Competition and Post-Transplant Outcomes in Cadaveric Liver Transplantation under the MELD Scoring System

Previous researchers have modelled the decision to accept a donor organ for transplantation as a Markov decision problem, the solution to which is often a control-limit optimal policy: accept any organ whose match quality exceeds some health-dependent threshold; otherwise, wait for another. When competing transplant centers vie for the same organs, the decision rule changes relative to no competition; the relative size of competing centers affects the decision rules as well. Using center-specific graft and patient survival-rate data for cadaveric adult livers in the United States, we have found empirical evidence supporting these predictions.liver transplantation, competition, optimal stopping

Competition and Post-Transplant Outcomes in Cadaveric Liver Transplantation under the MELD Scoring System

Previous researchers have modelled the decision to accept a donor organ for transplantation as a Markov decision problem, the solution to which is often a control-limit optimal policy: accept any organ whose match quality exceeds some health-dependent threshold; otherwise, wait for another. When competing transplant centers vie for the same organs, the decision rule changes relative to no competition; the relative size of competing centers affects the decision rules as well. Using center-specific graft and patient survival-rate data for cadaveric adult livers in the United States, we have found empirical evidence supporting these predictions.liver transplantation; competition; optimal stopping

Optimal Policies for the Acceptance of Living- and Cadaveric-Donor Livers

Transplantation is the only viable therapy for end-stage liverdiseases (ESLD) such as hepatitis B. In the United States,patients with ESLD are placed on a waiting list. When organsbecome available, they are offered to the patients on this waitinglist. This dissertation focuses on the decision problem faced bythese patients: which offer to accept and which to refuse? Thisdecision depends on two major components: the patient's currentand future health, as well as the current and future prospect fororgan offers. A recent analysis of liver transplant data indicatesthat 60\% of all livers offered to patients for transplantationare refused.This problem is formulated as a discrete-time Markov decisionprocess (MDP). This dissertation analyzes three MDP models, eachrepresenting a different situation. The Living-Donor-Only Modelconsiders the problem of optimal timing of living-donor livertransplantation, which is accomplished by removing an entire lobeof a living donor's liver and implanting it into the recipient.The Cadaveric-Donor-Only Model considers the problem ofaccepting/refusing a cadaveric liver offer when the patient is onthe waiting list but has no available living donor. In this model,the effect of the waiting list is incorporated into the decisionmodel implicitly through the probability of being offered a liver.The Living-and-Cadaveric-Donor Model is the most general model.This model combines the first two models, in that the patient isboth listed on the waiting list and also has an available livingdonor. The patient can accept the cadaveric liver offer, declinethe cadaveric liver offer and use the living-donor liver, ordecline both and continue to wait.This dissertation derives structural properties of all threemodels, including several sets of conditions that ensure theexistence of intuitively structured policies such as control-limitpolicies. The computational experiments use clinical data, andshow that the optimal policy is typically of control-limit type

Cost-effectiveness of adjuvant paclitaxel and trastuzumab for early-stage node-negative, HER2-positive breast cancer

Adjuvant paclitaxel and trastuzumab has been shown to be an effective regimen with low risk of cancer recurrence and treatment-related toxicities in early-stage node-negative, HER2-positive breast cancer. We investigated the cost-effectiveness of this regimen

OM Forum-challenges and strategies in managing nonprofit operations: an operations management perspective

The operations management (OM) community is paying increasing attention to the analysis of nonprofit operations. However, what is it about this type of operation that makes it particularly interesting to OM scholars? We address this question by studying the objectives, actors, and main activities of nonprofit operations and the most common challenges they face. In addition, we suggest tactical and operational strategies to address these challenges by considering works in the for-profit sector and in different applied areas. The ultimate goal of this paper is to inspire and stimulate OM researchers to develop significant theoretical and empirical models in this novel stream of literature

ANTICIPATING U.S. POPULATION-LEVEL HEALTH AND ECONOMIC IMPACTS USING DISCRETE-EVENT SIMULATION TO GUIDE HEALTH POLICY DECISIONS

This dissertation presents two applications of discrete-event simulation (DES) to represent clinical processes: (1) a model to quantify the risk of the maternal obese and diabetic intrauterine environment influence on progression to adult obesity and diabetes, and (2) a model to evaluate health and economic outcomes of different smoking cessation strategies. The first application considers the public health impact of the diabetic and obese intrauterine environment\u27s effect on the prevalence of diabetes and obesity across subsequent generations. We first develop a preliminary DES model to investigate and characterize the epidemiology of diabetes during pregnancy and birth outcomes related to maternal obesity and diabetes. Using data from the San Antonio Heart Study (SAHS), the 1980 Census and the NCHS we are able to verify a simplified initial version of our model. Our methodology allows us to quantify the impact of maternal disparities between different racial/ethnic groups on future health disparities at the generational level and to estimate the extent to which intrauterine exposure to diabetes and obesity could be driving these health disparities. The populace of interest in this model is women of child-bearing age. The preliminary model is next modified to accommodate data and assumptions representing the United States population. We use a mixed-methods approach, incorporating both statistical methods and discrete event simulation, to examine trends in weight-gain over time among white and black women of child-bearing age in the US from 1980 to 2008 using United States Census projections and National Health and Nutrition Examination Survey (NHANES) data. We use BMI as a measure of weight adjusted for height. We establish an underlying population representative of the population prior to the onset of the obesity epidemic. Assessing the rate of change in body mass index (BMI) of the population prior to the obesity epidemic allows us to make \u27unadjusted\u27 projections, assuming that subsequent generations carry the same risk as the initial cohort. Unadjusted projections are compared to actual trends in the US population. This comparison allows us to quantify the trends in weight-gain over time. This model is interesting as a first step in understanding the trans-generational impact of obesity during pregnancy at the population level. The aim of the second application is to understand the impact of different pharmacologic interventions for smoking cessation in achieving long-term abstinence from cigarette smoking is an important health and economic issue. We design and develop a clinically-based DES model to provide predictive estimates of health and economic outcomes associated with different smoking cessation interventions. Interventions assessed included nicotine replacement therapy, oral medications (bupropion and varenicline), and abstinence without pharmacologic assistance. We utilized data from multiple sources to simulate patients\u27 actions and associated responses to different interventions along with co-morbidities associated with smoking. Outcomes of interest included estimates of sustained abstinence from smoking, quality adjusted life years, cost of treatment, and additional health-related costs due to long-term effects of smoking (lung cancer, chronic obstructive pulmonary disease, stroke, coronary heart disease). Understanding the comparative effectiveness and intrinsic value of alternative smoking cessation strategies can improve clinical and patient decision-making and subsequent health and economic outcomes at the population level. This dissertation contributes to the field of industrial engineering in healthcare. US population-level data structures are not always available in the desired format and there is not one method for managing the data. The key element is to be able to link the mathematical model with the available data. We illustrate various methods (i.e. bootstrap techniques, mixed-effects regression, application of probability distributions) for extracting information from different types of data (i.e. longitudinal data, cross-sectional data, incidence rates) to make population-level predictions. Methods used in cost-effectiveness evaluations (i.e. incremental cost-effectiveness ratio, bootstrap confidence intervals, cost-effectiveness plane) are applied to output measures obtained from the simulation to compare alternative smoking cessation strategies to deduce additional information. While the estimates resulting from the two models are topic-specific, many of the modules created for these studies are generic and can easily be transferred to other disease models. It is believed that these two models will aid decision makers in recognizing the impact that preventative-care initiatives will have, and to evaluate possible alternatives