Web of Science: 35 cites, Scopus: 39 cites, Google Scholar: cites,
Assessment of portal hypertension severity using machine learning models in patients with compensated cirrhosis
Reiniš, Jiří (CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences)
Petrenko, O. (Medical University of Vienna)
Simbrunner, Benedikt (Medical University of Vienna)
Hofer, Benedikt Silvester (Medical University of Vienna)
Schepis, Filippo (Università degli Studi di Modena e Reggio Emilia (UNIMORE))
Scoppettuolo, Marco (Università degli Studi di Modena e Reggio Emilia (UNIMORE))
Saltini, Dario (Università degli Studi di Modena e Reggio Emilia (UNIMORE))
Indulti, Federica (Università degli Studi di Modena e Reggio Emilia (UNIMORE))
Guasconi, Tomas (Università degli Studi di Modena e Reggio Emilia (UNIMORE))
Albillos, Agustín (Universidad de Alcalá)
Téllez, L. (Universidad de Alcalá)
Villanueva, Càndid (Institut d'Investigació Biomèdica Sant Pau)
Brujats Rubirola, Anna (Institut d'Investigació Biomèdica Sant Pau)
García-Pagán, JC (Hospital Clínic i Provincial de Barcelona)
Pérez Campuzano, Valeria (Hospital Clínic i Provincial de Barcelona)
Hernández-Gea, Virginia (Hospital Clínic i Provincial de Barcelona)
Rautou, Pierre-Emmanuel (Université de Paris)
Moga, Lucile (Université de Paris)
Vanwolleghem, Thomas (University of Antwerp)
Kwanten, Wilhelmus (University of Antwerp)
Francque, Sven (University of Antwerp)
Trebicka, Jonel (WWU Münster)
Gu, Wenyi (European Foundation for the Study of Chronic Liver Failure)
Ferstl, Philip G. (European Foundation for the Study of Chronic Liver Failure)
Gluud, Lise Lotte (University of Copenhagen)
Bendtsen, Flemming (University of Copenhagen)
Møller, Søren (University of Copenhagen)
Kubicek, Stefan (CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences)
Mandorfer, Mattias (Medical University of Vienna)
Reiberger, Thomas (Medical University of Vienna)
Universitat Autònoma de Barcelona

Data: 2023
Resum: In individuals with compensated advanced chronic liver disease (cACLD), the severity of portal hypertension (PH) determines the risk of decompensation. Invasive measurement of the hepatic venous pressure gradient (HVPG) is the diagnostic gold standard for PH. We evaluated the utility of machine learning models (MLMs) based on standard laboratory parameters to predict the severity of PH in individuals with cACLD. A detailed laboratory workup of individuals with cACLD recruited from the Vienna cohort (NCT03267615) was utilised to predict clinically significant portal hypertension (CSPH, i. e. , HVPG ≥10 mmHg) and severe PH (i. e. , HVPG ≥16 mmHg). The MLMs were then evaluated in individual external datasets and optimised in the merged cohort. Among 1,232 participants with cACLD, the prevalence of CSPH/severe PH was similar in the Vienna (n = 163, 67. 4%/35. 0%) and validation (n = 1,069, 70. 3%/34. 7%) cohorts. The MLMs were based on 3 (3P: platelet count, bilirubin, international normalised ratio) or 5 (5P: +cholinesterase, +gamma-glutamyl transferase, +activated partial thromboplastin time replacing international normalised ratio) laboratory parameters. The MLMs performed robustly in the Vienna cohort. 5P-MLM had the best AUCs for CSPH (0. 813) and severe PH (0. 887) and compared favourably to liver stiffness measurement (AUC: 0. 808). Their performance in external validation datasets was heterogeneous (AUCs: 0. 589-0. 887). Training on the merged cohort optimised model performance for CSPH (AUCs for 3P and 5P: 0. 775 and 0. 789, respectively) and severe PH (0. 737 and 0. 828, respectively). Internally trained MLMs reliably predicted PH severity in the Vienna cACLD cohort but exhibited heterogeneous results on external validation. The proposed 3P/5P online tool can reliably identify individuals with CSPH or severe PH, who are thus at risk of hepatic decompensation. We used machine learning models based on widely available laboratory parameters to develop a non-invasive model to predict the severity of portal hypertension in individuals with compensated cirrhosis, who currently require invasive measurement of hepatic venous pressure gradient. We validated our findings in a large multicentre cohort of individuals with advanced chronic liver disease (cACLD) of any cause. Finally, we provide a readily available online calculator, based on 3 (platelet count, bilirubin, international normalised ratio) or 5 (platelet count, bilirubin, activated partial thromboplastin time, gamma-glutamyltransferase, choline-esterase) widely available laboratory parameters, that clinicians can use to predict the likelihood of their patients with cACLD having clinically significant or severe portal hypertension.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Hepatic venous pressure gradient ; Machine learning ; Non-invasive testing
Publicat a: Journal of hepatology, Vol. 78 Núm. 2 (february 2023) , p. 390-400, ISSN 1600-0641

DOI: 10.1016/j.jhep.2022.09.012
PMID: 36152767


12 p, 863.5 KB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut de Recerca Sant Pau
Articles > Articles de recerca
Articles > Articles publicats

 Registre creat el 2024-11-21, darrera modificació el 2025-09-25



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