Preinjury ASA score as an independent predictor of readmission after major traumatic injury
Bibliographic record
Abstract
BACKGROUND: Patients with trauma have a high predisposition for readmission after discharge. Unplanned solicitation of medical services is a validated quality of care indicator and is associated with considerable economic costs. While the existing literature emphasizes the severity of the injury, there is heterogeneity in defining preinjury health status. We evaluate the validity of the American Society of Anesthesiologists (ASA) Physical Status score as an independent predictor of readmission and compare it to the Charlson Comorbidity Index (CCI). METHODS: This is a single center, retrospective cohort study based on adult patients (>18 years of age) with trauma admitted to the Ottawa Hospital from January 1, 2004 to November 1, 2014. A multivariate logistic regression model is used to control for confounding and assess individual predictors. Outcome is readmission to hospital within 30 days, 3 months and 6 months. RESULTS: A total of 4732 adult patients were included in this analysis. Readmission rates were 6.5%, 9.6% and 11.8% for 30 days, 3 months and 6 months, respectively. Higher preinjury ASA scores demonstrated significantly increased risk of readmission across all levels in a dose-dependent manner for all time frames. The effect of preinjury ASA scores on readmission is most striking at 30 days, with patients demonstrating a 2.81 (1.88-4.22, P<0.0001), 3.59 (2.43-5.32, P<0.0001) and 7.52 (4.72-11.99, P<0.0001) fold odds of readmission for ASA class 2, 3 and 4, respectively, as compared with healthy ASA class 1 patients. The ASA scores outperformed the CCI at 30 days and 3 months. CONCLUSIONS: The preinjury ASA score is a strong independent predictor of readmission after traumatic injury. In comparison to the CCI, the preinjury ASA score was a better predictor of readmission at 3 and 6 months after a major traumatic injury. LEVEL OF EVIDENCE: Prognostic and Epidemiological Study, Level III.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".