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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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".