI and NSTEMI were 29% and 34% respectively less in the COVID-19 era compared with periods before COVID-19, which was statistically significantly (OR = 0.71; 95% CI: 0.65 –0.78; I2 = 93%; p < 0.001; 22 studies, OR = 0.66; 95% CI: 0.58–0.73; I2 = 95%; p < 0.001; 14 studies). The overall rate of in-hospital mortality in AMI patients increased by 26% in the COVID-19 era, which was not statistically significant (OR = 1.26; 95% CI: 1.0–1.59; I2 = 22%; p < 0.001; six studies). The rate of in-hospital mortality in STEMI and NSTEMI patients increased by 15% and 26% respectively in the COVID-19 era, which was not statistically significant (OR = 1.15; 95% CI: 0.85–1.57; I2 = 48%; p = 0.035; 11 studies, OR = 1.35; 95% CI: 0.64–2.86; I2 = 45%; p = 0.157; 3 articles). These observations highlight the challenges in the adaptation of health-care systems with the impact of the COVID-19 pandemic.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| 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".