The 22/11 Risk Prediction Model: A Validated Model for Predicting 30-Day Mortality in Patients With Cirrhosis and Spontaneous Bacterial Peritonitis
Bibliographic record
Abstract
OBJECTIVES: Clinicians do not have a validated tool for estimating the short-term mortality associated with spontaneous bacterial peritonitis (SBP). Accurate prognosis assessment is important for risk stratification and for individualizing therapy. We aimed therefore to develop and validate a model for the prediction of 30-day mortality in SBP patients receiving standard medical treatment (antibiotics and if indicated by guidelines, intravenous albumin therapy). METHODS: We retrospectively identified SBP patients treated at a tertiary care center between 2003 and 2011 (training set). Multivariate regression modeling and receiver operating characteristic (ROC) curves were utilized for statistical analysis. An external data set of 109 SBP patients was utilized for validation. RESULTS: Of the 184 patients in the training set, 66% were men with a median age of 55 years, a median MELD (Model for End-Stage Liver Disease) score of 20, and a 30-day mortality of 27%. Peripheral blood leukocyte count ≥11×10⁹ cells/l (odds ratio (OR) 2.5; 95% confidence interval CI: 1.2-5.2) and MELD score ≥22 (OR 4.6; 95% CI: 2.3-9.6) were independent predictors of 30-day mortality. Patients with neither, one, or both variables had 30-day mortality rates of 8%, 32%, and 52%, respectively. The findings in the validation set mirrored the training set. CONCLUSIONS: In cirrhotic patients with SBP receiving standard therapy, MELD score ≥22 and peripheral blood leukocyte count ≥11×10⁹ cells/l are validated independent predictors of mortality. The mortality in a patient without either poor prognostic variable is ≤10% and with both variables is ≥50%. Trials aiming to reduce mortality should target patients in the moderate-risk to high-risk groups.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".