Abstract 13607: Correlation in Hospital Survival Following Out-of-Hospital Cardiac Arrest With Variation in Post-Arrest Inpatient Intervention Rates in Michigan
Bibliographic record
Abstract
Introduction: Wide variations in rates of survival to hospital discharge exist for survivors of out-of-hospital cardiac arrest (OHCA). The potential influence of variation in post-OHCA hospital care has not been adequately explored. We hypothesized that variation of in hospital survival rates may be influenced by variation of in-hospital care in Michigan. Methods: We performed a secondary analysis of a statewide cardiac arrest database constructed from two probabilistically-linked cardiac arrest registries [Cardiac Arrest Registry to Enhance Survival (CARES) and Michigan Inpatient Database (MIDB)] from 2014 - 2017. A novel composite rank score was created to characterize post-arrest in-hospital care, incorporating four specific interventions: left heart catheterization within 24 hours (LHC), emergent mechanical circulatory support (EMCS), targeted temperature management (TTM), and do-not-resuscitate order placed within 72 hours of arrival (DNR). The highest score (1 of 38) was given to the hospital with highest procedure rate (LHC, TTM, LHC) and the lowest rate of early DNR. Spearman’s correlation coefficients assessed the relationship between the equal weight composite rank score and rate of hospital survivors. Results: We included 3,644 patients admitted to 38 hospitals who treated >30 OHCA patients during the study period. Patient mean age was 62.4 years, and 59.3% were male. Survival, rank scores and correlation coefficients are listed below: We observed four-fold variation in survival for all patients and witnessed arrest, with a non-significant correlation with care provision. However, we identified a sixteen-fold variation in survival among unwitnessed arrests, which was significantly correlated with a higher rank of care provided. Conclusions: In Michigan, the greatest variation in survival was identified among unwitnessed arrests. This variation was robustly associated with a composite rank of in-hospital post-arrest interventions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".