Development and Validation of a Model for Prediction of End-Stage Liver Disease in People With HIV
Bibliographic record
Abstract
BACKGROUND: End-stage liver disease (ESLD) is a leading cause of non-AIDS-related death among people with HIV (PWH). Factors that increase the progression of liver disease include comorbidities and HIV-specific factors, but we currently lack a tool to apply this evidence into clinical practice. METHODS: We developed and validated a risk prediction model for ESLD among PWH who received care in 12 cohorts of the North American AIDS Cohort Collaboration on Research and Design between 2000 and 2016 and had fibrosis-4 index > 1.45. The first occurrence of ascites, variceal bleed, spontaneous bacterial peritonitis, or hepatic encephalopathy was verified by standardized medical record review. The Bayesian model averaging was used to select predictors among biomarkers and diagnoses and the Harrell C statistic to assess model discrimination. RESULTS: Among 13,787 PWH in the training set, 82% were men and 54% were Black with a mean age of 48 years. Three hundred ninety ESLD events occurred over a mean 5.4 years. Among the ESLD cases, 52% had hepatitis C virus, 15% hepatitis B virus, and 31% alcohol use disorder. Twelve factors together predicted ESLD risk moderately well (C statistic 0.79, 95% confidence interval: 0.76 to 0.81): age, sex, race/ethnicity, chronic hepatitis B or C, and routinely collected laboratory values reflecting hepatic impairment (serum albumin, aspartate aminotransferase, total bilirubin, and platelets) and lipid metabolism (triglycerides, high-density lipoprotein, and total cholesterol). Our model performed well in the test set (C statistic 0.81, 95% confidence interval: 0.76 to 0.86). CONCLUSION: This model of readily accessible clinical parameters predicted ESLD in a large diverse population of PWH.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".