Abstract 20190: Sudden Cardiac Death in the Young is Frequently Associated With Cardiac Disease and Drug Use
Bibliographic record
Abstract
Background: Out-of-Hospital-Cardiac Arrest (OHCA) in young individuals is a tragic and unexpected event, often occurring in seemingly healthy individuals. We examined the etiologies and medications in a large urban cohort of young sudden death patients in order to ascertain the accuracy of this assumption. Methods: A prospective population-based registry of all OHCAs in the Toronto area was used to identify cases from 2009-2012. Eligible cases were defined as OHCA patients aged 2-45 who had “no obvious cause” as defined by Emergency Medical Services (EMS) and hospital records, who died, and had a coroner record available. All cases were subsequently classified as primary cardiac or non-cardiac etiology by 3 reviewers (KA, AP, PD) using all available sources of information, including EMS reports, in-hospital data coroner investigative statements, autopsy, toxicology and police reports. Disagreement was resolved by consensus. Cardiac etiology was classified as one of: structural heart disease (HD), ischemic HD, primary arrhythmic, other or undetermined. A positive drug screen was defined as any psychoactive drug, ethanol, or drug of abuse detected at therapeutic, significant or toxic concentrations post-mortem. Cases were further subdivided by age (2-34 vs. 35-45) and gender. Results: A total of 470 patients were identified as having adjudicated primary cardiac etiologies. Causes of death and drug information are detailed in Table 1. Both men and women had a relatively high rate of psychoactive drug use (antipsychotics, antidepressants, and opioids). Overall, women had higher rates of medications compared to men. Conclusions: Younger, seemingly healthy OHCA patients die more often from structural heart disease than from primary arrhythmia disorders. The high rates of psychoactive drug use, as well as high rates of positive toxicology screens in this cohort suggests that prior illness and drugs may play an important role in the cause of sudden death.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".