The Effects of Preexisting Medical Comorbidities on Mortality and Length of Hospital Stay in Acute Burn Injury
Bibliographic record
Abstract
OBJECTIVE: To determine whether and to what extent preexisting medical comorbidities influence mortality risk and length of hospitalization in patients with acute burn injury. SUMMARY BACKGROUND DATA: The effects on mortality and length of stay of a number of important medical comorbidities have not been examined in acute burn injury. Existing studies that have investigated the effects of medical comorbidities on outcomes in acute burn injury have produced inconsistent results, chiefly due to the use of relatively small samples from single burn centers. METHODS: Records of 31,338 adults who were admitted with acute burn injury to 70 burn centers from the American Burn Association National Burn Repository, were reviewed. A burn-specific list of medical comorbidities was derived from diagnoses included in the Charlson Index of Comorbidities and the Elixhauser method of comorbidity measurement. Logistic regression was used to assess the effects of preexisting medical conditions on mortality, controlling for demographic and burn injury characteristics. Ordinal least squares regression with a logarithmic transformation of the dependent variable was used to assess the relationship of comorbidities with length of stay. RESULTS: In-hospital mortality was significantly predicted by HIV/AIDS (odds ratio [OR] = 10.2), renal disease (OR = 5.1), liver disease (OR = 4.8), metastatic cancer (OR = 4.6), pulmonary circulation disorders (OR = 2.9), congestive heart failure (OR = 2.4), obesity (OR = 2.1), non-metastatic malignancies (OR = 2.1), peripheral vascular disorders (OR = 1.8), alcohol abuse (OR = 1.8), neurological disorders (OR = 1.6), and cardiac arrhythmias (OR = 1.5). Increased length of hospital stay among survivors was significantly predicted by paralysis (90% increase), dementia (60%), peptic ulcer disease (53%), other neurological disorders (52%), HIV/AIDS (49%), renal disease (44%), a psychiatric diagnosis (42%), cerebrovascular disease (41%), cardiac arrhythmias (40%), peripheral vascular disorders (39%), alcohol abuse (36%), valvular disease (32%), liver disease (30%), diabetes (26%), congestive heart failure (23%), drug abuse (20%), and hypertension (17%). CONCLUSIONS: A number of preexisting medical conditions influence outcomes in acute burn injury. Patients with preburn HIV/AIDS, metastatic cancer, liver disease, and renal disease have particularly poor prognoses.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".