Assessment of portal hypertension severity using machine learning models in patients with compensated cirrhosis
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Background & Aims: 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. Methods: 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. Results: 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). Conclusions: 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. Impact and implications: 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.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Journal of Hepatology |
| Vol/bind | 78 |
| Udgave nummer | 2 |
| Sider (fra-til) | 390-400 |
| Antal sider | 11 |
| ISSN | 0168-8278 |
| DOI | |
| Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
This study did not receive specific funding. Some authors report financial support of their research outside of this study: SF holds a senior clinical investigator fellowship from the Research Foundation Flanders (FWO) (1802154N). JT is further supported by grants from the Deutsche Forschungsgemeinschaft (SFB TRR57 to P18 and CRC1382 A09), European Union's Horizon 2020 Research and Innovation Programm (Galaxy, No. 668031, MICROB-PREDICT, No. 825694 and DECISION No.84794), and Societal Challenges - Health, Demographic Change and Wellbeing (Liverhope No. 731875). P-E.R.’s laboratory receives financial supports from the “Institut National de la Santé et de la Recherche Médicale” (ATIP AVENIR), the “Agence Nationale pour la Recherche” (ANR-18-CE14-0006-01, RHU QUID-NASH, ANR-18-IDEX-0001), “Émergence, Ville de Paris”, Fondation ARC and from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 847949 (DECISION). TR, OP, BS, BH, MM were co-supported by the Austrian Federal Ministry for Digital and Economic Affairs , the National Foundation for Research, Technology and Development , Boehringer-Ingelheim, and the Christian Doppler Research Association .
Funding Information:
This study did not receive specific funding. Some authors report financial support of their research outside of this study: SF holds a senior clinical investigator fellowship from the Research Foundation Flanders (FWO) (1802154N). JT is further supported by grants from the Deutsche Forschungsgemeinschaft (SFB TRR57 to P18 and CRC1382 A09), European Union's Horizon 2020 Research and Innovation Programm (Galaxy, No. 668031, MICROB-PREDICT, No. 825694 and DECISION No.84794), and Societal Challenges - Health, Demographic Change and Wellbeing (Liverhope No. 731875). P-E.R.’s laboratory receives financial supports from the “Institut National de la Santé et de la Recherche Médicale” (ATIP AVENIR), the “Agence Nationale pour la Recherche” (ANR-18-CE14-0006-01, RHU QUID-NASH, ANR-18-IDEX-0001), “Émergence, Ville de Paris”, Fondation ARC and from the European Union's Horizon 2020 research and innovation programme under grant agreement No 847949 (DECISION). TR, OP, BS, BH, MM were co-supported by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, Boehringer-Ingelheim, and the Christian Doppler Research Association.The research group of TR (OP, BS, BSH) was co-supported by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, the Christian Doppler Research Association, and Boehringer-Ingelheim, which is gratefully acknowledged. Elements of the graphical abstract were created with BioRender.com.
Funding Information:
The research group of TR (OP, BS, BSH) was co-supported by the Austrian Federal Ministry for Digital and Economic Affairs , the National Foundation for Research, Technology and Development , the Christian Doppler Research Association , and Boehringer-Ingelheim, which is gratefully acknowledged. Elements of the graphical abstract were created with BioRender.com .
Publisher Copyright:
© 2022 The Authors
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