Hospital Variation in Survival Trends for In‐hospital Cardiac Arrest
Bibliographic record
Abstract
BACKGROUND: During the past decade, survival after in-hospital cardiac arrest has improved markedly. It remains unknown whether the improvement in survival has occurred uniformly at all hospitals or was driven by large improvements at only a few hospitals. METHODS AND RESULTS: We identified 93 342 adults with an in-hospital cardiac arrest at 231 hospitals in the Get With The Guidelines(®)-Resuscitation registry during 2000-2010. Using hierarchical regression models, we evaluated hospital-level trends in survival to discharge. Mean age was 66 years, 59% were men, and 21% were black. Between 2000 and 2010, there was a significant decrease in age, prevalence of heart failure and myocardial infarction, and cardiac arrests due to shockable rhythms (P<0.001 for all) and an increase in prevalence of sepsis, respiratory insufficiency, renal insufficiency, intensive care unit location, and mechanical ventilation before arrest (P<0.001 for all). After adjustment for temporal trends in baseline characteristics, hospital rates of in-hospital cardiac arrest survival improved by 7% per year (odds ratio [OR] 1.07, 95% CI 1.06 to 1.08, P<0.001). Improvement in survival varied markedly and ranged from 3% in the bottom hospital quartile to 11% in the top hospital quartile. Compared with minor teaching hospitals (OR 1.04, 95% CI 1.02 to 1.06), hospital rate of survival improvement was greater at major teaching (OR 1.08, 95% CI 1.06 to 1.10) and nonteaching hospitals (OR 1.07, 95% CI 1.05 to 1.09, P value for interaction=0.03). CONCLUSION: Although in-hospital cardiac arrest survival has improved during the past decade, the magnitude of improvement varied across hospitals. Future studies are needed to identify hospital processes that have led to the largest improvement in survival.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".