Targeted Temperature Management Processes and Outcomes After Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
OBJECTIVES: Targeted temperature management has been shown to improve survival with good neurological outcome in patients after out-of-hospital cardiac arrest. The optimal approach to inducing and maintaining targeted temperature management, however, remains uncertain. The objective of this study was to evaluate these processes of care with survival and neurological function in patients after out-of-hospital cardiac arrest. DESIGN: An observational cohort study evaluating the association of targeted temperature management processes with survival and neurological function using bivariate and generalized estimating equation analyses. SETTING: Thirty-two tertiary and community hospitals in eight urban and rural regions of southern Ontario, Canada. PATIENTS: Consecutive adult (≥ 18 yr) patients admitted between November 1, 2007, and January 31, 2012, and who were treated with targeted temperature management following nontraumatic out-of-hospital cardiac arrest. INTERVENTIONS: Evaluate the association of targeted temperature management processes with survival and neurologic function using bivariate and generalized estimating equation analyses. MEASUREMENTS AND MAIN RESULTS: There were 5,770 consecutive out-of-hospital cardiac arrest patients, of whom 747 (12.9%) were eligible and received targeted temperature management. Among patients with available outcome data, 365 of 738 (49.5%) survived to hospital discharge and 241 of 675 (35.7%) had good neurological outcomes. After adjusting for the Utstein variables, a higher temperature prior to initiation of targeted temperature management was associated with improved neurological outcomes (odds ratio, 1.27 per °C; 95% CI, 1.08-1.50; p = 0.004) and survival (odds ratio, 1.26 per °C; 95% CI, 1.09-1.46; p = 0.002). A slower rate of cooling was associated with improved neurological outcomes (odds ratio, 0.74 per °C/hr; 95% CI, 0.57-0.97; p = 0.03) and survival (odds ratio, 0.73 per °C/hr; 95% CI, 0.54-1.00; p = 0.049). CONCLUSIONS: A higher baseline temperature prior to initiation of targeted temperature management and a slower rate of cooling were associated with improved survival and neurological outcomes. This may reflect a complex relationship between the approach to targeted temperature management and the extent of underlying brain injury causing impaired thermoregulation in out-of-hospital cardiac arrest patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".