Predictors Of Use Of Hydroxyurea And Its Impact On Clinical And Economic Outcomes Among Children With Sickle Cell Disease

Objective: The objective of this study was to assess the prevalence and predictors of use of hydroxyurea (HU) and its impact on clinical and economic outcomes in pediatric patients with sickle cell disease (SCD) enrolled in Medicaid. Methods: A cohort of patients with SCD was identified in 2006 using ICD-9-CM codes from Medicaid claims from 40 US states. Patients who filled three prescriptions of HU in within 6 months in 2007 were identified as HU users. HU users were then matched with non-users and the impact of HU use on the presence of crises and economic outcomes including presence of hospitalizations and emergency department visits were assessed using conditional logistic regression stratified on matched pairs. Length of stay and medical costs were compared in the matched sample using generalized linear models. An additional clinical outcome, number of crises, was evaluated in the unmatched sample of HU users and non-users using conventional multivariable regression. Estimates obtained using this approach, were then compared with those obtained by minimizing selection bias using regional variation and physician preference-based instrumental variables (IVs). Results: Prevalence of HU use in children with SCD enrolled in Medicaid was found to be 10.72%. Age, gender, race, disease severity, previous office visits, presence of a comprehensive sickle cell center within the state of residence, and prior opioid use were all found to be significant predictors of HU use in this population (p\u3c0.0001). HU users had a significantly greater likelihood of having a hospitalization (OR:2.09; 95% CI:1.28-3.43) and a longer LOS (β=0.49; 95% CI:0.14-0.84) compared to non-users. Even though the conventional multivariable model shothat HU users had a significantly greater number of crises compared to non-users (β=0.93; p\u3c0.0001), analysis using IVs found no statistically significant relationship (β=-2.75; p=0.2013). Conclusion: HU use is not very prevalent among children with SCD enrolled in Medicaid. Based on the identified predictors, it seems that physicians follow guidelines when prescribing HU in this population. Since this study failed to corroborate the benefit associated with the use of HU on clinical outcomes and resource utilization, physicians should be wary in prescribing HU in this population

Doctor of Philosophy in Computing

dissertationStatistical shape analysis has emerged as an important tool for the quantitative analysis of anatomy in many medical imaging applications. The correspondence based approach to evaluate shape variability is a popular method, based on comparing configurations of carefully placed landmarks on each shape. In recent years, methods for automatic placement of landmarks have enhanced the ability of this approach to capture statistical properties of shape populations. However, biomedical shapes continue to present considerable difficulties in automatic correspondence optimization due to inherent geometric complexity and the need to correlate shape change with underlying biological parameters. This dissertation addresses these technical difficulties and presents improved shape correspondence models. In particular, this dissertation builds on the particle-based modeling (PBM) framework described by Joshua Cates' 2010 Ph.D. dissertation. In the PBM framework, correspondences are modeled as a set of dynamic points or a particle system, positioned automatically on shape surfaces by optimizing entropy contained in the model, with the idea of balancing model simplicity against accuracy of the particle system representation of shapes. This dissertation is a collection of four papers that extend the PBM framework to include shape regression and longitudinal analysis and also adds new methods to improve modeling of complex shapes. It also includes a summary of two applications from the field of orthopaedics. Technical details of the PBM framework are provided in Chapter 2, after which the first topic related to the study of shape change over time is addressed (Chapters 3 and 4). In analyses of normative growth or disease progression, shape regression models allow characterization of the underlying biological process while also facilitating comparison of a sample against a normative model. The first paper introduces a shape regression model into the PBM framework to characterize shape variability due to an underlying biological parameter. It further confirms the statistical significance of this relationship via systematic permutation testing. Simple regression models are, however, not sufficient to leverage information provided by longitudinal studies. Longitudinal studies collect data at multiple time points for each participant and have the potential to provide a rich picture of the anatomical changes occurring during development, disease progression, or recovery. The second paper presents a linear-mixed-effects (LME) shape model in order to fully leverage the high-dimensional, complex features provided by longitudinal data. The parameters of the LME shape model are estimated in a hierarchical manner within the PBM framework. The topic of geometric complexity present in certain biological shapes is addressed next (Chapters 5 and 6). Certain biological shapes are inherently complex and highly variable, inhibiting correspondence based methods from producing a faithful representation of the average shape. In the PBM framework, use of Euclidean distances leads to incorrect particle system interactions while a position-only representation leads to incorrect correspondences around sharp features across shapes. The third paper extends the PBM framework to use efficiently computed geodesic distances and also adds an entropy term based on the surface normal. The fourth paper further replaces the position-only representation with a more robust distance-from-landmark feature in the PBM framework to obtain isometry invariant correspondences. Finally, the above methods are applied to two applications from the field of orthopaedics. The first application uses correspondences across an ensemble of human femurs to characterize morphological shape differences due to femoroacetabular impingement. The second application involves an investigation of the short bone phenotype apparent in mouse models of multiple osteochondromas. Metaphyseal volume deviations are correlated with deviations in length to quantify the effect of cancer toward the apparent shortening of long bones (femur, tibia-fibula) in mouse models

Mixed-effects shape models for estimating longitudinal changes in anatomy

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Entropy-based particle correspondence for shape populations

Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies

Longitudinal Parameter Estimation in 3D Electromechanical Models: Application to Cardiovascular Changes in Digestion

International audienceComputer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However the number of simulation parameters in these models can be high and expert knowledge is required to properly design studies involving these models, and analyse the results. In particular it is important to know how the parameters vary in various clinical or physiological settings. In this paper we build a data-driven model of cardiovascular parameter evolution during digestion, from a clinical study involving more than 80 patients. We first present a method for longitudinal parameter estimation in 3D cardiac models, which we apply to 21 patient-specific hearts geometries at two instants of the study, for 6 parameters (two fixed and four time-varying parameters). From these personalised hearts, we then extract and validate a law which links the changes of cardiac output and heart rate under constant arterial pressure to the evolution of these parameters, thus enabling the fast simulation of hearts during digestion for future patients

Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study

International audienceComputer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive power. However the personalisation step to go from a generic model to a patient-specific one is still a scientific challenge. In particular it is still difficult to quantify the uncertainty on the estimated parameters and predicted values. In this manuscript we present a new pipeline to evaluate the impact of fibre uncertainty on the personalisation of an electromechanical model of the heart from ECG and medical images. We detail how we estimated the variability of the fibre architecture among a given population and how the uncertainty generated by this variability impacts the following personalisation. We first show the variability of the personalised simulations, with respect to the principal variations of the fibres. Then discussed how the variations in this (small) healthy population of fibres impact the parameters of the personalised simulations

Longitudinal Analysis using Personalised 3D Cardiac Models with Population-Based Priors: Application to Paediatric Cardiomyopathies

International audiencePersonalised 3D modelling of the heart is of increasing interest in order to better characterise pathologies and predict evolution. The personalisation consists in estimating the parameter values of an electromechanical model in order to reproduce the observed cardiac motion. However, the number of parameters in these models can be high and their estimation may not be unique. This variability can be an obstacle to further analyse the estimated parameters and for their clinical interpretation. In this paper we present a method to perform consistent estimations of electromechanical parameters with prior probabilities on the estimated values, which we apply on a large database of 84 different heartbeats. We show that the use of priors reduces considerably the variance in the estimated parameters, enabling better conditioning of the parameters for further analysis of the cardiac function. This is demonstrated by the application to longitudinal data of paediatric cardiomyopathies, where the estimated parameters provide additional information on the pathology and its evolution

Particle based shape regression of open surfaces with applications to developmental neuroimaging

journal articleShape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. In this paper we propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The statistical significance of the dependence is evaluated using permutation tests designed to estimate the likelihood of achieving the observed statistics under numerous rearrangements of the shape parameters with respect to the explanatory variable. We demonstrate the method on synthetic data and provide a new results on clinical MRI data related to early development of the human head