Incidence and mortality risk in coronavirus disease 2019 patients complicated by acute cardiac injury: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The prevalence and prognostic implications of acute cardiac injury (ACI), as a complication of coronavirus disease 2019 (COVID-19), remain unclear. OBJECTIVES: We conducted a systematic review and meta-analysis to investigate the relationship between ACI and mortality risk in COVID-19 patients. METHODS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE, Scopus and Web of Science to locate all articles published up to 10 April 2020 reporting data of COVID-19 survivors and nonsurvivors developing ACI as a complication of the infection. Quality assessment was performed using the Newcastle-Ottawa quality assessment scale. Data were pooled using the Mantel-Haenszel random effects models with odds ratio as the effect measure with the related 95% confidence interval. Statistical heterogeneity between groups was measured using the Higgins I statistic. RESULTS: Eight studies, enrolling 1686 patients (mean age 59.5 years), met the inclusion criteria and were included in the final analysis. Data regarding the outcome of patients complicated with ACI were available for 1615 patients. Of these, 387 (23.9%) experienced ACIs as COVID-19 complications during the hospitalization. The incidence of ACI was significantly higher among non survivors when compared with survivors (61.6 vs. 6.7%, P < 0.0001). The pooled analysis confirmed a significantly increased risk of death in COVID-19 patients complicated with ACI during the disease (odds ratio: 21.6, 95% confidence interval: 8.6-54.4, P < 0.0001, I = 82%). CONCLUSION: Development of ACI during COVID-19 significantly increases the risk of death during the infection.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".