Improving Use of Targeted Temperature Management After Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
RATIONALE: International guidelines recommend use of targeted temperature management following resuscitation from out-of-hospital cardiac arrest. This treatment, however, is often neglected or delayed. OBJECTIVE: To determine whether multifaceted quality improvement interventions would increase the proportion of eligible patients receiving successful targeted temperature management. SETTING: A network of 6 regional emergency medical services systems and 32 academic and community hospitals serving a population of 8.8 million people providing post arrest care to out-of-hospital cardiac arrest. INTERVENTIONS: Comparing interventions improve the implementation of targeted temperature management post out-of-hospital cardiac arrest through passive (education, generic protocol, order set, local champions) versus additional active quality improvement interventions (nurse specialist providing site-specific interventions, monthly audit-feedback, network educational events, internet blog) versus no intervention (baseline standard of care). MEASUREMENTS AND MAIN RESULTS: The primary process outcome was proportion of eligible patients receiving successful targeted temperature management, defined as a target temperature of 32-34ºC within 6 hours of emergency department arrival. Secondary clinical outcomes included survival and neurological outcome at hospital discharge. Four thousand three hundred seventeen out-of-hospital cardiac arrests were transported to hospital; 1,737 (40%) achieved spontaneous circulation, and 934 (22%) were eligible for targeted temperature management. After accounting for secular trends, patients admitted during the passive quality improvement phase were more likely to achieve successful targeted temperature management compared with those admitted during the baseline period (25.7% passive vs 9.0% baseline; odds ratio, 2.76; 95% CI, 1.76-4.32; p < 0.001). Active quality improvement interventions conferred no additional improvements in rates of successful targeted temperature management (26.9% active vs 25.7% passive; odds ratio, 0.96; 95% CI, 0.63-1.45; p = 0.84). Despite a significant increase in rates of successful targeted temperature management, survival to hospital discharge was unchanged. CONCLUSION: Simple quality improvement interventions significantly increased the rates of achieving successful targeted temperature management following out-of-hospital cardiac arrest in a large network of hospitals but did not improve clinical outcomes.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".