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Background: Non‐invasive tests (NITs), such as Fibrosis‐4 index (FIB‐4) and the aspartate aminotransferase‐to‐platelet ratio index (APRI), developed using classical statistical methods, are increasingly used for determining liver fibrosis stages and recommended in treatment guidelines replacing the liver biopsy. Application of conventional cutoffs of FIB‐4 and APRI resulted in high rates of misclassification of fibrosis stages. Aim: There is an unmet need for more accurate NITs that can overcome the limitations of FIB‐4 and APRI. Patients and methods: Machine learning with the random forest algorithm was used to develop a non‐invasive index using retrospective data of 7238 patients with biopsy‐proven chronic hepatitis C from two centers in Egypt; derivation dataset (n = 1821) and validation set in the second center (n = 5417). Receiver operator curve analysis was used to define cutoffs for different stages of fibrosis. Performance of the new score was externally validated in cohorts from two other sites in Egypt (n = 560) and seven different countries (n = 1317). Fibrosis stages were determined using the METAVIR score. Results were also compared with three established tools (FIB‐4, APRI, and the aspartate aminotransferase‐to‐alanine aminotransferase ratio [AAR]). Results: Age in addition to readily available laboratory parameters such as aspartate, and alanine aminotransferases, alkaline phosphatase, albumin (g/dl), and platelet count (/cm3) correlated with the biopsy‐derived stage of liver fibrosis in the derivation cohort and were used to construct the model for predicting the fibrosis stage by applying the random forest algorithm, resulting in an FIB‐6 index, which can be calculated easily at http://fib6.elriah.info. Application of the cutoff values derived from the derivation group on the validation groups yielded very good performance in ruling out cirrhosis (negative predictive value [NPV] = 97.7%), compensated advance liver disease (NPV = 90.2%), and significant fibrosis (NPV = 65.7%). In the external validation groups from different countries, FIB‐6 demonstrated higher sensitivity and NPV than FIB‐4, APRI, and AAR. Conclusion: FIB‐6 score is a non‐invasive, simple, and accurate test for ruling out liver cirrhosis and compensated advance liver disease in patients with chronic hepatitis C and performs better than APRI, FIB‐4, and AAR. [ABSTRACT FROM AUTHOR]

The hepatitis C pandemic has been systematically studied and characterized in North America and Europe, but this important public health problem has not received equivalent attention in other regions. The objective of this systematic review was to characterize hepatitis C virus (HCV) epidemiology in selected countries of Asia, Australia and Egypt, i.e. in a geographical area inhabited by over 40% of the global population. Data references were identified through indexed journals and non-indexed sources. In this work, 7770 articles were reviewed and 690 were selected based on their relevance. We estimated that 49.3-64.0 million adults in Asia, Australia and Egypt are anti-HCV positive. China alone has more HCV infections than all of Europe or the Americas. While most countries had prevalence rates from 1 to 2% we documented several with relatively high prevalence rates, including Egypt (15%), Pakistan (4.7%) and Taiwan (4.4%). Nosocomial infection, blood transfusion (before screening) and injection drug use were identified as common risk factors in the region. Genotype 1 was common in Australia, China, Taiwan and other countries in North Asia, while genotype 6 was found in Vietnam and other Southeast Asian countries. In India and Pakistan genotype 3 was predominant, while genotype 4 was found in Middle Eastern countries such as Egypt, Saudi Arabia and Syria. We recommend implementation of surveillance systems to guide effective public health policy that may lead to the eventual curtailment of the spread of this pandemic infection. [ABSTRACT FROM AUTHOR]