Liver Stiffness by Transient Elastography Predicts Liver-Related Complications and Mortality in Patients with Chronic Liver Disease
Bibliographic record
Abstract
BACKGROUND: Liver stiffness measurement (LSM) by transient elastography (TE, FibroScan) is a validated method for noninvasively staging liver fibrosis. Most hepatic complications occur in patients with advanced fibrosis. Our objective was to determine the ability of LSM by TE to predict hepatic complications and mortality in a large cohort of patients with chronic liver disease. METHODS: In consecutive adults who underwent LSM by TE between July 2008 and June 2011, we used Cox regression to determine the independent association between liver stiffness and death or hepatic complications (decompensation, hepatocellular carcinoma, and liver transplantation). The performance of LSM to predict complications was determined using the c-statistic. RESULTS: Among 2,052 patients (median age 51 years, 65% with hepatitis B or C), 87 patients (4.2%) died or developed a hepatic complication during a median follow-up period of 15.6 months (interquartile range, 11.0-23.5 months). Patients with complications had higher median liver stiffness than those without complications (13.5 vs. 6.0 kPa; P<0.00005). The 2-year incidence rates of death or hepatic complications were 2.6%, 9%, 19%, and 34% in patients with liver stiffness <10, 10-19.9, 20-39.9, and ≥40 kPa, respectively (P<0.00005). After adjustment for potential confounders, liver stiffness by TE was an independent predictor of complications (hazard ratio [HR] 1.05 per kPa; 95% confidence interval [CI] 1.03-1.06). The c-statistic of liver-stiffness for predicting complications was 0.80 (95% CI 0.75-0.85). A liver stiffness below 20 kPa effectively excluded complications (specificity 93%, negative predictive value 97%); however, the positive predictive value of higher results was sub-optimal (20%). CONCLUSIONS: Liver stiffness by TE accurately predicts the risk of death or hepatic complications in patients with chronic liver disease. TE may facilitate the estimation of prognosis and guide management of these patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".