ArticleScientific reports2026
A novel non-invasive nomogram for predicting advanced liver fibrosis in autoimmune hepatitis.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autoimmune hepatitis (AIH) is a relatively rare, immune-mediated chronic inflammatory liver disease characterized predominantly by hepatocellular injury. Liver fibrosis assessment is crucial in AIH management. We aimed to develop and validate a novel non-invasive nomogram to predict advanced liver fibrosis in AIH patients. Patients with AIH who had undergone liver biopsy and met the inclusion and exclusion criteria were included in this retrospective study; subsequently, they were randomly assigned to the training set and the validation set in a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation identified predictors from candidate variables. Multivariable logistic regression established independent predictors used to construct the nomogram. Performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA). This study ultimately included 141 patients with AIH (n = 98 for the training set and n = 43 for the validation set). LASSO and multivariate logistic regression analysis showed that liver stiffness measurement (LSM), platelet (PLT), and prothrombin time (PT) were independent risk factors for advanced liver fibrosis in patients with AIH, and a nomogram for diagnosing advanced liver fibrosis was established based on these factors. Calibration was excellent (Hosmer-Lemeshow P > 0.05), and DCA showed significant clinical net benefit. The nomogram demonstrates excellent discriminatory ability: the AUC of the training set is 0.851 (95% CI: 0.777–0.925), and the AUC of the validation set is 0.922 (95% CI: 0.847–0.997). It outperforms single indicators (LSM, PLT, PT), as well as well-known serological models including aspartate aminotransferase to platelet ratio (APRI) and fibrosis-4 (FIB-4). This novel nomogram model has excellent diagnostic performance and can more intuitively and personalizedly assess the probability of advanced liver fibrosis in patients with AIH, thereby potentially reducing the reliance on liver biopsy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.