The cost-effectiveness of mild hypercapnia after out-of-hospital cardiac arrest: a health economic evaluation alongside the TAME study
Bibliographic record
Abstract
BACKGROUND: Out of hospital cardiac arrest (OHCA) is a significant public health problem associated with high mortality and high healthcare costs. The Targeted Therapeutic Mild Hypercapnia after Resuscitated Cardiac Arrest (TAME) randomised clinical trial showed that targeted mild hypercapnia did not lead to better neurologic outcomes at 6 months compared to normocapnia after OHCA. We aimed to estimate the cost-effectiveness of mild hypercapnia compared to targeted normocapnia using data from the TAME trial. METHODS: Pre-specified, prospective cost-effectiveness analysis alongside the TAME RCT from a healthcare perspective using a 6-month time horizon. The analysis included 1586 patients across 63 intensive care units (ICUs), in 17 countries. The primary measure of cost-effectiveness was the cost per quality-adjusted life year (QALY). Costs were estimated for each patient by multiplying resource use data by the relevant country-specific resource unit cost. QALYs were calculated using utility scores derived from the EQ-5D-5L administered at 6-month follow-up. FINDINGS: There were no significant differences in costs or QALYs at 6 months between groups. The incremental net monetary benefit was also not significant at a willingness-to-pay threshold of $50,000 per QALY, with the 95 % CI including both negative and positive values. INTERPRETATION: This analysis found that the cost-effectiveness of mild hypercapnia is highly uncertain when compared with normocapnia in adult patients following OHCA, with no significant differences in either costs or outcomes. Further research is required to determine the specific circumstances under which mild hypercapnia may provide value for money.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".