Predicting survival post-cardiac arrest: An observational cohort study
Bibliographic record
Abstract
Introduction: Over 400,000 out-of-hospital cardiac arrest (OHCA) occur each year in Canada and the United States with less than 10% survival to hospital discharge. Cardiac arrest is a heterogenous condition and patient outcomes are impacted by a multitude of factors. Prognostication is recommended at 72 hours after return of spontaneous circulation (ROSC), however there may be other factors that could predict patient outcome earlier in the post-arrest period. The objective of our study was to develop and internally validate a novel clinical prediction rule to risk stratify patients early in the post-cardiac arrest period. Methods: We performed a retrospective cohort study of adult (≥18 years) post-cardiac arrest patients between 2010 and 2015 from the Epistry Cardiac Arrest database in Toronto. Our primary analysis used ordinal logistic regression to examine neurologic outcome at discharge using the modified Rankin Scale (mRS). Our secondary analysis used logistic regression for neurologic outcome and survival to hospital discharge. Models were internally validated using bootstrap validation. Results: A total of 3432 patients met our inclusion criteria. Our clinical prediction model was able to predict neurologic outcome on an ordinal scale using our predefined variables with an AUC of 0.89 after internal validation. The predictive performance was maintained when examining neurologic outcome as a binary variable and survival to hospital discharge. Conclusion: We were able to develop a model to accurately risk stratify adult cardiac arrest patients early in the post-cardiac arrest period. Future steps are needed to externally validate this model in other healthcare settings.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".