Evaluation of APACHE-IV Predictive Scoring in Surgical Abdominal Sepsis: A Retrospective Cohort Study
Bibliographic record
Abstract
Introduction: Evaluation of the effectiveness of care and clinical outcomes in critically ill patients is dependent on predictive scoring models that calculate measures of disease severity and an associated likelihood of mortality. The APACHE scoring system is a logistic regression model incorporating physiologic and laboratory parameters. APACHE-IV is the most updated scoring system for ICU mortality prediction. However, APACHE scores may not accurately predict mortality in patients who require surgery for abdominal sepsis, whose trajectory is modulated by source control procedures. Aim: To evaluate the accuracy of APACHE-IV mortality prediction in a cohort of ICU patients with surgical abdominal sepsis (SABS) requiring emergent laparotomy for source control. Materials and Methods: The study was conducted in a combined medical and surgical intensive care unit in a large urban Canadian tertiary care hospital. Retrospective review of 211 consecutive adult ICU admissions that fulfilled the 2012 ACCP/SCCM criteria for severe sepsis/septic shock due to abdominal source was performed. APACHE-IV score and predicted mortality rate (PMR) were calculated and evaluated using area under the ROC curve (AUROC). Results: Overall in-hospital mortality was 28.4%. There was overestimation of PMR by the APACHE-IV model in the overall cohort with an absolute difference of 16.6% (relative difference 36.9%). APACHE-IV crudely distinguished between survivors and non-survivors, with a PMR of 40% vs. 59% (p<0.001). AUROC of the APACHE-IV score was 0.67, 95% CI (0.58, 0.76) while the AUROC for the PMR was 0.72, 95% CI (0.64, 0.80), indicating poor performance in this cohort. Conclusion: APACHE-IV has poor discrimination in SABS. Future research should explore disease-specific prediction models.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".