Unexpected High Prevalence of Cardiovascular Disease Risk Factors and Psychiatric Disease Among Young People With Sudden Cardiac Arrest
Bibliographic record
Abstract
Background It is believed that most sudden cardiac arrests ( SCA s) in young people occur in previously healthy people with rare risk factors for sudden death. Few studies have investigated large populations with complete ascertainment. Our objective was to use multisource records to identify and classify all out-of-hospital cardiac arrests in the Greater Toronto Area (population 6.6 million) in people aged 2 to 45 years from 2009 to 2012. Methods and Results Expert reviewers employed a systematic process, with emergency medical services, in-hospital and coroner records, to adjudicate the cause of death as SCA from cardiac or noncardiac causes. We report the adjudicated etiologies, circumstances, triggers, and characteristics of the SCA cohort. Of 2937 eligible out-of-hospital cardiac arrest cases, 608 (20.7%) SCA s had an adjudicated etiology of cardiac cause (120 survivors and 488 nonsurvivors). Two thirds of these SCA patients had a history of cardiovascular disease, and over 50% had been diagnosed with ≥1 cardiovascular disease risk factor. Moreover, 20.1% of SCA s were diagnosed with psychiatric disease and 30% had central nervous system drugs prescribed. Over 30% of SCA patients had central nervous system active drugs, including drugs of abuse detected postmortem, with opioids and ethanol being detected most frequently. Potentially heritable structural cardiac diseases accounted for only 6.9% of SCA events, with acquired cardiac diseases comprising the rest. Conclusions The underlying causes of SCA , in people aged 2 to 45 years, often occur in those with previously diagnosed cardiovascular diseases, and are associated with contributory factors including prescribed medications, recreational drugs, and a concomitant psychiatric history.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".