Operations Research & Statistical Learning Methods to Monitor the Progression of Glaucoma and Chronic Diseases

This thesis focuses on advancing operations research and statistical learning methods for medical decision making to improve the care of patients diagnosed with chronic conditions. Because the National Center for Disease Prevention (2020) estimates chronic conditions affect approximately 60% of the US adult population, improving the care of patients with chronic conditions will improve the lives of most Americans. Patients diagnosed with chronic conditions face lifestyle changes, rising treatment costs, and frequently reductions in quality of life. To improve the way in which clinicians treat patients with chronic conditions, treatment decisions can be supplemented by evidenced-based, data driven algorithmic decision-making methods. This thesis provides data-driven methodologies of a general nature that are instantiated for several medical decision-making problems. In chapter two we proactively identify the time of a patient’s primary open angle glaucoma (POAG) progression under high measurement error conditions using a soft voting ensemble classification model. When medical tests have low residual variability (e.g., empirical difference between the patient's true and recorded value is small) they can effectively, without the use of sophisticated methods, identify the patient's current disease phase; however, when medical tests have moderate to high residual variability this may not be the case. We present a solution to the latter case. We find rapid progression disease phases can be proactively identified with the combination of denoising and supervised classification methods. In chapter three, we determine the optimal time to next follow-up appointment for patients with the chronic condition of ocular hypertension (OHTN). Patients with OHTN are at increased risk of developing glaucoma and should be observed over their lifetime. Follow-up appointment schedules that are chosen poorly can result in, at minimum, delay in the detection of a patient’s progression to glaucoma, and at worse, yield poor patient outcomes. To this end, we present a personalized decision support algorithm that uses the fitted Q-iteration reinforcement learning algorithm to recommend personalized time-to-next follow-up schedules that are based on a patient’s medical state. We find personalized follow-up appointments schedules produced by reinforcement learning methods are superior to both 1-year and 2-year fixed interval follow-up appointment schedules. In chapters four and five, we examine and compare several criteria for determining progression from OHTN to POAG and evaluate the use of a collective POAG conversion rule in predicting future occurrences of patients' POAG conversion. We find age, race, and sex are statistically significant determinants in progression for all compared criteria. However, there exists broad conversion discordance between the criteria, as demonstrated by statistically different survival curves and the limited overlap in eyes that progressed by multiple criteria. Ultimately, to permit machine learning models to predict conversion from OHTN to POAG, it is essential to have quantitative reference standards for POAG conversion for researchers to use. Additionally, using the collective POAG conversion rule, we find machine learning models can successfully predict future OHTN conversion events to POAG. This research was conducted in collaboration with clinical disease/domain experts. All the medical decision-making research herein addresses real world healthcare issues, that, if solved, have the potential to improve vision care if implemented. While these methodologies primarily focus on chronic conditions affecting the eyes (e.g., OHTN and POAG), it is important to note that much of the work produced offers methods applicable to other chronic diseases.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169926/1/isaacaj_1.pd

Personalized Medicine in Chronic Disease Management.

Chronic diseases are persistent medical conditions which affect half of all adults in the United States. The nature of these long-term chronic conditions present monitoring and treatment challenges to practicing clinicians and medical researchers: (1) how to use information learned about each patient's disease characteristics over time to tailor monitoring and treatment decisions, (2) how to make sequential decisions when each decision has strong implications for future decisions, and (3) how to incentivize adherence to prescribed medications. By combining operations research with the principles of personalized medicine, this work develops novel mathematical models to answer high impact clinical questions faced when managing patients with chronic conditions. We begin our research by understanding how information about a single patient can be used to personalize the patient's forecasted disease dynamics and likelihood of disease progression. Next, we consider how models of heterogeneity in disease characteristics and patient behavior can be embedded within an optimization framework to design individualized treatment plans. Finally, we develop a model for copayment restructuring to improve patient adherence to individualized treatment plans.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111447/1/schellg_1.pd

Exploring circadian blood pressure patterns

Despite our comprehensive knowledge of the importance of reducing mean levels of blood pressure (BP), we are less informed about the benefits of reducing other parameters of BP, specifically BP variability (BPV). Ambulatory blood pressure monitoring (ABPM), which can be used to obtain estimates of BP over a 24h period, offers a powerful tool in the analysis of circadian patterns and short-term BPV. The main aims of this thesis were to explore and identify circadian BP patterns between individuals and groups, and extract meaningful measures that describe these patterns while appropriately accounting for the inherent cyclical structure of ABPM data. Specifically, the thesis includes a systematic review which identifies summary measures of BPV, such as standard deviation. A meta-analysis exploring the correlation between short-term BPV and subclinical target organ damage (TOD) is included. The association between the identified summary measures and subclinical TOD is then explored in a group of middle aged adults. In an attempt to maximise the power of the repeated cyclical readings in ABPM and incorporate the data together in one model, different random-effects models were explored which allowed us to obtain estimates of both within and between-individual variation of model parameters. A piece-wise linear mixed-effects model was considered as a simple but suitable approach to capture BP trajectory throughout the day. Finally, a two-component cosinor random-effects model is outlined where derivatives of the model fit presents a novel alternative method to locate and quantify the magnitude of slopes at critical points along the trajectory. This is used to obtain a measure of morning BP surge. We compare the random-effects from this model to principle component scores obtained through functional principle component analysis. Our motivating data comes from the Mitchelstown Study, a population based study of Irish adults where a subsample underwent 24h ABPM

Ultrasonic Pulse Wave Imaging for in vivo Assessment of Vascular Wall Dynamics and Characterization of Arterial Pathologies

Arterial diseases such as hypertension, carotid stenosis, and abdominal aortic aneurysm (AAA) may progress silently without symptoms and contribute to acute cardiovascular events such as heart attack, stroke, and aneurysm rupture, which are consistently among the leading causes of death worldwide. The arterial pulse wave, regarded as one of the fundamental vital signs of clinical medicine, originates from the heart and propagates throughout the arterial tree as a pressure, flow velocity, and wall displacement wave, giving rise to the natural pulsation of the arteries. The dynamic properties of the pulse wave are intimately related to the physical state of the cardiovascular system. Thus, the assessment of the arterial wall dynamics driven by the pulse wave may provide valuable insights into vascular mechanical properties for the early detection and characterization of arterial pathologies. The focus of this dissertation was to develop and clinically implement Pulse Wave Imaging (PWI), an ultrasound elasticity imaging-based method for the visualization and spatio-temporal mapping of the pulse wave propagation at any accessible arterial location. Motion estimation algorithms based on cross-correlation of the ultrasound radio-frequency (RF) signals were used to track the arterial walls and capture the pulse wave-induced displacements over the cardiac cycle. PWI facilitates the image-guided measurement of clinically relevant pulse wave features such as propagation speed (pulse wave velocity, or PWV), uniformity, and morphology as well as derivation of the pulse pressure waveform. A parametric study investigating the performance of PWI in two canine aortas ex vivo and 10 normal, healthy human arteries in vivo established the optimal image acquisition and signal processing parameters for reliable measurement of the PWV and wave propagation uniformity. Using this framework, three separate clinical feasibility studies were conducted in patients diagnosed with hypertension, AAA, and carotid stenosis. In a pilot study comparing hypertensive and aneurysmal abdominal aortas with normal controls, the AAA group exhibited significantly higher PWV and lower wave propagation uniformity. A “teetering” motion upon pulse wave arrival was detected in the smaller aneurysms ( 5.5 cm in diameter). While no significant difference in PWV or propagation uniformity was observed between normal and hypertensive aortas, qualitative differences in the pulse wave morphology along the imaged aortic segment may be an indicator of increased wave reflection caused by elevated blood pressure and/or arterial stiffness. Pulse Wave Ultrasound Manometry (PWUM) was introduced as an extension of the PWI method for the derivation of the pulse pressure (PP) waveform in large central arteries. A feasibility study in 5 normotensive, 9 pre-hypertensive, and 5 hypertensive subjects indicated that a significantly higher PP in the hypertensive group was detected in the abdominal aorta by PWUM but not in the peripheral arteries by alternative devices (i.e. a radial applanation tonometer and the brachial sphygmomanometer cuff). A relatively strong positive correlation between aortic PP and both radial and brachial PP was observed in the hypertensive group but not in the normal and pre-hypertensive groups, confirming the notion that PP variation throughout the arterial tree may not be uniform in relatively compliant arteries. The application of PWI in 10 stenotic carotid arteries identified phenomenon such as wave convergence, elevated PWV, and decreased cumulative displacement around and/or within regions of atherosclerotic plaque. Intra-plaque mapping of the PWV and cumulative strain demonstrated the potential to quantitatively differentiate stable (i.e. calcified) and vulnerable (i.e. lipid) plaque components. The lack of correlation between quantitative measurements (PWV, modulus, displacement, and strain) and expected plaque stiffness illuminates to need to consider several physiological and imaging-related factors such as turbulent flow, wave reflection, imaging location, and the applicability of established theoretical models in vivo. PWI presents a highly translational method for visualization of the arterial pulse wave and the image-guided measurement of several clinically relevant pulse wave features. The aforementioned findings collectively demonstrated the potential of PWI to detect, diagnose, and characterize vascular disease based on qualitative and quantitative information about arterial wall dynamics under pathological conditions

Quantifying the Effects of Altered-Gravity and Spaceflight Countermeasures on Acute Cardiovascular and Ocular Hemodynamics

The cardiovascular system is strongly dependent on the gravitational environment. Gravitational changes cause mechanical fluid shifts and, in turn, autonomic effectors influence systemic circulation and cardiac control. For future long-duration spaceflight, these gravitational effects could be related to decreased cardiovascular performance, the pathoetiology of spaceflight associated neuro-ocular syndrome (SANS), and increased venous thromboembolism (VTE) risk. The development of novel countermeasure protocols using, for example, lower body negative pressure (LBNP) or short-radius centrifugation (SRC) requires a full understanding of the detailed cardiovascular response to gravity and to different levels of countermeasure intervention. In this research effort, we use a complementary experimental and modeling approach to generate acute dose-response curves for systemic, autonomic, and cephalad parameters of the cardiovascular system in graded tilt (as an analog for altered-gravity), graded LBNP, and graded SRC. In the experimental approach, 24 subjects (12 male and 12 female) experienced 1) a graded tilt profile in the range of 45° head-up tilt to 45° head-down tilt in 15° increments; and 2) a graded LBNP profile from 0 mmHg to –50 mmHg in 10 mmHg increments. Using two different statistical techniques (mixed-effects modeling and Bayesian hierarchical multivariate modeling) we generate dose-response curves for the cardiovascular and ocular response. In the computational approach, we further develop an existing lumped-parameter model of the cardiovascular system to incorporate cephalad hemodynamics and the effects of body tissue weight. In addition, we also further develop a complementary lumped-parameter model of the eye. We simulate the same tilt and LBNP profiles, along with a graded SRC profile and a gravitational field change using simulated 50th percentile male and female subjects. The quantification of cardiovascular hemodynamics as a function of changes in the gravitational vector or the presence of countermeasure interventions presented here provides a terrestrial model to reference spaceflight-induced changes, contributes to the assessment of the pathogenesis of SANS and spaceflight VTE events, and informs the development of countermeasures

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Progress Report No. 16

Progress report of the Biomedical Computer Laboratory, covering period 1 July 1979 to 30 June 1980