Using the age-adjusted Charlson comorbidity index to predict outcomes in emergency general surgery
Bibliographic record
Abstract
BACKGROUND: We evaluated the role of the Charlson age-comorbidity index (CACI), a weighted comorbidity index that reflects cumulative increased likelihood of 1-year mortality, in predicting perioperative outcomes in an emergency general surgery population at a large Canadian teaching hospital. METHODS: A retrospective chart review of emergency general surgery admissions in 2010 was conducted. Patients who had surgery were identified. Mode of surgery and CACI were recorded, as well as measures of outcome, including 30-day mortality and intensive care unit (ICU) admission. A multivariate stepwise logistic regression model was created to assess the effect of age-adjusted Charlson comorbidity index on postoperative outcomes while controlling for the effect of possible confounders. The prediction ability of CACI for mortality was assessed using receiver operating characteristic analyses considering the area under the curve and its 95% confidence intervals (CIs). RESULTS: Of the 529 admissions to general surgery from the emergency department, 257 patients underwent a surgical intervention. The CACI scores ranged from 0 to 16. We described a total of 11 deaths (4.3%) and 30 ICU admissions (11.7%). CACI was associated with an increased risk of 30-day mortality (adjusted odds ratio,1.39; 95% CI, 1.11-1.73; p = 0.0034). Receiver operating characteristic analysis was consistent with high accuracy of CACI for mortality prediction alone, resulting in area under the curve or c statistic of 0.90 (95% CI, 0.84-0.95). CACI was similar in predicting mortality to a multivariate model. CACI was also found to be associated with ICU admission (adjusted odds ratio, 1.17; 95% CI, 1.01-1.37; p < 0.0382). CACI is not as good a predictor for ICU admission when compared with the multivariate model. CONCLUSION: We have shown that the CACI is a valid tool for 30-day mortality prediction in the context of emergency general surgery. LEVEL OF EVIDENCE: Prognostic 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".