Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection

Background & Aims: Hepatocellular carcinoma (HCC) prediction models can inform clinical decisions about HCC screening provided their predictions are robust. We conducted an external validation of 6 HCC prediction models for UK patients with cirrhosis and a HCV virological cure. Methods: Pati...

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Autores principales:	Hamish Innes, Peter Jepsen, Scott McDonald, John Dillon, Victoria Hamill, Alan Yeung, Jennifer Benselin, April Went, Andrew Fraser, Andrew Bathgate, M. Azim Ansari, Stephen T. Barclay, David Goldberg, Peter C. Hayes, Philip Johnson, Eleanor Barnes, William Irving, Sharon Hutchinson, Indra Neil Guha
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Prognosis Risk prediction Primary liver cancer External validation Genetic risk scores Diseases of the digestive system. Gastroenterology RC799-869
Acceso en línea:	https://doaj.org/article/776f0b9432e44ddca1f7bb8a8c5de874
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spelling	oai:doaj.org-article:776f0b9432e44ddca1f7bb8a8c5de8742021-11-20T05:12:10ZPerformance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection2589-555910.1016/j.jhepr.2021.100384https://doaj.org/article/776f0b9432e44ddca1f7bb8a8c5de8742021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2589555921001609https://doaj.org/toc/2589-5559Background & Aims: Hepatocellular carcinoma (HCC) prediction models can inform clinical decisions about HCC screening provided their predictions are robust. We conducted an external validation of 6 HCC prediction models for UK patients with cirrhosis and a HCV virological cure. Methods: Patients with cirrhosis and cured HCV were identified from the Scotland HCV clinical database (N = 2,139) and the STratified medicine to Optimise Treatment of Hepatitis C Virus (STOP-HCV) study (N = 606). We calculated patient values for 4 competing non-genetic HCC prediction models, plus 2 genetic models (for the STOP-HCV cohort only). Follow-up began at the date of sustained virological response (SVR) achievement. HCC diagnoses were identified through linkage to nation-wide cancer, hospitalisation, and mortality registries. We compared discrimination and calibration measures between prediction models. Results: Mean follow-up was 3.4–3.9 years, with 118 (Scotland) and 40 (STOP-HCV) incident HCCs observed. The age-male sex-ALBI-platelet count score (aMAP) model showed the best discrimination; for example, the Concordance index (C-index) in the Scottish cohort was 0.77 (95% CI 0.73–0.81). However, for all models, discrimination varied by cohort (being better for the Scottish cohort) and by age (being better for younger patients). In addition, genetic models performed better in patients with HCV genotype 3. The observed 3-year HCC risk was 3.3% (95% CI 2.6–4.2) and 5.1% (3.5–7.0%) in the Scottish and STOP-HCV cohorts, respectively. These were most closely matched by aMAP, in which the mean predicted 3-year risk was 3.6% and 5.0% in the Scottish and STOP-HCV cohorts, respectively. Conclusions: aMAP was the best-performing model in terms of both discrimination and calibration and, therefore, should be used as a benchmark for rival models to surpass. This study underlines the opportunity for ‘real-world’ risk stratification in patients with cirrhosis and cured HCV. However, auxiliary research is needed to help translate an HCC risk prediction into an HCC-screening decision. Lay summary: Patients with cirrhosis and cured HCV are at high risk of developing liver cancer, although the risk varies substantially from one patient to the next. Risk calculator tools can alert clinicians to patients at high risk and thereby influence decision-making. In this study, we tested the performance of 6 risk calculators in more than 2,500 patients with cirrhosis and cured HCV. We show that some risk calculators are considerably better than others. Overall, we found that the ‘aMAP’ calculator worked the best, but more work is needed to convert predictions into clinical decisions.Hamish InnesPeter JepsenScott McDonaldJohn DillonVictoria HamillAlan YeungJennifer BenselinApril WentAndrew FraserAndrew BathgateM. Azim AnsariStephen T. BarclayDavid GoldbergPeter C. HayesPhilip JohnsonEleanor BarnesWilliam IrvingSharon HutchinsonIndra Neil GuhaElsevierarticlePrognosisRisk predictionPrimary liver cancerExternal validationGenetic risk scoresDiseases of the digestive system. GastroenterologyRC799-869ENJHEP Reports, Vol 3, Iss 6, Pp 100384- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Prognosis Risk prediction Primary liver cancer External validation Genetic risk scores Diseases of the digestive system. Gastroenterology RC799-869
spellingShingle	Prognosis Risk prediction Primary liver cancer External validation Genetic risk scores Diseases of the digestive system. Gastroenterology RC799-869 Hamish Innes Peter Jepsen Scott McDonald John Dillon Victoria Hamill Alan Yeung Jennifer Benselin April Went Andrew Fraser Andrew Bathgate M. Azim Ansari Stephen T. Barclay David Goldberg Peter C. Hayes Philip Johnson Eleanor Barnes William Irving Sharon Hutchinson Indra Neil Guha Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection
description	Background & Aims: Hepatocellular carcinoma (HCC) prediction models can inform clinical decisions about HCC screening provided their predictions are robust. We conducted an external validation of 6 HCC prediction models for UK patients with cirrhosis and a HCV virological cure. Methods: Patients with cirrhosis and cured HCV were identified from the Scotland HCV clinical database (N = 2,139) and the STratified medicine to Optimise Treatment of Hepatitis C Virus (STOP-HCV) study (N = 606). We calculated patient values for 4 competing non-genetic HCC prediction models, plus 2 genetic models (for the STOP-HCV cohort only). Follow-up began at the date of sustained virological response (SVR) achievement. HCC diagnoses were identified through linkage to nation-wide cancer, hospitalisation, and mortality registries. We compared discrimination and calibration measures between prediction models. Results: Mean follow-up was 3.4–3.9 years, with 118 (Scotland) and 40 (STOP-HCV) incident HCCs observed. The age-male sex-ALBI-platelet count score (aMAP) model showed the best discrimination; for example, the Concordance index (C-index) in the Scottish cohort was 0.77 (95% CI 0.73–0.81). However, for all models, discrimination varied by cohort (being better for the Scottish cohort) and by age (being better for younger patients). In addition, genetic models performed better in patients with HCV genotype 3. The observed 3-year HCC risk was 3.3% (95% CI 2.6–4.2) and 5.1% (3.5–7.0%) in the Scottish and STOP-HCV cohorts, respectively. These were most closely matched by aMAP, in which the mean predicted 3-year risk was 3.6% and 5.0% in the Scottish and STOP-HCV cohorts, respectively. Conclusions: aMAP was the best-performing model in terms of both discrimination and calibration and, therefore, should be used as a benchmark for rival models to surpass. This study underlines the opportunity for ‘real-world’ risk stratification in patients with cirrhosis and cured HCV. However, auxiliary research is needed to help translate an HCC risk prediction into an HCC-screening decision. Lay summary: Patients with cirrhosis and cured HCV are at high risk of developing liver cancer, although the risk varies substantially from one patient to the next. Risk calculator tools can alert clinicians to patients at high risk and thereby influence decision-making. In this study, we tested the performance of 6 risk calculators in more than 2,500 patients with cirrhosis and cured HCV. We show that some risk calculators are considerably better than others. Overall, we found that the ‘aMAP’ calculator worked the best, but more work is needed to convert predictions into clinical decisions.
format	article
author	Hamish Innes Peter Jepsen Scott McDonald John Dillon Victoria Hamill Alan Yeung Jennifer Benselin April Went Andrew Fraser Andrew Bathgate M. Azim Ansari Stephen T. Barclay David Goldberg Peter C. Hayes Philip Johnson Eleanor Barnes William Irving Sharon Hutchinson Indra Neil Guha
author_facet	Hamish Innes Peter Jepsen Scott McDonald John Dillon Victoria Hamill Alan Yeung Jennifer Benselin April Went Andrew Fraser Andrew Bathgate M. Azim Ansari Stephen T. Barclay David Goldberg Peter C. Hayes Philip Johnson Eleanor Barnes William Irving Sharon Hutchinson Indra Neil Guha
author_sort	Hamish Innes
title	Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection
title_short	Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection
title_full	Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection
title_fullStr	Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection
title_full_unstemmed	Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection
title_sort	performance of models to predict hepatocellular carcinoma risk among uk patients with cirrhosis and cured hcv infection
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/776f0b9432e44ddca1f7bb8a8c5de874
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Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection

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