Enhanced diagnosis of advanced fibrosis and cirrhosis in individuals with NAFLD using FibroScan-based Agile scores
Bibliographic record
Abstract
BACKGROUND & AIMS: Currently available non-invasive tests, including fibrosis-4 index (FIB-4) and liver stiffness measurement (LSM by VCTE), are highly effective at excluding advanced fibrosis (AF) (F ≥3) or cirrhosis in people with non-alcoholic fatty liver disease (NAFLD), but only have moderate ability to rule-in these conditions. Our objective was to develop and validate two new scores (Agile 4 and Agile 3+) to identify cirrhosis or AF, respectively, with optimized positive predictive value and fewer indeterminate results, in individuals with NAFLD attending liver clinics. METHODS: This international study included seven adult cohorts with suspected NAFLD who underwent liver biopsy, LSM and blood sampling during routine clinical practice or screening for trials. The population was randomly divided into a training set and an internal validation set, on which the best-fitting logistic regression model was built, and performance and goodness of fit were assessed, respectively. Furthermore, both scores were externally validated on two large cohorts. Cut-offs for high sensitivity and specificity were derived in the training set to rule-out and rule-in cirrhosis or AF and then tested in the validation set and compared to FIB-4 and LSM. RESULTS: Each score combined LSM, AST/ALT ratio, platelets, sex and diabetes status, as well as age for Agile 3+. Calibration plots for Agile 4 and Agile 3+ indicated satisfactory to excellent goodness of fit. Agile 4 and Agile 3+ outperformed FIB-4 and LSM in terms of AUROC, percentage of patients with indeterminate results and positive predictive value to rule-in cirrhosis or AF. CONCLUSIONS: The two novel non-invasive scores improve identification of cirrhosis or AF among individuals with NAFLD attending liver clinics and reduce the need for liver biopsy in this population. IMPACT AND IMPLICATIONS: Non-invasive tests currently used to identify patients with advanced fibrosis or cirrhosis, such as fibrosis-4 index and liver stiffness measurement by vibration-controlled transient elastography, have high negative predictive values but high false positive rates, while results are indeterminate for a large number of cases. This study provides scores that will help the clinician diagnose advanced fibrosis or cirrhosis. These new easy-to-implement scores will help liver specialists to better identify (1) patients who need more intensive follow-up, (2) patients who should be referred for inclusion in therapeutic trials, and (3) which patients should be treated with pharmacological agents when effective therapies are approved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".