{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T07:26:57Z","timestamp":1775806017038,"version":"3.50.1"},"reference-count":54,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T00:00:00Z","timestamp":1741996800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T00:00:00Z","timestamp":1741996800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Liver International"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Early assessment of hepatocellular carcinoma (HCC) risk could improve long\u2010term outcomes in people with chronic hepatitis B virus (HBV) infection. Some existing HCC predictive scores are not easily implementable. We developed easy\u2010to\u2010use HCC predictive scores based on behavioural and routine bio\u2010clinical data in people with chronic HBV infection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Eight\u2010year follow\u2010up data was analysed from people with chronic HBV infection enrolled in the French ANRS CO22 HEPATHER cohort. Patients were randomly split into two samples (training\/testing). A multivariable Cox model for time to HCC was estimated on the training sample. The HCC predictive score was computed by summing the points assigned to model predictors, normalising their coefficients over a 10\u2010year age increment, and rounding to the nearest integer. The Youden index identified the score's optimal risk threshold. Comparisons with existing predictive scores were performed on the testing sample.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the study population (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20094370; 63% of men; 65% of &lt;\u200950\u2009years old), 56 HCC cases occurred during 25,900 follow\u2010up person\u2010years. Two HCC predictive scores were defined: SADAPTT (daily soft drink consumption, age, hepatitis Delta infection, unhealthy alcohol use, platelet count, heavy tobacco smoking, and HBV treatment) and ADAPTT (the same predictors except for daily soft drink consumption), with ranges 0\u201313 and 0\u201314, respectively, and values \u2265\u20093 indicating a high HCC risk. Their performances were similar to existing scores.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed two effective behaviour\u2010based HCC predictive scores, implementable in many settings, including primary care and decentralised areas. Further studies are needed to validate these scores in other datasets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/liv.70065","type":"journal-article","created":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T03:50:17Z","timestamp":1742010617000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Behaviour\u2010Based Predictive Scores of Hepatocellular Carcinoma in People With Chronic Hepatitis B (\n                    <scp>ANRS CO22 HEPATHER<\/scp>\n                    )"],"prefix":"10.1111","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-0263","authenticated-orcid":false,"given":"Cl\u00e9mence","family":"Ramier","sequence":"first","affiliation":[{"name":"Aix Marseille Univ, INSERM, IRD, SESSTIM, Sciences \u00c9conomiques and Sociales de la Sant\u00e9 and Traitement de l'Information M\u00e9dicale  Marseille 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INSERM U1016, AP\u2010HP, H\u00f4pital Cochin  Paris France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8853-3829","authenticated-orcid":false,"given":"Fabienne","family":"Marcellin","sequence":"additional","affiliation":[{"name":"Aix Marseille Univ, INSERM, IRD, SESSTIM, Sciences \u00c9conomiques and Sociales de la Sant\u00e9 and Traitement de l'Information M\u00e9dicale  Marseille France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabrice","family":"Carrat","sequence":"additional","affiliation":[{"name":"Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale (INSERM), Institut Pierre Louis d'Epid\u00e9miologie et de Sant\u00e9 Publique, Sorbonne Universit\u00e9  Paris France"},{"name":"H\u00f4pital Saint\u2010Antoine, Unit\u00e9 de Sant\u00e9 Publique, Assistance Publique\u2010H\u00f4pitaux de Paris (AP\u2010HP)  Paris 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