Abstract 332: Long-term Post-discharge Survival and Healthcare Utilization Following Out-of-hospital Cardiac Arrest: Insights From a Novel Province-wide Linkage
Bibliographic record
Abstract
Introduction: Out-of-hospital cardiac arrest (OHCA) is associated with poor short-term outcomes. However, the impact of pre- and in-hospital factors on long-term outcomes is ill-defined, mainly related to challenges combining disparate data sources. Methods: We linked adult non-traumatic EMS-treated OHCAs from the British Columbia Cardiac Arrest Registry (Jan 2009 - Dec 2016) to provincial datasets describing co-morbidities, medications, procedures, mortality, and hospital admission and discharge. Among hospital-discharge survivors, we examined the 3-year composite endpoint of mortality ± all-cause readmission using the Kaplan-Meier (KM) method and multivariable Cox model for predictors. Results: Of 10,876 successfully linked OHCAs, 1325 survived to hospital discharge: mean age 62.8 years, 77.9% male, 72.6% shockable rhythms, 60.1% non-public locations, 69.1% bystander CPR, and 30.3% STEMI. During admission, 78.6% required mechanical ventilation, 69.1% received coronary angiography (37.5% PCI, 10.3% CABG), and 24.8% received an ICD. At 3 years post-discharge, the estimated KM event rates were 15.9% (95% CI 13.9%, 19.3%) for mortality and 68.2% (95% CI 65.3%, 71.0%) for mortality and readmission, which differed by age, initial rhythm, and arrest location ( Figure ). Following multivariable analysis, patients with a history of HF [HR 1.62 (95% CI 1.34 - 1.96)], age >75 [1.62 (1.35, 1.96)], anticoagulation use [2.55 (1.36, 4.79)], non-shockable rhythm [1.29 (1.07, 1.55)] and non-public arrest location [1.21(1.04, 1.40)] were more likely to experience the composite endpoint; those receiving coronary angiography were less likely [0.79 (0.64, 0.98)]. Conclusions: The long-term death or readmission risk persists even among OHCA hospital-discharge survivors, and is associated with both pre- and in-hospital factors. An enriched, linked dataset detailing the entire OHCA “journey” may be a promising tool to identify care and treatment gaps.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".