Browsing by Author "Josephson, Colin B."
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- ItemOpen AccessAdult patient perspectives of the unknowns of living with epilepsy - results from a focus group study.(2019-11-24) Lee, Jeanie Y. Y.; Gelfand, Jennifer; Khan, Sundus; Crooks, Rachel E.; Josephson, Colin B.; Wiebe, Samuel; Patten, Scott B.; Korngut, Lawrence; Smith, Eric Edward; Roach, Pamela M.Background/Objectives: Epilepsy is one of the most common and debilitating neurological conditions that affects nearly 50 million people worldwide, yet there remains a stigma around this condition, which can impact the information-seeking behaviours of patients. As the Brain and Mental Health Research Clinics develop a website about registry-based research, including patient-facing areas, it is important to understand how patients look for information, and the types of information they are seeking out. The objective of this study was to encourage conversation and understand the patient perspectives of existing knowledge gaps between epilepsy patients and the resources they use to obtain information. Methods: A total of thirteen patients (mean (SD) age = 46.4 (16.1) years) from the Calgary Comprehensive Epilepsy Program Registry and four caregivers participated in one of the three focus groups completed in order to meet our aims. There were eight female and five male patients. A semi-structured guide was used to understand the patients’ experiences, top concerns, informational resources currently used, and resources or knowledge that patients felt are lacking. The focus groups were audio-recorded and transcribed verbatim. Thematic content analysis was conducted by two researchers who independently open-coded the transcripts using NVivo 11. The final analysis was done by team discussion and ongoing analysis of the codes to create themes and sub-themes. Results: The major themes that emerged from the data included: 1) daily management; 2) resources; and 3) medications and treatment. For daily management, the participants reported concerns about the effects of epilepsy on day-to-day activities such as driving, working, and the barriers they face in society due to their perceived lack of awareness and education about seizure management in the general public. The participants felt negatively impacted by the stigma and compared their experience with epilepsy with other disorders such as cancer or diabetes which they feel are much more accepted in society. The geographical location of the patient also plays a role in the support they receive for epilepsy management, with participants citing challenges and feelings of isolation in rural areas. To acquire more information about epilepsy, participants reported that they primarily asked their physicians or searched online. However, despite the conveniences of the internet, some individuals felt the volume and variation of quality of online information was overwhelming. Instead, they would prefer to go to trusted resources that are provided by healthcare professionals or websites affiliated with hospitals or universities. Updated information on medication, side effects, and research are examples of resources the patients would like to see provided on such websites. Conclusion: Overall, it is clear from our focus groups that resources and support for self-management and day-to-day living for individuals with epilepsy is paramount to reduce knowledge gaps. Not only is it important to provide daily management and medication information to patients through trusted organizational resources, but it is equally important to increase public awareness about epilepsy and seizure disorders to reduce the stigma attached to these conditions.
- ItemOpen AccessAge-dependent analysis of cerebral structures and arteries in a large database(2022-06-30) Mouches, Pauline; Forkert, Nils D.; Goodyear, Bradley G.; Josephson, Colin B.Aging of the population is expected to lead to a rapid increase of neurological diseases. Such diseases can progress quickly and detrimentally affect the daily life of patients. Prognosis improves with early diagnosis, but early detection is diffcult. It is crucial to be able to differentiate early stage pathological alteration from normal age-related changes. Thus, there is a need for a better understanding of brain aging and reliable biomarkers. Within that context, the overarching aim of this work is to study normal aging patterns in brain tissues and arteries using a large database of magnetic resonance imaging and angiography data, as well as cardiovascular risk factors from the whole adult life span. To do so, the objectives of this thesis are: (1) to quantify artery morphology variability among adults and identify the impact of age, sex and cardiovascular risk factors on cerebrovascular structures; (2) to combine brain tissue and artery information for biological brain age prediction; (3) to explore the impact of cardiovascular risk factors on the brain age gap, which is a biomarker representing the difference between the biological brain age and chronological age. To achieve these objectives, first, a statistical cerebrovascular atlas is generated from multi-centre adult data. Image analyses and multivariate regression methods are then employed to find associations between brain artery morphology and aging. Second, multi-modal explainable deep learning models are used to accurately estimate the biological brain age and identify predictive brain regions. Third, an exploratory causal analysis is performed to isolate the effects of individual factors on the brain age gap. The results of this work offer a novel insight on brain tissue and artery aging patterns. An in-depth analysis of the brain age gap biomarker is carried out. Novel approaches are proposed to improve brain age prediction models in terms of accuracy and explainability. Finally, innovative methods are used to study cause and effects relationships between brain aging and cardiovascular risk factors. This work aims to uncover clinically relevant findings and represents valuable methodological advancements that could be used in other neuroimaging clinical applications, for instance, to ameliorate predictive models for decision-support.
- ItemOpen AccessPhenotype-based prediction of incident cardiovascular hospitalization and inpatient care costs in patients referred for cardiovascular magnetic resonance imaging: Applications of traditional statistical modelling and machine learning(2021-09-24) Lei, Lucy Y; White, James A; Fine, Nowell M.; Lee, Joon; Quan, Hude; Josephson, Colin B.Background: Cardiovascular disease has an estimated lifetime prevalence of 48% in adults and imposes the highest economic burden on health care systems among noncommunicable diseases. These costs are largely related to chronic disease management, clinical procedures, and hospitalization, particularly for major adverse cardiovascular events (MACE). Importantly, health expenditures incurred by cardiovascular care are expected to increase substantially as the global population ages and life expectancies continue to rise. To improve health system efficiency and resource allocation in preparation for future cardiovascular care needs, it is necessary to improve baseline patient characterization and offer more accurate personalized risk predictions to optimally plan for opportunities to improve cardiovascular health while controlling costs.Aims: The aim of this thesis was to develop and validate models for the prediction of MACE and one-year cumulative inpatient care costs in a large cohort of patients referred for cardiovascular magnetic resonance imaging.Methods: Patients were recruited from the Cardiovascular Imaging Registry of Calgary, a prospective clinical outcomes registry that provides automated linkages of data abstracted from electronic health records, cardiovascular magnetic resonance imaging reports, and patient-reported health questionnaires. These data were used for predictive modelling using both traditional statistical methodologies and machine learning approaches.Results: Random survival forest and Cox proportional hazards models were developed for time-to-event prediction of hospitalization for MACE. Both models achieved time dependent AUCs of 0.83 in holdout validation. Patients with predicted risk in the upper tertile experienced 29- and 21-fold (p < 0.001) increased risk of MACE, respectively. A two-part hurdle model was developed for cost regression to predict one-year cumulative inpatient expenditures following cardiovascular magnetic resonance imaging. When binning the cost predictions into zero-, low-, and high-cost brackets, the model achieved 0.73 precision, 0.76 recall, and 0.74 F1. The best performing machine learning classification model combined predictions from random forest and artificial neural network algorithms to achieve 0.76 precision, 0.82 recall, and 0.79 F1.Conclusions: The results of this thesis demonstrate the prognostic capacity of multi-domain health data and its utility in the development of patient-specific risk models for adverse cardiovascular events and cumulative inpatient care costs. Additionally, while machine learning modelling methodologies offer advantages in handling large health care data sets, the interpretability of traditional statistical models remains valuable for delineating relationships between health-related variables and outcomes.
- ItemOpen AccessStreamlining the Epilepsy Pre-surgical Evaluation Workflow with Virtual Reality(2023-04-28) Aminolroaya, Zahra; Maurer, Frank; Wiebe, Samuel; Willett, Wesley; Josephson, Colin B.We introduce RealityFlow, a novel virtual reality (VR) system designed to assist neurologists in a clinical workflow of planning epilepsy surgery. We describe RealityFlow ’s prototyping process and present our video-based approach for the prototyping feedback elicitation from physicians with limited availability. Currently, the clinical workflow is laborious, time-consuming, and requires high mental loads of physicians. Neurologists with tight schedules use desktop-based systems with 2D magnetic resonance imaging (MRI) representations of the brain to analyze the brain, mentally imagining how seizures propagate through the brain. Then, they write summaries of their analyzes and present seizure propagation information in meetings. Also, while designing a tool to help neurologists requires their engagement in a design process, the neurologists’ limited availabilities reduce opportunities for them to give feedback on critical design decisions. RealityFlow aims to assist neurologists in preparing and presenting seizure propagation data. RealityFlow offers 3D direct VR manipulation to prepare data and integrates required spatio-temporal information of a seizure spread for demonstration in a 3D space. It introduces a novel visualization of seizure propagation to help neurologists better understand and present user-defined seizure propagation types. The system’s visualization aims to enable neurologists to identify seizure changes and compare different seizure propagation types in a VR environment. Neurologists will be able to place different layouts showing seizure spread information in RealityFlow for analysis and presentation of data. Based on experts’ reflections, we discuss the criteria for integrating RealityFlow into surgery planning rounds. Feedback from domain experts suggests a promising future for RealityFlow. Participants stressed that the new VR tool can provide easier interactions with a 3D brain improving anatomical orientation compared to traditional desktop-based systems. It also potentially supports a better understanding of seizure propagation than a current clinical workflow and can be used as an educational tool. The successful integration of RealityFlow’s VR technology in clinical practice depends on neurologists’ adaptation to its use. The incorporation of a new VR tool like RealityFlow in the clinical process should enhance the clinical workflow while eliminating unnecessary steps, like inserting temporal information in VR instead of reading the available information from medical tools. Also, in our RealityFlow prototyping process, we developed a remote feedback collection process in which we created videos of the VR design process and used these videos to ground iterative input from neurologist collaborators. We utilized the videos from the high-fidelity prototype to elicit feedback from the neurologists who are VR beginners and to help them better grasp the 3D design concepts compared to low-fidelity prototyping approaches, like traditional paper prototyping. The short videos were easily accessible through the Internet for neurologists with limited availability. Using the recorded videos allowed us to elicit feedback from neurologists based on their availability and to develop the VR prototype in a fast-paced prototyping process. We describe our approaches, takeaways, and challenges for developing RealityFlow and the video-based feedback collection to play a role in future VR prototyping.
- ItemOpen AccessThe use of patient-reported measures in epilepsy care: the Calgary Comprehensive Epilepsy Program experience(2021-10-12) Delgado-García, Guillermo; Wiebe, Samuel; Josephson, Colin B.Abstract The regular use of patient-reported measures (PRMs) has been associated with greater patient satisfaction and outcomes. In this article, we will review the Calgary Comprehensive Epilepsy Program's successful experience with PRMs in both clinical and research settings, as well as our current challenges and future directions. Our experience will illustrate that is feasible and convenient to implement PRMs, and especially electronic PRMs (ePRMs), into epilepsy clinics. These PRMs have direct clinical and research applications. They inform clinical decision making through readily interpretable scales to which clinicians can expeditiously respond. Equally, they are increasingly forming an integral and central component of intervention and outcomes-based research. However, implementation studies are necessary to address knowledge gaps and facilitate adoption and dissemination of this approach. A natural symbiosis of the clinical and research realms is precision medicine. The foundations of precision-based interventions are now being set whereby we can maximize the quality of life and psychosocial functioning on an individual level. As illustrated in this article, this exciting prospect crucially depends on the routine use of ePRMs in the everyday care of people with epilepsy. Increasing ePRMs uptake will clearly be a catalyst propelling precision epilepsy from aspiration to clinical reality.