Respiratory Mechanics and Outcomes in Immunocompromised Patients With ARDS
Bibliographic record
Abstract
BACKGROUND: In view of the high mortality rate of immunocompromised patients with ARDS, it is important to identify targets for improvement. RESEARCH QUESTION: This study investigated factors associated with mortality in this specific ARDS population, including factors related to respiratory mechanics (plateau pressure [Pplat,rs], compliance [Crs], and driving pressure [ΔPrs]). STUDY DESIGN AND METHODS: This study consisted of a predefined secondary analysis of the EFRAIM data. Overall, 789 of 1,611 patients met the Berlin criteria for ARDS, and Pplat,rs, ΔPrs, and Crs were available for 494 patients. A hierarchical model was used to assess factors at ARDS onset independently associated with hospital mortality. RESULTS: Hospital mortality was 56.3%. After adjustment, variables independently associated with hospital mortality included ARDS of undetermined etiology (OR, 1.66; 95% CI, 1.01-2.72), need for vasopressors (OR, 1.91; 95% CI, 1.27-2.88), and need for renal replacement therapy (OR, 2.02; 95% CI, 1.37-2.97). ARDS severity according to the Berlin definition, neutropenia on admission, and the type of underlying disease were not significantly associated with mortality. Before adjustment, higher Pplat,rs, higher ΔPrs, and lower Crs were associated with higher mortality. Addition of each of these individual variables to the final hierarchical model revealed a significant association with mortality: ΔPrs (OR, 1.08; 95% CI, 1.05-1.12), Pplat,rs (OR, 1.07; 95% CI, 1.04-1.11), and Crs (OR, 0.97; 95% CI, 0.95-0.98). Tidal volume was not associated with mortality. INTERPRETATION: In immunocompromised patients with ARDS, respiratory mechanics provide additional prognostic information to predictors of hospital mortality. Studies designed to define lung-protective ventilation guided by these physiological variables may be warranted in this specific population.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".