Abstract 335: Distinguishing Cardiac From Non-Cardiac Causes of Sudden Death: The Value of Novel Factors to Identify Risk of SCD
Bibliographic record
Abstract
Introduction: Identifying those at risk for Sudden Cardiac Death (SCD) is imperative to prevent future events. There may be risk factors (RFs), triggers or warning symptoms preceding the SCD that could help identify those at greatest risk. Methods: This retrospective study included out-of-hospital cardiac arrests (OHCAs) of “no obvious cause” in the Greater Toronto Area, ages 2-45 from 2009-2012. Expert reviewers systematically adjudicated EMS, coroner, autopsy, toxicology and police reports to classify the etiology of arrest as SCD or SD due to non-cardiac etiologies (non-cardiac SDs). We compared past medical history, triggers and symptoms in the adjudicated SCD and non-cardiac SD groups and assessed their potential associations with SCD. Results: 872 OHCAs were classified as SCDs (488; 56%) or non-cardiac SDs (384; 44%). The SCDs were mostly CAD (203; 41.6%), structural (155; 31.8%), congenital (8; 1.6%) and primary arrhythmic (76; 15.6%). The non-cardiac SDs were due to acute infection (67; 17.4%), metabolic (75, 19.5%), epilepsy (69, 18.0%), vascular (106; 27.6%) and respiratory (35, 9.1%). Cardiac RFs and exercise as a trigger were observed significantly more in SCDs vs. non-cardiac SDs (Table 1). Symptoms ≤24 hours prior were similar between groups except for chest pain and diaphoresis. After adjusting for patient and resuscitation variables, factors associated with increased risk of SCD were: increasing age (OR 1.03; 95%CI 1.01-1.05), male sex (OR 1.90; 95%CI 1.30-2.70), HTN (OR 1.80; 95%CI 1.11-3.10), smoking (1.60; 95%CI 1.02-2.60), public location (OR 2.10; 95%CI 1.13-3.80), exercise (OR 3.0 95%CI 1.56-5.59) and chest pain ≤24 hours prior (OR 2.90 95%CI 1.30-6.42). Conclusion: Many young SCDs have previously diagnosed cardiac RFs and acute symptoms suggestive of ischemia prior to their event. These findings highlight the potential value of better public awareness in the younger age group for signs and symptoms of ischemia and potential risk for SCD.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".