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Nevin Manimala Statistics

Development of a simple dynamic algorithm for individualized HCC risk-based surveillance using pre- and post-treatment GES score

Liver Int. 2021 Jun 26. doi: 10.1111/liv.14995. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: With the growing number of treated hepatitis C patients, the current “one size fits all” HCC surveillance strategies for patients with advanced fibrosis represents a great burden on healthcare systems. An individualized HCC risk strategy incorporates the dynamic changes of HCC risk are lacking.

METHODS: This single-center observational study included 3075 patients, with advanced fibrosis (≥ F3) who achieved SVR following DAAs at Egyptian Liver research institute and hospital (ELRIAH) with follow up period (range 6-72 months). The performance of a recently developed GES HCC risk stratification score was calculated pre- and post-treatment using Harrell’s c statistic. Times to HCC and cumulative incidences were calculated with Kaplan-Meier method and compared using log-rank (Mantel-Cox) test.

RESULTS: Pre-treatment GES score stratified patients into low (60.4%), intermediate (23.4%), and (16.2%) high-risk score where 5-year cumulative incidences of HCC were 1.66%, 4.45% and 7.64 respectively. Harrell’s c statistic was 0.801. Post-treatment GES score stratified patients into low (57.4%), intermediate (30.7%) and (11.9%) high-risk score where 5-year cumulative incidences of HCC were 1.35%,3.49% and 11.09% respectively. The cumulative HCC incidence increased significantly with higher scores (p<0.001). Harrell’s c statistic was 0.818. Using pretreatment and post treatment GES score, GES algorithm was developed with higher predictive value. The cumulative HCC incidence increased significantly with higher scores (p<0.001). Harrell’s c statistic was 0.832.

CONCLUSION: A dynamic algorithm incorporating both pre and post-GES scores have better performance and predictive value compared to only pre-treatment assessments. The proposed algorithm would help stratify those who need intensive or being excluded from screening.

PMID:34174150 | DOI:10.1111/liv.14995

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