Liver international : official journal of the International Association for the Study of the Liver · 2022
Machine learning in primary biliary cholangitis: A novel approach for risk stratification.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Gerussi Alessio, Verda Damiano, Bernasconi Davide Paolo, Carbone Marco, Komori Atsumasa, Abe Masanori, Inao Mie, Namisaki Tadashi, Mochida Satoshi, Yoshiji Hitoshi, Hirschfield Gideon, Lindor Keith, Pares Albert, Corpechot Christophe, Cazzagon Nora, Floreani Annarosa, Marzioni Marco, Alvaro Domenico, Vespasiani-Gentilucci Umberto, Cristoferi Laura, Valsecchi Maria Grazia, Muselli Marco, Hansen Bettina E, Tanaka Atsushi, Invernizzi Pietro
Аннотация
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.
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.