Effect of SARS-CoV-2 infection on out-of-hospital cardiac arrest outcomes – systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: The COVID-19 pandemic caused by the SARS-CoV-2 virus has recently presented the world with an unprecedented challenge. The purpose of this systematic review and meta-analysis is to investigate the relationship between SARS-CoV-2 infection and out-of-hospital cardiac arrest (OHCA) by comparing data from infected and non-infected individuals. The study adds to our understanding of the broader effects of the pandemic on public health and emergency care by examining the influence of COVID-19 on OHCA. MATERIAL AND METHODS: A comprehensive systematic literature search was performed using PubMed, EMBASE, Scopus, Web of Science, the Cochrane Library and Google Scholar from 1 January 2020 - 24 May 2023. Incidence rates and odds ratios (ORs) or mean differences (MDs) with 95% confidence intervals (CIs) for risk factors were recorded from individual studies, and random-effects inverse variance modelling used to generate pooled estimates. RESULTS: Six studies, involving 5,523 patients, met the criteria for inclusion in the meta-analysis. Survival to hospital admission, defined as admission to the emergency department with sustained return of spontaneous circulation (ROSC), among patients with and without on-going infection was 12.2% and 20.1%, respectively (p=0.09). Survival to hospital discharge/30-day survival rate was 0.8% vs. 6.2% (p<0.001). Two studies reported survival to hospital discharge in good neurological condition; however, the difference was not statistically significant (2.1% vs. 1.8%; p=0.37). CONCLUSIONS: Compared to the non-infected patients, the ongoing SARS-CoV-2 infection was associated with worse OHCA outcomes.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".