The role of contributing factors, triggers, and prodromal symptoms in the etiological classification of out-of-hospital cardiac arrest; A scoping review
Bibliographic record
Abstract
BACKGROUND: Current Utstein etiological classifications for out-of-hospital cardiac arrest (OHCA) are heterogenous and inaccurate when compared with robust sources. This heterogeneity may influence reporting incidence and outcomes and patient enrollment in observational studies and clinical trials. Circumstance-related factors may contribute to cardiac arrest; however, the role of these factors in improving the etiological classification of OHCA is unknown. OBJECTIVE: This scoping review was proposed to explore current evidence to identify the role of contributing factors, triggers, and prodromal symptoms of out-of-hospital cardiac arrest in the reported etiology of cardiac arrest based on emergency medical services data, medical records, or autopsy reports. METHOD: We searched Medline, Embase, and EMB review-Cochrane databases from 1946 to 2024. Studies were selected if the included population was adults with OHCA for whom the initial etiology was assigned, and any contributing factors, triggers, or prodromal symptoms of OHCA were reported. A descriptive review of the included studies was conducted. RESULT: The search yielded 24,833 citations. Seventy studies met the inclusion criteria. These studies were published predominantly in Europe and Asia between 2010 and 2024 and classified as contributing factors (n = 24), exercise (n = 13), environmental triggers (n = 24), and prodromal symptoms (n = 9). The etiology of cardiac arrest initially assigned to cardiac or obvious non-cardiac classification may be precipitated by seizures (n = 8), trauma (n = 7), alcohol or drug intoxication (n = 6), Covid-19 infection (n = 5), myocardial infarction (n = 4), suicide (n = 4), antipsychotic medications (n = 4), and illicit drug use (n = 3). Exercise and environmental factors (e.g., particulate matter (PM) 2.5µ and ambient temperature) may trigger cardiac arrest predominantly due to cardiac etiologies. Based on EMS data, approximately 50% of patients with OHCA experienced symptoms prior to cardiac arrest which suggested cardiac and non-cardiac etiologies. CONCLUSION: Many circumstance-related factors may directly or indirectly contribute to cardiac arrest etiology classification. Listing these factors in the reporting template may help prehospital personnel and data abstractors gather enough information to identify more accurately the etiology of OHCA.
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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 | 0.020 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.033 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".