Development and Validation of the Mortality Risk for Trauma Comorbidity Index
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop and validate a comorbidity index to predict the risk of mortality associated with chronic health conditions following a traumatic injury. SUMMARY BACKGROUND DATA: Currently available comorbidity adjustment tools do not account for certain chronic conditions, which may influence outcome following traumatic injury or they have not been fully validated for trauma. Controlling for comorbidity in trauma patients is becoming increasingly important as the population ages and elderly patients are more active, as well as to adjust for bias in trauma mortality studies. METHODS: Cohort study using data from the National Study on the Costs and Outcome of Trauma. Subject pool (N = 4644/Weighted Number = 14,069) was randomly divided in half; the first half of subjects was used to derive the risk scale, the second to validate the instrument. To construct the Mortality Risk Score for Trauma (MoRT), univariate analysis and odds ratios were performed to determine relative risk of mortality at hospital discharge comparing those persons with a comorbid condition to those without. Conditions significantly associated with mortality (P < 0.05) were included in the multivariate model. The variables in the final model were used to build the MoRT. The predictive ability of the MoRT and the Charlson Comorbidity Index (CCI) for discharge and 1-year mortality were estimated using the c-statistic in the validation sample. RESULTS: Six comorbidity factors were independently associated with the risk of mortality and formed the basis for the MoRT: severe liver disease, myocardial infarction, cerebrovascular disease, cardiac arrhythmias, dementia, and depression. The MoRT had a similar overall discrimination as the CCI for mortality at hospital discharge in injured adults (c-statistic: 0.56 vs. 0.56) although neither by itself performed well. The addition of age and gender improved the predictive ability of the MoRT (0.59; 95% CI: 0.56, 0.62) and the CCI (0.59; 0.56, 0.62). Similar results were seen at 1-year postinjury. The further addition of Injury Severity Score significantly improved the predictive ability of the MoRT (0.77, 95% CI: 0.74, 0.79) and the CCI (0.77, 95% CI: 0.75, 0.80). CONCLUSIONS: The MoRTs primary advantage over current instruments is its parsimony, containing only 6 items. In the present study, the comorbid conditions found to be predictive of mortality had some overlap with the CCI, but this study identified 2 novel predictors: cardiac arrhythmias and depression. Inclusion and reporting of these items within trauma registries would therefore be an important step to allow further validation and use of the MoRT.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".