Lactate and number of organ failures predict intensive care unit mortality in patients with acute‐on‐chronic liver failure
Bibliographic record
Abstract
BACKGROUND AND AIMS: Patients with acute-on-chronic liver failure (ACLF) have high mortality rates. Most prognostic scores were not developed for the intensive care unit (ICU) setting. We aimed to improve risk stratification for patients with ACLF in the ICU. METHODS: A training set with 240 patients with cirrhosis and organ failures (Chronic Liver Failure Sequential Organ Failure Assessment score [CLIF-SOFA]) from Curry Cabral Hospital (Portugal) and University of Alberta Hospital (Canada) in 2010-2016 was used to derive a prognostic model for ICU mortality. A validation set with 237 patients with cirrhosis and organ failures from Vancouver General Hospital (Canada) in 2000-2011 was used to evaluate its performance. RESULTS: Amongst patients in the training set, ICU and hospital mortality rates were 39.2% and 54.6% respectively. Median lactate (4.4 vs 2.5 mmol/L) and number of organ failures (3 vs 2) on admission to ICU were associated with higher likelihood of ICU mortality (P < 0.001 for both). The lactate and organ failures predictive model (LacOF) was derived to predict ICU mortality: -2.420 + 0.072 × lactate + 0.569 × number of organ failures (area under-the-curve [AUC], 0.76). In the validation set, the LacOF model discriminative ability (AUC, 0.85) outperformed the CLIF-SOFA (AUC, 0.79), Chronic Liver Failure Consortium Acute-on-Chronic Liver Failure (AUC, 0.73), Model for End-stage Liver Disease score (AUC, 0.78) and Acute Physiology and Chronic Health Evaluation II scores (AUC, 0.74; P < 0.05 for all). The LacOF model calibration was good up to the 25% likelihood of ICU mortality. CONCLUSIONS: In patients with ACLF, lactate and number of organ failures on admission to ICU are useful to predict ICU mortality. This early prognostic evaluation may help to better stratify the risk of ICU mortality and thus optimize organ support strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".