Fact checked byHeather Biele
Source:

Ramier C, et al. Abstract OS-092-YI: Development of a behavior-based score predicting hepatocellular carcinoma among patients with chronic hepatitis B virus infection in France (ANRS CO22 HEPATHER). Presented at: EASL Congress; June 21-24, 2023; Vienna (hybrid meeting).

Disclosures:Healio was unable to confirm relevant financial disclosures at the time of publication.
June 27, 2023
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本·avioral data ‘has the potential’ to predict HCC in patients with hepatitis B

Fact checked byHeather Biele
Source:

Ramier C, et al. Abstract OS-092-YI: Development of a behavior-based score predicting hepatocellular carcinoma among patients with chronic hepatitis B virus infection in France (ANRS CO22 HEPATHER). Presented at: EASL Congress; June 21-24, 2023; Vienna (hybrid meeting).

Disclosures:Healio was unable to confirm relevant financial disclosures at the time of publication.
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Scoring systems based on behavioral and clinical data outperformed existing scores in predicting risk for hepatocellular carcinoma in patients with chronic hepatitis B virus infection, according to data presented at the EASL Congress.

“The natural history of HCC begins with fibrosis, which leads to cirrhosis and then to HCC,”ClémenceRamier,女士,a doctoral student at Aix Marseille University, said. “Certain behavior may injure or foster liver disease progression. ... Accordingly, behavioral interventions are needed to prevent HCC occurrence. To date, 32prediction models for HCCexist, each with their own scores. Only seven have been validated in Caucasian populations.”

“Our two scores, ADAPTT and SADAPTT, have comparable performances with existing HCC scores and they are based on easy to collect data and can be implemented in all settings,” Clémence Ramier, MS, said.
“Our two scores, ADAPTT and SADAPTT, have comparable performances with existing HCC scores and they are based on easy to collect data and can be implemented in all settings,”Clémence Ramier , MS,said.
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Using data from the French ANRS CO22 HEPATHER cohort from 2012 to 2018, Ramier and colleagues developed scoring systems based on behaviors and routine medical data to predict HCC risk in 4,370 patients (63% men, 65% aged < 50 years, 3.6% infected with chronic hepatitis D) withchronic HBV.The study population was split into training (70%) and testing (30%) sets.

Researchers estimated time to HCC in the training set using Cox proportional hazards with points assigned to each variable in the final model. Area under the receiver operating characteristic curve was used to compare the score’s predictive performance with other published scores.

During a median follow-up of 6.4 years, the incidence of HCC in the study population was 0.21 per 100 person-years. Multivariable analysis showed age, unhealthy alcohol use, tobacco use, hepatitis delta virus infection, low platelet count and HBV treatment were associated with an increased risk for HCC.

Using this data, researchers developed the age, delta, alcohol, platelet, tobacco and treatment (ADAPTT) score, which ranged from 0 to 16, and SADAPTT, which also included daily soft drink consumption. A score of 6 or greater indicated a higher risk for HCC.

According to results, the ADAPTT score (AUROC = 0.848) outperformed PAGE-B (AUROC = 0.783), REAL-B (AUROC = 0.787) and THRI (AUROC = 0.776) scores.

“We showed that behavioral data should no longer be omitted from routine data collection, as it has the potential to predict HCC,” Ramier concluded. “Our two scores, ADAPTT and SADAPTT, have comparable performances with existing HCC scores and they are based on easy to collect data and can be implemented in all settings. ... We need to validate these two scores in other data sets in order to confirm the performances in other population.”