Postarrest Care Bundle Improves Quality of Care and Clinical Outcomes in the Normothermia Era
Bibliographic record
Abstract
Purpose Temperature targets in patients with cardiac arrest and return of spontaneous circulation (ROSC) have changed. Changes to higher temperature targets have been associated with higher breakthrough fevers and mortality. A post-ROSC normothermia bundle was developed to improve compliance with temperature targets. Methods In August 2021, “ad hoc” normothermia at the discretion of the attending intensivist was initiated. In December 2021, a post-ROSC normothermia protocol was implemented, incorporating a rigorous, stepwise approach to fever prevention (temperature ≥ 37.8). We conducted a before-after cohort study of all adult patients post-ROSC who survived to intensive care unit admission between August 1, 2021, and April 1, 2022. They were divided into “ad hoc” and “protocol” groups. Clinical outcomes compared included fevers, active cooling, and paralytic use. Results Fifty-eight post-ROSC patients were admitted; 24 in the “ad hoc” and 34 in the “protocol” groups. Patient demographics were similar between groups. The “ad hoc” group had more shockable rhythms (67% vs 24%, P = .001) and cardiac catheterizations (42% vs 15%, P = .03). The “protocol” group were significantly less likely to have a fever at 40 h (6% vs 40%, P < .001) and 72 h (14% vs 65%, P ≤ .001). Patients in the normothermia “protocol” used significantly less neuromuscular blocking agents (24% vs 50%, P = .05). The normothermia “protocol” resulted in similar mortality (56% vs 58%, P = 1.0). Conclusion Use of a normothermia “protocol” resulted in fewer fevers and less neuromuscular blocker administration compared to “ad hoc” management. A protocolized approach for improved quality of care should be considered in institutions adopting normothermia.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".