Browsing by Author "Wiebe, Samuel"
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- ItemOpen AccessA systematic approach to using regression modelling and ‘big data’ to derive a meaningful clinical decision rule for epilepsy(2018-08-22) Josephson, Colin Bruce; Wiebe, Samuel; Jetté, Nathalie; Sajobi, Tolulope T.; Marshall, Deborah A.Introduction: clinical decision rules (CDRs) have been developed in a number of medical fields resulting in improved patient outcomes, quality of care, and health economics. Aims: to identify all CDRs developed for epilepsy and to derive one that guides the prescription of the antiepileptic drug (AED), levetiracetam, according to its risk of a psychiatric adverse effect. Methods: a systematic review and meta-analysis was first performed to determine the state of the literature with respect to CDRs in epilepsy. The Health Improvement Network (THIN) electronic medical records register was used to identify patients with epilepsy by employing a modified validated case definition with a 5-year washout. Analyses were restricted to patients receiving AED monotherapy and the association between levetiracetam use and psychiatric adverse effects was explored Cox proportional hazards regression with timevarying covariates. Finally, logistic regression with parameter regularisation and k=5 fold cross validation was used to derive the CDR that predicts the development of psychiatric adverse effects following levetiracetam prescription. Results: the systematic review identified four epilepsy-specific CDRs, none of which guided AED prescription. A total of 9595 presumed incident cases of epilepsy (85.7 cases per 100,000 persons) were identified in THIN. Both carbamazepine (hazard ratio [HR]: 0.84, 95% confidence interval [95% CI]: 0.73– 0.97; p = 0.02) and lamotrigine (HR: 0.83, 95% CI: 0.70–0.99; p = 0.03) were associated with reduced hazards of a psychiatric sign, symptom, or disorder iii compared to no AED treatment. Levetiracetam was not associated with psychiatric adverse effects but the analyses were underpowered (n=202; 3%). All patients receiving levetiracetam (1173/7400; 16%) were included for CDR derivation. Prediction variables were incorporated into multiple logistic regression models with parameter regularisation. Odds of reporting a psychiatric complaint were elevated for females and those with a pre-exposure history of depression, anxiety, recreational drug use, or higher social deprivation. The prediction model performed well (area under the curve [AUC] 0.68; 95% confidence interval 0.58- 0.79 after stratified k=5 fold cross-validation). Using a cut-off threshold 0.1, the CDR had a specificity of 83%. Conclusion: If externally validated and properly implemented, this CDR could be used to guide prescription in clinical practice.
- 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 AccessClassification Models for Multivariate Non-normal Repeated Measures Data(2021-01-08) Brobbey, Anita; Sajobi, Tolulope T.; Wiebe, Samuel; Williamson, Tyler S.; Nettel-Aguirre, AlbertoMultivariate repeated measures data, in which multiple outcomes are repeatedly measured at two or more occasions, are commonly collected in several disciplines (e.g., medicine, ecology, environmental sciences), where investigators seek to discriminate between population groups or make predictions based on changes in multiple correlated outcomes over time. Repeated measures discriminant analysis have been developed and applied to address these research questions. These classification models, which have been mostly developed based on growth curve models, covariance pattern models, and mixed-effects models, are advantageous in that they can account for complex correlation structures in multivariate repeated measures data (e.g., within-outcome and between-outcome correlations) to improve their predictive accuracy. However, they largely rely on the assumption of multivariate normality, which is rarely satisfied in multivariate repeated measures data. To our knowledge, there has been limited investigation of the behavior of these existing models in multivariate non-normal repeated measures data. The overarching goal of this research was to develop robust repeated measures discriminant analysis classifiers for multivariate non-normal repeated measures data. Specifically, we developed repeated measures discriminant analysis based on maximum trimmed likelihood estimators (MTLE) and generalized estimating equations (GEE) estimators and examine their accuracy in comparison to classifiers based on maximum likelihood estimation (MLE) using Monte Carlo methods. The simulation conditions examined, included population distribution, sample size, covariance structure (between-outcomes and within-outcome), covariance heterogeneity, repeated number of occasions, and number of outcome variables. The Monte Carlo study results indicated that the proposed methods increased overall mean classification accuracy by 2% - 15% in multivariate non-normal repeated measures data compared to repeated measures discriminant analysis based on MLE under most scenarios. Data from two cohort studies were used to illustrate the implementation of the proposed repeated measures discriminant analysis methods. The outcomes of this research includes novel multivariate classifiers for predicting group membership in multivariate normal and non-normal repeated measures data. This research contributes to the advancement of statistical science on methods for analyzing multivariate repeated measures data.
- ItemOpen AccessEMD-Based EEG and fMRI Data Analysis and Integration for High Precision Brain Functional Imaging(2023-04-28) Moradi, Narges; Sotero Diaz, Roberto; Gordon Goodyear, Bradley; Protzner, Andrea; Wiebe, Samuel; Leung, Henry; Murari, Kartikeya; Hecker, Kenton; Meltzer, JedTo understand the spatiotemporal dynamics of brain function, both high spatial and temporal resolution imaging data are required. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two non-invasive and complementary functional brain imaging methods with high temporal and spatial resolution, respectively. Thus, combining EEG and fMRI data would permit mapping of brain function with high temporal and spatial resolution simultaneously. The accurate design of models for EEG-fMRI data integration has become an important research topic; however, to date, the success of these models has been limited. In this thesis, new analysis methods are proposed that combine mathematically decomposed components of EEG and fMRI data, guided by information about the underlying neural activity. Specifically, this thesis introduces and validates novel methods based on Empirical Mode Decomposition (EMD) to remove the global signal (GS) from fMRI, denoise the gamma frequency band of EEG, and to integrate EEG with fMRI data. EMD-based methods, ICEEDMAN and the 3D-EMD, are used to decompose EEG and fMRI data into temporal- and spatial-Intrinsic Mode Functions (IMFs) (TIMFs and SIMFs, respectively). First, we show that GS can be removed from the fMRI by removing low-frequency SIMFs causing spurious high global connectivity in the brain. Second, we denoise EEG Gamma-band by removing low-power TIMFs from its frequency- and amplitude-modulated components corresponding to the noise. Finally, we improve EEG source localization precision by adding fMRI’s high spatial-frequency-based weights to the EEG inverse problem’s gain matrix, thereby improving EEG's spatial resolution, especially when deep regions are involved. This thesis thus develops novel methods to increase the precision of imaging of brain function with less artifacts and high spatial and temporal resolutions. A more complete representation of brain function is crucial for better brain function realization, accurate diagnosis, and development of effective treatments for brain diseases such as epilepsy and ADHD. It could aid by specifying sources of seizures and distinguishing patterns and networks involved in ADHD and epileptic activities with high precision.
- ItemOpen AccessExploring Visual Analytics in the Workplace: A Case Study with the Calgary Epilepsy Program(2015-12-22) Paredes, Julia; Maurer, Frank; Wiebe, Samuel; Engbers, JordanVisual data mining tools integrate machine learning, information visualization, and human pattern recognition capabilities for effective data exploration and analysis. When data is large and high dimensional, humans cannot easily derive conclusions from it, which means data must often be reduced and abstracted. Generally, domain experts are not involved in data abstraction steps and are not informed of the resulting information loss, which can affect informed decision-making. To evaluate how useful it is to involve domain experts and their knowledge in data abstraction steps, which are required to understand and visualize information, we conducted a case study with the Calgary Epilepsy Program. We designed CEP-Vis, a patient centric visual analytics tool, using a user-centered approach to allow clinicians to compare a patient to other patients with epilepsy. We evaluated CEP-Vis through usability studies and interviews and presented the results and challenges of integrating this tool in a real work setting.
- ItemOpen AccessGuidelines for Epilepsy Care – Gaps, Knowledge and Implementation(2016) Sauro, Khara; Wiebe, Samuel; Jetté, Nathalie; Quan, Hude; Holroyd-Leduc, Jayna; DeCoster, CarolynEpilepsy is the second most common neurological condition and can be associated with significant morbidity, premature mortality, and high resource use. Epilepsy is a spectrum disorder due its diverse presentation, making it challenging to manage. As a result, treatment gaps exist. Clinical practice guidelines should facilitate the care of people with epilepsy. While evidence exists that guidelines are effective in improving the quality of care in some clinical settings, this has not been demonstrated for epilepsy. The objectives of this thesis are to: 1) Identify gaps in epilepsy guidelines, 2) Determine barriers and facilitators to implementation of epilepsy guidelines, and 3) Develop a knowledge translation (KT) strategy to optimize dissemination and implementation of epilepsy guidelines. Several methods were used to achieve the study objectives. A systematic review of epilepsy guidelines was conducted to identify gaps. A mixed-methods approach (quantitative survey and focus groups) was used to identify the determinants of guideline use among neurologists. Based on the results of the study examining determinants of guideline use, a theory-based KT strategy was proposed to facilitate future implementation of epilepsy guidelines in clinical practice. The systematic review identified 63 guidelines for the care of epilepsy covering 19 populations/conditions. Gaps in the availability of guidelines for high priority areas (i.e. elderly) and significant heterogeneity in quality were identified. Despite the number of guidelines available for the care of people with epilepsy, use of these guidelines clinically is poor. Reasons for the poor implementation of these guidelines among neurologists (end-users of epilepsy guidelines) were identified here, and include: lack of knowledge, poor credibility, applicability and motivation, insufficient resources, and lack of clarity regarding the target users. A three-pillared KT strategy to overcome the barriers of guideline use, and leverage facilitators is proposed to improve implementation in clinical practice. This body of work provides novel evidence into the current state of epilepsy guidelines, and the factors that determine their use clinically. This novel insight helps bridge a knowledge gap while the KT strategy outlined here provides the tools required to move towards improving implementation of guidelines for the care of people with epilepsy internationally.
- ItemOpen AccessIntranasal Insulin for Treatment of Diabetic Polyneuropathy(2013-11-19) Korngut, Lawrence; Wiebe, Samuel; Jetté, NathalieIntranasal insulin administration is a novel approach to slow the progression of diabetic polyneuropathy (DPN). We performed a pilot randomized controlled trial of intranasal insulin in 12 type 1 diabetes mellitus patients with DPN to assess safety. We administered intranasal insulin for 6 weeks using biweekly dose-escalation up to 160 IU/d or intranasal saline. The primary outcome measure was frequency of hypoglycaemia. Frequency of mild (mHG) and serious hypoglycaemic (sHG) events was recorded. Secondary outcomes included clinical (Utah Early Neuropathy Score (UENS)) and laboratory (corneal confocal microscopy and electrophysiology) measures. There were no differences in glycemia between groups after supervised initial administration. The 40 IU/d and 80 IU/d doses were safe and well tolerated with comparable mHG events between groups. One intranasal insulin subject suffered a sHG at home while receiving 160 IU/d. Intranasal insulin was safe and well tolerated at 40 and 80 IU/d.
- ItemOpen AccessProtocol for the development of an international Core Outcome Set for treatment trials in adults with epilepsy: the EPilepsy outcome Set for Effectiveness Trials Project (EPSET)(2022-11-17) Mitchell, James W.; Noble, Adam; Baker, Gus; Batchelor, Rachel; Brigo, Francesco; Christensen, Jakob; French, Jacqueline; Gil-Nagel, Antonio; Guekht, Alla; Jette, Nathalie; Kälviäinen, Reetta; Leach, John P.; Maguire, Melissa; O’Brien, Terence; Rosenow, Felix; Ryvlin, Philippe; Tittensor, Phil; Tripathi, Manjari; Trinka, Eugen; Wiebe, Samuel; Williamson, Paula R.; Marson, TonyAbstract Background A Core Outcome Set (COS) is a standardised list of outcomes that should be reported as a minimum in all clinical trials. In epilepsy, the choice of outcomes varies widely among existing studies, particularly in clinical trials. This diminishes opportunities for informed decision-making, contributes to research waste and is a barrier to integrating findings in systematic reviews and meta-analyses. Furthermore, the outcomes currently being measured may not reflect what is important to people with epilepsy. Therefore, we aim to develop a COS specific to clinical effectiveness research for adults with epilepsy using Delphi consensus methodology. Methods The EPSET Study will comprise of three phases and follow the core methodological principles as outlined by the Core Outcome Measures in Effectiveness Trials (COMET) Initiative. Phase 1 will include two focused literature reviews to identify candidate outcomes from the qualitative literature and current outcome measurement practice in phase III and phase IV clinical trials. Phase 2 aims to achieve international consensus to define which outcomes should be measured as a minimum in future trials, using a Delphi process including an online consensus meeting involving key stakeholders. Phase 3 will involve dissemination of the ratified COS to facilitate uptake in future trials and the planning of further research to identify the most appropriate measurement instruments to use to capture the COS in research practice. Discussion Harmonising outcome measurement across future clinical trials should ensure that the outcomes measured are relevant to patients and health services, and allow for more meaningful results to be obtained. Core Outcome Set registration COMET Initiative as study 118 .
- 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.
- ItemOpen AccessUsing Machine Learning Towards Decision Support for Refractory Epilepsy Cases(2023-01-25) Farhoudi, Bijan; Maurer, Frank; Wiebe, Samuel; Federico, Paolo; Josephson, Colin BruceBetween 0.5% to 1.0% of people in North America suffer from epilepsy, and around 30% of patients are drug-resistant. Some drug-resistant patients are candidates for surgery and up to 60% to 70% of patients who undergo surgery become seizure-free. Finding a magnetic resonance imaging (MRI) abnormality on pre-operative imaging increases the chance of surgical success. However, up to 30% to 40% of pre-operative MRIs have no clear lesion in people with drug-resistant epilepsy, and only up to 40% to 50% of non-lesional MRI cases become seizure-free after surgery. The focus of this work was to design decision support tools to help clinicians evaluate patients for surgery. As the first step, we investigated the possibility of segregating MRIs with abnormality from MRIs without any abnormality using Deep Learning models. Such models would help clinicians when they examine MRIs to find an abnormality. Considering the value of predicting surgery results, in our next step, we explored the possibility of predicting the outcome of surgery using MRI and Deep Learning. Our results indicate that both lesional and non-lesional MRIs of patients with epilepsy contain signals that Deep Learning models can harness to predict the operative success., Finally, we explored the possibility of finding an abnormality in MRIs that were reported by radiologists as non-lesional by using Deep Learning.