Multicenter validation of FIB-6 as a novel machine learning non-invasive score to rule out liver cirrhosis in biopsy-proven MAFLD
Yükleniyor...
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Lippincott Williams & Wilkins
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Background and aims: We previously developed and validated a non-invasive diagnostic index based on routine laboratory parameters for predicting the stage of hepatic fibrosis in patients with chronic hepatitis C (CHC) called FIB-6 through machine learning with random forests algorithm using retrospective data of 7238 biopsy-proven CHC patients. Our aim is to validate this novel score in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). Method: Performance of the new score was externally validated in cohorts from one site in Egypt (n = 674) and in 5 different countries (n = 1798) in Iran, KSA, Greece, Turkey and Oman. Experienced pathologists using METAVIR scoring system scored the biopsy samples. Results were compared with FIB-4, APRI, and AAR. Results: A total of 2472 and their liver biopsy results were included, using the optimal cutoffs of FIB-6 indicated a reliable performance in diagnosing cirrhosis, severe fibrosis, and significant fibrosis with sensitivity = 70.5%, specificity = 62.9%. PPV = 15.0% and NPV = 95.8% for diagnosis of cirrhosis. For diagnosis of severe fibrosis (F3 and F4), the results were 86.5%, 24.0%, 15.1% and 91.9% respectively, while for diagnosis of significant fibrosis (F2, F3 and F4), the results were 87.0%, 16.4%, 24.8% and 80.0%). Comparing the results of FIB-6 rule-out cutoffs with those of FIB-4, APRI, and AAR, FIB-6 had the highest sensitivity and NPV (97.0% and 94.7%), as compared to FIB-4 (71.6% and 94.7%), APRI (36.4% and 90.7%), and AAR (61.2% and 90.9%). Conclusion: FIB-6 score is an accurate, simple, NIT for ruling out advanced fibrosis and liver cirrhosis in patients with MAFLD.
Açıklama
Anahtar Kelimeler
Multicenter Validation, FIB-6, MAFLD, Biopsy, Liver
Kaynak
European Journal of Gastroenterology & Hepatology
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
35
Sayı
11
Künye
Anushiravani, A., Alswat, K., Dalekos, G. N., Zachou, K., Örmeci, N., Al-Busafi, S., Abdo, A., Sanai, F., Mikhail, N.N., Soliman, R., & Shiha, G. (2023). Multicenter validation of FIB-6 as a novel machine learning non-invasive score to rule out liver cirrhosis in biopsy-proven MAFLD. European Journal of Gastroenterology & Hepatology, 35(11), pp. 1284-1288. https://doi.org/10.1097/MEG.0000000000002641
