Development of a simple dynamic algorithm for individualized hepatocellular carcinoma risk‐based surveillance using pre‐ and post‐treatment general evaluation score. (8th July 2021) - Shiha, Gamal; Soliman, Reham; Mikhail, Nabiel; Hassan, Ayman; Eslam, Mohammed
Abstract: Background and Aims: With the growing number of treated hepatitis C patients, the current 'one‐size‐fits‐all' hepatocellular carcinoma (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‐centre 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 General Evaluation Score (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 < .001). Harrell's c statistic was 0.818. Using pre‐ and post‐treatment GES score, GES algorithm was developed with higher predictive value. The cumulative HCC incidence increased significantly with higher scores ( P < .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 with only pre‐treatment assessments. The proposed algorithm would help to stratify those who need intensive or being excluded from screening.