1128 Prediction Model of Anemia Using Itpa Genotype Identifies Patients at High Risk of Anemia and Treatment Failure With Pegylated-Interferon Plus Ribavirin for Chronic Hepatitis C

ITPase Activity Modulates the Severity of Anaemia in HCV-Related Cirrhosis Treated with Ribavirin-Containing Interferon-Free Regimens

“…The polymorphisms rs1127354 and rs7270101 were found to be associated with a haemoglobin reduction at week 4 of treatment in 304 genotype-1 CHC patients receiving PEG-IFN plus RBV, while the minor alleles of each variant protected against a haemoglobin reduction; in particular, a 3 g reduction in haemoglobin levels was a rare occurrence in 22 (2%) patients with a reduction in the ITPA activity of less than 30% and in 45% of 212 with normal enzyme activity [33]. These data were confirmed in other investigations both in HCV-genotype-1 [34,35] and HCV-non-1 genotypes [13,32] as well as in patients receiving PEG-IFN plus RBV and a first-generation protease inhibitor [15,36,37].…”

“…In fact, the international guidelines for patients with cirrhosis suggest IFN-free regimens with daily weight-based RBV for 12 weeks to achieve a high SVR rate [7,35], whereas the patients with cirrhosis and contraindications to the use of RBV or with poor tolerance to RBV on treatment should receive the IFN-free regimens for an extended period (24 weeks). The use of RBV and the duration of the treatment remained a question unsolved; however, RBV allows shorter course of therapy from 24 to 12 weeks with probably high SVR [42].…”

Identifying candidates with favorable prognosis following liver transplantation for hepatocellular carcinoma: Data mining analysis

“…The IBM-SPSS Modeler 13 (IBM SPSS Inc., Chicago, IL) was used for decision tree analysis (SPSS Statistics 21 [ditto] was used for the rest of the analyses). The statistical methods used have been documented previously [16][17][18][20][21][22][23]. Briefly, this analysis belongs to a family of nonparametric regression methods based on binary recursive partitioning of data.…”

“…These relevant factors are then combined in an orderly sequence to identify rules for predicting the incidence of the target outcome [15]. Data mining analysis has already been reported to be useful in identifying prognostic factors in liver diseases [16][17][18][19][20][21][22][23][24], as well as other non-liver diseases [25][26][27][28]. As documented in these previous publications in the field, the findings of data mining analysis are expressed as flowcharts that are easily understood and available for clinical use without a specialized knowledge of statistics [29].…”

Pharmacogenomic Testing in the Era of Patient-Tailored HCV Treatment

“…However, considering that anemia is one of the main ADRs leading to premature withdrawal of therapy, any marker able to predict the risk of severe anemia before treatment would be of extreme importance [7]. For this purpose, two studies have designed predictions models incorporating ITPA genotype along with creatinine clearance, baseline hemoglobin and quantitative hemoglobin decline at week 2 of treatment [90,91]. Further validation before entering these algorithms into clinical practice is necessary [7].…”