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Embargo
The Safety and Feasibility of Transcranial Direct Current Stimulation for the Treatment of Chronic Cervicogenic Headaches
(2024-06-24) Jobin, Kaiden D.; Debert, Chantel; Smith, Ashley; Schneider, Kathryn; Kirton, Adam
Cervicogenic headaches (CGH) are a highly debilitating condition whereby individuals primarily experience neck pain, headaches, and impaired neck function. Onset occurs frequently after whiplash injury, concussion, and degeneration of the cervical joints. Although therapies such as pharmacotherapy and exercise provide relief to some patients suffering from CGH, many patients do not find benefit from these interventions alone. Neuromodulation therapies such as transcranial direct current stimulation (tDCS) have recently shown promise in other headache and pain conditions. As such, we designed a double-blinded, sham-controlled, randomized trial to consider the safety and feasibility of tDCS combined with exercise therapy (ET) for patients with chronic CGH. Secondarily, we sought to explore the efficacy of active tDCS/+ET compared to sham tDCS/+ET on outcomes evaluating headache, pain, quality of life, and neck function. We found this intervention to be both safe and feasible for individuals with CGH, demonstrating high recruitment, retention, and adherence as well as no serious adverse events. Furthermore, we found significant group-time interactions favouring the active tDCS/+ET group with respect to headache intensity and frequency as well as pain and fine motor control of the deep cervical flexors. From these findings, a larger, adequately powered, clinical trial is warranted.
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Open Access
Brain Magnetic Resonance Spectroscopy: Advances and Applications to Chronic Pain in Knee Osteoarthritis
(2024-06-24) Leech, Samantha; Manske, Sarah; Harris, Ashley; Dunn, Jeffrey; Ng, Richard; Goodyear, Bradley; Dydak, Ulrike
This dissertation investigates advancements in brain proton magnetic resonance spectroscopy (1H-MRS) measures and their application to chronic pain in knee osteoarthritis. 1H-MRS measures proton signals, which can be converted into absolute concentrations using the properties of water, brain tissue, and neurochemicals. These concentrations serve as markers of brain health or dysfunction. Current methods to quantify absolute neurochemical concentrations assume an equal distribution of neurochemicals between white matter (WM) and gray matter (GM), an assumption not thoroughly examined. To address this, I determined the distribution of six neurochemicals between WM and GM to establish correction factors to replace assumptions with calculated values. After validation using an independent dataset, I created an open-source tool to implement the calculated correction factors, improving 1H-MRS accuracy by 30-55%. I used quantitative synthetic imaging to measure water properties — relaxation rates (T1 and T2) and proton density (PD) — in different brain tissues of healthy adults. I assessed the impact of inter-individual differences in T1, T2, and PD on neurochemical concentration measures by comparing concentrations calculated using literature-based constants (as is typically performed) to concentrations calculated using individual measures from quantitative maps. In a young, healthy population, individual measures contributed to subtle yet significant variations in calculated neurochemical concentrations, suggesting that using uniform literature values may not be accurate for every individual. Sensitivity analyses indicated that these inaccuracies are likely greater across a wider age range or in individuals with clinical disorders. Applying 1H-MRS, I identified potential neurochemicals and brain regions associated with chronic pain in knee osteoarthritis to understand the brain’s role in this condition. Knee osteoarthritis is a leading cause of chronic pain, with limited research on the specific neurochemicals and brain regions involved. I compared neurochemical levels and their association with pain measures in four brain regions between patients with knee osteoarthritis and healthy controls as well as longitudinally in patients three months after total knee replacement surgery. I found opposing relationships in brain regions associated with pain's sensory and affective dimensions. This dissertation enhances the accuracy of neurochemical concentration quantification and refines the understanding of the brain's contribution to knee osteoarthritis pain.
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Embargo
Some Contributions to Understanding the Heterogeneity of Treatment Effects in Stroke Trials
(2024-06-20) Ademola, Ayoola; Sajobi, Tolulope; Hildebrand, Kevin A.; Hill, Michael D.; Thabane, Lehana
Background: Stroke is a neurological disease that is the third leading cause of death and the tenth-largest known cause of disability-adjusted life years in Canada. Fortunately, clinical trial evidence has identified a few treatments that improve patients’ outcomes, resulting in faster reperfusion, better functional outcomes, lower mortality rates, and improved quality of life. Despite the overall positive benefits of these interventions, there remain differences in the impact of the treatment at the individual level, with some patients experiencing positive benefits and others showing neutral or adverse effects of interventions. Such heterogeneity of treatment effects (HTE) could be attributed to differences in patients’ socio-demographic or clinical characteristics, study designs, inclusion/exclusion criteria, and geographic or regional healthcare systems. Conventional statistical approaches for accounting for within-study and between-study HTE have primarily relied on within-trial subgroup analysis and meta-analysis. However, these approaches are limited because they are based on restrictive distributional assumptions, which may only be tenable in some clinical trials. Methods: This dissertation investigates relevant methodologies for characterizing and accommodating treatment effects within- and between-study heterogeneity in stroke trials. The specific objectives of this dissertation are to: 1) assess the credibility of subgroup analyses reported in published stroke trials; 2) investigate the comparative performance of methods for subgroup identification in clinical trials with binary endpoints when there is no a priori knowledge of patients’ characteristics associated with HTE, and 3) examine the performance of random-effects models when synthesizing evidence from trials with different study design characteristics. This study uses a combination of knowledge synthesis methodology and computer simulations to address these objectives. For objective 1, we conducted a systematic review to examine the credibility of reported subgroups in stroke trials. We used the Instrument for Assessing the Credibility of Effect Modification Analyses (ICEMAN) checklist to evaluate the quality of the subgroup analyses conducted for each study. For Objectives 2 and 3, computer simulations were used to examine the comparative performance of subgroup identification methods for identifying relevant variables/biomarkers associated with HTE in clinical trials of binary endpoints and meta-analytic methods for synthesizing treatment effects obtained from explanatory and pragmatic trials, respectively. Results: The systematic review of reporting quality of subgroup analyses in stroke trials revealed that the credibility of reported subgroup analyses is poor, with most studies not providing a priori rationale for the type and number of subgroup analyses conducted. Among all the subgroup identification methods investigated, the model-based recursive partitioning (MOB) method had the best control of Type I and higher statistical power to detect HTE. The random-effects model based on t-distribution (robustRE) and the mixture random-effects model (mixRE) are more appropriate for meta-analysis data with substantial HTE. However, the conventional random-effects model (RE model) remains reliable for estimating pooled treatment effects in data with moderate HTE. Conclusion: Understanding and capturing treatment effect heterogeneity is critical for generating evidence about treatment effectiveness in clinical trials. More statistical methods that account for heterogeneity in the study population and design characteristics are recommended to analyze and synthesize evidence from clinical trials.
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Embargo
An Examination of Patients’ Experiences with Navigation Services in Alberta’s Healthcare System
(2024-06-20) Rabi, Sarah; Tang, Karen; Santana, Maria; McBrien, Kerry; Dimitropoulos, Gina
Background: The concept of patient navigation (PN) was first envisioned to assist marginalized cancer patients access appropriate and timely healthcare resources. While this understanding of PN may still hold for a subgroup of programs today, the expansion of PN over the past 30 years has resulted in a diverse set of interventions with distinct care settings, patient eligibility criteria, navigator training, and program objectives. Noting this, our study sought to better understand how PN has evolved by gathering information on patients’ perspectives and interactions with PN programs across Alberta. Our objectives were to (i) explore patients’ current experiences with PN programs, and (ii) identify the features of PN felt to be of particular value to patients. Methods: To address these objectives, we conducted an interpretive descriptive study to collate the experiences of adult patients with longitudinal exposure to Albertan PN programs (involvement for greater than or equal to one month). Participant recruitment occurred via key informant sampling with navigators across Alberta. One-on-one semi-structured interviews were conducted to explore patient experiences with PN and their understanding of it as a broader concept. Inductive thematic analysis and interpretive exercises were subsequently performed to construct a coherent message from the data. Continued collaboration with two patient partners was maintained throughout the study to ensure responsiveness to patient priorities. Results: This study involved 23 participants with experience using nurse navigators, transition navigators, and lay community health navigators. Irrespective of navigation type, the participants’ stories were tethered by their navigators’ promotion of seamless and personalized care, as well as their ability to seemingly humanize the healthcare system. This was accomplished through a set of participant-identified navigator characteristics, including approachability, accessibility, and comprehensive systems knowledge. While the identified functions and characteristics of navigators were consistent across participants, how these components were operationalized varied based on the program’s setting and the particular needs of each patient. Conclusion: As a patient-centred intervention, understanding patients’ experiences and valuations of PN is critical to distilling the essence of the intervention. This research directly addresses ongoing knowledge gaps surrounding contemporary understandings of PN, particularly from patients’ perspectives.
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Embargo
Applications of Machine Learning to the High-Dose-Rate Cervical Brachytherapy Workflow: Applicator Prediction and Late Toxicity Modelling
(2024-06-20) Stenhouse, Kailyn; McGeachy, Philip; Roumeliotis, Michael; Yanushkevich, Svetlana; Wilms, Matthias; Rink, Alexandra; Roumeliotis, Michael
Cervical cancer is a significant global health issue, with brachytherapy being crucial for treating locally advanced disease. Brachytherapy involves inserting an applicator through the vaginal cavity to escalate radiation dose to the affected areas. Applicator geometries vary and impact the achievable dose distribution. Limited criteria exist to guide applicator selection, making it dependent on physician experience, presenting challenges in selection consistency and outcome variability. Suboptimal treatment may increase the likelihood of adverse effects post-treatment that impact quality of life. Additionally, there are currently limited comprehensive predictive models for treatment toxicities based on multi-institutional datasets. Machine learning can identify complex relationships and generate predictive models, and thus this thesis explored it’s potential to enhance the cervical cancer brachytherapy workflow by developing tools that assist physicians in making more informed treatment decisions. We first developed a decision-support tool for selecting the optimal treatment applicator, including hybrid interstitial needle arrangement. Using algorithm comparison and analyzing feature importance on retrospective data, we identified that boosted tree-based models combined with geometric features of the target volume provided the highest predictive accuracy. These models, validated through a prospective study, demonstrated comparable accuracy to expert radiation oncologists, with accuracies of 70% for applicator prediction and 82.5% for hybrid interstitial needle prediction. Machine learning predictions improved organ-at-risk dose compared to clinical predictions, demonstrating potential dosimetric benefit. Using a robust, multi-institutional dataset, we developed a Bayesian network approach to model late treatment toxicities, aiding in personalized and adaptive treatment strategies. We first developed a customized simulated annealing framework for optimizing network structures, integrating expert knowledge to ensure generated models have a logical structure that represent current clinical understanding. This framework demonstrated predictive performance comparable to out-of-box optimization methods, while providing a highly interpretable network structure. We explored potential clinical applications of these networks, including risk stratification, risk factor identification, and centre bias analysis. This thesis highlights novel applications of machine learning in supporting key aspects of the brachytherapy workflow for cervical cancer, potentially enhancing treatment quality and consistency, reducing treatment errors, and providing powerful clinical decision-support tools to physicians.