Unplanned readmission after traumatic injury: A long-term nationwide analysis
Bibliographic record
Abstract
BACKGROUND: Long-term outcomes after trauma admissions remain understudied. We analyzed the characteristics of inpatient readmissions within 6 months of an index hospitalization for traumatic injury. METHODS: Using the 2010 to 2015 Nationwide Readmissions Database, which captures data from up to 27 US states, we identified patients at least 15 years old admitted to a hospital through an emergency department for blunt trauma, penetrating trauma, or burns. Exclusion criteria included hospital transfers, patients who died during their index hospitalizations, and hospitals with fewer than 100 trauma patients annually. After calculating the incidences of all-cause, unplanned inpatient readmissions within 1 month, 3 months, and 6 months, we used multivariable logistic regression models to identify predictors of readmissions. Analyses adjusted for patient, clinical, and hospital factors. RESULTS: Among 2,763,890 trauma patients, the majority had blunt injuries (92.5%), followed by penetrating injuries (6.2%) and burns (1.5%). Overall, rates of inpatient readmissions were 11.1% within 1 month, 21.6% within 6 months, and 29.8% within 6 months, with limited variability by year. After adjustment, the following were associated with all-cause 6 months inpatient readmissions: male sex (adjusted odds ratio [aOR], 1.10; 95% confidence interval [95% CI], 1.09-1.10), comorbidities (aOR, 1.21; 95% CI, 1.21-1.22), low-income quartiles (first and second) (aOR, 1.08; 95% CI, 1.07-1.10 and aOR, 1.04; 95% CI, 1.03-1.06, respectively), Medicare (aOR, 1.65; 95% CI, 1.62-1.69), Medicaid (aOR, 1.51; 95% CI, 1.48-1.53), being treated at private, investor-owned hospitals (aOR, 1.15; 95% CI, 1.12-1.18), longer hospital length of stay (aOR, 1.01; 95% CI, 1.01-1.01) and patient disposition to short-term hospital (aOR, 1.55; 95% CI, 1.49-1.62), skilled nursing facility (aOR, 1.43; 95% CI, 1.42-1.45), home health care (aOR, 1.27; 95% CI, 1.25-1.28), or leaving against medical advice (aOR, 1.85; 95% CI, 1.78-1.92). CONCLUSION: Unplanned readmission after trauma is high and remains this way 6 months after discharge. Understanding the factors that increase the odds of readmissions within 1 month, 3 months, and 6 months offer a focus for quality improvement and have important implications for hospital benchmarking. LEVEL OF EVIDENCE: Epidemiological study, level III.
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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 | 0.002 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".