1536: ARDS OUTCOMES IN TRAUMA 2007-2019: COMPREHENSIVE PATIENT AND CENTER-LEVEL ANALYSIS
Bibliographic record
Abstract
Introduction: Acute respiratory distress syndrome (ARDS) remains a significant complication in trauma survivors. Yet the epidemiology of ARDS in trauma remains incompletely characterized. We sought to define trends in ARDS incidence and the effect of temporal, patient, and center-level factors on outcomes with the hypothesis that ARDS independently predicts mortality. Methods: We conducted a retrospective cohort study of the American College of Surgeons National Trauma Databank from 2007-2019. All patients ≥18 years old on mechanical ventilation (MV) for ≥2 days were included, and patients with ARDS were compared to those without ARDS. A subgroup with 24-hour transfusion data was also identified. We created multivariable logistic regression models by year and adjusted for patient demographics, center characteristics, and blood products to identify factors associated with ARDS incidence and 30-day mortality. Results: Of 384,032 injured patients on MV, 34,251 (9%) developed ARDS with a significant decrease over the study period (28% in 2007 vs 4% in 2019, p< 0.001). Patient-level risk factors independently associated with ARDS were blunt injury (OR 1.30, 95%CI 1.25-1.35), sepsis (OR 2.22, 95%CI 2.12-2.33), pneumonia (OR 2.80, 95%CI 2.72-2.87), and acute kidney injury (AKI, OR 2.97, 95%CI 2.84-3.11). Crude ARDS mortality increased over the study period (2007, 15.6% vs 2019, 28.7%, p< 0.001), and after adjusting for significant differences, ARDS was independently associated with 30-day mortality (OR 1.25, 95%CI 1.21-1.30). Independent risk factors for 30-day mortality in patients with ARDS included head injury (OR 1.54, 95%CI 1.44-1.65), sepsis (OR 1.47, 95%CI 1.35-1.61), and AKI (OR 2.67, 95%CI 2.47-2.89). In the transfusion subset, 24-hour plasma (OR 1.02, 95%CI 1.00-1.03) and platelets (OR 1.03, 95%CI 1.01-1.05) were independently associated with ARDS. Patients with ARDS managed in PETAL/ELSO centers were less likely to die (OR 0.81, 95%CI 0.75-0.87). Conclusions: From 2007 to 2019, ARDS decreased significantly in trauma patients. Over the same time, mortality increased to nearly 30%, and after adjusting for other risks factors, ARDS was strongly associated with 30-day mortality. Future studies should examine modifiable patient and center-level factors to improve mortality in these high-risk patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".