FibroScan, APRI, FIB4, and GUCI: Role in prediction of fibrosis and response to therapy in Egyptian patients with HCV infection

Yosry, Ayman; Fouad, Rabab; Alem, Shereen Abdel; Elsharkawy, Aisha; Elsayed, Mohammad; Asem, Noha; Hassan, Ehsan; A, Ismail; Esmat, Gamal

doi:10.1016/j.ajg.2016.05.002

Cited by 32 publications

(18 citation statements)

References 30 publications

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“…The results of this study revealed IL28B rs12979860 C T genotype as the commonest genotype (68.9 ) and is in agreement with previous studies done on Egyptian patients (19,(30)(31)(32), suggesting a possible role of this genotype in the transition of a normal hepatocyte into an abnormal one. This genotype distribution was suggested to be a consequence of environmental or ethnic variation (30,32).…”

Section: Discussionsupporting

confidence: 81%

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Data Mining and Machine Learning Algorithms Using <i>IL28B</i> Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C

et al. 2018

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Section: Discussionsupporting

confidence: 81%

“…The aspartate aminotransferase-to-platelet ratio index (APRI) and the fibrosis-4 (FIB-4) score are simple, noninvasive, easy to perform, inexpensive, and reproducible algorithms that were found to have satisfactory performance in the diagnosis of moderate to severe stages of fibrosis (17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

Data Mining and Machine Learning Algorithms Using <i>IL28B</i> Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C

et al. 2018

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“…Therefore, such assessment is gradually being replaced by non-invasive methods currently used in clinical practice [44] . In the present study, after treatment, the regression of liver fibrosis was assessed in both groups with the use of fibrosis markers such as PLT, FIB-4, and APRI, that have been demonstrated to be accurate predictors of hepatic fibrosis in HCV patients [34][35][36] . Several studies have demonstrated a significant decline in liver fibrosis, as determined by FIB-4 and APRI scores in treatment with Sofosbuvir-based regimens [45,46] .…”

Section: Discussionmentioning

confidence: 99%

“…One used in assessing liver fibrosis is a common chemical test mostly for estimating the degree of fibrosis. FIB-4 and the aspartate aminotransferase-to-platelet ratio index (APRI) in this category have been demonstrated to be accurate in staging chronic liver diseases before antiviral treatment and in the prediction of hepatic fibrosis in HCV patients [34][35][36] . Fibrosis biomarkers classified in another category, such as hyaluronic acid, bone morphogenetic protein 7 (BMP-7), and connective tissue growth factor (CTGF), are used to assess liver fibrosis pathophysiologically, and have been reported to increase in patients with chronic liver disease, liver damage, and cirrhosis.…”

mentioning

confidence: 99%

Comparison of Sofosbuvir Plus Ribavirin Treatment with Pegylated Interferon Plus Ribavirin Treatment for Chronic Hepatitis C Genotype 2

Seo

Kim²,

Kim³

et al. 2017

Dig Dis

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Background: Sofosbuvir plus ribavirin (RBV) therapy showed higher sustained virological response at 12 weeks after treatment (SVR12) than pegylated interferon (peg-IFN) plus RBV; however, liver function, fibrosis, and hepatocellular carcinoma markers have not been assessed so far. Summary: Patients (n = 21) receiving Sofosbuvir plus RBV and those (n = 24) receiving peg-IFN plus RBV were enrolled in this study. Changes in alanine aminotransferase (ALT) and α-fetoprotein (AFP) levels, platelet (PLT) counts, FIB-4, and aspartate aminotransferase-to-platelet ratio index (APRI) in both groups were assessed in patients achieving SVR12. Also, fibrosis regression was assessed using pathophysiological biomarkers, such as hyaluronic acid, bone morphogenetic protein 7 (BMP-7), and connective tissue growth factor (CTGF) in the Sofosbuvir plus RBV group. In both groups, while the reduction in ALT levels was significant that of AFP was not. Compared with the baseline, although serum PLT count at the end of treatment (EOT) was significantly higher in the Sofosbuvir plus RBV group, it was significantly lower in the peg-IFN plus RBV group. Although a significant decline in fibrosis markers such as FIB-4 and APRI was observed between the baseline and at EOT in the Sofosbuvir plus RBV group, no significant change of these markers was observed in the peg-IFN plus RBV group. Moreover, BMP-7 and CTGF were significantly lower at EOT than the baseline in the Sofosbuvir plus RBV group. Key Message: The treatment with Sofosbuvir plus RBV results in not only a higher SVR, but also improves the liver function and the degree of fibrosis.

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“…Fibroscan ultrasonography is non-invasive and reliable methods for diagnosis of the stage of liver fibrosis, compared to liver biopsy [5]. The Aspartate aminotransferase-toplatelet ratio index (APRI), and FIB-4 scores are simple, noninvasive, easy to perform, inexpensive and reproducible algorithms for diagnosing advanced fibrosis stages [5][6][7], but they are not consistently accurate in classifying fibrosis stages [8].…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction and Decision Tree Pruning to Simplify Liver Fibrosis Prediction Algorithms A Cohort Study

Mabrouk¹,

Awad²,

Shousha³

et al. 2019

9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)

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Background: Assessment of liver fibrosis is a vital need for enabling therapeutic decisions and prognostic evaluations of chronic hepatitis. Liver biopsy is considered the definitive investigation for assessing the stage of liver fibrosis but it carries several limitations. FIB-4 and APRI also have a limited accuracy. The National Committee for Control of Viral Hepatitis (NCCVH) in Egypt has supplied a valuable pool of electronic patients' data that data mining techniques can analyze to disclose hidden patterns, trends leading to the evolution of predictive algorithms. Aim: to collaborate with physicians to develop a novel reliable, easy to comprehend noninvasive model to predict the stage of liver fibrosis utilizing routine workup, without imposing extra costs for additional examinations especially in areas with limited resources like Egypt. Methods: This multi-centered retrospective study included baseline demographic, laboratory, and histopathological data of 69106 patients with chronic hepatitis C. We started by data collection preprocessing, cleansing and formatting for knowledge discovery of useful information from Electronic Health Records EHRs. Data mining has been used to build a decision tree (Reduced Error Pruning tree (REP tree)) with 10-fold internal cross-validation. Histopathology results were used to assess accuracy for fibrosis stages. Machine learning feature selection and reduction (CfsSubseteval / best first) reduced the initial number of input features (N=15) to the most relevant ones (N=6) for developing the prediction model. Results: In this study, 32419 patients had F(0-1), 25073 had F(2) and 11615 had F(3-4). FIB-4 and APRI revalidation in our study showed low accuracy and high discordance with biopsy results, with overall AUC 0.68 and 0.58 respectively. Out of 15 attributes machine learning selected Age, AFP, AST, glucose, albumin, and platelet as the most relevant attributes. Results for REP tree indicated an overall classification accuracy up to 70% and ROC Area 0.74 which was not nearly affected by attribute reduction, and pruning. However attribute reduction, and 316 Computer Science & Information Technology (CS & IT) tree pruning were associated with simpler model easy to understand by physician with less time for execution. Conclusion: This study we had the chance to study a large cohort of 69106 chronic hepatitis patients with available liver biopsy results to revise and validate the accuracy of FIB-4 and APRI. This study represents the collaboration between computer scientist and hepatologists to provide clinicians with an accurate novel and reliable, noninvasive model to predict the stage of liver fibrosis.

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FibroScan, APRI, FIB4, and GUCI: Role in prediction of fibrosis and response to therapy in Egyptian patients with HCV infection

Cited by 32 publications

References 30 publications

Data Mining and Machine Learning Algorithms Using <i>IL28B</i> Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C

Data Mining and Machine Learning Algorithms Using <i>IL28B</i> Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C

Comparison of Sofosbuvir Plus Ribavirin Treatment with Pegylated Interferon Plus Ribavirin Treatment for Chronic Hepatitis C Genotype 2

Attribute Reduction and Decision Tree Pruning to Simplify Liver Fibrosis Prediction Algorithms A Cohort Study

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