Escalation of Oxygenation Modalities and Mortality in Critically Ill Immunocompromised Patient With Acute Hypoxemic Respiratory Failure: A Clustering Analysis of a Prospectively Multicenter, Multinational Dataset
Bibliographic record
Abstract
OBJECTIVES: Acute hypoxemic respiratory failure in immunocompromised patients remains the leading cause of admission to the ICU, with high case fatality. The response to the initial oxygenation strategy may be predictive of outcome. This study aims to assess the response to the evolutionary profiles of oxygenation strategy and the association with survival. DESIGN: Post hoc analysis of EFRAIM study with a nonparametric longitudinal clustering technique (longitudinal K-mean). SETTING AND PATIENTS: Multinational, observational prospective cohort study performed in critically ill immunocompromised patients admitted for an acute respiratory failure. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 1547 patients who did not require invasive mechanical ventilation (iMV) at ICU admission were included. Change in ventilatory support was assessed and three clusters of change in oxygenation modality over time were identified. Cluster A: 12.3% iMV requirement and high survival rate, n = 717 patients (46.3%); cluster B: 32.9% need for iMV, 97% ICU mortality, n = 499 patients (32.3%); and cluster C: 37.5% need for iMV, 0.3% ICU mortality, n = 331 patients (21.4%). These clusters demonstrated a high discrimination. After adjustment for confounders, clusters B and C were independently associated with need for iMV (odds ratio [OR], 9.87; 95% CI, 7.26-13.50 and OR, 19.8; 95% CI, 13.7-29.1). CONCLUSIONS: This study identified three distinct highly performing clusters of response to initial oxygenation strategy, which reliably predicted the need for iMV requirement and hospital mortality.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".