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Original Investigation |

The Risk of Long-term Morbidity and Mortality in Patients With Chronic Hepatitis C Results From an Analysis of Data From a Department of Veterans Affairs Clinical Registry FREE

Jeffrey McCombs, PhD1; Tara Matsuda, BA1,2; Ivy Tonnu-Mihara, PharmD2; Sammy Saab, MD3; Patricia Hines, BA4; Gilbert L’Italien, PhD4; Timothy Juday, PhD4; Yong Yuan, PhD4
[+] Author Affiliations
1Department of Clinical Pharmacy and Pharmaceutical Economics and Policy, School of Pharmacy, Leonard Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles
2Veterans Affairs Long Beach Healthcare System, Long Beach, California
3Departments of Medicine and Surgery, David Geffen School of Medicine, University of California, Los Angeles
4Global Health Economics and Outcomes Research, Bristol-Myers Squibb, Plainsboro New Jersey
JAMA Intern Med. 2014;174(2):204-212. doi:10.1001/jamainternmed.2013.12505.
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Published online

Importance  The impact of viral load suppression, genotype, race, and other factors on the risk of late-stage liver-related events in patients with hepatitis C (HCV) has been assessed previously using data from small observational cohorts or clinical trials. Data from large real-world practice samples are needed to improve risk factor estimates for late-stage liver events and death in HCV.

Objective  To describe the natural history of HCV in real-world clinical practice.

Design, Setting, and Participants  Observational cohort study. Patients with a detectable viral load (>25 IU/mL) and a recorded baseline genotype were selected from the Veterans Affairs (VA) HCV clinical registry (CCR), which compiles electronic medical records data from 1999 to present.

Exposures  Risk factors included genotype, race, age, sex, and time to achieving an observed undetected viral load.

Main Outcomes and Measures  The primary outcomes were time to death and time to a composite of liver-related clinical events. Secondary outcomes included the components of the composite clinical outcome. Outcomes were measured using a time-to-event format and were analyzed using Cox proportional hazards models.

Results  A total of 28 769 of 360 857 unique HCV CCR patients met all study criteria. Only 24.3% of patients received treatment, and 16.4% of treated patients (4.0% of all patients) achieved an undetectable viral load. The unadjusted death rates were 6.8 (95% CI, 6.0-7.7) per 1000 person-years for patients who achieved viral load suppression vs 21.8 (95% CI, 21.5-22.2) deaths per 1000 person-years in patients who did not achieve this goal. Cox model results found that achieving viral suppression reduced risk of the composite clinical end point by 27% (hazard ratio [HR], 0.73 [95% CI, 0.66-0.82]) and the risk of death by 45% (HR, 0.55 [95% CI, 0.47-0.64]). Genotype 2 patients were at significantly lower risk, and genotype 3 patients were at higher risk for all study outcomes relative to genotype 1. Black patients were at lower risk for all liver events than white patients.

Conclusion and Relevance  Achieving an undetectable viral load was associated with decreased hepatic morbidity and mortality. It remains to be determined whether newer treatment regimens can offer higher response rates with fewer adverse effects in real-world settings.

Hepatitis C virus (HCV) affects approximately 130 to 170 million people worldwide,1,2 and an estimated 3.2 million people in the United States.3 This US estimate may be low because high-risk groups such as the incarcerated and the homeless were not included in the data. Alternative estimates put the number of US patients with chronic HCV at between 5.2 million and 7 million.4

Patients with HCV are at risk of developing liver-related complications such as cirrhosis, liver failure, and hepatocellular carcinoma (HCC).1,2,57 Using data from the US Department of Veterans Affairs (VA), Butt et al8 found that infection with HCV increased the risk of death by 37%.8Hepatitis C is also the leading indication for liver transplant, and the US incidence of HCC is increasing.9 Davis et al10 used a simulation model to project that 13.1% of US patients living with HCV in 2005 will die of liver-related causes by 2030. Rein et al11 extended this projection to 2060, at which time they estimate that 36.8% of the 2005 HCV cohort will have died due to liver causes.

The impact of genotype and demographic factors on the clinical course of HCV may be significant. For example, genotype 1 is thought to be highly correlated with disease progression, although Seeff5 casts some doubt on this conclusion. Kallwitz et al12 found that BMI and Hispanic ethnicity were associated with disease progression, while African Americans had a lower rate of disease progression than white patients.

Sustained viral response (SVR) to treatment is associated with decreased liver-related morbidity and mortality. The REVEAL HCV study13 found a significant association between undetectable viral load and liver-related events in Taiwan. Van der Meer et al14 found that the 36% of patients with advanced hepatic fibrosis who achieved SVR had reduced all-cause mortality and reduced incidence of liver-related events compared with those who did not achieve SVR.14

To our knowledge, no previous studies have investigated a wide range of the risk factors associated with mortality and morbidity in a large real-world cohort of patients at all levels of disease progression. The objective of the present research is to use HCV RNA data to quantify the impact of viral load suppression on liver-related morbidity and overall mortality using a large cohort of patients with HCV, including those in the early stages of disease progression, while controlling for the impact of genotype, race, age, sex, and other factors.

Data

The data used in this study were taken from the VA clinical case registry (CCR) system for HCV-infected patients. The VA institutional review board approved the study. Potential patients with HCV were identified by the presence of an HCV-related International Classification of Diseases, Ninth Revision (ICD-9) diagnosis code or a positive viral load assessment using the hepatitis C antibody test, the hepatitis C RIBA (recombinant immunoblot assay), or the qualitative hepatitis C RNA test. A local CCR coordinator then manually confirmed or rejected the patient for HCV CCR inclusion. After confirmation, all historical data from the patient’s electronic medical record (EMR) were pulled and added to the CCR. The VA EMR system was fully implemented in 1999, and the data period for this study covers the entire period over which EMR data were available from all VA regions from 1999 to 2010.15

An intermediate patient-level analytic database was created consisting of summary variables for each month before and after the patient’s CCR enrollment (index date). Summary data were organized as follows:

  1. Patient demographic data were recorded (age in months at baseline, sex, race, ethnicity); race and ethnicity data were based on patient self-report.

  2. The patient’s diagnostic profile was created, consisting of monthly dichotomous variables reflecting the diagnoses recorded each month.

  3. Monthly dichotomous variables were created for hospital admissions for any diagnosis and for liver-related diagnoses.

  4. Prescription drug data were used to create monthly variables indicating when patients received HCV-related treatment (peginterferon alfa 2a or 2b, interferon alfa 2a or 2b, interferon alfacon-1, boceprevir, or telaprevir). The use of ribavirin alone was not considered to be a drug therapy for HCV.

  5. The objective of treatment is to suppress the patient’s HCV viral load to undetectable levels. A primary objective of this research was to document the impact of viral load suppression, accounting for the temporal relationship between achieving an undetectable viral load and event date. To achieve this, we calculated the time to the first undetectable viral load test result as our covariate of interest and defined our primary and secondary outcomes using time-to-event formats. This approach is less stringent than measuring time to SVR, the gold standard for measuring treatment response. Time to SVR is significantly more difficult to calculate, requiring the determination of the time at which the patient maintained consistent viral load suppression for a minimum of 6 months following the termination of treatment.

Sample Selection Criteria

To estimate the risk reduction achieved if patients achieved viral load suppression, all patients were screened for a detectable baseline HCV viral load (>25 IU/mL). Study patients were also screened for a recorded genotype within 6 months of their index date. Longitudinal data were then used to measure time-to-outcome events and estimate the impact of achieving an undetectable viral load and other factors on event risk.

Primary and Secondary Outcomes

Patients infected with HCV are at risk for progressive liver disease and related complications such as cirrhosis, liver failure, HCC, and death.1,2,57 The primary outcomes specified for this analysis were all-cause mortality and a composite of newly diagnosed cirrhosis (compensated and decompensated), HCC, or a liver-related hospitalization. The time to the composite event was set at the earliest event date for any of the composite events. Because HCV infections commonly go undiagnosed or untreated until complications are observed, sensitivity analysis was conducted in which clinical composite variables were measured using a 1-year postindex washout period during which the clinical events were not counted. The secondary outcomes included the individual elements of the clinical composite analyzed individually.

Monthly dichotomous variables were created for the outcomes of the study based on recorded diagnostic codes (eg, diagnosis of cirrhosis) and selected CPT-4 codes (Current Procedural Terminology, Fourth Edition) included in data from hospital admissions and outpatient services. Hospitalizations were defined as being liver related if the primary diagnosis for the hospitalization was one of those listed in the eAppendix in the Supplement, building on the fact that all study patients had a positive HCV viral load. Cirrhosis and HCC outcomes were compiled by searching the inpatient, outpatient, and problem lists for ICD-9 codes 571.5, 571.2, and 571.6; and 155, 155.1, and 155.2, respectively. Decompensated cirrhosis was defined as a diagnosis of cirrhosis and a diagnosis of hepatic coma (70.44, 71.71, 348.3, 348.31, 572.2), portal hypertension (572.3), hepatorenal syndrome (572.4), jaundice (782.4), ascites (789.59), or esophageal varices (456, 456.1, 456.2, 456.21) or a FIB-4 score16 higher than 3.25. The FIB-4 score can also be segmented into 3 categories that have been found to correctly classify nearly 73% of liver biopsies and to have an 82.1% positive predictive value to confirm the existence of significant fibrosis in an HCV-infected cohort.16

Statistical Methods

The time-to-event variables for primary and secondary outcomes were analyzed using Cox proportional hazards models to test the correlation between potential predictors and study end points. Time to first observed undetected viral load was included in the analyses as a time-dependent independent variable to measure the impact of viral load suppression on each primary and secondary liver-related event controlling for genotype, race, age, sex, and other factors. Race and ethnicity were initially included as separate categories, but the significant correlation between race and ethnicity in the VA sample resulted in only race being included in the final model specifications. The impact of a diabetes diagnosis at baseline and any hospital admission in the 6 months prior to the patient’s index date were included in our list of risk factors based on statistical significance.

A sensitivity analysis was conducted using only those patients with a baseline FIB-4 score to test if the core results on the impact of viral load suppression, genotype, and other factors were sensitive to controlling statistically for the patient’s baseline fibrosis level.

Descriptive Statistics

The HCV CCR database contained information on 360 857 unique patients from which a population of 128 769 patients met all study inclusion criteria, including a detectable viral load and genotype data at baseline. Only 24.3% of patients in the analytic sample received treatment at any time following HCV diagnosis, while only 16.4% of treated patients achieved an undetectable viral load after treatment (Table 1). The mean (SD) postindex period was 6.1 (3.0) years. The VA patients with HCV were predominately men of either white or black race (51.4% and 31.3%, respectively). The mean (SD) age was 52.0 (6.9) years, and close to 80% of patients were genotype 1. Just over 42% of study sample patients had baseline data for their fibrosis stage at baseline (FIB-4 score), and only 19% had a FIB-4 score higher than 3.25, which is correlated with a Metavir fibrosis stage of F3 to F416 or an Ishak fibrosis stage of F4 to F6.17 The FIB-4 score was not used in the core analysis owing to this high level of missing data. Instead, a sensitivity analysis was conducted using only those patients with baseline FIB-4 scores, and the patient’s FIB-4 category was entered as a potential risk factor.

Absolute Risk of Liver-Related Events and Death

Table 2 lists data on the absolute risk of the composite event and death across the risk factors of interest in this analysis. There were a total of 35 253 composite events and 15 458 deaths in our sample over a total of 734 829 person-years of data. Significantly higher event rates and death rates were experienced by male patients, white patients, and patients with genotype 3. Higher unadjusted composite event rates were found in treated patients than in untreated patients and in those who achieved viral suppression than in those who did not. However, patients who achieved viral suppression exhibited lower unadjusted death rates, which may reflect the delays in therapy until patient became symptomatic.

Table Graphic Jump LocationTable 2.  Absolute Event Risk by Risk Group
Predictors of Liver-Related Events

The factors associated with our primary outcomes are listed in Table 3.

Table Graphic Jump LocationTable 3.  Impact of Viral Clearance and Other Risk Factors on the Risk of Late-Stage Liver Eventsa
Viral Load Suppression and Genotype

Patients who achieved an undetectable viral load significantly reduced their risk of the composite clinical end point by 27% (hazard ratio [HR], 0.73 [95% CI, 0.66-0.82]) and their risk of death by 45% (HR, 0.55 [95% CI, 0.47-0.60]) relative to patients with a detectable viral load over their entire postindex period. The risk reduction associated with the composite clinical end point measured after a 1-year washout period increased slightly to a reduction of 28% (HR, 0.72 [95% CI, 0.64-0.81]).

Patients with genotype 2 were consistently at lower marginal risk for liver-related events compared with patients with the more common genotype 1, controlling for viral load suppression and other risk factors. The risk reduction for the composite end point was 23% (HR, 0.77 [95% CI, 0.74-0.80]). The risk of all-cause mortality for genotype 2 patients was reduced by 20% relative to genotype 1 patients (HR, 0.80 [95% CI, 0.76-0.84]). Patients with genotype 3 were consistently at higher risk than patients with genotype 1. The estimates of marginal increased risk for those with genotype 3 ranged between 11% for the composite clinical end point (HR, 1.11 [95% CI, 1.07-1.16]) to a 17% increase in the risk of death (HR, 1.17 [95% CI, 1.11-1.24]). Other genotypes were infrequent in the VA patient population and were collapsed into a single category for which the estimated HRs were not generally significant.

Patient Characteristics

Male sex significantly increased the risk of the composite clinical outcome by 11% (HR, 1.11 [95% CI, 1.04-1.19]) and the risk of death by 58% (HR, 1.58 [95% CI, 1.38-1.80]). Each additional year of age increased the risk of death by 6% (HR, 1.06 [95% CI, 1.05-1.06]) but only increased the risk of the composite outcome by less than 1% (HR, 1.0001 [95% CI, 1.0012-1.0016]). Whites were consistently at higher risk for all late-stage liver events than blacks and other races. A diagnosis of diabetes at baseline and a hospital admission within 6 months prior to the index date were significantly associated with liver-related events.

Secondary Outcomes

The risk prediction models for the individual late-stage liver events that compose the composite clinical outcome are listed in Table 4. As with the primary outcomes, achieving an undetectable viral load significantly reduced the risk of all clinical events. Other estimates were consistent with the results for the composite event.

Table Graphic Jump LocationTable 4.  Impact of Viral Clearance and Other Risk Factors on the Risk of Secondary Outcomesa
Sensitivity Analysis: Impact of Fibrosis Stage

Risk models for the composite end point and all-cause mortality were reestimated using only patients with a baseline FIB-4 score (n = 54 420), and FIB-4 stage was entered as a potential risk factor. Several results are noteworthy (Table 5). First, our core estimates of the impact of achieving an undetectable viral load on event risk were very robust (Table 3). If anything, the estimated risk reduction associated with viral load suppression increased when the analysis took into account baseline levels of fibrosis. Second, risk of the composite event and all-cause mortality was monotonically and positively related to the patient’s baseline fibrosis level. FIB-4 stage 2 increased risk of the composite event by 47% (HR, 1.47 [95% CI, 1.42-1.54]), while stage 3 HR for the composite event was 3.44 (95% CI, 3.29-3.61). The corresponding HRs for the risk of death were 1.46 (95% CI, 1.37-1.55) for stage 2 and 3.77 (95% CI, 3.55-4.00) for stage 3. In the FIB-4 analyses, each additional year of age was estimated to decrease event risk, but this is likely owing to age appearing in the FIB-4 calculation. Genotype 3 was also associated with lower risk than genotype 1 in the FIB-4 sensitivity analyses, but the estimated HRs were not statistically significant.

Table Graphic Jump LocationTable 5.  Impact of Viral Clearance on the Risk of Late-Stage Liver Events Adjusting for Fibrosis Stagea

We found that viral load suppression was associated with decreased risk of liver-related events using data from a large cohort of real-world patients with HCV at various stages of disease progression while controlling for other risk factors, including genotype. This study considered a wide range of liver-related events as study outcome variables and measured these outcomes and viral load suppression as time-dependent outcomes over as long as 10 years, depending on each patient’s availability of data. Finally, the risk factors studied here were derived from EMR data and included both baseline data (sex, race, genotype) and other time-dependent risk factors (BMI, age) that take full advantage of the temporal relationships between events and patient risk factors.

Previous research has clearly documented that patients with an undetectable viral load18 or who achieve SVR due to treatment are at significantly lower risk of late-stage liver events and death.14,19 Our results are consistent with these earlier studies. Patients who achieve an undetectable viral load reduce their risk of death by 45% and reduce their risk of our composite end point of liver-related events by 27% relative to those patients whose viral load was detectable over the entire period following their diagnosis. More importantly, very few patients achieve an undetectable viral load without treatment (39 of 97 485 untreated patients).

While antiviral therapy can lead to viral eradication and reduced event risk, its effectiveness under real-world clinical conditions is limited by adverse effects and other factors. In this study, only 1 in 4 patients with HCV and a detectable viral load were willing to initiate treatment. Once treated, only a fraction of patients achieved the minimum treatment response of a single undetectable viral load test. Our rate of treatment “success” of 16.4% is consistent with that of other studies. For example, Kramer et al19 documented that the SVR rates achieved using standard antiviral therapy in real-world clinical settings ranged from 14% to 24% for HCV genotype 1 and 37% to 52% for genotypes 2 or 3. In the present study, 16.4% of treated patients achieved viral suppression. The independent role of genotype on the risk of liver-related events has been controversial, mostly because of limited number of patients with non–genotype 1 infection.20,21 Our results are consistent with those of Larsen et al,22 who demonstrated that genotypes 1 and 3 may be associated with greater rates of disease progression than other HCV genotypes. Our results are also consistent with prior observations demonstrating low risk of disease progression in African American patients.23,24 Kallwitz et al12 found that both Hispanics and non-Hispanic whites had a higher risk of cirrhosis than blacks (odds ratios, 1.6 and 2.4, respectively). This study found that incidence rates of HCC were also slightly lower in blacks.

Our sensitivity analysis using baseline FIB-4 scores to define fibrosis stages found these staging variables to be highly predictive of hepatic morbidity and mortality. This supports the expanded use of noninvasive fibrosis staging methods as substitutes for liver biopsy. It is noteworthy that the inclusion of baseline FIB-4 levels into the statistical models eliminated the estimated increase in risk associated with genotype 3 relative to genotype 1. The exact reason for the differential risk effects associated with genotype 3 is unclear but may be related to the higher risk of hepatic steatosis in genotype 3 patients.2527

The use of HCV protease inhibitors is associated with a significant increase in SVR rates relative to standard therapy but also with increases in frequency and severity of adverse effects such as anemia, neutropenia, thrombocytopenia, rash, and gastrointestinal events.28 Early reports suggest non–interferon-based therapies will deliver increased SVR rates while reducing associated substantial adverse effects that limit tolerability.2933 Clearly, new therapeutic options might offer significant benefits to patients and the health care system if their introduction improves the willingness of patients to initiate therapy and the likelihood that the patient will achieve viral suppression leading to SVR. Natural history data and an understanding of the challenges and expectations from patients are essential to help both providers and patients make informed decisions about when to initiate antiviral therapy and to motivate patient adherence.34

There are several important limitations in our study. First the VA study population differs significantly from the US population, consisting mostly of white and black men. Therefore, results for the risk associated with sex and the catch-all category of “other race” should be viewed with caution. Nevertheless, most US patients with HCV are male,3,4 and VA is the largest provider of care to chronically HCV-infected patients in the United States.35

We did not measure SVR, which requires that an undetectable viral load be maintained for 6 months following the termination of treatment. Instead, we used time to the patient’s first undetectable viral load test. It is possible that patients achieving viral load suppression at 1 point can relapse. Nevertheless, our findings are consistent with those of previous studies that even suboptimal therapy is associated with survival benefits.8

The sensitivity of HCV viral load tests has changed over time, presented a challenge in defining an “undetectable” viral load. Many older tests used prior to 2004 have a lower threshold of 600 IU/mL, below which the result would be reported as undetectable. Newer tests are sensitive down to 10 IU/mL. For patients with more sensitive tests, we chose to define reported values under 25 IU/mL as undetectable. This overclassification of cases as being undetectable excluded some patients with baseline detectable viral loads from the study sample. Misclassification of some patients as having achieved viral suppression was much less likely because these measurements were made later in the data period. If viral suppression is based on an older laboratory technology, then some “detectable” postindex test findings would be miscategorized as viral suppression. This created a conservative bias in our estimates of the impact of achieving viral suppression because some patients in this category would have viral loads as high as 600 IU/mL and higher risk.

For 2 reasons, this study did not estimate or control for the effects of treatment on clinical end points and death. First, viral suppression without treatment was exceedingly rare, consisting of only 39 patients of 97 485 untreated patients. Second, the parameters with which to determine if a patient completed an adequate course of therapy vary by genotype and other factors, such as allowable duration on breaks in treatment. While developing counts of continuous days of therapy has been used by this research team in the past,36 we elected to use viral load suppression as our measure of treatment success.

Finally, our study did not capture medical care outside the VA system, such as the Medicare program, which may cloud the relationship between viral load suppression and event risk. For example, viral load suppression is highly correlated with expensive treatment that was not well covered in the Medicare program before Part D became effective in 2006. Even when the treatment is covered, drug copayments and the cost of physician visits for injections constitute a significant financial burden. This suggests that treated patients use the VA system, which likely rolls over into treatment for any future liver-related events. If true, this would result in an underestimate of the impact of viral load suppression on event risk. Fortunately, the problem of missing outcome data does not apply to mortality, where viral load suppression has a larger estimated effect.

Corresponding Author: Jeffrey McCombs, PhD, Department of Clinical Pharmacy and Pharmaceutical Economics and Policy, School of Pharmacy, Leonard Schaeffer Center for Health Policy and Economics, University of Southern California, 3335 S Figueroa St, Ste A, Los Angeles, CA 90007 (jmccombs@usc.edu).

Accepted for Publication: September 30, 2013.

Published Online: November 5, 2013. doi:10.1001/jamainternmed.2013.12505.

Author Contributions: Drs Tonnu-Mihara and Matsuda had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: McCombs, Tonnu-Mihara, Saab, Hines, L’Italien, Yuan.

Acquisition of data: Tonnu-Mihara.

Analysis and interpretation of data: McCombs, Matsuda, Tonnu-Mihara, Saab, L’Italien, Juday, Yuan.

Drafting of the manuscript: McCombs, Matsuda, Saab, L’Italien.

Critical revision of the manuscript for important intellectual content: McCombs, Tonnu-Mihara, Saab, Hines, L’Italien, Juday, Yuan.

Statistical analysis: McCombs, Matsuda, Tonnu-Mihara, Hines, L’Italien, Yuan.

Obtained funding: McCombs, Yuan.

Administrative, technical, or material support: McCombs, Tonnu-Mihara, Juday, Yuan.

Study supervision: McCombs, Tonnu-Mihara, Saab.

Conflict of Interest Disclosures: Dr McCombs received salary and travel support under the terms of the research grant between Bristol-Myers Squibb and the University of Southern California. Ms Matsuda was supported as a University of Southern California graduate research assistant under the terms of the grant. Dr Tonnu-Mihara is employed by the US Department of Veterans Affairs (VA). Dr Saab maintains a consultancy with Bristol-Myers Squibb. Ms Hines and Drs L’Italien, Juday, and Yuan are employees of Bristol-Myers Squibb. The study did not involve specific drugs or drugs marketed by the sponsor. No other conflicts were reported.

Funding/Support: Financial support for this research was provided by Bristol-Myers Squibb. The University subcontracted with the VA for access to the data.

Role of the Sponsor: The University of Southern California maintained all rights to publication subject to a time-limited period for review and comment by Bristol-Myers Squibb. In addition, those coauthors of this study who are also employees of Bristol-Myers Squibb (Ms Hines and Drs L’Italien, Juday, and Yuan) were fully involved with all aspects of this research.

Previous Presentation: The manuscript for this article was accepted as a podium presentation at the Liver Meeting; November 5, 2013; Washington, DC.

Additional Contributions: The authors acknowledge Anupama Kalsekar, PhD, from Bristol-Myers Squibb for her scientific critique and Tim Morgan, MD, from the Long Beach VA center for his support of the project. Neither acknowledged person was compensated for the work on this article.

Additional Information: Dr McCombs is an experienced investigator and well trained in the multivariate statistical techniques used in this analysis. Dr McCombs served as the independent statistical analyst for the research.

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Gordon  FD, Poterucha  JJ, Germer  J,  et al.  Relationship between hepatitis C genotype and severity of recurrent hepatitis C after liver transplantation. Transplantation. 1997;63(10):1419-1423.
PubMed   |  Link to Article
Larsen  C, Bousquet  V, Delarocque-Astagneau  E, Pioche  C, Roudot-Thoraval  F, Desenclos  JC; HCV Surveillance Steering Committee; HCV Surveillance Group.  Hepatitis C virus genotype 3 and the risk of severe liver disease in a large population of drug users in France. J Med Virol. 2010;82(10):1647-1654. doi:10.1002/jmv.21850.
PubMed   |  Link to Article
Crosse  K, Umeadi  OG, Anania  FA,  et al.  Racial differences in liver inflammation and fibrosis related to chronic hepatitis C. Clin Gastroenterol Hepatol. 2004;2(6):463-468.
PubMed   |  Link to Article
Sterling  RK, Stravitz  RT, Luketic  VA,  et al.  A comparison of the spectrum of chronic hepatitis C virus between Caucasians and African Americans. Clin Gastroenterol Hepatol. 2004;2(6):469-473.
PubMed   |  Link to Article
Castéra  L, Hézode  C, Roudot-Thoraval  F,  et al.  Effect of antiviral treatment on evolution of liver steatosis in patients with chronic hepatitis C: indirect evidence of a role of hepatitis C virus genotype 3 in steatosis. Gut. 2004;53(3):420-424.
PubMed   |  Link to Article
Nkontchou  G, Ziol  M, Aout  M,  et al.  HCV genotype 3 is associated with a higher hepatocellular carcinoma incidence in patients with ongoing viral C cirrhosis. J Viral Hepat. 2011;18(10):e516-e522.
PubMed   |  Link to Article
Tapper  EB, Afdhal  NH.  Is 3 the new 1: perspectives on virology, natural history and treatment for hepatitis C genotype 3. J Viral Hepat. 2013;20(10):669-677.
PubMed   |  Link to Article
Gaetano  JN, Reau  N.  Hepatitis C: management of side effects in the era of direct-acting antivirals. Curr Gastroenterol Rep. 2013;15(1):305.
PubMed   |  Link to Article
McHutchison  JG, Everson  GT, Gordon  SC,  et al; PROVE1 Study Team.  Telaprevir with peginterferon and ribavirin for chronic HCV genotype 1 infection. N Engl J Med. 2009;360(18):1827-1838.
PubMed   |  Link to Article
Poordad  F, McCone  J  Jr, Bacon  BR,  et al; SPRINT-2 Investigators.  Boceprevir for untreated chronic HCV genotype 1 infection. N Engl J Med. 2011;364(13):1195-1206.
PubMed   |  Link to Article
Bacon  BR, Gordon  SC, Lawitz  E,  et al; HCV RESPOND-2 Investigators.  Boceprevir for previously treated chronic HCV genotype 1 infection. N Engl J Med. 2011;364(13):1207-1217.
PubMed   |  Link to Article
Jacobson  IM, McHutchison  JG, Dusheiko  G,  et al; ADVANCE Study Team.  Telaprevir for previously untreated chronic hepatitis C virus infection. N Engl J Med. 2011;364(25):2405-2416.
PubMed   |  Link to Article
Zeuzem  S, Andreone  P, Pol  S,  et al; REALIZE Study Team.  Telaprevir for retreatment of HCV infection. N Engl J Med. 2011;364(25):2417-2428.
PubMed   |  Link to Article
Fusfeld  L, Aggarwal  J, Dougher  C,  et al.  Assessment of motivating factors associated with the initiation and completion of treatment for chronic hepatitis C virus (HCV) infection. BMC Infect Dis. 2013;13(1):234-245.
PubMed   |  Link to Article
Center for Quality Management in Public Health. The State of Care for Veterans With Chronic Hepatitis C. Palo Alto, California: US Dept of Veteran Affairs, Public Health Strategic Health Care Group, Center for Quality Management in Public Health; 2010.
McCombs  JS, Shin  J, Hines  P, Yuan  Y, Saab  S.  Impact of drug therapy adherence in patients with hepatitis C. Am J Pharmacy Benefits.2012;4(special issue):SP19-SP27.

Figures

Tables

Table Graphic Jump LocationTable 2.  Absolute Event Risk by Risk Group
Table Graphic Jump LocationTable 3.  Impact of Viral Clearance and Other Risk Factors on the Risk of Late-Stage Liver Eventsa
Table Graphic Jump LocationTable 4.  Impact of Viral Clearance and Other Risk Factors on the Risk of Secondary Outcomesa
Table Graphic Jump LocationTable 5.  Impact of Viral Clearance on the Risk of Late-Stage Liver Events Adjusting for Fibrosis Stagea

References

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van der Meer  AJ, Veldt  BJ, Feld  JJ,  et al.  Association between sustained virological response and all-cause mortality among patients with chronic hepatitis C and advanced hepatic fibrosis. JAMA. 2012;308(24):2584-2593.
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Vallet-Pichard  A, Mallet  V, Nalpas  B,  et al.  FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection: comparison with liver biopsy and fibrotest. Hepatology. 2007;46(1):32-36.
PubMed   |  Link to Article
Sterling  RK, Lissen  E, Clumeck  N,  et al; APRICOT Clinical Investigators.  Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317-1325.
PubMed   |  Link to Article
Ng  V, Saab  S.  Effects of a sustained virologic response on outcomes of patients with chronic hepatitis C. Clin Gastroenterol Hepatol. 2011;9(11):923-930.
PubMed   |  Link to Article
Kramer  JR, Kanwal  F, Richardson  P, Mei  M, El-Serag  HB.  Gaps in the achievement of effectiveness of HCV treatment in national VA practice. J Hepatol. 2012;56(2):320-325.
PubMed   |  Link to Article
Zhou  S, Terrault  NA, Ferrell  L,  et al.  Severity of liver disease in liver transplantation recipients with hepatitis C virus infection: relationship to genotype and level of viremia. Hepatology. 1996;24(5):1041-1046.
PubMed   |  Link to Article
Gordon  FD, Poterucha  JJ, Germer  J,  et al.  Relationship between hepatitis C genotype and severity of recurrent hepatitis C after liver transplantation. Transplantation. 1997;63(10):1419-1423.
PubMed   |  Link to Article
Larsen  C, Bousquet  V, Delarocque-Astagneau  E, Pioche  C, Roudot-Thoraval  F, Desenclos  JC; HCV Surveillance Steering Committee; HCV Surveillance Group.  Hepatitis C virus genotype 3 and the risk of severe liver disease in a large population of drug users in France. J Med Virol. 2010;82(10):1647-1654. doi:10.1002/jmv.21850.
PubMed   |  Link to Article
Crosse  K, Umeadi  OG, Anania  FA,  et al.  Racial differences in liver inflammation and fibrosis related to chronic hepatitis C. Clin Gastroenterol Hepatol. 2004;2(6):463-468.
PubMed   |  Link to Article
Sterling  RK, Stravitz  RT, Luketic  VA,  et al.  A comparison of the spectrum of chronic hepatitis C virus between Caucasians and African Americans. Clin Gastroenterol Hepatol. 2004;2(6):469-473.
PubMed   |  Link to Article
Castéra  L, Hézode  C, Roudot-Thoraval  F,  et al.  Effect of antiviral treatment on evolution of liver steatosis in patients with chronic hepatitis C: indirect evidence of a role of hepatitis C virus genotype 3 in steatosis. Gut. 2004;53(3):420-424.
PubMed   |  Link to Article
Nkontchou  G, Ziol  M, Aout  M,  et al.  HCV genotype 3 is associated with a higher hepatocellular carcinoma incidence in patients with ongoing viral C cirrhosis. J Viral Hepat. 2011;18(10):e516-e522.
PubMed   |  Link to Article
Tapper  EB, Afdhal  NH.  Is 3 the new 1: perspectives on virology, natural history and treatment for hepatitis C genotype 3. J Viral Hepat. 2013;20(10):669-677.
PubMed   |  Link to Article
Gaetano  JN, Reau  N.  Hepatitis C: management of side effects in the era of direct-acting antivirals. Curr Gastroenterol Rep. 2013;15(1):305.
PubMed   |  Link to Article
McHutchison  JG, Everson  GT, Gordon  SC,  et al; PROVE1 Study Team.  Telaprevir with peginterferon and ribavirin for chronic HCV genotype 1 infection. N Engl J Med. 2009;360(18):1827-1838.
PubMed   |  Link to Article
Poordad  F, McCone  J  Jr, Bacon  BR,  et al; SPRINT-2 Investigators.  Boceprevir for untreated chronic HCV genotype 1 infection. N Engl J Med. 2011;364(13):1195-1206.
PubMed   |  Link to Article
Bacon  BR, Gordon  SC, Lawitz  E,  et al; HCV RESPOND-2 Investigators.  Boceprevir for previously treated chronic HCV genotype 1 infection. N Engl J Med. 2011;364(13):1207-1217.
PubMed   |  Link to Article
Jacobson  IM, McHutchison  JG, Dusheiko  G,  et al; ADVANCE Study Team.  Telaprevir for previously untreated chronic hepatitis C virus infection. N Engl J Med. 2011;364(25):2405-2416.
PubMed   |  Link to Article
Zeuzem  S, Andreone  P, Pol  S,  et al; REALIZE Study Team.  Telaprevir for retreatment of HCV infection. N Engl J Med. 2011;364(25):2417-2428.
PubMed   |  Link to Article
Fusfeld  L, Aggarwal  J, Dougher  C,  et al.  Assessment of motivating factors associated with the initiation and completion of treatment for chronic hepatitis C virus (HCV) infection. BMC Infect Dis. 2013;13(1):234-245.
PubMed   |  Link to Article
Center for Quality Management in Public Health. The State of Care for Veterans With Chronic Hepatitis C. Palo Alto, California: US Dept of Veteran Affairs, Public Health Strategic Health Care Group, Center for Quality Management in Public Health; 2010.
McCombs  JS, Shin  J, Hines  P, Yuan  Y, Saab  S.  Impact of drug therapy adherence in patients with hepatitis C. Am J Pharmacy Benefits.2012;4(special issue):SP19-SP27.

Correspondence

CME


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