Global performance of non-invasive tests in MASLD: Insights from the G-MASLD study.
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Younossi Zobair M, de Avila Leyla, Petta Salvatore, Hagström Hannes, Kim Seung Up, Nakajima Atsushi, Crespo Javier, Castera Laurent, Alkhouri Naim, Zheng Ming-Hua, Treeprasertsuk Sombat, Ananchuensook Prooksa, Shalimar, Tsochatzis Emmanuel, Trivikrama Shenoy Kotacherry, Balakumaran Leena Kondarappassery, Fan Jian-Gao, Roberts Stuart K, Alswat Khalid, Wong Vincent Wai-Sun, Yilmaz Yusuf, Dunn Winston, Francque Sven, Cordie Ahmed, Yu Ming-Lung, Ekstedt Mattias, Goh George Boon-Bee, Oliveira Claudia P, Pessoa Mario Guimaraes, Chan Wah Kheong, Fernandez Marlen Ivon Castellanos, Duseja Ajay, Arab Juan Pablo, Papatheodoridis George, Sebastiani Giada, Villela-Nogueira Cristiane, D'Ambrosio Roberta, Lampertico Pietro, Alnaamani Khalid, Holleboom A G, Valsan Arun, Venu Arathi, El-Kassas Mohamed, Pennisi Grazia, Shang Ying, Liu Wen-Yue, Lee Hye Won, Kobayashi Takashi, Kakizaki Satoru, Caussy Cyrielle, Pearlman Brian, Iruzubieta Paula, Nadeem Rida, Cinque Felice, Neonaki Antonia, Zoncapé Mirko, Yang Rui-Xu, Song Sherlot Juan, Dunn Nicholas, Gadi Zouhir, Yeh Ming-Lun, Kim-Jun Kevin, Mahadeva Sanjiv, Fabian Licet Gonzalez, Almohsen Ahmed, Leite Nathalie, Pugliese Nicola, Vessby Johan, Xie Chencheng, Choudhary Narendra Singh, Friend Ethan, Poca Maria, Kawaguchi Takumi, Russo Francesco Paolo, Gadano Adrian, Diaz Luis Antonio, Singal Ashwani K, Segrestin Berenice, Gunn Nadege, Mauricio Didac, Arrese Marco, Fracanzani Anna, Lombardi Rosa, Lam Brian, Racila Andrei, Alqahtani Saleh A, Stepanova Maria, Global NASH/MASH Council (GNC)
Аннотация
METHODS: MASLD patients with liver biopsies and NIT data [fibrosis-4 index (FIB-4), enhanced liver fibrosis (ELF), and liver stiffness measurement (LSM)] were enrolled through the Global NASH Council collaboration (G-MASLD). FibroScan-AST (FAST) and Agile-3+/Agile-4 were calculated. NITs' performance for predicting ≥F2 (significant fibrosis), ≥F3 (advanced fibrosis), or cirrhosis (F4) was determined in patients from different regions.
RESULTS: A total of 17,792 MASLD patients from 41 countries were included: 14% had F0, 32% F1, 18% F2, 22% F3, 13% F4 (cirrhosis); 48% NAS ≥5. Advanced fibrosis prediction by NITs was variable across regions for FIB-4 [pooled AUC (95% CI)=0.80 (0.79-0.81)], the lowest in Latin America [0.75 (0.71-0.79)], the highest in MENA [0.84 (0.82-0.87)], and ELF [pooled AUC=0.77 (0.76-0.79)], the lowest in Europe [0.72 (0.69-0.76)], the highest in North America [0.80 (0.78-0.82)]. Prediction of advanced fibrosis by LSM [pooled AUC=0.84 (0.83-0.85)] was similar across regions except North America [0.78 (0.76-0.81)]. In addition, FAST [AUC=0.75 (0.74-0.76)] and Agile-3+ [AUC=0.87 (0.86-0.88)] performed similarly across regions. Similar trends were observed for the NITs predicting significant fibrosis. Finally, the accuracy of Agile-4 for predicting cirrhosis [AUC=0.90 (0.89-0.91)] was the lowest in North America [0.85 (0.83-0.87)], the highest in MENA [0.96 (0.94-0.98)].
CONCLUSIONS: The diagnostic performance of common NITs for fibrosis in MASLD varies across the world. In the large multinational G-MASLD sample, the most accurate NITs were Agile-3+ and Agile-4 composite scores.