HCC screening with ultrasound: assessment of quality using ultrasound LI-RADS score.

PURPOSE: To describe ultrasound (US) quality for hepatocellular carcinoma (HCC) screening/surveillance using the US LI-RADS scoring system, and to assess predictive factors of worse US quality scores. METHODS: This retrospective study included adult patients (n = 470; M/F 264/206, median age 59y) at risk for HCC that underwent US for HCC screening/surveillance. US examinations were independently reviewed by 2 radiologists that assigned a visualization score (A: no/minimal, B: moderate, C: severe limitation) and US diagnostic category (US LI-RADS 1: negative, US LI-RADS 2: subthreshold, US LI-RADS 3: positive) to each study. A generalized linear mixed model was used to assess the predictive factors of worse visualization score using OR (odds ratio) statistics. Simple Kappa coefficient (K) assessed inter-reader agreement. RESULTS: For readers 1 and 2, 295/320 (62.8%/68.1%) cases were scored A, 153/134 (32.6%/28.5%) were scored B, and 22/16 (4.6%/3.4%) were scored C, respectively. There was moderate inter-reader agreement for US LI-RADS visualization score (K = 0.478) and 100% concordance for US diagnostic category (K = 1), with 30 (6.4%) cases scored as positive (US LI-RADS 3). Cirrhosis and obesity were significant independent predictors of worse visualization scores (B/C) (cirrhosis: OR 10.4 confidence intervals: [4.25-25.48], p < 0.001; obesity: OR 3.61 [2.11-6.20], p < 0.001). Of the 30 lesions scored as US LI-RADS 3, 9 were characterized as probable or definite HCC on confirmatory CT/MRI, yielding a PPV of 30% (9/30) and a false-positive rate of 70% (21/30). CONCLUSION: Moderate to severe limitations in quality of US performed for HCC screening/surveillance was observed in approximately one-third of patients. Patients with cirrhosis and/or elevated BMI have poorer quality US studies and may benefit from other screening modalities such as CT or MRI.

Comparative assessment of standard and immune response criteria for evaluation of response to PD-1 monotherapy in unresectable HCC.

PURPOSE: To assess response to programmed death-1 (PD-1) monotherapy (nivolumab) in hepatocellular carcinoma (HCC) patients using RECIST1.1, modified RECIST (mRECIST), and immune RECIST (iRECIST). A secondary objective was to identify clinicolaboratory and imaging variables predictive of progressive disease (PD) and overall survival (OS). METHODS: Patients with HCC treated with nivolumab at a single institution from 5/2016 to 12/2019 with MRI or CT performed ≥ 4 weeks post treatment were retrospectively assessed. Patients who received concurrent locoregional, radiation, or other systemic therapies were excluded. Response was assessed by 2 observers in consensus using RECIST1.1, mRECIST, and iRECIST at 3/6/9/12-month time points. Time to progression (TTP) and OS were recorded. Clinicolaboratory and imaging variables were evaluated as predictors of PD and OS using uni-/multivariable and Cox regression analyses. RESULTS: Fifty-eight patients (42M/16F) were included. 118 target lesions (TL) were identified before treatment. Baseline mean TL size was 49.1 ± 43.5 mm (range 10-189 mm) for RECIST1.1/iRECIST and 46.3 ± 42.3 mm (range 10-189 mm) for mRECIST. Objective response rate (ORR) was 21% for mRECIST/iRECIST/RECIST1.1, with no cases of pseudoprogression. Median OS and median TTP were 717 days and 127 days for RECIST1.1/mRECIST/iRECIST-iUPD (unconfirmed PD). Older age, MELD/Child-Pugh scores, AFP, prior transarterial radioembolization (TARE), and larger TL size were predictive of PD and/or poor OS using mRECIST/iRECIST. The strongest predictor of PD (HR = 2.49, 95% CI 1.29-4.81, p = 0.007) was TARE. The strongest predictor of poor OS was PD by mRECIST/iRECIST at 3 months (HR = 2.26, 95% CI 1.00-5.10, p = 0.05) with borderline significance. CONCLUSION: Our results show ORR of 21%, equivalent for mRECIST, iRECIST, and RECIST1.1 in patients with advanced HCC clinically treated with nivolumab.

Outcomes assessment in intrahepatic cholangiocarcinoma using qualitative and quantitative imaging features.

BACKGROUND: To assess the performance of imaging features, including radiomics texture features, in predicting histopathologic tumor grade, AJCC stage, and outcomes [time to recurrence (TTR) and overall survival (OS)] in patients with intrahepatic cholangiocarcinoma (ICC). METHODS: Seventy-three patients (26 M/47F, mean age 63y) with pre-operative imaging (CT, n = 37; MRI, n = 21; CT and MRI, n = 15] within 6 months of resection were included in this retrospective study. Qualitative imaging traits were assessed by 2 observers. A 3rd observer measured tumor apparent diffusion coefficient (ADC), enhancement ratios (ERs), and Haralick texture features. Blood biomarkers and imaging features were compared with histopathology (tumor grade and AJCC stage) and outcomes (TTR and OS) using log-rank, generalized Wilcoxon, Cox proportional hazards regression, and Fisher exact tests. RESULTS: Median TTR and OS were 53.9 and 79.7 months. ICC recurred in 64.4% (47/73) of patients and 46.6% (34/73) of patients died. There was fair accuracy for some qualitative imaging features in the prediction of worse tumor grade (maximal AUC of 0.68 for biliary obstruction on MRI, p = 0.032, observer 1) and higher AJCC stage (maximal AUC of 0.73 for biliary obstruction on CT, p = 0.002, observer 2; and AUC of 0.73 for vascular involvement on MRI, p = 0.01, observer 2). Cox proportional hazards regression analysis showed that CA 19-9 [hazard ratio (HR) 2.44/95% confidence interval (CI) 1.31-4.57/p = 0.005)] and tumor size on imaging (HR 1.13/95% CI 1.04-1.22/p = 0.003) were significant predictors of TTR, while CA 19-9 (HR 4.08/95% CI 1.75-9.56, p = 0.001) and presence of metastatic lymph nodes at histopathology (HR 2.86/95% CI 1.35-6.07/p = 0.006) were significant predictors of OS. On multivariable analysis, satellite lesions on CT (HR 2.79/95%CI 1.01-7.15/p = 0.032, observer 2), vascular involvement on MRI (HR 0.10/95% CI 0.01-0.85/p = 0.032, observer 1), and texture feature MRI variance (HR 0.55/95% CI 0.31-0.97, p = 0.040) predicted TTR once adjusted for the independent predictors CA 19-9 and tumor size on imaging. Several qualitative and quantitative features demonstrated associations with TTR, OS, and AJCC stage at univariable analysis (range: HR 0.35-19; p < 0.001-0.045), however none were predictive of OS at multivariable analysis when adjusted for CA 19-9 and metastatic lymph nodes (p > 0.088). CONCLUSIONS: There was reasonable accuracy in predicting tumor grade and higher AJCC stage in ICC utilizing certain qualitative and quantitative imaging traits. Serum CA 19-9, tumor size, presence of metastatic lymph nodes, and qualitative imaging traits of satellite lesions and vascular involvement are predictors of patient outcomes, along with a promising predictive ability of certain quantitative texture features.

Drug therapies for chronic hepatitis C infection: a cost-effectiveness analysis.

BACKGROUND: Before 2011, pegylated interferon plus ribavirin was the standard therapy for chronic hepatitis C. Interferon-free direct-acting antiviral agents were then approved. Although these treatments appear to be more effective, they are substantially more expensive. In anticipation of the need for information regarding the comparative cost-effectiveness of new regimens in a recent therapeutic review, we conducted the analysis to inform listing decision in Canada. METHODS: A state-transition model was developed in the form of a cost-utility analysis. Regimens included in the analysis were comprehensive. The cohort under consideration had a mean age of 50 years. The cohort was defined by treatment status and cirrhosis status. Inputs for the model were derived from published sources and validated by clinical experts. RESULTS: For each genotype 1 population, at least 1 of the interferon-free agents appeared to be economically attractive compared with pegylated interferon-ribavirin, at a willingness-to-pay of $50 000 per quality-adjusted life-year. The drug that was the most cost-effective varied by population. For genotype 2-4 population, the direct-acting antiviral therapies appeared not to be economically attractive compared with pegylated interferon-ribavirin for the treatment-naive; however, there were direct-acting antiviral therapies that appeared to be attractive when compared with no treatment for the treatment-experienced. INTERPRETATION: Public health policy should be informed by consideration of health benefit, social and ethical values, feasibility and cost-effectiveness. Our analysis assists the development of reimbursements and policies for interferon-free direct-acting antiviral agent regimens for chronic hepatitis C infection by informing the last criterion. Considering the rapid development of treatments for chronic hepatitis C, further update and expanded reviews will be necessary.

Reimbursement-Based Economics--What Is It and How Can We Use It to Inform Drug Policy Reform?

BACKGROUND: In Ontario, approximately $3.8 billion is spent annually on publicly funded drug programs. The annual growth in Ontario Public Drug Program (OPDP) expenditure has been limited to 1.2% over the course of 3 years. Concurrently, the Ontario Drug Policy Research Network (ODPRN) was appointed to conduct drug class review research relating to formulary modernization within the OPDP. Drug class reviews by ODPRN incorporate a novel methodological technique called reimbursement-based economics, which focuses on reimbursement strategies and may be particularly relevant for policy-makers. OBJECTIVES: To describe the reimbursement-based economics approach. METHODS: Reimbursement-based economics aims to identify the optimal reimbursement strategy for drug classes by incorporating a review of economic literature, comprehensive budget impact analyses, and consideration of cost-effectiveness. This 3-step approach is novel in its focus on the economic impact of alternate reimbursement strategies rather than individual therapies. RESULTS: The methods involved within the reimbursement-based approach are detailed. To facilitate the description, summary methods and findings from a recent application to formulary modernization with respect to the drug class tryptamine-based selective serotonin receptor agonists (triptans) used to treat migraine headaches are presented. CONCLUSIONS: The application of reimbursement-based economics in drug policy reforms allows policy-makers to consider the cost-effectiveness and budget impact of different reimbursement strategies allowing consideration of the trade-off between potential cost savings vs increased access to cost-effective treatments.

Budget impact analysis-principles of good practice: report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force.

BACKGROUND: Budget impact analyses (BIAs) are an essential part of a comprehensive economic assessment of a health care intervention and are increasingly required by reimbursement authorities as part of a listing or reimbursement submission. OBJECTIVES: The objective of this report was to present updated guidance on methods for those undertaking such analyses or for those reviewing the results of such analyses. This update was needed, in part, because of developments in BIA methods as well as a growing interest, particularly in emerging markets, in matters related to affordability and population health impacts of health care interventions. METHODS: The Task Force was approved by the International Society for Pharmacoeconomics and Outcomes Research Health Sciences Policy Council and appointed by its Board of Directors. Members were experienced developers or users of BIAs; worked in academia and industry and as advisors to governments; and came from several countries in North America and South America, Oceania, Asia, and Europe. The Task Force solicited comments on the drafts from a core group of external reviewers and, more broadly, from the membership of the International Society for Pharmacoeconomics and Outcomes Research. RESULTS: The Task Force recommends that the design of a BIA for a new health care intervention should take into account relevant features of the health care system, possible access restrictions, the anticipated uptake of the new intervention, and the use and effects of the current and new interventions. The key elements of a BIA include estimating the size of the eligible population, the current mix of treatments and the expected mix after the introduction of the new intervention, the cost of the treatment mixes, and any changes expected in condition-related costs. Where possible, the BIA calculations should be performed by using a simple cost calculator approach because of its ease of use for budget holders. In instances, however, in which the changes in eligible population size, disease severity mix, or treatment patterns cannot be credibly captured by using the cost calculator approach, a cohort or patient-level condition-specific model may be used to estimate the budget impact of the new intervention, accounting appropriately for those entering and leaving the eligible population over time. In either case, the BIA should use data that reflect values specific to a particular decision maker's population. Sensitivity analysis should be of alternative scenarios chosen from the perspective of the decision maker. The validation of the model should include at least face validity with decision makers and verification of the calculations. Data sources for the BIA should include published clinical trial estimates and comparator studies for the efficacy and safety of the current and new interventions as well as the decision maker's own population for the other parameter estimates, where possible. Other data sources include the use of published data, well-recognized local or national statistical information, and, in special circumstances, expert opinion. Reporting of the BIA should provide detailed information about the input parameter values and calculations at a level of detail that would allow another modeler to replicate the analysis. The outcomes of the BIA should be presented in the format of interest to health care decision makers. In a computer program, options should be provided for different categories of costs to be included or excluded from the analysis. CONCLUSIONS: We recommend a framework for the BIA, provide guidance on the acquisition and use of data, and offer a common reporting format that will promote standardization and transparency. Adherence to these good research practice principles would not necessarily supersede jurisdiction-specific BIA guidelines but may support and enhance local recommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.

Using effectiveness and cost-effectiveness to make drug coverage decisions: a comparison of Britain, Australia, and Canada.

CONTEXT: National public insurance for drugs is often based on evidence of comparative effectiveness and cost-effectiveness. This study describes how that evidence has been used across 3 jurisdictions (Australia, Canada, and Britain) that have been at the forefront of evidence-based coverage internationally. OBJECTIVES: To describe how clinical and cost-effectiveness evidence is used in coverage decisions both within and across jurisdictions and to identify common issues in the process of evidence-based coverage. DESIGN, SETTING, AND PARTICIPANTS: Descriptive analysis of retrospective data from the Common Drug Review (CDR) of Canada, National Institute for Health and Clinical Excellence (NICE) in Britain, and Pharmaceutical Benefits Advisory Committee (PBAC) of Australia. All publicly available information as of December 31, 2008, was gathered from each committee's Web site (data set begins in January 2004 [CDR], February 2001 [NICE], and July 2005 [PBAC]). MAIN OUTCOME MEASURE: Listing recommendations for each drug by disease indication. RESULTS: NICE recommended 87.4% (174/199) of submissions for listing compared with a listing rate of 49.6% (60/121) and 54.3% (153/282) for the CDR and PBAC, respectively. Significant uncertainty around clinical effectiveness, typically resulting from inadequate study design or the use of inappropriate comparators and unvalidated surrogate end points, was identified as a key issue in coverage decisions. Recommendations varied considerably across countries, possibly because of differences in the medications reviewed; different agency processes, including the willingness to negotiate on price; and the approach to "me too" drugs. The data suggest that the 3 agencies make recommendations that are consistent with evidence on effectiveness and cost-effectiveness but that other factors are often important. CONCLUSIONS: NICE, PBAC, and CDR face common issues with respect to the quality and strength of the experimental evidence in support of a clinically meaningful effect. However, comparative effectiveness and cost-effectiveness, along with other relevant factors, can be used by national agencies to support drug decision making. The results of the evaluation process in different countries are influenced by the context, agency processes, ability to engage in price negotiation, and perhaps differences in social values.

The role of models within economic analysis: focus on type 2 diabetes mellitus.

Economic analysis of healthcare interventions is increasingly reliant on decision models to estimate the long-term costs and benefits of new therapies. Models permit analysts to take short-term clinical data to forecast long-term costs and benefits. Models should follow certain basic principles and can be appraised in terms of three broad characteristics: clinical relevance, transparency and analytical ability. The purpose of this paper is to explore the role of modelling in the economic analysis of interventions for type 2 diabetes mellitus. A review of existing models for diabetes identified five complex disease models appropriate for economic analysis. These models were broadly similar in structure and in source of input parameters. However, models did vary according to the coverage of relevant disease complications and the complexity of analysis possible. Models could be enhanced by improving their transparency and by using data relevant to type 2 diabetes. In addition, enhancing clinical knowledge through the provision of long-term data on effectiveness may reduce concern relating to the appropriateness of the assumptions currently required within models. The value of such further information must be weighed against the costs of its acquisition.