Showing posts with label Adherence to therapy. Show all posts
Showing posts with label Adherence to therapy. Show all posts

Friday, July 11, 2014

Factors Influencing Adherence in Hepatitis-C Infected Patients

Factors Influencing Adherence in Hepatitis-C Infected Patients

A Systematic Review
Tim Mathes, Sunya-Lee Antoine, Dawid Pieper

BMC Infect Dis. 2014;14(203)

Background: Adherence is a crucial point for the successful treatment of a hepatitis-C virus infection. Studies have shown that especially adherence to ribavirin is important.

The objective of this systematic review was to identify factors that influence adherence in hepatitis-C infected patients taking regimes that containing ribavirin.

Methods: A systematic literature search was performed in Medline and Embase in March 2014 without limits for publication date. Titles and abstracts and in case of relevance, full-texts were screened according to predefined inclusion criteria. The risk of bias was assessed. Both process steps were carried out independently by two reviewers. Relevant data on study characteristics and results were extracted in standardized tables by one reviewer and checked by a second. Data were synthesized in a narrative way using a standardized procedure.

Results: Nine relevant studies were identified. The number of analyzed patients ranged between 12 and 5706 patients. The study quality was moderate. Especially the risk of bias regarding the measurement of influencing factors was mostly unclear.

"Psychiatric disorders" (N = 5) and having to take "higher doses of ribavirin" (N = 3) showed a negative influence on adherence. In contrast, a "HIV co-infection" (N = 2) and the "hemoglobin level" (N = 2) were associated with a positive influence on adherence. Furthermore, there is the tendency that male patients are more adherent than female patients (N = 6). "Alcohol consumption" (N = 2), "education", "employment status", "ethnic group","hepatitis-C virus RNA" (N = 4), "genotype" (N = 5), "metavir activity" (N = 1) and "weight" (N = 3) showed mostly no effect on adherence. Although, some studies showed statistically significant results for "age", "drug use", "genotype", "medication dose interferon", and "treatment experience" the effect is unclear because effect directions were partly conflicting.

The other factors were heterogeneous regarding the effect direction and/or statistical significance.

Conclusion There are some factors that seem to show an influence on adherence. However, due to the heterogeneity (e.g. patient characteristics, regimes, settings, countries) no general conclusions can be made. The results should rather be considered as indications for factors that can have an influence on adherence in hepatitis-C infected patients taking regimes that containing ribavirin. 

Discussion Only
Full Text Available @ Medscape 
This is the first review that systematically analyzes adherence influencing factors in hepatitis-C infected patients taking ribavirin. There are several factors that seem to influence adherence in hepatitis-C infected patients taking ribavirin. "Psychiatric disorders/depression", "higher doses ribavirin" seem to have a negative influence on adherence. In contrast "HIV co-infection" and "hemoglobin level" seem to have a positive influence on adherence. Furthermore, there is the tendency that male patients are more adherent than female patients. "Alcohol consumption", "education", "employment status", "ethnic group", "hepatitis-C virus RNA", "genotype", "metavir activity" and "weight" seem to have no effect on adherence. The remaining the results differed between studies.

The findings are in accordance with research findings for other indications. A meta-analysis found a statistically significant negative effect of depression on adherence in chronic conditions.[28] This might be attributable to a reduced motivation in depressed patients. The question is therefore, whether the treatment of the psychiatric disorder can help to increase adherence.

The negative influence of higher doses ribavirin on adherence is probably caused by the higher risk of side effects. For example, systematic reviews in HIV infected patients have shown that side effects are a predictor for non-adherence.[29,30] The assumption that ribavirin intake can be associated with depression is justified. A low hemoglobin level is associated with fatigue which can possibly result in low motivation to take medication. Furthermore, also a low hemoglobin level and respectively the associated fatigue is a possible side effect of ribavirin. Therefore, the hemoglobin level is perhaps also an indicator for side effects.

The two studies that analyzed the influence of an HIV-co-infection are adjusted for drug use.[8,24] The reason why this confounder is adjusted for the positive effect of an HIV-co-infection might be due to the experience in handling complex treatment regimens in HIV-infected individuals. Furthermore research has indicated, that access to care is higher in co-infected individuals.[31]

Due to the heterogeneity no general conclusions can be made that can be applied to all settings, countries, patient groups, etc. This pertains also for the factors that were highlighted as having an influence, The results should rather be considered explorative as indications for factors that can have an influence on adherence in hepatitis-C infected patients treated with regimes that contain ribavirin. To be of sufficient significance to make decisions in clinical practice, the factor/s has/have to be evaluated in detail for the specific context of the decision. The main reasons for heterogeneity between studies are the sample size, the analyses methods, different regimens and different patient characteristics. Furthermore, all studies revealed methodological flaws. In particular the measurement of influencing factors was mostly unclear. Also the time point of measurement can have an influence on adherence. A more recent study shows that at the first measurement time point younger age and African American ethnicity were statistically significant associated with lower ribavirin adherence. At the second measurement time point these factors were not statistically significant anymore, but publicly insured and employed patients showed a statistically significant effect in ribavirin adherence.

The measurement of adherence is performed with various instruments. All types of the applied adherence measurement instruments are associated with the tendency to overestimate adherence.[32] Most studies use self-reports. In particular for self-reporting instruments a higher estimation of intake rather than the true adherence rate has been shown.[32] Indeed pill counts and prescription refill are a more objective adherence measures but also these measurement methods imply the tendency to overestimate adherence (e.g. trashing tablets). In none of the included studies timing adherence was assessed. Thus, for example compensating one missing ribavirin tabled by double taking on another day would not have been revealed. However, for a more detailed and precise assessment usually additional effort is necessary which is often not feasible in clinical practice.

To have a substantial virologic response, patients have to reach a certain adherence level. Taking this into account, the proportion of patients reaching this cut-off value should be chosen as the operationalization of adherence, instead of the mean of the entire trial population, as the overall mean does not allow for a clinically significant estimation of how many patients can reach the required adherence. To our knowledge, a precise lower bound of required adherence (dose and timing) for an adequate suppression of RNA replication has not yet been proven.[7] Thus, the cut-off values used in the studies are not proven. It has to be taken into account that also the variation between patients and regimens should be analyzed in detail in this context because the needed adherence to reach a substantial virologic response probably depends on patient characteristics and/or the regimen. Furthermore, prior research has shown that a categorization of variables can result in different predictors in prognostic models and in poor performance of the model.[33] However, the mean adherence is only used as operationalization for adherence in two studies.[4,23] Apart from this, it is unlikely that adherence is influenced by only one factor but it is rather a multifactorial problem.[9]

The different adherence operationalization and measurements are furthermore a limitation for the comparability of results and probably one reason for different results regarding the statistical significance and effect direction between studies. But also the influencing factors differ regarding operationalization and measurement. For example in all studies age is operationalized in two categories or continuously. However, studies on other indications have shown that adherence presents a concave shape i.e. adherence is highest in the middle age and declines with younger or older age.[34] Such information is lost (no statistically significant results) if e.g. only two categories are used or age is treated as a continuous variable. The effect of different categorizations for the same influencing factor on the results is analyzed in none of the included studies (sensitivity analysis).

Another comparability limiting point is that the analyses are adjusted for different factors. Especially the unadjusted analysis should be interpreted with caution because confounders or effect modifiers are not accounted for. But also the multivariate analyses are adjusted for different factors and consequently the comparability is limited. Although, it was sought to consider confounding in the evidence synthesis, i.e. to identify factors that are independently associated with adherence, a risk of bias in the results cannot be excluded.

In two studies, variables that do not contribute to the explanation of the variance of adherence were not eliminated from the analysis. Consequently the probability of statistically non-significant results due to inter-correlation might be raised.[25,35] In the other multivariate analyses indeed the model is fitted by eliminating variables without a statistically significant influence on adherence. However, in none of the multivariate analysis the inter-correlations (e.g. drug use and alcohol use) between influencing factors were analyzed. Thus, variables that measure basically the same phenomena (e.g. mental illness) probably show no influence in the analysis, because most of the variance in adherence is explained by one factor (e.g. drug use) leaving little potential for explaining additional variance in adherence by adding the other factor (e.g. alcohol use). The actual influencing factor or underlying phenomena can therefore be concealed. In addition some factors that have shown an influence in other conditions like copayments and other barriers to access to care were not analyzed in any of the included studies.[36]

The observed high adherence rates in some studies suggest a "ceiling effect". A high overall adherence level implies that adherence differences become marginal. Probably the high adherence is due to the fact that patients participating in studies are often more adherent than those patients, who refuse study participation.[37] Furthermore, it can be presumed that access to medication is ensured for study participants. The high baseline adherence implies that a large sample size is needed to show statistical significance of the results. However, most studies were small and thus probably underpowered.

The presented systematic review has some limitations. Firstly, missing relevant literature published in other languages could not be excluded because we included only English and German literature.[38] Secondly, we did not evaluate the quality of registry data in register based studies. The extent of this source of bias is therefore unknown. Thirdly, we did not evaluate the risk of bias for each individual factor, because in most studies for none of the factors the measurement was described in detail and consequently all factors would have had to be rated with unclear risk of bias. But an unclear risk of bias was judged differently depending on the factor in the evidence synthesis (e.g. age vs. social support).

In this systematic review only implementation adherence to antiretroviral hepatitis-C therapy was considered because, persistence and implementation adherence should been analyzed separately.[15] It could be hypothesized that early implementation non-adherence is associated with discontinuation. However, in a study that analyzed many various potential influencing factors only younger age showed an influence on discontinuation and also on ribavirin implementation adherence. Another study showed no statistically significant association between adherence and cannabis users, but cannabis users were statistically significant more likely to continuing treatment.[25] Also other studies indicate that the factors influencing implementation adherence and discontinuation differ. Thus, this systematic review indicates an association between depression and adherence.[39] Again, a study on the influence of depression on discontinuation in intravenous drug users found not statistically significant association. Another study showed a statistically non-significant influence of drug addiction and a non-significant effect of psychiatric deterioration on discontinuation.[40] Also these results were contrary to the presented results for implementation adherence.

In clinical practice the factors can be an indication for non-adherence, especially if various factors pertain in one patient. Due to the explorative nature of our analysis, adherence influencing factors in hepatitis-C infected patients receiving combination therapy with ribavirin should further be investigated to get deeper insights into the reasons for non-adherence. Detailed knowledge of adherence influencing factors would facilitate the identification of patients at risk for non-adherence e.g. the development of screening tools for non-adherence. The knowledge of adherence influencing factors can also contribute to the development of tailored, multifactorial adherence enhancing interventions.

Continue reading @ Medscape

Thursday, June 6, 2013

Obstacles to Hepatitis C Therapy: Effective Regimens Are Not Enough

Obstacles to Hepatitis C Therapy: Effective Regimens Are Not Enough

Source-Clinical Care Options

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Hepatitis C Virus Epidemiology, Pathogenesis, Diagnosis, and Natural History

Donald P. Kotler, MD - 6/4/2013 

Although the outlook for HCV-infected patients who enter treatment has improved substantially with the introduction of direct-acting agents, various host factors remain as barriers to effective management for some patients. My colleagues and I treat patients in the New York City community of Harlem and its surrounding neighborhoods. As most providers in similar communities are aware, we face challenges including higher rates of comorbidities such as alcoholism, lower response rates to current therapies, and disparities of access to healthcare. Our conclusion is that many patients will not derive the full potential benefit from advances in HCV therapy unless all of these factors are addressed.

Each Patient Brings Their Own Unique Challenges
The patients from our community are affected by a number of important host-related factors that influence their access to and the outcomes of medical care in general and HCV treatment in particular.

Substance abuse, particularly alcoholism, is a significant problem for our patients and is especially concerning since active alcoholism represents a significant challenge to anti-HCV therapy. Surprisingly, alcoholism has received little attention, despite the fact that it accelerates hepatic fibrosis. A prospective, case-controlled study performed at Harlem Hospital Center approximately 20 years ago demonstrated that the combination of HCV infection and heavy alcohol intake, but neither HCV infection nor alcoholism alone, significantly promoted the development of chronic liver disease. Whereas those results suggest the need for greater attention to the management of alcoholism, they also illustrate the presence of a subgroup of patients at serious risk of disease progression who typically are excluded from treatment. Recent studies from the United States and Europe have documented the ability to successfully treat patients with a history of alcoholism, typically by providing some form of integrated care. Maintaining adherence to anti-HCV treatment may be as important or even a more important goal than enforcing abstinence from alcohol intake.

In our community, other patient-related factors may also affect treatment adherence including mistrust of the healthcare system, poor health literacy, inequalities in access to healthcare, as well as outright discrimination. For our patients, these factors might be responsible for poorer adherence as well as poorer outcomes of therapy. This atmosphere of mistrust works in concert with patients’ nihilism and perceived stigma to confound attempts at therapy. Although efforts to improve cultural competence in managing patients and combating host-related obstacles to effective therapy are now routine medical education and core competency initiatives, the reality is that these barriers still exist. The lack of faith may extend beyond the patient and become imbedded in the local healthcare system. A culture of low expectations may influence caregivers so that they do not aggressively promote therapy to their patients. As with all cultures, these biases may be resistant to change.

Stumbling Over Institutional Steps
We have found that it is especially difficult for the medical establishment to engage patients who exist outside the formal healthcare system, especially when the individual harbors a fundamental mistrust in the healthcare system. We have found that some of this mistrust may be mitigated by effective community-based organizations that foster trust by establishing an environment in which people are treated in a nonjudgmental manner.

In addition to efficacious medications, effective HCV therapy also requires appropriate diagnosis and evaluation, recommendation for therapy, access to therapy, acceptance of the diagnosis and its implications by the patient, and adherence to therapy. Medication development is the purview of the pharmaceutical industry, and it has vigorously pursued them, but the diagnosis, treatment recommendations, and treatment access are tasks for the healthcare system and those of us at the front line of care. Our patients bear the responsibility of accepting the diagnosis and adhering to therapy and form the other basis for successful therapeutic outcomes. An increasing amount of attention is being given to screening and linkage to care, but less attention has been placed on assuring treatment access and very little attention has been given to care coordination. To the extent that care coordination and community involvement enhance acceptance and adherence, they will avoid the wasted resources associated with refusal of care and early treatment suspension, as well as liver disease progression.

Your Thoughts?
Do you share our concerns that many patients are effectively excluded from successful treatment by these host-related factors? What is your experience with patients in your communities? Have you tried any interventions to overcome these barriers in your patients? We are keen to hear your experiences.

Topics: HCV - Outcome

Friday, December 28, 2012

Interventions To Improve Patient Adherence to Hepatitis C Treatment: Comparative Effectiveness

Research Review - Final – Dec. 20, 2012 

Interventions To Improve Patient Adherence to Hepatitis C Treatment: Comparative Effectiveness

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Executive Summary .....................................ES-1
Introduction ........................................................1
Condition Definition .......................................... 1
Prevalence and Disease Burden .......................... 1
Etiology and Natural History of Hepatitis C Infection ............ 1
Hepatitis C Virus Genotypes and Detection ................. 2
Treatment of Chronic Hepatitis C Infection ..............2
Adherence in the Context of Chronic Hepatitis C Treatment .......... 4
Risk Factors for Nonadherence to Antiviral Treatment .............. 5
Association of Adherence With Sustained Viral Response .............. 6
Interventions for Improving Adherence ................. 7
Scope and Purpose .............. 7
Key Questions ...................... 8
Topic Development and Refinement ........................ 9
Analytic Framework ................. 9
Literature Search Strategy .................... 10
Process for Study Selection ................... 10
Data Abstraction and Data Management ................ 12
Individual Study Quality Assessment ...................... 12
Data Synthesis ............................. 13
Grading the Strength of Evidence .................. 13
Applicability ...................... 14
Review Process .................. 14
Literature Search ................. 15
Characteristics of Included Studies ............. 16
Results of Included Studies .......................... 30

Key Question 1 (Intermediate and Final Health Outcomes) and Key Question 2
(Treatment Adherence) .......................... 30
System-Level Interventions Versus Usual Care .......................30
Regimen-Related Interventions Versus Usual Care ..................33
Patient-Level Interventions Versus Usual Care ........................34
Adverse Event Management Interventions Versus Usual Care/Placebo .........37

Does the Comparative Effectiveness of Treatment Adherence Interventions Differ
by Patient Subgroups?.........................40
Key Question 3. Harms ...................... 40
Summary and Discussion .....................47
Overview of Main Findings ................. 47
Outcomes of Adherence Interventions .................. 48
Strength of Evidence .............................. 48
Health Outcomes ..................................... 48
Intermediate Outcomes ............................ 49
Findings in Relationship to What Is Already Known .................... 53
Applicability of the Evidence to the United States Health Care System ....... 53
Limitations ............................ 54
Potential Limitations of Our Approach................54
Limitations of the Literature ................................55
Implications for Clinical and Policy Decisionmaking............... 56
Evidence Gaps................................... 57
Future Research ................................ 57

Monday, December 17, 2012

Predictors of Response to Chronic Hepatitis C Treatment

Future Virology

Predictors of Response to Chronic Hepatitis C Treatment

Ezequiel Ridruejo

Future Virology. 2012;7(11):1089-1101.

Chronic hepatitis C is a growing health problem worldwide that has attracted increased attention in recent years. Treatment with peginterferon and ribavirin combination had previously been the standard of care. In 2011, a new treatment with protease inhibitors, telaprevir and boceprevir was approved, and a new standard of care was defined. Previous predictors of response have been redefined, and while IL28B, fibrosis stage and hepatitis C virus viral load testing continue to have their value, viral kinetics during treatment defining viral response have emerged as the strongest predictor for achieving sustained virologic response with the new treatment. New therapies are expected in the near future, and current treatment predictors of response may soon change.

Chronic hepatitis C is a growing health problem worldwide that has attracted increased attention in recent years. Approximately 3% of the world's population (130–170 million people) is chronically infected with HCV. [1,2] HCV is one of the most prevalent blood-borne infections, with a higher prevalence than HIV (~1.1 million infected) and HBV (0.8–1.4 million infected) in western countries. [3] Chronic HCV infection progresses asymptomatically, and almost 75% of patients are unaware of the diagnosis when they present with complications of cirrhosis, portal hypertension or develop a hepatocellular carcinoma (HCC) many years later. [4] In western countries HCV infection is one of the most frequent causes of death from end-stage liver diseases and HCC. [5] In the last decade, HCV-related morbidity doubled, and HCC related to HCV increased almost threefold. [6] Its impact on liver-related morbidity and mortality is expected to reach its peak in the next decade. [7]

The objective with antiviral therapy is to obtain a sustained virologic response (SVR), defined as undetectable HCV RNA 24 weeks after the end of treatment. In the last decade, treatment with pegylated interferon 2a or 2b (pegIFN) combined with weight-based ribavirin (RBV) for 48 weeks (genotypes 1, 4, 5 and 6) or 24 weeks (genotypes 2 and 3), has been considered the standard of care (SOC) for HCV treatment. With pegIFN/RBV treatment, SVR rates were 40–50% in genotype 1 and 70–80% in genotypes 2/3-infected patients in western countries. [8] Some studies showed that prolonging treatment up to 72 weeks in genotype 1 slow responders (HCV RNA detectable at week 12 but undetectable at week 24), can increase SVR rates. [9–12] But, prolonged treatment is associated with higher costs, increases in adverse events, and cannot be used in treatment-intolerant patients.
Many viral and host factors affect treatment response, and not achieving a SVR might be related to a combination of them. HCV genotype and IL28B host genotype are the strongest predictors of pegIFN/RBV therapy outcome. [13]IL28B designates single-nucleotide polymorphisms (SNPs) in the interferon λ gene region in chromosome 19, and it is related to interferon responsiveness. Other factors such as high viral load, older age, black race and advanced fibrosis or cirrhosis negatively influence SVR rates. [14,15]

In 2011 the US FDA and the EMA approved telaprevir (TVR) (Incivek/Incivo™, Vertex) and boceprevir (BOC) (Vitrelis™, Merck) for its use in HCV genotype 1 infected patients. These two NS3/4A serine protease inhibitors (PIs) are the first generation of direct-acting antiviral (DAA) drugs to be approved for its use in clinical practice. Used in combination with pegIFN/RBV, PIs increased SVR rates up to 68–75% in naive patients and to 41–52% in previous nonresponders. [16–19] With the new treatment regime of PI-based triple therapy, some of the predictors of response to dual pegIFN/RBV-based therapy are less important. In fact, some host factors are used differently. Nonetheless, baseline host and viral factors and early viral kinetics are still important determinants for patient counseling and management using BOC or TVR combination treatment.

Why Do We Need Predictors of Response to Treatment?
The factors that determine the likelihood of achieving SVR are called predictors of response. They can be classified as viral- or host-related, or as pre- or on-treatment depending on the time point of evaluation.
Treatment with pegIFN/RBV dual therapy is costly and is associated with side effects. The triple therapy regimen including BOC or TVR increases the costs and the complexity of treatment further, and more frequent and severe adverse events are expected with these newer therapies. Before initiating treatment, it is useful for patients and physicians to determine the likelihood of achieving a SVR, so that they can decide whether treatment benefits outweigh its costs and risks.
In the pegIFN/RBV dual therapy era, predictors of response helped the patient and the physician to decide whether or not to start treatment. There were no other treatment options, and antivirals were just a promise. Today, in naive patients, it is possible to predict response to dual or triple therapy and choose between them. There are a small percentage of patients, with good predictors of response, who are still candidates for pegIFN/RBV dual therapy. Also, naive patients with poor predictors of response with mild liver disease may be candidates for awaiting the new generation of antivirals. In the future, these regimens might be more effective, less toxic, and perhaps pegIFN-free.
Before the approval of DAA, patients who had failed a previous course of pegIFN/RBV had few treatment options. These included consensus interferon or retreatment with pegIFN/RBV. Previous relapsers and null responders can achieve 24–50% and 4–14% SVR rates, respectively. [20–23] Even though PIs increase SVR rates to 41–52%, there is still a percentage of nonresponders to the first generation of PIs. [18,19] In this subset of patients it is very important to try to predict which patients will benefit with this treatment, and which patients are candidates to wait for treatment with the next generation of DAAs.
Many interesting viral and host predictors of response have been described. We will discuss those that are widely available to medical practitioners and suitable for being used in routine clinical practice.

Pretreatment Predictors of Response
Predictors of response to pegIFN/RBV dual therapy have been described, and are shown in Box 1. Some host predictors are fixed (e.g., age) but others can be modified (e.g., body weight). Data about predictors of response to triple therapy are beginning to be demonstrated. Because interferon responsiveness continues to be important with the newer regimes, the same factors may influence treatment response to PI-based therapy. Viral genotype and baseline viral load still influence PI treatment response, but mutations in certain regions of the viral genome appear as new treatment predictors. Due to different rates of resistance, SVR rates to PIs may be different between subtypes a and b in genotype 1 (G1) infected patients. [24]

Viral Factors

HCV Genotype.
HCV genotype is the strongest baseline predictor for response for dual therapy: the highest SVR rates have been achieved in G2 (~80%) and the lowest in G1 (~40–50%)-infected patients. [8] Other smaller studies suggested that G4 is poorly responsive; [25] that G5 is similar to G3; [26] that G3 is less responsive than G2; [27] and that, similarly to G2, G6 is very responsive. [25] The first generation of PIs has been approved for G1 treatment, but these drugs are ineffective in most non-G1-infected patients. A second-generation of NS3/4A PIs, nucleos(t)ide and nonnucleoside inhibitors of the NS5B RNA polymerase and NS5A complex inhibitors, have now reached Phase II and even Phase III clinical stage development for treatment across all HCV genotypes. [28] HCV genotype value as a predictor of SVR in this scenario will need to be redefined.
HCV G1 Subtype. G1 has two subtypes, 1a and 1b. Subtype 1b has a higher prevalence in Europe and 1a in the USA. Patients infected with G1 subtype b achieved slightly higher SVR rates with triple therapy (~7–10% difference), than those infected with subtype a. Given that subtype 1a has a lower barrier to resistance to PIs, resistance-associated variants (RAVs) are more frequent with this subtype than with viral subtype 1b. [29] In cases of treatment failure, 1a-associated RAVs appear earlier, 1b-associated RAVs disappear faster, and the double V36M + R155K mutation needed for RAV appearance occurs almost exclusively in subtype 1a. [30]

Baseline HCV Viral Load.
HCV RNA levels before initiating treatment predict the likelihood of obtaining SVR with pegIFN/RBV dual therapy. [27,31] This measure has a lower predictive value for the more IFN-sensitive genotypes associated with higher SVR rates, such as G2 and 3 compared with G1. HCV G3-infected patients with <400,000, 400,000–800,000 and >800,000 IU/ml baseline viral loads, achieved SVR rates of 81, 70, and 59%, respectively, when treated with pegIFN alfa-2a and RBV 800 mg per day for 24 weeks. In G2 patients, SVR rates for the same HCV RNA levels were 82, 79, and 73%, respectively. [32]

On the contrary, in G1 patients low baseline HCV RNA level (<600,000–800,000 IU/ml or less) was shown to be an independent predictor of achieving SVR. [32,33] Based on these results, an abbreviated regimen (24-week treatment with pegIFN/RBV) may be indicated for patients with G1 and a low versus high baseline viral load, and undetectable HCV RNA after 4 weeks of treatment. There is no current agreement on the most discriminatory HCV RNA level, which ranges between 400,000 and 800,000 IU/ml (5.6–5.9 log 10 IU/ml) ( Table 1). [34]

Baseline HCV RNA level is less predictive of SVR when using PIs in G1 patients. In the SPRINT-2 study, patients treated with pegIFN/RBV/BOC with <800,000 IU/ml baseline HCV RNA levels achieved 76–85% SVR rates, while for patients with ≥800,000 IU/ml, SVR rates were 61–63%. SVR rates in the SOC control arm were 64 and 33% for ≥800,000 and <800,000 IU/ml, respectively. [16] Patients with baseline viral load ≤400,000 (vs >400,000 IU/ml) had a higher SVR rate, with odds ratio (OR): 3.9 (95% CI 2.1–7.1), p < 0.001. [16] In the ADVANCE study, patients treated with pegIFN/RBV/TVR achieved high SVR rates irrespective of their baseline viral load: 78% with <800,000 IU/ml and 74% with ≥800,000 IU/ml. The SVR rates in the SOC control arm were 70 and 36%, respectively ( Table 2 & Table 3. [7] Both studies showed that PI-treated patients with baseline lower viral loads tend to have a slightly higher likelihood of achieving SVR, but the differences in SVR rates between high and low HCV RNA levels in these patients is less marked than in pegIFN/RBV-treated patients. [30]

Host Factors
Age. Univariate and multivariate analyses performed in most of the randomized control trials in patients treated with pegIFN/RBV dual therapy showed that younger age significantly correlated with the likelihood of obtaining SVR. Furthermore, higher SVR rates were obtained in patients younger than 40–45 years old. [32] In Phase III clinical trials using PIs, younger patients achieved slightly higher SVR rates. Patients younger than 40 years old treated with BOC achieved higher SVR rate than those treated with SOC: OR: 1.5 (95% CI 1.0–2.1), p = 0.03. [16] Patients younger than 45 years old treated with TVR had a SVR rate of 83% versus 70% in those older than 45; in the SOC arm, SVR rates were 52 and 38%, respectively ( Table 2). [17]

Sex. Female patients had been shown to achieve higher SVR rates than males in two studies using the old combination of standard IFN and RBV (p < 0.004). However, in both of the pegIFN/RBV registration trials, no statistically significant correlation was found between sex and SVR. [32] According to data in the Phase III clinical trials, sex has no impact on SVR response to PIs. [16,17] SVR rates were 74% in males and 75% in females in the TVR arm, and 45 and 43%, respectively, in the SOC arm ( Table 2). [17]

African–Americans had lower SVR rates than non-African–Americans patients when treated with pegIFN/RBV therapy. In randomized control trials SVR rates were 19–28% in African–Americans versus 39–52% in non-African–Americans. These lower SVR rates were observed in genotype 1-infected patients treated with pegIFN/RBV for 48 weeks. There was no difference in SVR rates in patients infected with other genotypes. [32] Although not as extensively studied, Latinos (Hispanic) patients treated with pegIFN/RBV also showed lower SVR rates than Caucasians. However, another study including Latino patients of European ancestry showed similar SVR rates to those observed in Caucasians. [35] Finally, Asian HCV-infected patients tended to achieve higher SVR rates than Caucasians. [32] How ethnicity influences HCV treatment outcome is unknown, but race is strongly correlated with differences in favorable IL28B allele frequencies. Host genetics may be the explanation.
In black patients, PI-based therapy improves SVR rates when compared with pegIFN/RBV. However, SVR rates in these patients are still lower than in nonblack patients. IFN-responsive black patients achieving early virologic responses showed higher SVR rates. Among nonblack patients, the rate of a SVR was 40% with SOC and was significantly higher with BOC: 67 and 68% (p < 0.001). Among black patients, the SVR rate was 23% in SOC, and 42 and 53% in BOC group (p = 0.004, vs SOC). Black patients treated with BOC showed significantly lower SVR rates than nonblack patients: OR: 0.5 (95% CI 0.3–0.7), p < 0.001. [16] Among black patients treated with TVR, SVR rate was 62 and 58%, as compared with 25% in the pegIFN/RBV group. In nonblack patients, TVR-treated patients achieved 75% SVR rates, while in the pegIFN/RBV arm it was 46%. [17] Even though Hispanic patients represent a small percentage of the treated population (~10%), they obtained a similar response rate to Caucasians: 74% in the TVR arm and 35% in the pegIFN/RBV arm ( Table 2). [17] There is no information on SVR rates in Asians in the Phase III registration trials. In a study from Japan, SVR rates in response to TVR for 12 weeks combined with pegIFN/RBV for 24 weeks were higher when compared with SOC (73.0 vs 49.2%, respectively, p = 0.002), and similar to SVR rates in nonblacks in Phase III trials. [36]

In patients with chronic HCV infection advanced fibrosis, especially cirrhosis, is associated with a diminished treatment response. Multilogistic regression analyses in pegIFN/RBV randomized controlled trials did not show a significant association between cirrhosis and lower SVR rates. However, lower SVR rates can be demonstrated when they were directly compared between cirrhotic (44% for pegIFN α2b and 43% for pegIFN α2a) and noncirrhotic patients (57% for pegIFN α2b and 58% for pegIFN α2a). [32] One possible explanation for the lack of correlation between treatment outcome and cirrhosis in the multivariate analysis is the small percentage of cirrhotic patients in these studies. The mechanisms of reduced IFN responsiveness in cirrhotic patients is unknown, and is possibly multifactorial.
Patients with advanced fibrosis or cirrhosis treated with PI triple therapy also showed diminished SVR rates, possibly related to reduced IFN responsiveness in patients with advanced liver disease. However, cirrhotic patients treated with PI-based triple therapy have higher SVR rates than those treated with pegIFN/RBV alone. On the contrary, patients treated with BOC or TVR-based therapy have very high SVR rates if they have mild liver disease (F0–F2 stages of fibrosis).
SVR rates in patients with advanced fibrosis were lower than in those with mild fibrosis, although the numbers of patients with a Metavir fibrosis score of 3 or 4 were small (7–9%). SVR rates according to fibrosis stage were: F0–2: 67% in BOC versus 38% in SOC group (p < 0.001); and F3–4: 41–52% in BOC arm versus 38% in SOC arm (p > 0.05). Absence of cirrhosis was a good predictor for obtaining a SVR in the SPRINT-2 trial: OR: 2.5 (95% CI 1.4–4.6) p = 0.003. [16] In the ADVANCE trial the numbers of patients with advance fibrosis were also small: 14–16% F3 and 6–7% F4 in the TVR arm. In this subgroup of patients SVR rates were 62% in the TVR arm and 33% in the SOC arm; in F0–2 patients, SVR rates were 81% in the TVR arm and 46% in the SOC arm ( Table 2 & Table 3. [17]

IL28B Genotype.
Genome-wide association studies have identified two single SNPs near the IL28B gene or lambda interferon 3 in chromosome 19. These SNPs are strongly associated with HCV clearance, whether spontaneous or treatment-related. [37–39] Given that IL28B genotype strongly influences dual pegIFN/RBV treatment outcome, its predictive value is more relevant in the more difficult-to-treat HCV genotypes: 1 and 4.
Caucasian G1 patients with the rs12979860 CC, CT, and TT genotypes treated with pegIFN/RBV dual therapy achieved 69, 33, and 27% SVR rates, respectively; SVR rates for black patients were 48, 15, and 13%, respectively. [40] Caucasians and Asian patients with the rs8099917 TT, GT and GG genotypes treated with pegIFN/RBV, showed similar SVR rates. [38] Similar SVR rates with both genotypes had been reported in Hispanic patients with European ancestry. [41] Based on these results, IL28B CC genotype has been considered the strongest baseline predictor of SVR (OR: 5.2 vs non-CC genotype; p <0.0001) ( Table 1). [40] The mechanisms by which IL28B influences pegIFN/RBV treatment outcome, are unknown.
However, IL28B predictive value is diminished in patients treated with PIs. In all of the Phase III trials, data on IL28B genotype was retrospectively obtained in a proportion of patients. Naive patients with unfavorable IL28B genotypes treated with TVR or BOC in the ADVANCE and SPRINT-2 trials achieved higher SVR rates than those treated with SOC. [42–44] Incremental benefits of BOC or TVR-based therapy were less remarkable in patients with the favorable CC genotype. However, it allowed the shortening of therapy in most patients.
A subanalysis from the ADVANCE trial showed that treatment with TVR combined with pegIFN/RBV increased SVR rates across all IL28B genotypes, with the largest increases shown in patients with the CT or TT genotype. SVR rates among patients with the CC genotype were 90% with TVR 12 weeks plus pegIFN/RBV, 84% with TVR 8 weeks plus pegIFN/RBV, and 64% with pegIFN/RBV alone. For the same treatment groups, SVR rates were 71, 57, and 25%, respectively, in CT genotype patients; and 73, 59 and 23%, respectively, for TT genotype patients. In this trial, information about IL28B status was obtained in 42% of the patients, all of whom were white. [42]
Data from the SPRINT-2 study, also limited by the relatively small numbers of patients included (62% of the population), showed that the benefit of BOC in patients with the CC genotype seems less marked. In this subgroup, SVR rates were 82% with BOC and 78% in the pegIFN/RBV control arm. As in the TVR trial, patients with unfavorable IL28B genotypes achieved significantly higher SVR rates when treated with BOC-based triple therapy versus pegIFN/RBV alone. SVR rates were 65% in the BOC-based response-guided therapy (RGT) arm, 71% in the BOC for 48 weeks arm, and 28% in the pegIFN/RBV arm for patients with the CT genotype. For the same treatment arms SVR rates were 55, 59, and 27%, respectively in TT genotype patients. [43,44] In treatment-naive patients treated with BOC-based therapy, IL28B CC versus non-CC genotype predicted SVR in a multiple stepwise logistic regression model: OR 4.5 (p < 0.001) ( Table 3). [43]

In the same subanalysis of the SPRINT-2 trial, the predictive value of the IL28B rs8099917 SNP was evaluated. A crossanalysis of rs12979860 and rs8099917 SNPs revealed that most CC patients at the rs12979860 locus had the corresponding favorable TT at the rs8099917 locus. However, the converse was not true because TT patients at rs8099917 were just as likely to be CC versus non-CC. SVR rates were 78% in TT, 67% in GT and 50% in GG rs8099917 BOC-treated patients. Combining the two SNPs did not offer enhanced predictability of SVR, and although both rs12979860 and rs8099917 have utility in predicting SVR, it appears that the rs12979860 locus is more predictive when considering all treatment groups, including the control groups. [44]

SVR rates in the CC genotype patients treated in the pegIFN/RBV control arms differs between the two PI trials. Patients treated in the ADVANCE trial achieved a 64% SVR rate, while in the SPRINT-2 trial this was 78%. SVR rates in the ADVANCE trial are lower than those previously reported, and this may be related to the small sample size. [37] It seems that BOC and TVR-based treatment slightly improves SVR rates in the favorable IL28B genotype, but significantly improves it in CT and TT genotypes. One major advantage detected in both studies was that most CC genotype naive patients were able to reduce treatment duration: 78% CC versus 57% of CT versus 45% of TT received shortened treatment with TVR, [42] and 89% CC versus 53% of CT versus 45% of TT patients received shortened treatment with BOC. [43,44]

Recent data add information on short courses of treatment in favorable IL28B genotypes. A retrospective analysis of the PROVE 2 trial showed that IL28B CC naive patients treated for 12 weeks with TVR/pegIFN/RBV achieved SVR in 100% of the treated patients (14 out of 14). [45] In IL28B CC patients treated with TVR for 12 weeks combined with pegIFN/RBV for 24 weeks, and in patients treated with SOC, SVR rates were 94 and 64%, respectively. SVR rates were lower in non-CC genotype patients. [45] Shortened therapy is of great benefit, even if SVR rates remain close to those obtained with pegIFN/RBV alone. Currently, there are ongoing studies in IL28B CC patients treated with reduced duration PI-based regimes. [101]

The predictive value of IL28B genotype with PI therapy is limited for treatment-experienced patients. In retrospective analyses of the REALIZE [46] and RESPOND-2 [43,44] trials, patients' previous treatment response appears to have a better predictive value than the IL28B genotype. SVR rates decrease in a stepwise mode from relapsers to partial responders to null responders in the REALIZE trial. [46] Also, SVR rates were lower in partial responders than in relapsers in the RESPOND-2 trial (this trial did not include null responders). CC patients receiving TVR-based therapy achieved higher SVR rates (79%) than non-CC patients (60% in CT and 61% in TT). However there was no SVR rate difference according to IL28B genotype according to previous treatment response: in previous relapsers, SVR rates were 88% in CC, and 85% in CT and TT patients; in previous partial responders they were 63% in CC, 58% in CT and 71% in TT; and they were 40% in CC, 29% in CT and 11% in TT previous null responders treated with TVR-based therapy in the REALIZE trial. [46]

Response to pegIFN based lead-in phase (LI) was the strongest predictor of treatment response (OR 2.2, p = 0.025) in the RESPOND-2 trial, even stronger than IL28B. Among previous partial responders or relapsers, CC and CT genotype patients treated with RGT or fixed-duration BOC therapy achieved higher SVR rates than those treated with pegIFN/RBV. Although the number of patients included in this subanalysis was small, TT genotype patients treated with fixed-duration BOC achieved higher SVR rates than those treated with either RGT BOC or pegIFN/RBV alone. [43] As demonstrated in naive patients, IL28B genotype predicted the possibility of shortening therapy in previous nonresponders: 79% CC versus 46% CT versus 63% TT patients received shortened treatment with BOC. [43,44]

Although IL28B genotype has major role in predicting the initial response to pegIFN treatment, there are other factors influencing SVR. Previous response to pegIFN is the strongest predictor of PI-based therapy SVR in treatment-experienced patients. Even though IL28B genotyping has limited value in this subgroup of patients, it may add information about patients' possibilities of achieving SVR and about the possibility of reducing treatment duration. Once the lead-in response is known, reflecting IFN responsiveness, IL28B value as a predictor of response becomes less important.

Previous Response to pegIFN/RBV.
As previously mentioned, nonresponders to pegIFN/RBV are only candidates for PI base therapy, since they increase SVR rates to 41–52%,. [18,19] But there are still a percentage of patients nonresponsive to the first generation of PIs, and one of the strongest predictors of SVR is the type of previous response to pegIFN/RBV. Three categories have been defined for these patients. Null responders are patients whose HCV RNA level did not decline by at least 2 log IU/ml at treatment week 12; partial responders are patients whose HCV RNA level dropped by at least 2 log IU/ml at treatment week 12, but in whom HCV RNA was still detectable at treatment week 24; and relapsers are patients whose HCV RNA became undetectable during treatment, but then reappeared after treatment ended. [8]

Phase 3 trials using BOC and TVR-based therapy have been performed in HCV G1 treatment-experienced patients. Categories of non-response have been used to predict SVR. As expected, SVR rates were higher in PI-based regimes than in pegIFN/RBV arms. In the two BOC therapy arms in the RESPOND-2 trial, SVR rates were 66 and 59%, and were 21% in the control group. In the BOC-containing arms, SVR rates were higher in prior relapsers (75 and 69%) than in prior partial responders (52 and 40%). In the SOC control arm, SVR rates were 29% for prior relapsers and 7% for prior partial responders; null responders were not included in this study. [18] Overall SVR rates in the TVR-containing groups in the REALIZE trial were 64% and 66%. In these groups SVR rates according to previous response were 83 and 88% in relapsers, 59 and 54% in partial responders, and 29 and 33% in null responders. In the control SOC group SVR rates were 24, 15 and 5%, respectively. [19] Previous response to pegIFN/RBV treatment influences the outcome of PI-based triple therapy. This issue highlights the importance of adequately documenting previous type of response. The highest SVR rates occurred in prior relapsers, a lower rate was achieved in partial responders, and the lowest rate occurred in null responders (only with TVR). [18,19]

Previous response to pegIFN/RBV can be combined with the other predictive factors mentioned previously to increase its predictive value. In the RESPOND-2 trial, F0–2 stage had similar SVR rates in both BOC groups: 68 and 66%, versus 23% in pegIFN/RBV group. However, in patients with F3–4 stages, SVR rates were higher in patients treated with the BOC 48-week duration regime (68%) than in those treated with BOC RGT (44%) or pegIFN/RBV alone (13%). [46] Relapsers had similar SVR rates in the TVR arm independent of fibrosis stage: F0–2 86%, F3 85% and F4 84%. But SVR rates were lower according to fibrosis stage in partial responders (F0–2 72%, F3 56% and F4 34%) and even lower in null responders (F0–2 41%, F3 39% and F4 14%). [19] The predictive values of the different factors evaluated in the BOC trials are shown in Table 3.
Thus, achieving SVR in treatment-experienced patients will mainly depend on the type of prior response to pegIFN/RBV, as well as on the other predictors mentioned. Also other reasons for treatment failure, such as inadequate dosing or side-effect management, have to be taken into account when deciding to retreat a patient.
Previous SOC required a moderately complex regimen of twice-daily oral RBV and weekly subcutaneous injections of pegIFN, as well as frequent monitoring of laboratory results and adverse effects. It had been demonstrated that strict adherence to pegIFN/RBV regimen was associated with higher SVR rates. [47–50] Triple PI-based therapy increases treatment complexity: it increases the number of pills to be taken with food, not low-fat in the case of TVR; increases the number of laboratory controls; increases the number, and in some cases the severity of side effects, some only related to the PIs (i.e., rash, disgeusia, etc.); and PI misuse increases the risk of RAV emergence. One benefit, is that treatment can be shortened in a large proportion of patients with PI-based therapy. [8,16–19] Studies using PIs outside clinical trials will show how this treatment is tolerated in clinical practice and how treatment adherence impacts SVR rates.

On-treatment Predictors of Response
Early viral kinetics are useful in predicting treatment response when treatment has been initiated. With pegIFN/RBV treatment, it was clear that early viral kinetics are the strongest predictor of achieving SVR: obtaining a rapid virologic response (RVR, defined as HCV RNA being undetectable at week 4 of therapy) was the strongest predictor of SVR, no matter what adverse baseline predictors may have been present. [51] Patients who have achieved RVR with PI-based triple therapy are also likely to achieve SVR. On the contrary, HCV RNA still being detectable at week 12 of therapy is associated with a low likelihood of obtaining SVR. [16,17]

Pretreatment host and viral characteristics affect early viral kinetics. Once treatment has been initiated, outcome depends on how fast HCV RNA becomes undetectable. A retrospective study of the IDEAL trial showed that the IL28B genotype loses its predictive value for SVR after including RVR in the analysis. A patient with CC genotype who did not obtain RVR, has a lower probability of achieving SVR than a patient who has CT or TT genotype but has achieved RVR. [40] According to IL28B genotype, patients achieving RVR (only 14% of the treated patients) had 85 (CC), 76 (CT) and 100% (TT) SVR rates (p = 0.25 CC vs non-CC genotype). On the contrary, in patients not achieving RVR, IL28B status predicted SVR rates: 66 (CC), 31 (CT) and 24% (TT) SVR rates (p < 0.001 CC vs non-CC genotype). [40] Another study showed that the strongest predictor of SVR is RVR (OR: 5.35; 95% CI: 2.80–10.19; p < 0.0001), while IL28B CC can still predict SVR in the multivariate analysis (OR: 2.66; 95% CI: 1.54–4.61; p < 0.0001). [52] It also showed that IL28B predicted HCV RNA undetectability at each of the week 4, week 8, and week 12 testing points during pegIFN/RBV treatment. These results are consistent with IL28B genotype being strongly associated with the viral kinetics of IFN response. On-treatment viral kinetics may represent a final common pathway of response reflecting IL28B genotype. So, once a virological response has been achieved, SVR is set and independent of IL28B. Based on these results, achieving RVR is considered to be the strongest predictor of SVR versus all baseline predictors, including IL28B genotype (OR: 9.1, vs no RVR and non-CC IL28B genotype; p < 0.001) ( Table 1). [40] In this setting of a RGT with pegIFN/RBV treatment, IL28B genotype might not add significant clinical information to early viral response.
In the PI-therapy era, early viral kinetics remained as crucial determinants of treatment response. On-treatment virologic response definitions varied between pegIFN and PI-based therapy ( Table 4). The pegIFN/RBV LI used in the BOC-containing regimes gives important data about IFN responsiveness. Naive patients treated with BOC-based triple therapy in the SPRINT-2 trial not achieving a >1 log 10 decline in HCV RNA during the LI had a 28–38% SVR rate following triple therapy, compared with a 79–81% SVR rate in those achieving a >1 log 10 decline. [16] In a multivariate analysis of the Phase III trials data, LI response was the strongest predictor for achieving SVR (OR: 9.0; p < 0.001). [53] When LI response and IL28B are included in the analysis, the latter loses its predictive value for SVR ( Table 3). [44]

In treatment experienced patients, a subanalysis of RESPOND-2 trial showed that BOC regime, type of previous non-response, low baseline viral load, and mild fibrosis were significantly associated with SVR (without including IL28B genotype). [54] However, when lead-in response was added into the model it was found to be the strongest predictor of SVR (OR: 5.2; p < 0.001), even stronger than previous type of non-response (OR: 3.0; p < 0.0001). When IL28B genotype was added to the model (available for 66% of patients), its predictive value was lost, and response to LI continued to be a significant predictor for SVR (OR: 1.8; p < 0.0001) ( Table 3). [44]

BOC-based therapy requires HCV RNA responses at week 4 (after LI) and at week 8 to implement RGT. IL28B genotype was found to be a strong predictor of both responses in a retrospective analysis performed after SPRINT-2 and RESPOND-2 trials. [43,44] It was found that most IL28B CC genotype naive (89%) and previously treated (82%) patients presented as HCV RNA-negative at week 8, and therefore were able to reduce treatment duration with BOC. In CT and TT genotypes, 52% of naive and 51% of previously treated patients were able to reduce treatment duration. Also, the favorable IL28B CC genotype strongly predicted HCV RNA decline ≥1 log 10 after LI in naive patients (OR: 15.8; 95% CI: 6.3–39.8; p < 0.001) and in previous nonresponders (OR: 4.5; 95% CI: 1.5–13.7; p = 0.007) in the same analysis. Nonetheless, the highest SVR rates were achieved by BOC-treated patients, who achieved a ≥1 log 10 decline in HCV RNA levels after LI, regardless of the IL28B genotype ( Table 3). [43,44]

Even with TVR, response to LI can be particularly useful to establish whether a prior null responder patient has chances to achieve SVR with a new course of treatment. In patients treated with TVR-based therapy, response type to prior treatment was shown to be a stronger predictor of SVR than on-treatment response to LI in a retrospective analysis of the REALIZE trial. [55] In the TVR LI arm 88% of prior relapsers, 54% of prior partial responders and 33% of prior null responders achieved SVR. Moreover, TVR-treated patients achieving >1 log 10 decline in HCV RNA levels after LI achieved SVR in 94% of prior relapsers, and 59% of prior partial responders; in the same arm, patients not achieving this >1 log 10 decline had SVR rates of 62 and 56%, respectively, for the same categories of nonresponse. In prior null responders, achieving ≥1 log 10 versus <1 log 10 decline in HCV RNA levels after LI was associated with a >3-fold higher likelihood of achieving SVR: 54 versus 15%, respectively. [55] Lead-in phase response to pegIFN/RBV in previous null responders may help in making the decision whether or not to proceed to PI-based triple therapy in this subgroup of patients.
Early viral responses help in identifying possible patients failing to treatment. Patients failing response after LI at week 4 of treatment, as well as those not achieving low HCV RNA levels after initiating PI-based treatment, are unlikely to obtain SVR. In the TVR arms in the ADVANCE and ILLUMINATE trials, none of those patients with HCV RNA levels >1000 IU/ml at week 4 of therapy obtained SVR. So, this criterion was established as a futility rule for TVR to stop all treatment at this point. [17,56,57] Almost none of the BOC-treated patients with HCV RNA levels ≥100 IU/ml at week 12 of treatment achieved SVR. So, this criterion was established as a futility rule for BOC to stop all treatment at this point. [58,59]

Patients treated in the BOC-containing arms in RESPOND-2 and SPRINT-2 trials who become HCV RNA-undetectable by week 8 achieved 81–100% SVR rates, regardless of IL28B genotype. [44] None of the patients failing to achieve >1 log 10 decline after LI at week and >3 log 10 decline in HCV RNA at week 8 achieved SVR (0 out of 44). [44,60] Despite having a <1 log 10 decline at week 4, patients who became HCV RNA-negative by week 8, achieved SVR in 83% of cases. [44] In this subgroup of patients, 3 log decline by week 8 may be used as an additional stopping rule.
Following futility rules to stop PI-based treatment is critical, because they strongly predicted the possibility for not achieving SVR ( Table 5). If PI-based therapy is maintained despite its failure, not only is resistance emergence almost certain, but this will increase resistant virus fitness and may compromise future therapies.

The first generation of PI-based therapy, BOC and TVR, combined with pegIFN/RBV, has increased SVR rates across many viral and host factors. Almost all patients are more likely to achieve SVR with PIs than with the older SOC. However, baseline patient characteristics may help in making decisions regarding whether to treat or not, or how to treat a given patient. Also they may help in providing information to the patients about their probabilities of achieving SVR and the probable duration of therapy. In HCV genotype 1-infected patients treated with PI-based triple combination therapy, it is clear that baseline factors help to predict SVR, but on-treatment virologic response appears to be the strongest factor predicting SVR.

Future Perspective
New generations of direct antiviral therapies are in the pipeline for several companies. Even IFN-free regimes could be a possibility in the near future, perhaps in 3–6 years. These new drugs will change the HCV therapeutic scenario, as well as the predictors of response. We will see in the future how these treatments work, and how we can predict which treatment is the best option for which patient. There is no doubt that HCV treatment will become 'personalized', and there is hope for finding suitable predictors of response for each drug for a given patient.

Thursday, December 13, 2012

Adherence to hepatitis C treatment: unravelling the complexities

Adherence to hepatitis C treatment: unravelling the complexities

Rob Camp, Keith Alcorn
Published: 13 December 2012
Successful adherence to hepatitis C treatment may require physicians and care teams to address a wide range of factors, according to research from the United States and Germany presented at The Liver Meeting 2012, the 63rd annual meeting of the American Association for the Study of Liver Diseases (AASLD) in Boston last month.

Hepatitis C treatment presents a number of adherence challenges that are distinct from other disease areas, due to the use of pegylated interferon, which causes both physical and psychological side-effects that affect numerous areas of a patient’s life during a treatment course that may last up to 48 weeks, and which may be undergone several times if the first course is unsuccessful.

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Thursday, November 15, 2012

Towards HCV extinction with modern HCV treatment? “Yes we can!”

Towards HCV extinction with modern HCV treatment? “Yes we can!

C Torti1*, A Focà2 and G Carosi3
* Corresponding author: C Torti Author Affiliations 1 Unit of Infectious Diseases, University “Magna Graecia”, Catanzaro, Italy 2 Unit of Microbiology, University “Magna Graecia”, Catanzaro, Italy 3 Department of Infectious Diseases, University of Brescia, Brescia, Italy

BMC Infectious Diseases 2012, 12(Suppl 2):S1 doi:10.1186/1471-2334-12-S2-S1
The electronic version of this article is the complete one and can be found online at:
Published:12 November 2012

© 2012 Torti et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

With the availability of new drugs (Directly Acting Antivirals, DAA), the ambition to eradicate HCV infection even in patients infected by “difficult-to-treat” genotypes is becoming a reality. Benefits are enormous from an individual and a global perspective since about 3% people in the world are chronically infected, with a burden of 350,000 deaths related to the long-term complications of the disease (cirrhosis and hepato-cellular carcinoma).

The lessons we learned from HIV treatment (Highly Active Antiretroviral Therapy, HAART) are that antivirals pay-off by saving costs of clinical complications, patients’ stress and sufferance, and an increase in productivity of the infected individuals can be achieved for the sake of the society. Moreover, control of HIV replication induced by HAART can reduce the infection burden in the general population by decreasing HIV transmissibility [1]. This is extremely important for infection control when coupled with behavioural interventions since for HIV (as for HCV) an effective vaccine is not yet available. So, with highly active anti-HCV treatments we can now follow in HAART’s footsteps and even get ahead of HAART cost-effectiveness because HCV is eradicable and treatment can be stopped; by contrast, HAART must be continued life-long with a consequent incremental cost.

However, the road is still long and we should overcome several obstacles along the way. First, benefits of HCV treatment are maximal if treatment is prescribed, ideally, to all patients in need. Unfortunately, HCV epidemic is largely underground as most HCV infected individuals are unaware of their infection status. It is clear that screening policy should be optimized to detect as many cases as possible but it is unclear what is the most cost-effective strategy. The article by Zaltron et al., in this supplement discusses the epidemiological and clinical relevance of chronic HCV infection, including a review of the screening strategies and their cost-effectiveness [2]. The US Department of Health and Human Services has recently recommended HCV testing for “baby boomers” (i.e., adults born in the period 1945-65) as well as for adults of any ages reporting risk factors for HCV infection.

Second, in the current economic crisis, the paradox is that we should treat as many patients as possible but resources are limited in nature. Therefore, our focus must be on improving cost-effectiveness. With this objective in mind, three main questions for clinicians are: (i) Whom to treat? (ii) How to treat? (iii) When to treat? The expert-opinion paper by Petta & Craxì tries to answer to these questions [3]. For example, these authors underline that, by considering the initial virological response (Rapid Virological Response, RVR) and a new pharmacogenomics test (IL28B genotype) as predictors of treatment success, it is possible to limit the use of DAA with saving in cost and drug toxicity. Indeed, a standard treatment with Peg-interferon and ribavirin provides a similar rate of success as a “triple” regimen including a DAA in 25-33% of patients, provided that a prognostic score (including pharmacogenomics screening) is favourable and a RVR is achieved. In their papers, Nucara et al., review the key data on the use of IL28B genotype in clinical practice [4], while Colucci describes the molecular tests available as diagnostic tools and surrogate markers of treatment response [5].

Third, a lesson we learned is the importance of treatment adherence and retention into care [6]. So, clinicians should provide the maximum support to improve patients’ adherence to treatment and increase the rate of success. A dedicated approach with a strict follow-up should be in place, not only dealing with adverse events and assessing the virological response for a rapid adaptation of treatment, but also providing a psychosocial support to patients in need. Only through an inter-disciplinary collaboration following a patient-centred approach –with clinicians (specialists in infectious diseases, gastroenterology, and hepathology), virologists, and psychologists working together - we can improve our standard of care.

Fourth, the holy grail of HCV eradication at the population level could be achieved only if HCV transmission is controlled both with treatment and behavioural intervention. Along this line, it is important to define HCV transmission routes, identify the most-at-risk population and implement targeted prevention strategies. As appropriately underlined in the papers by Liberto et al., [7] and by Ciccozzi et al., [8], monitoring the genetic evolution of HCV is an essential step to develop efficient preventative measures for controlling HCV epidemic. Also, epidemiologic estimates and HCV genotyping will help public health officials to allocate appropriate health-care resources for managing this important condition.

What is the way forward?
New weapons against HCV are in the pipeline, allowing the construction of combination regimens taken orally. Phase 2 trials conducted in patients naïve to treatment support the efficacy of these convenient regimens without the addition of Peg-interferon and ribavirin. In their paper, Puoti et al., conclude that the advent of these regimens is eagerly awaited but caveats in the results of phase 2 trials suggest to dampen enthusiasm and conduct more studies on how to better use these innovative drugs [9].

Against this background we feel that it is important to set-up collaborative networks to implement operational research. The SINERGIE (South Italian Network for Rational Guidelines and International Epidemiology) project –object of the last article of the supplement [10]– will focus on upcoming research questions, with the final aim of improving the identification, care of patients, prevention strategies and health resource allocation in the Calabria Region (Southern Italy). This is a “global”, patient-centred research. We hope that this project will be extended to other Regions. Indeed, it is now time for a coordinated, inter-disciplinary effort to better use the new weapons that we have and those available in the future. Only with a better knowledge and understanding we can improve our skills and ability to fight an insidious enemy such as HCV, driving it to extinction.
Competing interests
The authors declare that they have no competing interests related to the contents of this paper.
Publication of this supplement was partly supported by an unrestricted grant provided by Roche. The articles were independently prepared by the authors with no input from Roche. Roche were not involved in selecting the articles for the supplement. The Peg-interferon treatment mentioned in this article is produced by Roche.
This article has been published as part of BMC Infectious Diseases Volume 12 Supplement 2, 2012: Proceedings of the Second Workshop of the Regional Study Group on HCV in the Calabria Region (Southern Italy). The virus-host-therapy pathway in HCV disease management: from bench to bedside in the era of Directly Acting Antivirals. The full contents of the supplement are available online at

  1. Cohen MS, Chen YQ, McCauley M, et al.: Prevention of HIV-1 infection with early antiretroviral therapy. New England Journal of Medicine 2011, 365:493-505. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  2. Zaltron S, Spinetti A, Biasi L, Baiguera C, Castelli F: Chronic HCV infection: epidemiological and clinical relevance. BMC Infectious Diseases 2012, 12(Suppl 2):S2. OpenURL

  3. Petta S, Craxì A: Therapeutic algorithms for chronic hepatitis C in the DAA era during the current economic crisis: whom to treat? How to treat? When to treat? BMC Infectious Diseases 2012, 12(Suppl 2):S3. OpenURL

  4. Nucara S, Caroleo B, Guadagnino V, Perrotti N, Trapasso F: Natural history and clinical response: “It’s the virus, stupid, or is it the host?”. BMC Infectious Diseases 2012, 12(Suppl 2):S6. OpenURL

  5. Colucci G: Molecular diagnostic and predictive tests in the evolution of chronic hepatitis C anti-viral therapies. BMC Infectious Diseases 2012, 12(Suppl 2):S8. OpenURL

  6. Lo Re V, Amorosa VK, Localio AR, et al.: Adherence to hepatitis C virus therapy and early virologic outcomes. Clinical Infectious Diseases 2009, 48:186-193. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  7. Liberto MC, Marascio N, Zicca E, Matera G: Epidemiological features and specificities of HCV infection: a hospital-based cohort study in a University Medical Center of Calabria region. BMC Infectious Diseases 2012, 12(Suppl 2):S4. OpenURL

  8. Ciccozzi M, Lo Presti A, Ciccaglione AR, Zehender G, Ciotti M: Phylogeny and phylodinamic of Hepatitis C in Italy. BMC Infectious Diseases 2012, 12(Suppl 2):S5. OpenURL

  9. Puoti M, Rossotti R, Travi G, Orso M, Moioli MC: Top topics in HCV research arena. BMC Infectious Diseases 2012, 12(Suppl 2):S7. OpenURL

  10. Torti C, Zazzi M, Abenavoli L, Trapasso F, Cesario F, Corigliano M, Cosco L, Costa C, Curia RL, De Rosa M, Foti G, Giraldi C, Leone R, Liberto MC, Lucchino D, Marascio N, Masciari R, Matera G, Pisani V, Serrao N, Surace L, Zicca E, Castelli F, Ciccozzi M, Puoti M, Focà A, SINERGIE Study Group: Future research and collaboration: the “SINERGIE” project on HCV. BMC Infectious Diseases 2012, 12(Suppl 2):S9. OpenURL

Thursday, July 12, 2012

Adherence to Hepatitis C Treatment Interventions: A Comparative Effectiveness Review

Adherence to Hepatitis C Treatment Interventions: A Comparative Effectiveness Review
July 11 2012
This report is based on research conducted by the Evidence-based Practice Center (EPC) under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD. The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.

The information in this report is intended to help clinicians, employers, policymakers, and others make informed decisions about the provision of health care services. This report is intended as a reference and not as a substitute for clinical judgment.

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Summary and Discussion ONLY
Overview of Main Findings

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We identified 11 studies—including five RCTs and six cohort studies—that addressed the comparative effectiveness of adherence interventions on health outcomes, surrogate markers, and patient adherence in hepatitis-C patients treated with the standard dual combination viral therapy. This existing body of literature, however, had substantial methodological and clinical heterogeneity.

The five included RCTs were rated as primarily poor quality and all included small sample sizes (29-250). While two good-quality cohort studies
88,90 included a relatively large number of patients (674 and 1560, respectively) and reported effect estimates that adjusted for the influence of potential risk factors, the remaining cohort studies had serious methodological limitations and generally had small sample sizes. We also found important variations in patient populations in all of the included studies, such as including patients with differing genotypes, history of substance abuse, and history of antiviral treatment. These factors may represent potentially important risk factors for treatment response and/or adherence (see Table 1). Patient populations also differed in racial and ethnicity distribution, as well as patient comorbidities.

How these studies evaluated adherence interventions was another source of heterogeneity. While grouped into four general categories, studies within a single category often investigated interventions that differed in their components and intensity. Interventions for managing adverse effects, for example, included medications addressing different conditions (e.g. epoetin for preventing anemia vs. antidepressants for depression), the use of antidepressants to prevent or to manage depression once it occurred, and cognitive behavioral therapy to prevent depression. Similarly, the two system-level interventions had very different approaches. One intervention evaluated the effect of specialty compared with standard pharmacy services and the other evaluated direct observation treatments on QOL or intermediate outcomes. The most consistent grouping was among the four patient-level interventions that enhanced patient education and/or support in order to improve adherence. Despite this, we were not able to identify the most successful intervention components given the lack of detailed descriptions, differences in intervention providers (e.g., nurses vs. physicians vs. psychologists), and approaches in the various interventions.

The included studies rarely reported health outcomes, which hampered our ability to directly interpret the evidence. Even among intermediate outcomes, we were unable to pool these outcomes due to differing definitions and measurement methods for adherence. Although the completion of HCV treatment is a commonly used definition, studies used different thresholds for defining treatment completion. We encountered additional issues to cross-trial comparisons for these studies, including studies that may target the completion of different antiviral agents (i.e., ribavirin vs. pegIFN-α, vs. both) or fail to clarify which antiviral agents they measured. The methods of measuring adherence included self-reported questionnaire, one-on-one interviews, pill counts, treatment administration records, or chart reviews. Several studies did not report this information. While SVR was commonly reported, this outcome was generally not comparable across studies due to diverse patient populations (with different likelihood of responding to treatment) across the body of evidence. 44

Outcomes of adherence interventions
There is a paucity of evidence assessing the effect of adherence interventions on health outcomes, particularly hepatitis C complications and mortality. Only two small, poor-quality studies56,60 reported data on QOL. Both studies suggested a tendency towards improved QOL in the adherence intervention groups, compared with usual care, despite the interventions reflecting completely different approaches in very different patient populations: the use of epoetin to manage treatment-associated anemia in 67 patients56 and the use of DOT in methadone maintenance clinic attendees.60We cannot eliminate the possibility that these positive findings are affected by publication, reporting, or other biases. Nonetheless, the fact that the few studies that reported any health outcomes tended towards benefit and also did not indicate a decrement in intermediate measures of adherence and treatment response (i.e., SVR) should be encouraging to patients, clinicians, and researchers as this would be consistent with overall potential health benefit.

The association of adherence interventions with virological response, particularly SVR, was the most commonly investigated outcome in the available literature. In general, adherence interventions tended to result in greater proportions of patients achieving a SVR (and EVR where reported), but few studies showed statistically significant differences between groups. When considered by intervention type, the evidence for increased SVR was most consistent for patient-level adherence interventions. Whether viewed by intervention type or considered as a whole, however, the available evidence is very weak in suggesting a clear improvement in SVR through adherence interventions.

Almost all included studies that measured adherence showed that interventions tended to improve adherence, despite the varying quality, interventions, definitions, and measurements. Additionally, the magnitude of the association remained consistent (or increased) over time (12 vs. 24 vs. 48 weeks) in those studies reporting adherence data in multiple follow-up time points.
87,88,90 The two fair-quality studies – one evaluating the effect of specialized pharmacy care61 and the other evaluating the effect of cognitive behavioral therapy94 – that showed no impact on adherence (and suggested a possible increase in nonadherence) after the interventions were imprecise in their estimates and relatively small. The existing body of literature offers little data about the harms associated with adherence interventions.

Strength of Evidence
We present the strength of the evidence for health outcomes for all studies and by intervention group in Table 12. The strength of the evidence for intermediate outcomes for all studies and by intervention group are presented in Table 13. We summarize this information by outcome and intervention group in narrative below.

Health Outcomes
Overall, we found insufficient evidence to determine the effect of adherence interventions on health outcomes. No studies reported morbidity, all-cause mortality, or HCV-specific mortality. In addition, no studies reported on HCV transmission. One poor-quality RCT and one poor-quality cohort study provided insufficient evidence for quality-of-life improvements that resulted 45

from patient adherence interventions due to risk of bias, imprecision, and lack of sufficient number of studies.

Two poor-quality RCTs with a high risk of bias provided insufficient evidence for harms related to adherence interventions. Both of these studies tested the effect of medications (e.g., epoetin and citalopram) to help manage side effects related to HCV treatment. Both studies reported that no patients showed adverse effects related to the use of these medications, but provided no additional details.

Intermediate Outcomes
The strength of evidence is insufficient-to-low for SVR achievement through adherence interventions that manage adverse effects, provide patient education and support, or directly oversee HCV therapy in patients at high risk for nonadherence (methadone maintenance clinic patients). This rating is due to medium-to-high risk of bias, imprecision, and lack of sufficient numbers of comparable studies.

We also found insufficient evidence on how interventions affected EVR based on two RCTs with high risk of bias. One study presented inadequate data, which precluded us from determining estimates of overall consistency and precision.

We deemed the strength of evidence to be insufficient (based on one fair- and two poor-quality RCTs) or low (based on five primarily fair-to-good quality cohort studies) for improved adherence as a result of various types of interventions. In general, the cohort studies found that adherence interventions had a consistent benefit on patient adherence.

System-Level Interventions versus Usual Care
We found insufficient evidence regarding the impact of system-level interventions on QOL, SVR, and adherence. No evidence exists regarding mortality and morbidity.

Regimen-Related Interventions versus Usual Care
We found insufficient evidence on the association between regimen-related interventions and patient adherence. We found no evidence about other outcomes.

Patient-Level Interventions versus Usual Care
We judged the strength of evidence for the association between patient-level interventions and the achievement of SVR to be low. We made this valuation based on a medium risk of bias across three studies with consistent effects, despite imprecise estimates and the fact that these outcomes were indirect.

The studies provided generally consistent and precise effect estimates related to patient adherence. We judged the strength of evidence to be moderate given the relatively few studies (four) with overall medium risk of bias and the indirectness of the outcome. More research in this area may affect this estimate and our confidence in the effect estimate. Only one study examined the effect of a patient-level intervention on EVR. As a result, we found the strength of evidence to be insufficient. There was no evidence regarding health outcomes, including harms related to patient-level adherence interventions. 46

Adverse Effect Management Interventions versus Usual Care/Placebo
The strength of evidence on QOL was found to be insufficient, based on a relatively small poor-quality RCT. The evidence on harms was also insufficient given the high risk of bias and the lack of detail provided. Similarly, we judged the evidence on SVR, EVR, and adherence to be insufficient due to high risk of bias, the inconsistency and imprecision of the effects, and the indirectness of the outcomes. Again, no evidence addressed the effects of the intervention on mortality or morbidity.

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Table 12. Strength of evidence for final health outcomes

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Table 13. Strength of evidence for intermediate health outcomes

Applicability of the Evidence to United States Health Care System
The findings from included studies have generally good applicability to HCV patients in the United States receiving standard (dual) combination therapy of pegIFN-α and ribavrin. However, given the recent recommendation for adding protease inhibitors to the existing combination therapy for patients with genotype 1 HCV, which represents the preponderance of HCV infections in the United States,16 the available evidence is unlikely to be directly applicable to the present patients with genotype 1 HCV.30 In general, patient adherence to medication regimens often decreases as the complexity of the treatment regimen increases. It is plausible that the addition of a third agent administered multiple times per day is likely to further impact patients’ ability and likelihood of complying to treatment. In June 2012, the CDC called for universal HCV screening of the "baby boomer" population (i.e., individuals born between 1945 and 1965).96 Such screening could result in a rapid increase in the number of individuals being treated for HCV and subsequently struggling with adherence.

Seven of the 11 included studies were conducted in the United States. The remaining trials were conducted in France (k=2) or Italy (k=2). Two studies enrolled patients from a primary care setting,93,94 two from specialized hepatology units,56,61 two from addiction management centers,60,89 and four from multiple clinics.87,88,91,92 The other trial did not specify study setting . These studies included both academic and nonacademic centers.

Most studies had wide inclusion criteria, although a number of studies excluded those presumed to be less responsive to therapy (i.e., with coexisting infections or previous history of HCV treatment) or those at risk for poor adherence (i.e., with psychological illnesses or current or previous abuse of substance). Patients coinfected with HBV, HIV, and/or hepatitis D virus (HDV) were excluded in five studies,56,60,61,87,91 those with ongoing depression were excluded in two,91,93 and patients having a history of and/or active substance use were excluded in two studies.92,93 Across all studies, there were a larger proportion of males than females and the majority of patients were Caucasian. Patients with HCV genotype 1 to 4 were the primarily studied population, and the majority of patients had genotype 1 HCV in seven of the 11 included studies. Three studies60,89,94 exclusively enrolled patients currently abstinent from drugs and other substances, but seeking treatment for drug abuse in methadone maintenance or other addiction centers. These data, although limited, suggest that patients at risk for poorer adherence may be appropriate candidates for HCV therapy coupled with effective adherence interventions. Generally, patients included in those studies were representative of the prevalent HCV population in the United States.

Patients in the included studies exclusively used standard doses of combination antiviral therapy of pegIFN-α and ribavirin. The intended duration of treatment in all studies was 48 weeks for patients with genotype 1 or 4, and 24 weeks for those with genotype 2 or 3. Again, although the antiviral therapy was consistent with the current recommendations for patients with genotypes 2, 3, or 4, the currently recommended treatment for patients genotype 1 has shifted from the standard combination therapy to the triple therapy, in which a protease inhibitor is added to the combination of pegIFN-α plus ribavirin.11

A wide variety of adherence interventions were investigated in the included studies. These interventions included simplifying dosing, the use of medications or counseling for managing adverse effects, patient education and support by various parties to motivate antiviral medication use or help manage adverse effects, and provision of care within specialized care delivery systems (e.g., specialized pharmacies, methadone clinics). We found no studies that directly 50 compared the effectiveness of one type of intervention to another type of intervention. In addition, very little detail was given in the majority of the studies regarding the specific intervention components, messages, frequency, and duration. Thus, it is unclear how feasible or effective these interventions would be in real-world settings.

Potential Limitations of our Approach

Our approach has a number of potential limitations. Our systematic review methodology may not be the ideal method to synthesize findings across studies that are predominately poor quality, with a high level of heterogeneity. Additionally, there are likely major limitations in determining the effect of treatment adherence interventions on both intermediate and final health outcomes because of multiple confounding factors that also affect response to treatment (e.g., age, genotype, body mass index, viral load). Because we are limited to the data that are presented in the primary studies, we were unable to adjust for many of these potential confounders. We discuss other limitations of the literature below.

We also excluded studies with length of followup shorter than 12 weeks. Although these short-term results may be of interest, such studies can only provide evidence on rapid virological response and possibly early virological response, both of which were judged as much less important intermediate outcomes than SVR.

We did not include non-English language studies, and thus may be missed some relevant data. Our search found only 99 citations for potentially relevant studies that were published in languages other than English. The majority of these studies were written in Spanish, French, and German. More importantly, the vast majority of non-English studies may be less applicable to the United States health care system. Therefore, their findings may be of very limited value to the context of our review.

Limitations of the Literature
There are several major limitations of the available literature. First, the studies are limited to relatively small sample sizes and are of suboptimal quality. Three of the five RCTs had sample sizes smaller than 50, and the other two included 134 and 250 patients, respectively. One RCT was of fair quality, and the other four were considered poor. The quality of cohort studies varied. In the only two good-quality studies,88,90 a relatively large number of patients (674 and 1560) were included. Other cohort studies were generally small and had important methodological limitations, including the fact that almost all failed to adjust for the influence of potentially important confounding factors. Additionally, the subpopulations varied substantially in terms of their risk for nonadherence and nonresponse to treatment across studies, which hamper our ability to pool data or results across studies.

Second, inadequate reporting of details about study design and conduct was prevalent across all studies. This resulted in substantial difficulties collecting data and determining the quality and applicability of study findings. For example, limited information was available about the intensity and length of interventions and the parties that carried out interventions. Collectively, these issues represent particularly important potential limitations because most interventions were behavior-based, and lack of implementation details makes it challenging to judge the fidelity, comparability, and applicability of study findings. In another example, many cohort studies, particularly retrospective studies, failed to detail the sources of data, the approaches to 51 acquiring and measuring data, and strategies for controlling the influence of bias. Data on loss to followup were also inadequately reported. There was a significant and disproportionate loss to followup between intervention and control groups in three studies,60,90,94 which impedes our ability to interpret the true effect of interventions.

Third, there were several serious variations and ambiguities in the definition of adherence used across studies. For example, two studies61,89 defined adherence as "completion of treatment." However, it was unclear which agent or agents (pegIFN-α vs. ribavirin vs. both) this referred to, whether it allowed for any missed doses over the course of treatment, and to what extent it reflected patient- versus physician-initiated changes in treatment. Of the eight studies reporting adherence data, at least five different definitions were used (Tables 8-11). The widely varying definitions of adherence used by study investigators created a major obstacle in our ability to compare findings across studies; this also hampers the ability of clinicians, patients, and policy-makers in using the evidence for practice and decision making.

Many studies failed to distinguish between physician-initiated reductions in dosage or therapy duration and patient-directed nonadherence. Physician-initiated dose-modification or even discontinuation generally represents individualized patient care, which should not be considered as nonadherence. Patient-directed dose-reduction and discontinuation may be due to toxic effects, and many other reasons (e.g., patients not remembering dosing schedule, having difficulties in using pegIFN).97 Although debate continues about the inclusion of physician-directed treatment discontinuation or modification in defining "nonadherence,"42 for this review we decided that patient-directed nonadherence was the primary focus. Thus, we excluded many studies that did not present patient- and physician-directed treatment discontinuation separately in their analyses.

Populations varied substantially in terms of their risks for nonresponse to treatment (e.g., what genotypes, previous treatment history, or ages were represented) and their risks for potential nonadherence (i.e., current or past drug users). Within studies, these potentially important factors were not generally assessed for baseline comparability or controlled for in analyses. This was particularly true in prospective and retrospective cohort studies. Of the five cohort studies, only two adequately adjusted for the influence of confounding factors.88,90 Other studies either failed to adjust for or inadequately controlled for the influence of other important factors.

Another important limitation in this literature is the fact that all identified studies relied on surrogate outcomes. Likewise, none reported long-term health outcomes besides two that reported on quality of life. The goal of adherence interventions is to improve treatment response, typically SVR, and ultimately improve hepatitis C complications, such as cirrhosis and hepatocellular carcinoma. However, no evidence has examined whether interventions for adherence improve those final long-term health outcomes. Additionally, available evidence assessing the comparative effectiveness of interventions for intermediate outcomes such as SVR is very weak.

Finally, while treatment standards for HCV have been rapidly evolving, available studies have only included patients receiving dual therapy through a standard combination of pegIFN-α and ribavirin. Further research is needed to determine how patient adherence may change with the addition of a third antiviral agent into the standard treatment regimen, and how adherence interventions should be designed to incorporate the new class of drugs. Prior reviews examining medication adherence have found that patient adherence decrease as treatment regimens become 52

more complex.
54 However, it is unclear how adherence may change in patients undergoing antiviral therapy for chronic HCV infection with the new therapy regimen.

Clinical Implications
Available evidence does not provide a clear direction for clinical practice to improve adherence in hepatitis C treatment. The included studies suggest that adherence interventions tended towards improved adherence and/or SVR, particularly those focused on reducing pill burden or providing additional patient education and support. This result was evident despite including various patient populations, use of diverse interventions, and suboptimal quality. Our review found four studies with an overall moderate strength of evidence supporting patient-level interventions to improve treatment adherence. However, it continues to be uncertain which specific interventions are effective and what degree of improvement could be expected in current practice, particularly considering the recent updated recommendation for triple therapy in genotype-1 patients.

In general, the available evidence on guiding efforts to improve adherence to recommended treatments of patients with chronic hepatitis C remains very limited. While the uncertainty continues, care providers may not be compelled to formally initiate interventions in order to achieve a higher level of patient adherence to hepatitis C antiviral treatment. Nonetheless, general principles such as patient education and support and reducing pill burden that have been shown to increase patient adherence to treatment may be considered, since existing epidemiological studies suggest a consistency in the association between a higher level of adherence and an improved SVR.

Evidence Gaps
Substantial gaps exist for all types of adherence interventions. Across all trials, no trials investigated the impact of adherence interventions on long-term health outcomes, such as decompensated cirrhosis, hepatocellular carcinoma, and mortality. Nearly all studies included genotype-1 HCV patients that received the standard combination antiviral therapy. Therefore, the results may not be applicable to current clinical practice.

For system-level interventions, evidence was inconsistent regarding SVR and substantial uncertainty remains regarding adherence. While it appears that dose simplification is an effective regimen-related strategy to improve adherence, the evidence on SVR is lacking. While generally low, the evidence of patient-related interventions suggested a trend of improvement in SVR and adherence. The evidence for adverse effects management is conflicting, although studies with fair-quality RCTs suggest a trend of improvement in SVR.

We identified no studies that evaluated the effect of an intervention that targeted two or more levels of influence (e.g., system-level changes plus patient counseling). It is likely that the most effective interventions would include a combination of changes made to the systems and settings in which HCV care is received, the packaging and delivery of medications, the support and education provided to HCV patients including strategies to helping patients manage HCV treatment-related side effects through medications or counseling methods. There is a need in the HCV literature to design and test such comprehensive approaches. 53

Future Research
Future research should use more rigorous methods in the design and conduct of hepatitis C adherence intervention studies. Although various designs can assess the comparative effectiveness of adherence interventions, RCTs remain the optimal approach for hypothesis testing.
98 While cohort studies may be used, they are susceptible to selection bias and are less able to account for unknown prognostic factors than RCTs,99 despite the use of novel approaches such as propensity scoring.100 Future studies should have sufficient power for testing hypotheses, with longer follow-up periods to include long-term health outcomes. As noted earlier, the quality and design of the available literature was a serious limitation in our review.

Studies should also strive to use direct health-related outcomes such as HCV-morbidity, mortality, and quality of life, in addition to the surrogate outcomes that are most often reported in the current literature. While these outcomes will require longer follow-up and may be challenging when conducting studies, reporting these outcomes will improve the applicability of study findings to clinical practice. Longer-term outcome data, such as cirrhosis and hepatocellular carcinoma, are less readily available in RCTs. Nonetheless, it is possible to use cohort studies that rely on patient registries to address this issue.

The recommended treatment for genotype-1 patients has shifted from the standard combination therapy of pegIFN-α plus ribavirin to triple therapy including protease inhibitors.
11 As such, the available evidence is of very limited value to the treatment of genotype 1 HCV. Although the available literature base may provide indirect evidence regarding interventions for this population, it is unclear how adding new antiviral agent will affect patient adherence. In particular, the administration of the protease inhibitor is complex, and adding this agent to the standard combination therapy will further complicate the treatment. Uncertainty will remain until well designed and conducted trials are available, and adequately powered RCTs testing adherence interventions incorporating this new treatment regimen are conducted.

There is also a strong need for standardizing the definitions of adherence in the context of chronic hepatitis C treatment. Multiple components—including treatment duration, dosing, timing, and intensity—are used in the varying definitions of adherence that we found, and treatment adherence can be associated with one or more antiviral agents in hepatitis C treatment. The multiplicity of domains and components may result in many variants in the definitions about adherence to hepatitis C treatment. The "80/80/80" criterion is often used in hepatitis C literature but has two major limitations. First, this definition will no longer be applicable to the triple antiviral therapy for genotype 1 HCV patients. Second, there seems a continuous relationship between the level of adherence and the treatment response 41 so defining adherence vs. nonadherence based on an arbitrary threshold may thus be suboptimal.

Future studies should clearly distinguish physician-initiated dose-reduction or discontinuation from patient nonadherence to treatment. Although physician initiated dose-reduction or discontinuation seems related to adherence, this treatment change is typically due to vital adverse effects associated with antiviral therapy, and is based on the treatment protocol. The nature of this change differs from patient nonadherence, in which patients fail to match agreed treatment plan probably because of difficulties in remembering taking medications or following the complex treatments, unwillingness to continue the treatment, and reduced quality of life.

In our exploration of risk factors associated with treatment response, we have found a number of potentially important factors associated with treatment response and patient nonadherence (Table 1). Future studies, particularly observational studies, should consider the 54 issue about patient comparability in exposure and nonexposure groups. Efforts are needed to adequately adjust for the influence of those factors.

Finally, as noted earlier, many of the studies we found were of poor quality, with inadequate reporting of study design and intervention details. Future studies should include clearer and more detailed reporting of study design and conduct. Studies need to provide sufficient information about how adherence interventions are undertaken, including the parties of undertaking intervention, such details of interventions such as intervention components, intensity, and duration. Studies should also describe methodological characteristics in more details. RCTs should report details on patient selection, allocation, and followup. In the results, the data on loss to followup should be clearly reported. Cohort studies should provide detail on collected variables, sources of data, accuracy of measurements, and approaches that are used to minimize bias. In addition, studies should be more explicit and clear in defining and measuring adherence. Ideally, study reports should include a section to describe the definition and measurement of adherence.