The Attributable Mortality and Length of Stay of Trauma-Related Complications
Bibliographic record
Abstract
In Brief Objective: To determine the attributable mortality (AM) and excess length of stay because of complications or complication groupings in the National Trauma Data Bank. Summary Background Data: Resources devoted to performance improvement activities should focus on complications that significantly impact mortality and length of stay. To determine which post-traumatic complications impact these outcomes, we conducted a matched cohort study. AM is the proportion of all deaths that can be prevented if the complication did not occur. Methods: We identified severely injured patients (Injury Severity Score, ≥9) at centers that contribute complications to the National Trauma Data Bank. To estimate the AM, a patient with a specific complication was matched to 5 patients without the complication. Matching was based on demographics and injury characteristics. Residual confounding was addressed through a logistic regression model. To estimate excess length of stay, matching covariates were identified through a Poisson regression model. Each case was required to match the control on all variables, and one control was selected per case. Results: Of the 94,795 patients who met the inclusion criteria, 3153 died. The overall mortality rate was 3.33%, and 10,478 (11.1%) patients developed at least 1 complication. Four complication groupings (cardiovascular, acute respiratory distress syndrome, renal failure, and sepsis) were associated with significant AM. Infectious complications (surgical infections, sepsis, and pneumonia) were associated with the greatest excess length of stay. Conclusions: This study used AM and excess length of stay to identify trauma-related complications for external benchmarking. Guideline development and performance improvement activities need to be focused on these complications to significantly reduce the probability of poor outcomes following injury. We conducted a matched cohort study to determine the attributable mortality and excess length of stay because of complications in severely injured trauma patients; this will guide future performance improvement initiatives. Cardiovascular, acute respiratory distress syndrome, renal failure, and sepsis were associated with the greatest attributable mortality; infectious complications were associated with the greatest excess length of stay.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".