Association between prehospital arterial hypercapnia and mortality in acute heart failure: a retrospective cohort study
Bibliographic record
Abstract
Abstract Background Acute Heart Failure (AHF) is a potentially lethal pathology and is often encountered in the prehospital setting. Although an association between prehospital arterial hypercapnia in AHF patients and admission in high-dependency and intensive care units has been previously described, there is little data to support an association between prehospital arterial hypercapnia and mortality in this population. Methods This was a retrospective study based on electronically recorded prehospital medical files. All adult patients with AHF were included. Records lacking arterial blood gas data were excluded. Other exclusion criteria included the presence of a potentially confounding diagnosis, prehospital cardiac arrest, and inter-hospital transfers. Hypercapnia was defined as a PaCO2 higher than 6.0 kPa. The primary outcome was in-hospital mortality, and secondary outcomes were 7-day mortality and emergency room length of stay (ER LOS). Univariable and multivariable logistic regression models were used. Results We included 225 patients in the analysis. Prehospital hypercapnia was found in 132 (58.7%) patients. In-hospital mortality was higher in patients with hypercapnia (17.4% [23/132] versus 6.5% [6/93], p = 0.016), with a crude odds-ratio of 3.06 (95%CI 1.19–7.85). After adjustment for pre-specified covariates, the adjusted OR was 3.18 (95%CI 1.22–8.26). The overall 7-day mortality was also higher in hypercapnic patients (13.6% versus 5.5%, p = 0.044), and ER LOS was shorter in this population (5.6 h versus 7.1 h, p = 0.018). Conclusion Prehospital hypercapnia is associated with an increase in in-hospital and 7-day mortality in patient with AHF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".