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Record W4398175740 · doi:10.2196/56054

Out-of-Hospital Cardiac Arrest Before and During the COVID-19 Pandemic in Hong Kong: Registry-Based Study From 2017 to 2023

2024· article· en· W4398175740 on OpenAlexvenueno aff
Richard Huan Xu, R. Sun, Siu Ngor Fu

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Emergency medicineMedical emergencyEnvironmental healthVirologyOutbreakInternal medicineInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Background The COVID-19 pandemic has exerted a significant toll on individual health and the efficacy of health care systems. However, the influence of COVID-19 on the frequency and outcomes of out-of-hospital cardiac arrest (OHCA) within the Chinese population, both before and throughout the entire pandemic period, remains to be clarified. Objective This study aimed to fill the gaps by investigating the prevalence and outcomes of OHCA in Hong Kong (HK) both before and during the whole pandemic period. Methods This is a retrospective regional registry study. The researchers matched OHCA data with COVID-19–confirmed case records between December 2017 and May 2023. The data included information on response times, location of OHCA, witness presence, initial rhythm, bystander cardiopulmonary resuscitation (CPR), use of public-access defibrillation, resuscitation in the accident and emergency department, and survival to admission. Descriptive analyses were conducted, and statistical tests such as analysis of variance and χ2 were used to examine differences between variables. The incidence of OHCA and survival rates were calculated, and logistic regression analysis was performed to assess associations. The prevalence of OHCA and COVID-19 during the peak of the pandemic was also described. Results A total of 43,882 cases of OHCA were reported in HK and included in our analysis. Around 13,946 cases were recorded during the prepandemic period (2017-2019), and the remaining 29,936 cases were reported during the pandemic period (2020-2023). During the pandemic period, the proportion of female patients increased to 44.1% (13,215/29,936), and the average age increased slightly to 76.5 (SD 18.5) years. The majority of OHCAs (n=18,143, 61.1% cases) occurred at home. A witness was present in 45.9% (n=10,723) of the cases, and bystander CPR was initiated in 44.6% (n=13,318) of the cases. There was a significant increase in OHCA incidence, with a corresponding decrease in survival rates compared to the prepandemic period. The location of OHCA shifted, with a decrease in incidents in public places and a potential increase in incidents at home. We found that CPR (odds ratio 1.48, 95% CI 1.17-1.86) and public-access defibrillation (odds ratio 1.16, 95% CI 1.05-1.28) were significantly associated with a high survival to admission rate during the pandemic period. There was a correlation between the development of OHCA and the prevalence of COVID-19 in HK. Conclusions The COVID-19 pandemic has had a significant impact on OHCA in HK, resulting in increased incidence and decreased survival rates. The findings highlight the importance of addressing the indirect effects of the pandemic, such as increased stress levels and strain on health care systems, on OHCA outcomes. Strategies should be developed to improve OHCA prevention, emergency response systems, and health care services during public health emergencies to mitigate the impact on population health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.344
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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