In:
Alimentary Pharmacology & Therapeutics, Wiley, Vol. 57, No. 4 ( 2023-02), p. 409-417
Abstract:
In cirrhotic nonalcoholic steatohepatitis (NASH) clinical trials, primary efficacy endpoints have been hepatic venous pressure gradient (HVPG), liver histology and clinical liver outcomes. Important histologic features, such as septa thickness, nodules features and fibrosis area have not been included in the histologic assessment and may have important clinical relevance. We assessed these features with a machine learning (ML) model. Methods NASH patients with compensated cirrhosis and HVPG ≥6 mm Hg ( n = 143) from the Belapectin phase 2b trial were studied. Liver biopsies, HVPG measurements and upper endoscopies were performed at baseline and at end of treatment (EOT). A second harmonic generation/two‐photon excitation fluorescence provided an automated quantitative assessment of s epta, no dules and f ibrosis (SNOF). We created ML scores and tested their association with HVPG, clinically significant HVPG (≥10 mm Hg) and the presence of varices (SNOF‐V). Results We derived 448 histologic variables (243 related to septa, 21 related to nodules and 184 related to fibrosis). The SNOF score (≥11.78) reliably distinguished CSPH at baseline and in the validation cohort (baseline + EOT) [AUC = 0.85 and 0.74, respectively]. The SNOF‐V score (≥0.57) distinguished the presence of varices at baseline and in the same validation cohort [AUC = 0.86 and 0.73, respectively] . Finally, the SNOF‐C score differentiated those who had 〉 20% change in HVPG against those who did not, with an AUROC of 0.89. Conclusion The ML algorithm accurately predicted HVPG, CSPH, the development of varices and HVPG changes in patients with NASH cirrhosis. The use of ML histology model in NASH cirrhosis trials may improve the assessment of key outcome changes.
Type of Medium:
Online Resource
ISSN:
0269-2813
,
1365-2036
Language:
English
Publisher:
Wiley
Publication Date:
2023
detail.hit.zdb_id:
2003094-0
SSG:
15,3
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