Characteristics of out-of-hospital cardiac arrest patients in Riyadh province, Saudi Arabia: a cross-sectional study
Bibliographic record
Abstract
Introduction Little work has been done on out-of-hospital cardiac arrest (OHCA) in Saudi Arabia. Our goal is to report the characteristics of OHCA patients and predictors of bystander cardiopulmonary resuscitation (CPR). Materials and methods This cross-sectional study utilized data from the Saudi Red Crescent Authority (SRCA), a governmental emergency medical service (EMS). A standardized data collection form based on the “Utstein-style” guidelines was developed. Data were retrieved from the electronic patient care reports that SRCA providers fill out for every case. OHCA cases that were attended by SRCA in Riyadh province between June 1st, 2020 and May 31st, 2021 were included. Multivariate regression analysis was performed to assess independent predictors of bystander CPR. Results A total of 1,023 OHCA cases were included. The mean age was 57.2 (±22.6). 95.7% (979/1,023) of cases were adults and 65.2% (667/1,023) were males. Home was the most common location of OHCA [784/1,011 (77.5%)]. The initial recorded rhythm was shockable in 131/742 (17.7%). The EMS mean response time was 15.9 min (±11.1). Bystander CPR was performed in 130/1,023 (12.7%) and was more commonly performed in children as compared to adults [12/44 (27.3%) vs. 118/979 (12.1%), p = 0.003]. Independent predictors of bystander CPR were being a child (OR = 3.26, 95% CI [1.21–8.82], p = 0.02) and having OHCA in a healthcare institution (OR = 6.35, 95% CI [2.15–18.72], p = 0.001). Conclusion Our study reported the characteristics of OHCA cases in Saudi Arabia using EMS data. We observed young age at presentation, low rates of bystander CPR, and long response time. These characteristics are distinctly different from other countries and call for urgent attention to OHCA care in Saudi Arabia. Lastly, being a child and having OHCA in a healthcare institution were found to be independent predictors of bystander CPR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".