Abstract 430: Variation of Post Arrest Survival to Discharge and Cerebral Performance Category 1/2 in State of Michigan
Bibliographic record
Abstract
Introduction: Resuscitation from out-of-hospital cardiac arrest (OHCA) requires success in the entire chain of survival. Our prior work demonstrated substantial variation in provision of left heart catheterization (LHC) and TTM within Michigan (MI) hospitals but did not evaluate impact on survival. The objective of this study is to characterize the variation in post arrest survival to discharge and CPC-1/2 in MI Hospitals. Methods: We used the MI Cardiac Arrest to Enhance Survival (CARES) registry, to analyze all adult OHCA patients with ROSC from 2014 - 2017 that survived to hospital admission. Hospitals (N=40) were included if they managed > 30 cases/4-years for all rhythms and > 20 cases/4-years for shockable patients. We excluded transferred patients; those resuscitated with bystander CPR only and patients < 18 years old. We assessed provision of LHC and TTM by hospital, survival to hospital discharge and survival with good neurologic outcome (CPC 1 or 2). We report crude and adjusted survival rates by hospital with median and range. To account for variations between hospitals, the adjusted survival rates were estimated using multilevel multi-variable regression analyses, adjusting for OHCA variables predictive of survival. Results: There were 5486 patients included CARES patients, 4,715 ultimately included for analyses of survival to discharge and survival with good neurological outcome. Of included hospitals (N=40), the median of crude rate was 31.4% [Range: 12.5%, 51.9%] for survival at discharge and 25.1% [Range: 5.2%, 42.2%] for survival rate with CPC-1/2. In the multivariable analyses, adjusting for patient characteristics, the median of adjusted rate was 27.6% [Range: 18.1%, 42.0%] for survival and 21.3% [Range: 9.7%, 32.8%] for survival to discharge with CPC-1/2. Conclusion: We observed substantial disparities in inter-hospital rates of survival at discharge, with a 4-fold range of survival and 8-fold range of survival with CPC-1/2. This survival variation was ameliorated but still persisted in the adjusted modeling analysis. Variation in post arrest survival by hospital is not explained by patient and arrest characteristics, and identifies a need for hospital quality improvement activities to improve post arrest patient survival.
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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 | 0.003 |
| 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".