Risk-based analysis of femoral stem considering uncertainty in its design parameters

International audienceThe number of young people getting total hip arthroplasty surgery is on the rise and studies have shown that the average number of perfect health years after such surgery is being reduced to about 9 years; this is because of complications which can lead to the failure of such implants. Consequently, such failures cause the implant not to last as long as required. The uncertainty in design parameters, loading, and even the manufacturing process of femoral stems, makes it important to consider uncertainty quantification and probabilistic modeling approaches instead of the traditional deterministic approach when designing femoral stems. This paper proposes a probabilistic analysis method which considers uncertainties in the design parameters of femoral implants to determine its effect on the implant stiffness. Accordingly, this method can be used to improve the design reliability of femoral stems. A simplified finite element model of a femoral stem was considered and analyzed both deterministically and probabilistically using Monte Carlo simulation. The results showed that uncertainties in design parameters can significantly affect the resulting stiffness of the stem. This paper proposes an approach that can be considered a potential solution for improving, in general, the reliability of hip implants and the predicted stiffness values for the femoral stems so as to better mitigate the stress shielding phenomenon

Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer

Technological advances in biomarkers and imaging tests are creating new avenues to advance precision health for early detection of cancer. These advances have resulted in multiple layers of information that can be used to make clinical decisions, but how to best use these multiple sources of information is a challenging engineering problem due to the high cost and imperfect sensitivity and specificity of these tests. Questions that need to be addressed include which diagnostic tests to choose and how to best integrate them, in order to optimally balance the competing goals of early disease detection and minimal cost and harm from unnecessary testing. To study these research questions, we present new optimization-based models and data-driven analytic methods in three parts to improve early detection of prostate cancer (PCa). In the first part, we develop and validate predictive models to assess individual PCa risk using known clinical risk factors. Because not all men with newly-diagnosed PCa received imaging at diagnosis, we use an established method to correct for verification bias to evaluate the accuracy of published imaging guidelines. In addition to the published guidelines, we implement advanced classification modeling techniques to develop accurate classification rules identifying which patients should receive imaging. We propose a new algorithm for a classification model that considers information of patients with unverified disease and the high cost of misclassifying a metastatic patient. We summarize our development and implementation of state-wide, evidence-based imaging criteria that weigh the benefits and harms of radiological imaging for detection of metastatic PCa. In the second part of this thesis, we combine optimization and machine learning approaches into a robust optimization framework to design imaging guidelines that can account for imperfect calibration of predictions. We investigate efficient and effective ways to combine multiple medical diagnostic tests where the result of one test may be used to predict the outcome of another. We analyze the properties of the proposed optimization models from the perspectives of multiple stakeholders, and we present the results of fast approximation methods that we show can be used to solve large-scale models. In the third and final part of this thesis, we investigate the optimal design of composite multi-biomarker tests to achieve early detection of prostate cancer. Biomarker tests vary significantly in cost, and cause false positive and false negative results, leading to serious health implications for patients. Since no single biomarker on its own is considered satisfactory, we utilize simulation and statistical methods to develop the optimal diagnosis procedure for early detection of PCa consisting of a sequence of biomarker tests, balancing the benefits of early detection, such as increased survival, with the harms of testing, such as unnecessary prostate biopsies. In this dissertation, we identify new principles and methods to guide the design of early detection protocols for PCa using new diagnostic technologies. We provide important clinical evidence that can be used to improve health outcomes of patients while reducing wasteful application of diagnostic tests to patients for whom they are not effective. Moreover, some of the findings of this dissertation have been implemented directly into clinical practice in the state of Michigan. The models and methodologies we present in this thesis are not limited to PCa, and can be applied to a broad range of chronic diseases for which diagnostic tests are available.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143976/1/smerdan_1.pd

SIMULATION-BASED DESIGN AND MATERIAL MODELING FOR ENT IMPLANTS

Ph.DDOCTOR OF PHILOSOPH

What is valued most by patients with type 2 diabetes mellitus when selecting second-line antihyperglycemic medications in China

Objective: To estimate patient preferences for second-line antihyperglycemic medications in China. Methods: A face to face survey with the best-worst scaling (BWS) choices was administered in patients with diagnosed type 2 diabetes mellitus (T2DM). Study participants were asked to indicate which attribute they valued most and which attribute they valued least in 11 choice sets, each of which consisted of five alternatives out of 11 antihyperglycemic medication-specific attributes (treatment efficacy, weight change, hypoglycemic events, gastrointestinal side effects, cardiovascular health, urinary tract infection and genital infection side effects, edema, mode of administration, bone fracture, dosing frequency and out-of-pocket cost). A counting approach, a conditional logit model, and K-means clustering were used to estimate the relative importance of items and preference heterogeneity. Results: A total of 362 participants were included with a mean age of 63.6 (standard deviation: 11.8) years. There were 56.4% of participants were women, and 56.3% being diagnosed with diabetes for at least 5 years. Efficacy, cardiovascular health and hypoglycemic events were valued most, while dosing frequency, mode of administration and bone fracture were valued least. The K-means clustering further showed preference heterogeneity in out-of-pocket cost across the participants. Conclusion: Our study suggests that treatment efficacy, cardiovascular health and hypoglycemic events are valued most by Chinese patients with T2DM when selecting second-line antihyperglycemic medications. The study improves the understanding of patients’ preferences for second-line antihyperglycemic medications in China

The Fourteenth Annual Conference YUCOMAT 2012: Programme and the Book of Abstracts

The First Conference on materials science and engineering, including physics, physical chemistry, condensed matter chemistry, and technology in general, was held in September 1995, in Herceg Novi. An initiative to establish Yugoslav Materials Research Society was born at the conference and, similar to other MR societies in the world, the programme was made and objectives determined. The Yugoslav Materials Research Society (Yu-MRS), a nongovernment and non-profit scientific association, was founded in 1997 to promote multidisciplinary goal-oriented research in materials science and engineering. The main task and objective of the Society has been to encourage creativity in materials research and engineering to reach a harmonic coordination between achievements in this field in our country and analogous activities in the world with an aim to include our country into global international projects. Until 2003, Conferences were held every second year and then they grew into Annual Conferences that were traditionally held in Herceg Novi in September of every year. In 2007 Yu-MRS formed two new MRS: MRS-Serbia (official successor of Yu-MRS) and MRS-Montenegro (in founding). In 2008, MRS – Serbia became a member of FEMS (Federation of European Materials Societies)

An overview of the time trade-off method:concept, foundation, and the evaluation of distorting factors in putting a value on health

INTRODUCTION: Preference-based instruments measuring health status express the value of specific health states in a single number. One method used is time trade-off (TTO). Health-status values are key elements in calculating quality-adjusted life years (QALYs) and are pertinent for resource allocation. Since they are used in economic evaluations of healthcare, searching for a theoretical foundation of TTO in economics is justified. AREA COVERED: This paper provides an overview of TTO, including its relation to economic theory, and discusses biases and distortions, compiled from recent and older research. Inconsistencies between TTO and random utility theory were detected; The TTO is confounded by time preferences and by respondents' life expectancies. TTO is cognitively challenging, therefore guidance during the interviews is needed, producing interview effects. TTO does not measure one thing at a time, nor are the values independent of other states that are being valued in the same task. That is, TTO does not exhibit theoretical measurement properties such as unidimensionality and the invariance principle. EXPERT OPINION: We conclude that the TTO may be a pragmatic method of eliciting health state values, but the limitations in regard to measurement theory and practical elicitation problems makes it prone to inconsistencies and arbitrariness

Cost-effectiveness of Population Screening for BRCA Mutations in Ashkenazi Jewish Women Compared With Family History-Based Testing

BACKGROUND: Population-based testing for BRCA1/2 mutations detects the high proportion of carriers not identified by cancer family history (FH)-based testing. We compared the cost-effectiveness of population-based BRCA testing with the standard FH-based approach in Ashkenazi Jewish (AJ) women. METHODS: A decision-analytic model was developed to compare lifetime costs and effects amongst AJ women in the UK of BRCA founder-mutation testing amongst: 1) all women in the population age 30 years or older and 2) just those with a strong FH (≥10% mutation risk). The model assumes that BRCA carriers are offered risk-reducing salpingo-oophorectomy and annual MRI/mammography screening or risk-reducing mastectomy. Model probabilities utilize the Genetic Cancer Prediction through Population Screening trial/published literature to estimate total costs, effects in terms of quality-adjusted life-years (QALYs), cancer incidence, incremental cost-effectiveness ratio (ICER), and population impact. Costs are reported at 2010 prices. Costs/outcomes were discounted at 3.5%. We used deterministic/probabilistic sensitivity analysis (PSA) to evaluate model uncertainty. RESULTS: Compared with FH-based testing, population-screening saved 0.090 more life-years and 0.101 more QALYs resulting in 33 days' gain in life expectancy. Population screening was found to be cost saving with a baseline-discounted ICER of -£2079/QALY. Population-based screening lowered ovarian and breast cancer incidence by 0.34% and 0.62%. Assuming 71% testing uptake, this leads to 276 fewer ovarian and 508 fewer breast cancer cases. Overall, reduction in treatment costs led to a discounted cost savings of £3.7 million. Deterministic sensitivity analysis and 94% of simulations on PSA (threshold £20000) indicated that population screening is cost-effective, compared with current NHS policy. CONCLUSION: Population-based screening for BRCA mutations is highly cost-effective compared with an FH-based approach in AJ women age 30 years and older

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic