Pretreatment prediction of anemia progression by pegylated interferon alpha-2b plus ribavirin combination therapy in chronic hepatitis C infection: decision-tree analysis

Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia

Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954-5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092-7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden's index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden's index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans.

Predicting Unscheduled Emergency Department Return Visits Among Older Adults: Population-Based Retrospective Study

Background

Unscheduled emergency department return visits (EDRVs) are key indicators for monitoring the quality of emergency medical care. A high return rate implies that the medical services provided by the emergency department (ED) failed to achieve the expected results of accurate diagnosis and effective treatment. Older adults are more susceptible to diseases and comorbidities than younger adults, and they exhibit unique and complex clinical characteristics that increase the difficulty of clinical diagnosis and treatment. Older adults also use more emergency medical resources than people in other age groups. Many studies have reviewed the causes of EDRVs among general ED patients; however, few have focused on older adults, although this is the age group with the highest rate of EDRVs.

Objective

This aim of this study is to establish a model for predicting unscheduled EDRVs within a 72-hour period among patients aged 65 years and older. In addition, we aim to investigate the effects of the influencing factors on their unscheduled EDRVs.

Methods

We used stratified and randomized data from Taiwan’s National Health Insurance Research Database and applied data mining techniques to construct a prediction model consisting of patient, disease, hospital, and physician characteristics. Records of ED visits by patients aged 65 years and older from 1996 to 2010 in the National Health Insurance Research Database were selected, and the final sample size was 49,252 records.

Results

The decision tree of the prediction model achieved an acceptable overall accuracy of 76.80%. Economic status, chronic illness, and length of stay in the ED were the top three variables influencing unscheduled EDRVs. Those who stayed in the ED overnight or longer on their first visit were less likely to return. This study confirms the results of prior studies, which found that economically underprivileged older adults with chronic illness and comorbidities were more likely to return to the ED.

Conclusions

Medical institutions can use our prediction model as a reference to improve medical management and clinical services by understanding the reasons for 72-hour unscheduled EDRVs in older adult patients. A possible solution is to create mechanisms that incorporate our prediction model and develop a support system with customized medical education for older patients and their family members before discharge. Meanwhile, a reasonably longer length of stay in the ED may help evaluate treatments and guide prognosis for older adult patients, and it may further reduce the rate of their unscheduled EDRVs.

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice

Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their usefulness in medicine. However, no studies have used artificial neural networks for the prediction of adverse drug reactions. We aimed to validate the usefulness of artificial neural networks for the prediction of adverse drug reactions and focused on vancomycin -induced nephrotoxicity. For constructing an artificial neural network, a multilayer perceptron algorithm was employed. A 10-fold cross validation method was adopted for evaluating the resultant artificial neural network. In total, 1141 patients who received vancomycin at Hokkaido University Hospital from November 2011 to February 2019 were enrolled. Among these patients, 179 (15.7%) developed vancomycin -induced nephrotoxicity. The top three risk factors of vancomycin -induced nephrotoxicity which are relatively important in the artificial neural networks were average vancomycin trough concentration ≥ 13.0 mg/L and concomitant use of piperacillin–tazobactam and vasopressor drugs. The predictive accuracy of the artificial neural network was 86.3% and that of the multiple logistic regression model (conventional statistical method) was 85.1%. Moreover, area under the receiver operating characteristic curve (AUROC) of the artificial neural network was 0.83. In the 10-fold cross-validation, the accuracy obtained was 86.0% and AUROC was 0.82. The artificial neural network model predicting the vancomycin -induced nephrotoxicity showed good predictive performance. This appears to be the first report of the usefulness of artificial neural networks for an adverse drug reactions risk prediction model.

Construction of a risk prediction model of vancomycin-associated nephrotoxicity to be used at the time of initial therapeutic drug monitoring: A data mining analysis using a decision tree model

OBJECTIVES

In our previous study, we built a risk prediction model of vancomycin (VCM)-associated nephrotoxicity using decision tree (DT) analysis. However, this has several limitations in clinical applications. Our objective here was to construct a clinically applicable risk prediction model to be used at the time of initial therapeutic drug monitoring (TDM), in patients with uncomplicated infections.

METHOD

A retrospective study was conducted at Hokkaido University Hospital. Subjects that had received VCM were extracted between November 2011 and April 2017. Nephrotoxicity was defined as an increase in serum creatinine of 0.5 mg/dL or 50% or higher from baseline. The additional inclusion criteria in this study were as follows: (1) the target trough level of VCM was set to 10 to 15 mg/L, and (2) the duration of therapy was 7 to 14 days. Patients were assumed to have uncomplicated infections. Risk factors for nephrotoxicity were evaluated, which could be extracted at the initial TDM. In the DT analysis, a chi-squared automatic interaction detection algorithm was constructed.

RESULTS

A total of 402 patients were enrolled, and 56 (13.9%) patients developed nephrotoxicity. In the DT analysis, concomitant medications (furosemide, piperacillin-tazobactam, and vasopressor drugs) and an initial VCM trough concentration ≥ 15.0 mg/L were extracted as predictive variables by which patients were divided into six subgroups. The incidence of nephrotoxicity was 5.2% to 70.0%, with subgroups classified as low to high risk of nephrotoxicity. The accuracy of DT model was favourable (87.1%).

CONCLUSION

We propose that the DT model built in this study is applicable to clinical practice.

Phosphoinositide 3-kinase-delta could be a biomarker for eosinophilic nasal polyps

Nasal polyps (NP) cause diverse clinical symptoms of chronic rhinosinusitis (CRS). Chronic inflammation of sinonasal mucosa is known to be crucial in NP formation. We aimed to define the implications of phosphoinositide 3-kinase (PI3K)-δ in nasal inflammation associated with NP by analyzing NP tissue obtained from CRS patients. Results showed that expression of p110δ, a regulatory subunit of PI3K-δ, in NP tissue was increased compared to control tissue. Increased p110δ expression was closely correlated with more severe CRS features. Interestingly, p110δ expression was increased in eosinophilic NP, which are closely related to more complicated clinical courses of the disease. Furthermore, CRS patients possessing NP with higher p110δ expression displayed more eosinophils in NP tissue and blood, higher levels of IL-5 in NP tissue, and more severe features of the disease. Therefore, PI3K-δ may contribute to the formation of NP, especially eosinophilic NP associated with more severe clinical presentations and radiological features.

A model predicting short-term mortality in patients with advanced liver cirrhosis and concomitant infection

Infection is a common cause of death in patients with advanced cirrhosis. We aimed to develop a predictive model in Child-Turcotte-Pugh (CTP) class C cirrhotics hospitalized with infection for optimizing treatment and improving outcomes.Clinical information was retrospectively abstracted from 244 patients at Tianjin Third Central Hospital, China (cohort 1). Factors associated with mortality were determined using logistic regression. The model for predicting 90-day mortality was then constructed by decision tree analysis. The model was further validated in 91 patients at Mayo Clinic, Rochester, MN (cohort 2) and 82 patients at Seoul St. Mary's Hospital, Korea (cohort 3). The predictive performance of the model was compared with that of the CTP, model for end-stage liver disease (MELD), MELD-Na, Chronic Liver Failure-Sequential Organ Failure Assessment, and the North American consortium for the Study of End-stage Liver Disease (NACSELD) models.The 3-month mortality was 58%, 58%, and 54% in cohort 1, 2, and 3, respectively. In cohort 1, respiratory failure, renal failure, international normalized ratio, total bilirubin, and neutrophil percentage were determinants of 3-month mortality, with odds ratios of 16.6, 3.3, 2.0, 1.1, and 1.03, respectively (P < .05). These parameters were incorporated into the decision tree model, yielding area under receiver operating characteristic (AUROC) of 0.804. The model had excellent reproducibility in the U.S. (AUROC 0.808) and Korea cohort (AUROC 0.809). The proposed model has the highest AUROC and best Youden index of 0.488 and greatest overall correctness of 75%, compared with other models evaluated.The proposed model reliably predicts survival of advanced cirrhotics with infection in both Asian and U.S.

Usefulness of a decision tree model for the analysis of adverse drug reactions: Evaluation of a risk prediction model of vancomycin-associated nephrotoxicity constructed using a data mining procedure

OBJECTIVES

Several publications concerning decision tree (DT) analysis in medical fields have recently demonstrated its usefulness for defining prognostic factors in various diseases. However, there are minimal reports on the predictors of adverse drug reactions. We attempted to use DT analysis to discover combinations of multiple risk factors that would increase the risk of nephrotoxicity associated with vancomycin (VCM). To demonstrate the usefulness of DT analysis, we compared its predictive performance with that of multiple logistic regression analysis.

METHOD

A single-centre, retrospective study was conducted at Hokkaido University Hospital. A total of 592 patients, who received intravenous administrations of VCM between November 2011 and April 2016, were enrolled. Nephrotoxicity was defined as an increase in serum creatinine of ≥0.5 mg/dL or a ≥50% increase in serum creatinine from the baseline. Risk factors for VCM nephrotoxicity were extracted from previous reports. In the DT analysis, a chi-squared automatic interaction detection algorithm was constructed. For evaluating the established algorithms, a 10-fold cross validation method was adopted to calculate the misclassification risk of the model. Moreover, to compare the accuracy of the DT analysis, multiple logistic regression analysis was conducted.

RESULTS

Eighty-seven (14.7%) patients developed nephrotoxicity. A VCM trough concentration of ≥15.0 mg/L, concomitant medication (vasopressor drugs and furosemide), and a duration of therapy ≥14 days were extracted to build the DT model, in which the patients were divided into 6 subgroups based on variable rates of nephrotoxicity, ranging from 4.6 to 69.6%. The predictive accuracies of the DT and logistic regression models were similar (87.3%, respectively), indicating that they were accurate.

CONCLUSION

This study suggests the usefulness of DT models for the evaluation of adverse drug reactions.

Role of ITPA gene polymorphism in ribavirin-induced anemia and thrombocytopenia in Egyptian patients with chronic hepatitis C

BACKGROUND

Ribavirin (RBV)-induced anemia is one of the major causes of dose reduction and discontinuation of therapy for chronic hepatitis C (CHC) patients. We investigated the role of inosine triphosphate pyrophosphatase (ITPA) single nucleotide polymorphism (SNP) (rs1127354) in predicting RBV-induced anemia and thrombocytopenia among Egyptian patients with CHC genotype 4 infection.

METHODS

One hundred and twenty Egyptian patients with CHC genotype 4 who had received standard of care combination therapy were enrolled in this study. Single nucleotide polymorphism at ITPA (rs1127354) was genotyped by real-time detection polymerase chain reaction.

RESULTS

Hb levels between CC and non-CC groups were significantly different at weeks 4, 8, and 12. Hemoglobin decline was significantly higher among CC patient than non-CC patients at week 4 and week 8 of treatment. The RBV dose reduction was higher in CC than non-CC group. Platelet decline was significantly lower in CC patients than non-CC patients at baseline, 4, 12 weeks only.

CONCLUSION

Rs1127354 ITPA polymorphism was associated with RBV-induced anemia and thrombocytopenia in Egyptian patients with hepatitis C virus genotype 4 infection.

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

BACKGROUND AND OBJECTIVES

The optimal cutoff of each value in configuring selection criteria for pre-transplant assessment of hepatocellular carcinoma (HCC) remains uncertain.

METHODS

To build a predictive model for recurrent HCC, we performed data mining analysis on patients who underwent LT for HCC at University Health Network (n = 246). The model was externally validated using a cohort from the Scientific Registry of Transplant Recipients (SRTR) database (n = 9,769).

RESULTS

Among 246 patients, 14.6% (n = 36) experienced recurrent HCC within 2.5 years post-LT. The risk prediction model for recurrent HCC identified two subgroups with low-risk (total tumor diameter [TTD] <4 cm and serum alpha-fetoprotein [AFP] <73 ng/ml, n = 135) and with high-risk (TTD >4 cm and/or AFP >73 ng/ml, n = 111). The reproducibility of the model was validated through the SRTR database; overall patient survival rate was significantly better in low-risk group than high-risk group (P < 0.0001). Using Cox regression model, this yardstick, not Milan criteria, was revealed to efficiently predict post-transplant survival independent of underlying characteristics (P < 0.0001).

CONCLUSIONS

Grouping LT candidates with pre-LT HCC by the cutoffs of TTD 4 cm and AFP 73 ng/ml which were unearthed by data mining analysis efficiently classify patients according by the post-transplant prognosis.

Advanced molecular surveillance of hepatitis C virus

Hepatitis C virus (HCV) infection is an important public health problem worldwide. HCV exploits complex molecular mechanisms, which result in a high degree of intrahost genetic heterogeneity. This high degree of variability represents a challenge for the accurate establishment of genetic relatedness between cases and complicates the identification of sources of infection. Tracking HCV infections is crucial for the elucidation of routes of transmission in a variety of settings. Therefore, implementation of HCV advanced molecular surveillance (AMS) is essential for disease control. Accounting for virulence is also important for HCV AMS and both viral and host factors contribute to the disease outcome. Therefore, HCV AMS requires the incorporation of host factors as an integral component of the algorithms used to monitor disease occurrence. Importantly, implementation of comprehensive global databases and data mining are also needed for the proper study of the mechanisms responsible for HCV transmission. Here, we review molecular aspects associated with HCV transmission, as well as the most recent technological advances used for virus and host characterization. Additionally, the cornerstone discoveries that have defined the pathway for viral characterization are presented and the importance of implementing advanced HCV molecular surveillance is highlighted.

Administration of low-dose epoetin-alpha facilitates adherence to ribavirin in triple therapy with pegylated interferon-alpha-2b and telaprevir

AIM

Anemia frequently develops in patients given pegylated interferon, ribavirin (RBV), telaprevir (TVR) triple therapy and restricts treatment by forcing reduction or discontinuation of RBV administration. We investigated whether erythropoietin (EPO) could alleviate RBV-induced anemia to help maintain the RBV dose during the first 12 weeks, the triple therapy phase.

METHODS

Twenty-two patients with hepatitis C virus (HCV) genotype 1 were enrolled. Hemoglobin (Hb) concentration was measured every week. If Hb reduction from the baseline was 2 g/dL or more, 12 000 IU of epoetin-α was administrated. When further reduction (≥3 g/dL) was observed, 24 000 IU of epoetin-α was used. Inosine triphosphatase (ITPA) single nucleotide polymorphism (rs1127354) was genotyped for all patients.

RESULTS

Among the 22 patients enrolled in this study, three required RBV dose reduction due to anemia, two had to discontinue or reduce TVR and RBV due to creatinine elevation. The remaining 17 patients completed the treatment during the triple therapy phase without reduction of the RBV dose or adverse events attributable to EPO. Regardless of ITPA genotype, Hb decline was well controlled by EPO administration, whereas the total EPO dose tended to be higher in the CC genotype group. The average adherence to RBV during the triple therapy phase was 97.5%. SVR was achieved in 17 patients; two patients had viral breakthrough and three patients had relapse of HCV RNA.

CONCLUSION

EPO can be a favorable alternative to reduction of RBV to facilitate the adherence of patients on TVR-based triple therapy.

Serum albumin level is a notable profiling factor for non-B, non-C hepatitis virus-related hepatocellular carcinoma: A data-mining analysis

AIM

Various factors are underlying for the onset of non-B, non-C hepatitis virus-related hepatocellular carcinoma (NBNC-HCC). We aimed to investigate the independent risk factors and profiles associated with NBNC-HCC using a data-mining technique.

METHODS

We conducted a case-control study and enrolled 223 NBNC-HCC patients and 669 controls from a health checkup database (n = 176 886). Multivariate analysis, random forest analysis and a decision-tree algorithm were employed to examine the independent risk factors, factors distinguishing between the case and control groups, and to identify profiles for the incidence of NBNC-HCC, respectively.

RESULTS

In multivariate analysis, besides γ-glutamyltransferase (GGT) levels and the Brinkman index, albumin level was an independent negative risk factor for the incidence of NBNC-HCC (odds ratio = 0.67; 95% confidence interval = 0.60-0.70; P < 0.0001). In random forest analysis, serum albumin level was the highest-ranked variable for distinguishing between the case and control groups (98 variable importance). A decision-tree algorithm was created for albumin and GGT levels, the aspartate aminotransferase-to-platelet ratio index (APRI) and the Brinkman index. The serum albumin level was selected as the initial split variable, and 82.5% of the subjects with albumin levels of less than 4.01 g/dL were found to have NBNC-HCC.

CONCLUSION

Data-mining analysis revealed that serum albumin level is an independent risk factor and the most distinguishable factor associated with the incidence of NBNC-HCC. Furthermore, we created an NBNC-HCC profile consisting of albumin and GGT levels, the APRI and the Brinkman index. This profile could be used in the screening strategy for NBNC-HCC.

Hepatitis C virus treatment and survival in patients with hepatitis C and human immunodeficiency virus co-infection and baseline anaemia

The impact of pretreatment anaemia on survival in individuals with hepatitis C virus (HCV) and human immunodeficiency virus (HIV) co-infection is not known. Moreover, HCV treatment is offered less frequently to individuals with anaemia, due to haematological side effects of the treatment regimen. This study aimed to determine the effect of HCV treatment on survival among HCV/HIV co-infected individuals with pretreatment anaemia using the Electronically Retrieved Cohort of HCV-Infected Veterans (ERCHIVES). Individuals with HCV/HIV co-infection were included in current analyses. Participants were considered treated if they were prescribed ≥ 4 weeks of HCV treatment. All-cause mortality data were obtained using record linkage. Survival analyses were performed using Cox proportional hazard models. Among 5000 HCV/HIV co-infected individuals, 1671 (33.4%) had pretreatment anaemia. In a follow-up period of up to 7 years (19,500 person-years), individuals with anaemia had significantly higher mortality rate compared with those without anaemia [144.2 (95% CI: 134.5-154.7) vs 47.5 (44.0-51.2) per 1000 person-years, respectively]. Among individuals with anaemia, HCV treatment was associated with significantly lower mortality rate [66.6 (44.3-100.2) vs 149.6 (139.2-160.5) per 1000 person-years, for treated vs untreated, respectively]. Treatment remained associated with substantial survival benefit after taking into account the effect of multiple comorbidities (hazards ratio: 0.34, 95% CI: 0.21-0.62). These data suggest that HCV/HIV co-infected individuals with pretreatment anaemia have significantly higher mortality compared with those without anaemia. HCV treatment is associated with substantial survival benefit in this group. Additional studies are needed to determine strategies to improve HCV treatment rates among this group.

Therapy for hepatitis C virus infection increases survival of patients with pretreatment anemia

BACKGROUND & AIMS

Individuals with chronic hepatitis C virus (HCV) infection and pretreatment anemia are less likely to begin and complete a full course of treatment for HCV. However, among those who are treated for HCV infection, the effect of treatment on mortality is not clear.

METHODS

We performed a retrospective analysis of 200,139 HCV-infected veterans using data from the Electronically Retrieved Cohort of Hepatitis C-Infected veterans (2001-2008). The effects of treatment and treatment duration on survival were compared based on data from 1820 treated and 27,690 untreated anemic HCV-infected veterans. The association between HCV treatment and mortality was estimated using the Cox proportional hazard models, with adjustments for potential confounders. The main outcome was all-cause mortality.

RESULTS

In multivariable analysis, pretreatment anemia was associated significantly with African American race (odds ratio [OR], 2.03; 95% confidence interval [CI], 1.95-2.11), chronic kidney disease (OR, 3.36; 95% CI, 3.23-3.51), and decompensated liver disease (OR, 3.69; 95% CI, 3.53-3.86). All-cause mortality for treated, anemic, HCV-infected veterans was lower (54.2 per 1000 person-years; 95% CI, 49.2-59.7 per 1000 person years) than for untreated, anemic HCV-infected veterans (146.8 per 1000 person-years; 95% CI, 144.2-149.4 per 1000 person-years). The adjusted hazard ratio for treatment of HCV in anemic veterans was 0.45 (95% CI, 0.39-0.51), which was reduced after exclusion of comorbidities (hazard ratio, 0.28; 95% CI, 0.22-0.37).

CONCLUSIONS

Based on a retrospective analysis of a veterans database, HCV therapy increases survival rates of individuals with pretreatment anemia. Additional studies are needed to determine strategies to increase rates of HCV therapy for this group.

Anemia management in patients with chronic viral hepatitis C

OBJECTIVE

To review the literature regarding current strategies for the management of anemia associated with treatment for chronic viral hepatitis C (HCV) in adults.

DATA SOURCES

The MEDLINE/PubMed, EMBASE, and Cochrane databases were searched (January 1980-October 2012) for articles in English using the search terms anemia, ribavirin, dose reduction, erythropoietin stimulating agents, hepatitis C, HIV, liver transplant, telaprevir, and boceprevir.

STUDY SELECTION AND DATA EXTRACTION

All relevant original studies, meta-analyses, systematic reviews, guidelines, and review articles were assessed for inclusion. References from pertinent articles were examined for additional content not found during the initial search.

DATA SYNTHESIS

Standard of care for patients infected with HCV genotype 1 now requires a triple therapy regimen including an HCV NS3 protease inhibitor. These regimens lead to significantly higher rates of anemia compared to prior dual therapy regimens. Development of an optimal management strategy should begin with risk stratification. Ribavirin dose reductions have been recommended in the package inserts for the pegylated interferon products and studies have demonstrated the need for maintenance of 80% of the initial ribavirin dose to achieve optimal sustained virologic response (SVR) with dual therapy. The use of erythropoietin-stimulating agents has been shown to be effective for anemia caused by peginterferon and ribavirin without compromising SVR rates. Limited data have been published regarding the management of anemia with triple therapy; however, efficacy studies for boceprevir and telaprevir have used ribavirin dose reduction and erythropoietin-stimulating agents to successfully manage anemia.

CONCLUSIONS

Anemia is a common adverse event associated with the use of ribavirin, and, more recently, the new HCV protease inhibitors. Ribavirin dose reduction should continue to be used as an initial anemia management strategy, with the use of erythropoietin alfa 40,000 units once weekly reserved for patients whose hemoglobin does not adequately respond to initial management strategies.

Model incorporating the ITPA genotype identifies patients at high risk of anemia and treatment failure with pegylated-interferon plus ribavirin therapy for chronic hepatitis C

This study aimed to develop a model for predicting anemia using the inosine triphosphatase (ITPA) genotype and to evaluate its relationship with treatment outcome. Patients with genotype 1b chronic hepatitis C (n = 446) treated with peg-interferon alpha and ribavirin (RBV) for 48 weeks were genotyped for the ITPA (rs1127354) and IL28B (rs8099917) genes. Data mining analysis generated a predictive model for anemia (hemoglobin (Hb) concentration <10 g/dl); the CC genotype of ITPA, baseline Hb <14.0 g/dl, and low creatinine clearance (CLcr) were predictors of anemia. The incidence of anemia was highest in patients with Hb <14.0 g/dl and CLcr <90 ml/min (76%), followed by Hb <14.0 g/dl and ITPA CC (57%). Patients with Hb ≥ 14.0 g/dl and ITPA AA/CA had the lowest incidence of anemia (17%). Patients with two predictors (high-risk) had a higher incidence of anemia than the others (64% vs. 28%, P < 0.0001). At baseline, the IL28B genotype was a predictor of a sustained virological response [adjusted odds ratio 9.88 (95% confidence interval 5.01-19.48), P < 0.0001]. In patients who achieved an early virological response, the IL28B genotype was not associated with a sustained virological response, while a high risk of anemia was a significant negative predictor of a sustained virological response [0.47 (0.24-0.91), P = 0.026]. For high-risk patients with an early virological response, giving >80% of the planned RBV dose increased sustained virological responses by 24%. In conclusion, a predictive model incorporating the ITPA genotype could identify patients with a high risk of anemia and reduced probability of sustained virological response.

Several factors including ITPA polymorphism influence ribavirin-induced anemia in chronic hepatitis C

AIM: To construct formulae for predicting the likelihood of ribavirin-induced anemia in pegylated interferon α plus ribavirin for chronic hepatitis C.

METHODS: Five hundred and sixty-one Japanese patients with hepatitis C virus genotype 1b who had received combination treatment were enrolled and assigned randomly to the derivation and confirmatory groups. Single nucleotide polymorphisms at or nearby ITPA were genotyped by real-time detection polymerase chain reaction. Factors influencing significant anemia (hemoglobin concentration < 10.0 g/dL at week 4 of treatment) and significant hemoglobin decline (declining concentrations > 3.0 g/dL at week 4) were analyzed using multiple regression analyses. Prediction formulae were constructed by significantly independent factors.

RESULTS: Multivariate analysis for the derivation group identified four independent factors associated with significant hemoglobin decline: hemoglobin decline at week 2 [P = 3.29 × 10^-17, odds ratio (OR) = 7.54 (g/dL)], estimated glomerular filtration rate [P = 2.16 × 10^-4, OR = 0.962 (mL/min/1.73 m²)], rs1127354 (P = 5.75 × 10^-4, OR = 10.94) and baseline hemoglobin [P = 7.86 × 10^-4, OR = 1.50 (g/dL)]. Using the model constructed by these factors, positive and negative predictive values and predictive accuracy were 79.8%, 88.8% and 86.2%, respectively. For the confirmatory group, they were 83.3%, 91.0% and 88.3%. These factors were closely correlated with significant anemia. However, the model could not be constructed, because no patients with rs1127354 minor genotype CA/AA had significant anemia.

CONCLUSION: Reliable formulae for predicting the likelihood of ribavirin-induced anemia were constructed. Such modeling may be useful in developing individual tailoring and optimization of ribavirin dosage.

Difference in serum complement component C4a levels between hepatitis C virus carriers with persistently normal alanine aminotransferase levels or chronic hepatitis C

Certain hepatitis C virus (HCV) carriers exhibit persistently normal alanine aminotransferase (ALT) levels (PNALT) (≤30 IU/l) accompanied by normal platelet counts (≥15×10⁴/μl); these individuals show milder disease activity and slower progression to cirrhosis. This study aimed to elucidate the characteristics of HCV carriers with PNALT using serum proteomics. The first group of subjects, who underwent clinical evaluation in the hospital, consisted of 19 HCV carriers with PNALT (PNALT-1) and 20 chronic hepatitis C (CHC-1) patients. The second group of subjects was part of a cohort study on the natural history of liver disease, and included 37 PNALT (PNALT-2) and 30 CHC (CHC-2) patients. Affinity bead-purified serum protein was subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometry analysis. Serum proteomics showed that 6 protein peaks with mass-to-charge ratios ranging from 1,000 to 3,000 differed significantly between the PNALT-1 and CHC-1 groups. Among these peaks, a 1738-m/z peak protein was identified as a fragment of complement component 4 (C4) and correlated significantly with serum C4a concentrations as determined by enzyme immunoassay. Serum C4a levels were also significantly higher in the PNALT-2 group compared to the CHC-2 group and healthy volunteers. Furthermore, in the PNALT-2 group, serum C4a levels negatively correlated with transaminase levels, but not with other biochemical tests, HCV core antigen levels, peripheral blood cell counts or serum hepatic fibrosis markers. This study indicates that host factors such as C4a not only differ between HCV carriers with PNALT and CHC, but that proteomic approaches could also contribute to the elucidation of factors in PNALT as more differences are discovered.

Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C

BACKGROUND & AIMS

Assessment of the risk of hepatocellular carcinoma (HCC) development is essential for formulating personalized surveillance or antiviral treatment plan for chronic hepatitis C. We aimed to build a simple model for the identification of patients at high risk of developing HCC.

METHODS

Chronic hepatitis C patients followed for at least 5 years (n=1003) were analyzed by data mining to build a predictive model for HCC development. The model was externally validated using a cohort of 1072 patients (472 with sustained virological response (SVR) and 600 with nonSVR to PEG-interferon plus ribavirin therapy).

RESULTS

On the basis of factors such as age, platelet, albumin, and aspartate aminotransferase, the HCC risk prediction model identified subgroups with high-, intermediate-, and low-risk of HCC with a 5-year HCC development rate of 20.9%, 6.3-7.3%, and 0-1.5%, respectively. The reproducibility of the model was confirmed through external validation (r(2)=0.981). The 10-year HCC development rate was also significantly higher in the high-and intermediate-risk group than in the low-risk group (24.5% vs. 4.8%; p<0.0001). In the high-and intermediate-risk group, the incidence of HCC development was significantly reduced in patients with SVR compared to those with nonSVR (5-year rate, 9.5% vs. 4.5%; p=0.040).

CONCLUSIONS

The HCC risk prediction model uses simple and readily available factors and identifies patients at a high risk of HCC development. The model allows physicians to identify patients requiring HCC surveillance and those who benefit from IFN therapy to prevent HCC.