Screening for compensated advanced chronic liver disease using refined Baveno VI elastography cutoffs in Asian patients with nonalcoholic fatty liver disease

• Main Authors: Zhou, Yu‐Jie, Gao, Feng, Liu, Wen‐Yue, Wong, Grace Lai‐Hung, Mahadeva, Sanjiv, Raihan Nik Mustapha, Nik, Wang, Xiao‐Dong, Chan, Wah‐Kheong, Wong, Vincent Wai‐Sun, Zheng, Ming‐Hua
• Format: Article
• Published: 2021
• Subjects: R Medicine (General)
• Online Access: http://eprints.um.edu.my/26222/; https://doi.org/10.1111/apt.16487
• Institution: Universiti Malaya

Background Recently, Papatheodoridi et al proposed to refine the Baveno VI elastography dual-cutoffs and introduce an algorithm for the detection of compensated advanced chronic liver disease (cACLD) in asymptomatic European patients with chronic liver diseases. Aims To validate the performance of the dual-cutoffs (8/12 kPa) and the proposed algorithm to identify patients with cACLD in three well-characterised Asian nonalcoholic fatty liver disease (NAFLD) cohorts. Methods We included 830 patients with biopsy-proven NAFLD. Liver stiffness was measured using transient elastography (FibroScan). Results cACLD was found in 21.8% of patients. Compared with the original Baveno VI elastography criteria (10/15 kPa), the new cutoffs showed a comparable specificity and a higher sensitivity for identifying cACLD. We developed a simplified risk model incorporating age, liver stiffness value, and platelet count, which outperformed liver stiffness measurement alone in two Chinese cohorts (P = 0.001), and was further validated in a Malaysian cohort (P = 0.04). Overall, the "two-step" screening of cACLD improved classification rates from 73.5% by the original dual-cutoffs to 86.7%. Notably, usage of our simplified risk model resulted in significantly lower false-negative rate than the refined screening approach by Papatheodoridi et al (27.1% vs 41.4%; P = 0.01). Conclusions The dual elastography cutoffs of 8 and 12 kPa are more appropriate to identify cACLD in Asian patients with NAFLD. In combination with a simplified risk model in unclassified patients, the two-step approach showed a classification rate of about 85%.