Model for End‐Stage Liver Disease‐Lactate and Prediction of Inpatient Mortality in Patients With Chronic Liver Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Compared to other chronic diseases, patients with chronic liver disease (CLD) have significantly higher inpatient mortality; accurate models to predict inpatient mortality are lacking. Serum lactate (LA) may be elevated in patients with CLD due to both tissue hypoperfusion as well as decreased LA clearance. We hypothesized that a parsimonious model consisting of Model for End-Stage Liver Disease (MELD) and LA at admission may predict inpatient mortality in patients with CLD. APPROACH AND RESULTS: We examined all patients with CLD in two large and diverse health care systems in Texas (North Texas [NTX] and Central Texas [CTX]) between 2010 and 2015. We developed (n = 3,588) and validated (n = 1,804) a model containing MELD and LA measured at the time of hospitalization. We further validated the model in a second cohort of 14 tertiary care hepatology centers that prospectively enrolled nonelective hospitalized patients with cirrhosis (n = 726). MELD-LA was an excellent predictor of inpatient mortality in development (concordance statistic [C-statistic] = 0.81, 95% confidence interval [CI] 0.79-0.82) and both validation cohorts (CTX cohort, C-statistic = 0.85, 95% CI 0.78-0.87; multicenter cohort C-statistic = 0.82, 95% CI 0.74-0.88). MELD-LA performed especially well in patients with specific cirrhosis diagnoses (C-statistic = 0.84, 95% CI 0.81-0.86) or sepsis (C-statistic = 0.80, 95% CI 0.78-0.82). For MELD score 25, inpatient mortality rates were 11.2% (LA = 1 mmol/L), 19.4% (LA = 3 mmol/L), 34.3% (LA = 5 mmol/L), and >50% (LA > 8 mmol/L). A linear increase (P < 0.01) was seen in MELD-LA and increasing number of organ failures. Overall, use of MELD-LA improved the risk prediction in 23.5% of patients compared to MELD alone. CONCLUSIONS: MELD-LA (bswh.md/meldla) is an early and objective predictor of inpatient mortality and may serve as a model for risk assessment and guide therapeutic options.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".