Murray Krahn

Estimation of stage-specific fibrosis progression rates in chronic hepatitis C virus infection: a meta-analysis and meta-regression.

Published estimates of liver fibrosis progression in individuals with chronic hepatitis C virus (HCV) infection are heterogeneous. We aimed to estimate stage‐specific fibrosis progression rates and their determinants in these individuals. A systematic review of published prognostic studies was undertaken. Study inclusion criteria were as follows: (1) presence of HCV infection determined by serological assays; (2) available information about age at assessment of liver disease or HCV acquisition; (3) duration of HCV infection; and (4) histological and/or clinical diagnosis of cirrhosis. Annual stage‐specific transition probabilities (F0→F1, … , F3→F4) were derived using the Markov maximum likelihood estimation method and a meta‐analysis was performed. The impact of potential covariates was evaluated using meta‐regression. A total of 111 studies of individuals with chronic HCV infection (n = 33,121) were included. Based on the random effects model, the estimated annual mean (95% confidence interval) stage‐specific transition probabilities were: F0→F1 0.117 (0.104–0.130); F1→F2 0.085 (0.075–0.096); F2→F3 0.120 (0.109–0.133); and F3→F4 0.116 (0.104–0.129). The estimated prevalence of cirrhosis at 20 years after the infection was 16% (14%–19%) for all studies, 18% (15%–21%) for cross‐sectional/retrospective studies, 7% (4%–14%) for retrospective‐prospective studies, 18% (16%–21%) for studies conducted in clinical settings, and 7% (4%–12%) for studies conducted in nonclinical settings. Duration of infection was the most consistent factor significantly associated with progression of fibrosis. Conclusion: Our large systematic review provides increased precision in estimating fibrosis progression in chronic HCV infection and supports nonlinear disease progression. Estimates of progression to cirrhosis from studies conducted in clinical settings were lower than previous estimates. (HEPATOLOGY 2008.)

Natural history of hepatitis C virus infection in HIV-infected individuals and the impact of HIV in the era of highly active antiretroviral therapy: a meta-analysis.

Objectives:To estimate stage-specific transition probabilities in individuals coinfected with HIV and hepatitis C virus (HCV), to examine the effect of covariates on these rates, and to investigate the effect of HIV on HCV-related cirrhosis in the era of highly active antiretroviral therapy (HAART). Design:Systematic review of natural history studies among HCV-infected individuals. Methods:Markov maximum likelihood estimation method was used to estimate stage-specific transition probabilities. A meta-analysis was performed to obtain pooled transition probabilities, and a meta-regression to investigate the impact of covariates on these rates. Risk of cirrhosis between individuals monoinfected with HCV and coinfected with HIV/HCV were compared by HAART status. Results:The estimated mean (95% confidence intervals) annual transition probabilities of 3567 individuals coinfected with HIV/HCV (n = 17 studies) were as follows: fibrosis stage (F) F0 → F1 0.122 (0.098–0.153); F1 → F2 0.115 (0.095–0.140); F2 → F3 0.124 (0.097–0.159); and F3 → F4 0.115 (0.098–0.135) units/year. The prevalence of cirrhosis after 20 and 30 years of HCV infection was 21% (16–28%) and 49% (40–59%), respectively. Longer duration of HCV infection was significantly associated with slower rate of fibrosis progression. The overall rate ratio of cirrhosis between individuals coinfected with HIV/HCV and monoinfected with HCV (n = 27 studies) was 2.1 (1.5–3.0), 2.5 (1.8–3.4) in the non-HAART group, and 1.7 (1.1–2.8) in the HAART group. Conclusion:The rate of fibrosis progression among individuals coinfected with HIV/HCV appears constant. Our results confirm that chronic hepatitis C outcomes are worse among coinfected individuals. Over the period studied, HAART did not appear to fully correct the adverse effect of HIV infection on HCV prognosis.

Association of temporal trends in risk factors and treatment uptake with coronary heart disease mortality, 1994-2005.

CONTEXT Coronary heart disease (CHD) mortality has declined substantially in Canada since 1994. OBJECTIVE To determine what proportion of this decline was associated with temporal trends in CHD risk factors and advancements in medical treatments. DESIGN, SETTING, AND PATIENTS Prospective analytic study of the Ontario, Canada, population aged 25 to 84 years between 1994 and 2005, using an updated version of the validated IMPACT model, which integrates data on population size, CHD mortality, risk factors, and treatment uptake changes. Relative risks and regression coefficients from the published literature quantified the relationship between CHD mortality and (1) evidence-based therapies in 8 distinct CHD subpopulations (acute myocardial infarction [AMI], acute coronary syndromes, secondary prevention post-AMI, chronic coronary artery disease, heart failure in the hospital vs in the community, and primary prevention for hyperlipidemia or hypertension) and (2) population trends in 6 risk factors (smoking, diabetes mellitus, systolic blood pressure, plasma cholesterol level, exercise, and obesity). MAIN OUTCOME MEASURES The number of deaths prevented or delayed in 2005; secondary outcome measures were improvements in medical treatments and trends in risk factors. RESULTS Between 1994 and 2005, the age-adjusted CHD mortality rate in Ontario decreased by 35% from 191 to 125 deaths per 100,000 inhabitants, translating to an estimated 7585 fewer CHD deaths in 2005. Improvements in medical and surgical treatments were associated with 43% (range, 11% to 124%) of the total mortality decrease, most notably in AMI (8%; range, -5% to 40%), chronic stable coronary artery disease (17%; range, 7% to 35%), and heart failure occurring while in the community (10%; range, 6% to 31%). Trends in risk factors accounted for 3660 fewer CHD deaths prevented or delayed (48% of total; range, 28% to 64%), specifically, reductions in total cholesterol (23%; range, 10% to 33%) and systolic blood pressure (20%; range, 13% to 26%). Increasing diabetes prevalence and body mass index had an inverse relationship associated with higher CHD mortality of 6% (range, 4% to 8%) and 2% (range, 1% to 4%), respectively. CONCLUSION Between 1994 and 2005, there was a decrease in CHD mortality rates in Ontario that was associated primarily with trends in risk factors and improvements in medical treatments, each explaining about half of the decrease.

Health-state utilities and quality of life in hepatitis C patients

Abstract Objective Health-state utilities are global measurements of quality of life on a scale from 0 (death) to 1 (full health). Utilities are used to evaluate health outcomes and are the preferred outcome measure for policy models that determine the cost-effectiveness of treatments. Currently, utilities for hepatitis C virus (HCV)-infected patients have been estimated using expert judgments. The purpose of this study was to elicit HCV utilities directly from patients. Methods We assessed the utilities of 193 outpatients at various stages of chronic HCV progression by using a visual analog scale, the standard gamble technique, the Health Utilities Index Mark 3 survey, and the EuroQol Index survey. We also incorporated the nonutility-based Short Form-36v2 survey, which provides a detailed profile of health status. Results The mean standard gamble utilities were: 0.78 for patients without a recent liver biopsy and no signs of cirrhosis; 0.79 for mild to moderate chronic HCV infection; 0.80 for compensated cirrhosis; 0.60 for decompensated cirrhosis; 0.72 for hepatocellular carcinoma; 0.73 for transplant; and 0.86 for sustained virological responders to interferon ± ribavirin treatment. The Health Utilities Index Mark 3 survey and the EuroQol Index survey utilities were lower than Canadian population norms (p Conclusions These findings 1) suggest that quality of life (QOL) differences across the HCV clinical spectrum are smaller than previously believed; 2) support other evidence suggesting that QOL is significantly diminished in HCV patients; and 3) provide utility values derived directly from HCV patients.

PRIMER ON MEDICAL DECISION ANALYSIS : PART 3-ESTIMATING PROBABILITIES AND UTILITIES

This paper describes how to estimate probabilities and outcome values for decision trees. Probabilities are usually derived from published studies, but occasionally are derived from existing databases, primary data collection, or expert judgment. Outcome values represent quantitative estimates of the desirability of the outcome states, and are often expressed as utility values between 0 and 1. Utility values for different health states can be derived from the published literature, from direct measurement in appropriate subjects, or from expert opinion. Methods for assigning utilities to complex outcome states are described, and the concept of quality-adjusted life years is introduced. Key words: decision analysis; expected value; utility; sensitivity analysis; decision trees; probability. (Med Decis Making 1997;17:136-141)

Primer on Medical Decision Analysis: Part 5—Working with Markov Processes

Clinical decisions often have long-term implications. Analysts encounter difficulties when employing conventional decision-analytic methods to model these scenarios. This occurs because probability and utility variables often change with time and conventional decision trees do not easily capture this dynamic quality. A Markov analysis performed with current computer software programs provides a flexible and convenient means of modeling long-term scenarios. However, novices should be aware of several potential pitfalls when attempting to use these programs. When deciding how to model a given clinical problem, the analyst must weigh the simplicity and clarity of a conventional tree against the fidelity of a Markov analysis. In direct comparisons, both approaches gave the same qualitative answers. Key words: decision analysis; expected value; utility; sensitivity analysis; decision trees; probability. (Med Decis Making 1997; 17:152-159)

Primer on Medical Decision Analysis: Part 1—Getting Started:

This paper is Part 1 of a five-part series covering practical issues in the performance of decision analysis. The intended audience is individuals who are learning how to perform decision analyses, not just read them. The series assumes familiarity with the basic concepts of decision analysis. It imparts many of the recommendations the authors have learned in teaching a one-semester course in decision analysis to graduate students. Part 1 introduces the topic and covers questions such as choosing an appropriate question, determining the tradeoff between accuracy and simplicity, and deciding on a time frame. Key words: decision analysis; expected value; utility; sensitivity analysis; decision trees; probability. (Med Decis Making 1997;17:123-125)

The next step in guideline development: incorporating patient preferences.

CLINICAL PRACTICE GUIDELINES (CPGS) ARE SYSTEMatically developed statements to assist both patient and practitioner decisions. A fixture of modern medical care, guidelines link the practice of medicine more closely to the body of underlying evidence, shift the burden of evidence review from the individual practitioner to experts, and aim to improve the quality of care. But do guidelines take into account what patients want and value? Consider the following examples. A patient with mild to moderate hypertension has shown some lowering of blood pressure but has not achieved her guideline-recommended target with salt reduction, exercise, and weight reduction. After considering the potential risks and benefits, she prefers to avoid drugs and continue with her behavioral interventions. Another patient with atrial fibrillation prefers to begin taking warfarin rather than aspirin, even though he is at low risk of stroke. He is a surgeon, and a stroke would be a career-ending event. Both of these patients have made what appear to be rational choices, but choices that are at odds with what guidelines recommend. One potential reason for this discordance is that guidelines do not sufficiently take patient preferences into account. They may not include published evidence about preferences, include patient perspectives in the process of guideline formulation, acknowledge that an optimal decision in some circumstances is determined by preference, and actively encourage patients and practitioners to make decisions on the basis of preferences. The term preferences, in its broadest sense, represents the desirability of a health-related outcome, process, or treatment choice. For example, in considering options for atrial fibrillation, a patient may have strong feelings about preventing stroke (an outcome), taking warfarin and having her international normalized ratio monitored (the process), or warfarin as a treatment strategy, which includes the prospect of all potential outcomes (a treatment choice). Concepts of greatest relevance would include health values in the bioethics literature; concerns, desires, and expectations in the psychology literature; and utility in the decision analysis and economics literature. In the context of practice guidelines, the idea of tailoring treatment to preference is distinct from the notion of clinical tailoring. Tailoring treatment to age, sex, disease severity, overall risk profile, and combinations of comorbidity is an important part of the modern evolution of CPGs. This, however, is different than taking an individual’s values and priorities into account.

Tenofovir and entecavir are the most effective antiviral agents for chronic hepatitis B: a systematic review and Bayesian meta-analyses.

BACKGROUND & AIMS The relative efficacies of licensed antiviral therapies for treatment-naive chronic hepatitis B (CHB) infection in randomized controlled trials have not been determined. We evaluated the relative efficacies of the first 12 months of CHB treatments. METHODS Drugs evaluated were lamivudine, pegylated interferon, adefovir, entecavir, telbivudine, and tenofovir, as monotherapies and combination therapies, in treatment-naive individuals. Databases were searched for randomized controlled trials of the first 12 months of therapy in hepatitis B e antigen (HBeAg)-positive and/or HBeAg-negative patients with CHB published in English before October 31, 2009. Bayesian mixed treatment comparisons were used to calculate the odds ratios, including 95% credible intervals and predicted probabilities of surrogate outcomes to determine the relative effects of each treatment. RESULTS In HBeAg-positive patients, tenofovir was most effective in inducing undetectable levels of HBV DNA (predicted probability, 88%), normalization of alanine aminotransferase (ALT) levels (66%), HBeAg seroconversion (20%), and hepatitis B surface antigen loss (5%); it ranked third in histologic improvement of the liver (53%). Entecavir was most effective in improving liver histology (56%), second for inducing undetectable levels of HBV DNA (61%) and normalization of ALT levels (70%), and third in loss of hepatitis B surface antigen (1%). In HBeAg-negative patients, tenofovir was the most effective in inducing undetectable levels of HBV DNA (94%) and improving liver histology (65%); it ranked second for normalization of ALT levels (73%). CONCLUSIONS In the first year of treatment for CHB, tenofovir and entecavir are the most potent oral antiviral agents for HBeAg-positive patients; tenofovir is most effective for HBeAg-negative patients.

Conceptualizing a Model A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–2

The appropriate development of a model begins with understanding the problem that is being represented. The aim of this article is to provide a series of consensus-based best practices regarding the process of model conceptualization. For the purpose of this series of papers, the authors consider the development of models whose purpose is to inform medical decisions and health-related resource allocation questions. They specifically divide the conceptualization process into two distinct components: the conceptualization of the problem, which converts knowledge of the health care process or decision into a representation of the problem, followed by the conceptualization of the model itself, which matches the attributes and characteristics of a particular modeling type to the needs of the problem being represented. Recommendations are made regarding the structure of the modeling team, agreement on the statement of the problem, the structure, perspective and target population of the model, and the interventions and outcomes represented. Best practices relating to the specific characteristics of model structure, and which characteristics of the problem might be most easily represented in a specific modeling method, are presented. Each section contains a number of recommendations that were iterated among the authors, as well as the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.