Background & Aims: Machine learning (ML) provides new approaches for prognostication through the identification of novel subgroups of patients. We explored whether ML could support disease sub-phenotyping and risk stratification in primary biliary cholangitis (PBC). Methods: ML was applied to an international dataset of PBC patients. The dataset was split into a derivation cohort (training set) and a validation cohort (validation set), and key clinical features were analysed. The outcome was a composite of liver-related death or liver transplantation. ML and standard survival analysis were performed. Results: The training set was composed of 11,819 subjects, while the validation set was composed of 1,069 subjects. ML identified four clusters of patients characterized by different phenotypes and long-term prognosis. Cluster 1 (n = 3566) included patients with excellent prognosis, whereas Cluster 2 (n = 3966) consisted of individuals at worse prognosis differing from Cluster 1 only for albumin levels around the limit of normal. Cluster 3 (n = 2379) included young patients with florid cholestasis and Cluster 4 (n = 1908) comprised advanced cases. Further sub-analyses on the dynamics of albumin within the normal range revealed that ursodeoxycholic acid-induced increase of albumin >1.2 x lower limit of normal (LLN) is associated with improved transplant-free survival. Conclusions: Unsupervised ML identified four novel groups of PBC patients with different phenotypes and prognosis and highlighted subtle variations of albumin within the normal range. Therapy-induced increase of albumin >1.2 x LLN should be considered a treatment goal.

Gerussi, A., Verda, D., Bernasconi, D., Carbone, M., Komori, A., Abe, M., et al. (2022). Machine learning in primary biliary cholangitis: A novel approach for risk stratification. LIVER INTERNATIONAL, 42(3 (March 2022)), 615-627 [10.1111/liv.15141].

Machine learning in primary biliary cholangitis: A novel approach for risk stratification

Gerussi, Alessio
Co-primo
;
Bernasconi, Davide Paolo;Carbone, Marco;Cristoferi, Laura;Valsecchi, Maria Grazia;Invernizzi, Pietro
2022

Abstract

Background & Aims: Machine learning (ML) provides new approaches for prognostication through the identification of novel subgroups of patients. We explored whether ML could support disease sub-phenotyping and risk stratification in primary biliary cholangitis (PBC). Methods: ML was applied to an international dataset of PBC patients. The dataset was split into a derivation cohort (training set) and a validation cohort (validation set), and key clinical features were analysed. The outcome was a composite of liver-related death or liver transplantation. ML and standard survival analysis were performed. Results: The training set was composed of 11,819 subjects, while the validation set was composed of 1,069 subjects. ML identified four clusters of patients characterized by different phenotypes and long-term prognosis. Cluster 1 (n = 3566) included patients with excellent prognosis, whereas Cluster 2 (n = 3966) consisted of individuals at worse prognosis differing from Cluster 1 only for albumin levels around the limit of normal. Cluster 3 (n = 2379) included young patients with florid cholestasis and Cluster 4 (n = 1908) comprised advanced cases. Further sub-analyses on the dynamics of albumin within the normal range revealed that ursodeoxycholic acid-induced increase of albumin >1.2 x lower limit of normal (LLN) is associated with improved transplant-free survival. Conclusions: Unsupervised ML identified four novel groups of PBC patients with different phenotypes and prognosis and highlighted subtle variations of albumin within the normal range. Therapy-induced increase of albumin >1.2 x LLN should be considered a treatment goal.
Articolo in rivista - Articolo scientifico
artificial intelligence; autoimmune liver disease; cluster analysis; prognosis;
English
615
627
13
Gerussi, A., Verda, D., Bernasconi, D., Carbone, M., Komori, A., Abe, M., et al. (2022). Machine learning in primary biliary cholangitis: A novel approach for risk stratification. LIVER INTERNATIONAL, 42(3 (March 2022)), 615-627 [10.1111/liv.15141].
Gerussi, A; Verda, D; Bernasconi, D; Carbone, M; Komori, A; Abe, M; Inao, M; Namisaki, T; Mochida, S; Yoshiji, H; Hirschfield, G; Lindor, K; Pares, A; Corpechot, C; Cazzagon, N; Floreani, A; Marzioni, M; Alvaro, D; Vespasiani‐gentilucci, U; Cristoferi, L; Valsecchi, M; Muselli, M; Hansen, B; Tanaka, A; Invernizzi, P
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/363246
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