Predicting Mortality of Patients With Sepsis: A Comparison of APACHE II and APACHE III Scoring Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Acute Physiology, Age and Chronic Health Evaluation (APACHE) II and III scores were developed in 1985 and 1991, respectively, and are used mainly for critically ill patients of all disease categories admitted to the intensive care unit (ICU). They differ in how chronic health status is assessed, in the number of physiologic variables included (12 vs. 17), and in the total score. These two scoring systems have not been compared in predicting hospital mortality in patients with sepsis. METHODS: We retrospectively identified all septic patients admitted to our 54-bed medical-surgical ICU between June 2009 and February 2014 using the APACHE outcomes database. We calculated correlation coefficients for APACHE II and APACHE III scores in predicting hospital mortality. Receiver-operating characteristic (ROC) curves were also used to assess the mortality predictions. RESULTS: We identified a total of 2,054 septic patients. Average APACHE II score was 19 ± 7, and average APACHE III score was 68 ± 28. ICU mortality was 11.8% and hospital mortality was 18.3%. Both APACHE II (r = 0.41) and APACHE III scores (r = 0.44) had good correlations with hospital mortality. There was no statistically significant difference between the two correlations (P = 0.1). ROC area under the curve (AUC) was 0.80 (95% confidence interval (CI): 0.78 - 0.82) for APACHE II, and 0.83 (95% CI: 0.81 - 0.85) for APACHE III, suggesting that both scores have very good discriminative powers for predicting hospital mortality. CONCLUSIONS: This study shows that both APACHE II and APACHE III scores in septic patients were very strong predictors of hospital mortality. APACHE II was as good as APACHE III in predicting hospital mortality in septic patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it