Treatment patterns and survival in older adults with unresected nonmetastatic biliary tract cancers.

INTRODUCTION: The optimal treatment for unresected nonmetastatic biliary tract cancer (uBTC) is not well-established. The objective of this study was to analyze the treatment patterns and compare the differences in overall survival (OS) between different treatment strategies amongst older adults with uBTC. MATERIALS AND METHODS: We identified patients aged ≥65 years with uBTC using the Surveillance, Epidemiology, and End Results (SEER)-Medicare database (2004-2015). Treatments were classified into chemotherapy, chemoradiotherapy, and radiotherapy. The primary outcome was OS. The differences in OS were analyzed using Kaplan-Meier curves and multivariable Cox proportional hazard regression. RESULTS: A total of 4352 patients with uBTC were included. The median age was 80 years and median OS was 4.1 months. Most patients (67.3%, n = 2931) received no treatment, 19.1% chemotherapy (n = 833), 8.1% chemoradiotherapy (n = 354), and 5.4% radiotherapy alone (n = 234). Patients receiving no treatment were older and had more comorbidities. Chemotherapy was associated with significantly longer OS than no treatment in uBTC (hazard ratio [HR] 0.87, 95% confidence interval [CI] 0.79-0.95), but no difference was found in the subgroups of intrahepatic cholangiocarcinoma (iCCA; HR 0.87, 95% CI 0.75-1.00) and gallbladder carcinoma (GBC; HR 1.09, 95% CI 0.86-1.39). In the sensitivity analyses, capecitabine-based chemoradiotherapy showed significantly longer OS in uBTC compared to chemotherapy (adjusted HR 0.71, 95% CI 0.53-0.95). DISCUSSION: A minority of older patients with uBTC receive systemic treatments. Chemotherapy was associated with longer OS compared to no treatment in uBTC, but not in the subgroups of iCCA and GBC. The efficacy of chemoradiotherapy, especially in perihilar cholangiocarcinoma using capecitabine-based chemoradiotherapy, may be further evaluated in prospective clinical trials.

Resection of the Portal-Superior Mesenteric Vein in Pancreatic Cancer: Pathological Assessment and Recurrence Patterns.

OBJECTIVES: The portal vein (PV)-superior mesenteric vein (SMV) margin is the most affected margin in pancreatic cancer. This study investigates the association between venous resection, tumor invasion in the resected PV-SMV, recurrence patterns, and overall survival (OS). METHODS: This multicenter cohort study included patients who underwent pancreatoduodenectomy for pancreatic cancer (2010-2017). In addition, a systematic literature search was performed. RESULTS: In total, 531 patients were included, of which 149 (28%) underwent venous resection of whom 53% had tumor invasion in the resected PV-SMV. Patients with venous resection had a significant higher rate of R1 margins (69% vs 37%) and had more often multiple R1 margins (43% vs 16%). Patient with venous resection had a significant shorter time to locoregional recurrence and a shorter OS (15 vs 19 months). At multivariable analyses, venous resection and tumor invasion in the resected PV-SMV were not predictive for time to recurrence and OS. The literature overview showed that pathological assessment of the resected PV-SMV is not adequately standardized. CONCLUSIONS: Only half of patients with venous resection had pathology confirmed tumor invasion in the resected PV-SMV, and both are not independently associated with time to recurrence and OS. The pathological assessment of the resected PV-SMV needs to be standardized.

Low skeletal muscle mass is associated with increased hospital expenditure in patients undergoing cancer surgery of the alimentary tract.

BACKGROUND: Low skeletal muscle mass is associated with poor postoperative outcomes in cancer patients. Furthermore, it is associated with increased healthcare costs in the United States. We investigated its effect on hospital expenditure in a Western-European healthcare system, with universal access. METHODS: Skeletal muscle mass (assessed on CT) and costs were obtained for patients who underwent curative-intent abdominal cancer surgery. Low skeletal muscle mass was defined based on pre-established cut-offs. The relationship between low skeletal muscle mass and hospital costs was assessed using linear regression analysis and Mann-Whitney U-tests. RESULTS: 452 patients were included (median age 65, 61.5% males). Patients underwent surgery for colorectal cancer (38.9%), colorectal liver metastases (27.4%), primary liver tumours (23.2%), and pancreatic/periampullary cancer (10.4%). In total, 45.6% had sarcopenia. Median costs were €2,183 higher in patients with low compared with patients with high skeletal muscle mass (€17,144 versus €14,961; P<0.001). Hospital costs incrementally increased with lower sex-specific skeletal muscle mass quartiles (P = 0.029). After adjustment for confounders, low skeletal muscle mass was associated with a cost increase of €4,061 (P = 0.015). CONCLUSION: Low skeletal muscle mass was independently associated with increased hospital costs of about €4,000 per patient. Strategies to reduce skeletal muscle wasting could reduce hospital costs in an era of incremental healthcare costs and an increasingly ageing population.

Minimally invasive versus open distal pancreatectomy (LEOPARD): study protocol for a randomized controlled trial.

BACKGROUND: Observational cohort studies have suggested that minimally invasive distal pancreatectomy (MIDP) is associated with better short-term outcomes compared with open distal pancreatectomy (ODP), such as less intraoperative blood loss, lower morbidity, shorter length of hospital stay, and reduced total costs. Confounding by indication has probably influenced these findings, given that case-matched studies failed to confirm the superiority of MIDP. This accentuates the need for multicenter randomized controlled trials, which are currently lacking. We hypothesize that time to functional recovery is shorter after MIDP compared with ODP even in an enhanced recovery setting. METHODS: LEOPARD is a randomized controlled, parallel-group, patient-blinded, multicenter, superiority trial in all 17 centers of the Dutch Pancreatic Cancer Group. A total of 102 patients with symptomatic benign, premalignant or malignant disease will be randomly allocated to undergo MIDP or ODP in an enhanced recovery setting. The primary outcome is time (days) to functional recovery, defined as all of the following: independently mobile at the preoperative level, sufficient pain control with oral medication alone, ability to maintain sufficient (i.e. >50%) daily required caloric intake, no intravenous fluid administration and no signs of infection. Secondary outcomes are operative and postoperative outcomes, including clinically relevant complications, mortality, quality of life and costs. DISCUSSION: The LEOPARD trial is designed to investigate whether MIDP reduces the time to functional recovery compared with ODP in an enhanced recovery setting. TRIAL REGISTRATION: Dutch Trial Register, NTR5188 . Registered on 9 April 2015.

The combined analysis of uncertainty and patient heterogeneity in medical decision models.

The analysis of both patient heterogeneity and parameter uncertainty in decision models is increasingly recommended. In addition, the complexity of current medical decision models commonly requires simulating individual subjects, which introduces stochastic uncertainty. The combined analysis of uncertainty and heterogeneity often involves complex nested Monte Carlo simulations to obtain the model outcomes of interest. In this article, the authors distinguish eight model types, each dealing with a different combination of patient heterogeneity, parameter uncertainty, and stochastic uncertainty. The analyses that are required to obtain the model outcomes are expressed in equations, explained in stepwise algorithms, and demonstrated in examples. Patient heterogeneity is represented by frequency distributions and analyzed with Monte Carlo simulation. Parameter uncertainty is represented by probability distributions and analyzed with 2nd-order Monte Carlo simulation (aka probabilistic sensitivity analysis). Stochastic uncertainty is analyzed with 1st-order Monte Carlo simulation (i.e., trials or random walks). This article can be used as a reference for analyzing complex models with more than one type of uncertainty and patient heterogeneity.

Uncertainty and patient heterogeneity in medical decision models.

Parameter uncertainty, patient heterogeneity, and stochastic uncertainty of outcomes are increasingly important concepts in medical decision models. The purpose of this study is to demonstrate the various methods to analyze uncertainty and patient heterogeneity in a decision model. The authors distinguish various purposes of medical decision modeling, serving various stakeholders. Differences and analogies between the analyses are pointed out, as well as practical issues. The analyses are demonstrated with an example comparing imaging tests for patients with chest pain. For complicated analyses step-by-step algorithms are provided. The focus is on Monte Carlo simulation and value of information analysis. Increasing model complexity is a major challenge for probabilistic sensitivity analysis and value of information analysis. The authors discuss nested analyses that are required in patient-level models, and in nonlinear models for analyses of partial value of information analysis.

Value of information analyses of economic randomized controlled trials: the treatment of intermittent claudication.

OBJECTIVE: The aim of this study is to design the optimal study comparing endovascular revascularization and supervised exercise training for patients with intermittent claudication and to demonstrate value of information (VOI) analysis of patient-level data from an economic randomized controlled trial to guide future research. METHODS: We applied a net benefit framework to patient-level data on costs and quality-of-life of a previous randomized controlled trial. VOI analyses were performed using Monte Carlo simulation. We estimated the total expected value of perfect information (total EVPI), the total expected value of sample information (total EVSI), the partial expected value of perfect information (partial EVPI), and the partial expected value of sample information (partial EVSI). These VOI analyses identified the key parameters and the optimal sample size of future study designs. Sensitivity analyses were performed to explore the robustness of our assumptions about the population to benefit, the willingness-to-pay threshold, and the study costs. The VOI analyses are demonstrated in statistical software (R) and a spreadsheet (Excel) allowing other investigators to apply VOI analysis to their patient-level data. RESULTS: The optimal study design for the treatment of intermittent claudication involves a randomized controlled trial collecting data on the quality-adjusted life expectancy and additional admission costs for 525 patients per treatment arm. The optimal sample size remained between 400 and 600 patients for a willingness-to-pay threshold between euro30,000 and euro100,000/quality-adjusted life-years, for even extreme assumptions about the study costs, and for a range of 3 to 7 years that future patients will benefit from the results of the proposed study. CONCLUSIONS: 1) The optimal study for patients with intermittent claudication collects data on two key parameters for 525 patients per trial arm; and 2) we have shown that value of information analysis provides an explicit framework to determine the optimal sample size and identify key parameters for the design of future clinical trials.

Value of information analysis used to determine the necessity of additional research: MR imaging in acute knee trauma as an example.

PURPOSE: To help guide future outcomes research regarding the use of magnetic resonance (MR) imaging in patients with acute knee trauma in an emergency department setting, with use of prospective data from a randomized clinical trial and value of information analysis. MATERIALS AND METHODS: A total of 189 patients (123 male, 66 female; mean age, 33.4 years) were randomly assigned to undergo radiography alone (n = 93) or radiography and MR imaging (n = 96). Institutional review board approval and informed consent (parental consent for minors) were obtained. During 6 months of follow-up, data on quality of life and 39 cost parameters were collected. Value-of-information analysis was used to estimate the expected benefit of future research to eliminate the decision uncertainty that remained after trial completion. In addition, the parameters that were responsible for most of the decision uncertainty were identified, the expected benefits of various study designs were evaluated, and the optimal sample size was estimated. RESULTS: Only three parameters were responsible for most of the decision uncertainty: number of quality-adjusted life-years, cost of an overnight hospital stay, and friction costs. A study in which data on these three parameters are gathered would have an optimal sample size of 3500 patients per arm and would be expected to result in a societal benefit of euro 5.6 million or 70 quality-adjusted life-years. CONCLUSION: The optimal study design for use of MR imaging to evaluate acute knee trauma involves a trial in which there are 3500 patients per trial arm, and data on the number of quality-adjusted life-years, cost of an overnight hospital stay, and friction costs are collected.

Limitations of acceptability curves for presenting uncertainty in cost-effectiveness analysis.

Clinical journals increasingly illustrate uncertainty about the cost and effect of health care interventions using cost-effectiveness acceptability curves (CEACs). CEACs present the probability that each competing alternative is optimal for a range of values of the cost-effectiveness threshold. The objective of this article is to demonstrate the limitations of CEACs for presenting uncertainty in cost-effectiveness analyses. These limitations arise because the CEAC is unable to distinguish dramatically different joint distributions of incremental cost and effect. A CEAC is not sensitive to any change of the incremental joint distribution in the upper left and lower right quadrants of the cost-effectiveness plane; neither is it sensitive to radial shift of the incremental joint distribution in the upper right and lower left quadrants. As a result, CEACs are ambiguous to risk-averse policy makers, inhibit integration with risk attitude, hamper synthesis with other evidence or opinions, and are unhelpful to assess the need for more research. Moreover, CEACs may mislead policy makers and can incorrectly suggest medical importance. Both for guiding immediate decisions and for prioritizing future research, these considerable drawbacks of CEACs should make us rethink their use in communicating uncertainty. As opposed to CEACs, confidence and credible intervals do not conflate magnitude and precision of the net benefit of health care interventions. Therefore, they allow (in)formal synthesis of study results with risk attitude and other evidence or opinions. Presenting the value of information in addition to these intervals allows policy makers to evaluate the need for more empirical research.

Identifying key parameters in cost-effectiveness analysis using value of information: a comparison of methods.

Decisions in health care must be made, despite uncertainty about benefits, risks, and costs. Value of information analysis is a theoretically sound method to estimate the expected value of future quantitative research pertaining to an uncertain decision. If the expected value of future research does not exceed the cost of research, additional research is not justified, and decisions should be based on current evidence, despite the uncertainty. To assess the importance of individual parameters relevant to a decision, different value of information methods have been suggested. The generally recommended method assumes that the expected value of perfect knowledge concerning a parameter is estimated as the reduction in expected opportunity loss. This method, however, results in biased expected values and incorrect importance ranking of parameters. The objective of this paper is to set out the correct methods to estimate the partial expected value of perfect information and to demonstrate why the generally recommended method is incorrect conceptually and mathematically.