Browsing by Author "Wilton, Stephen"

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    Cardiac Rehabilitation and Secondary Prevention in Patients with Coronary Artery Disease and Atrial Fibrillation
    (2021-08-26) Liu, Hongwei; Wilton, Stephen; Tian, Ye; James, Matthew; Sajobi, Tolulope; Arena, Ross; Shaheen, Abdel-Aziz; Oh, Paul
    Background: Referral to and participation in cardiac rehabilitation (CR) in Canada and elsewhere remains suboptimal. The evidence for the benefits of CR in reducing incidence of atrial fibrillation (AF) in patients with coronary artery disease (CAD) is modest. Furthermore, whether multifactorial risk factor intervention is effective in improving prognosis in patients with AF remains unclear. Methods: We studied these questions by conducting two projects. Project 1 is a systematic review, which evaluated evidence on the effects of multifactorial risk factor intervention in patients with AF. Project 2 is a retrospective cohort study, which evaluated the relationships of CR completion status and cardiorespiratory fitness (CRF) across a CR program with the risk of incident AF. These analyses in Project 2 were performed by linking databases from an Alberta provincial cardiac catheterization registry, a city-wide CR program in Calgary, and Alberta provincial health administrative datasets. Results: In Project 1, the systematic review suggested that multifactorial risk factor intervention was positively associated with improved AF-related symptoms and health-related quality of life. In Project 2, we first used electrocardiography data to improve the diagnostic yield of administrative data-based AF identification algorithms. We further demonstrated that CR program completion was not associated with lower risk of incident AF after adjusting for baseline characteristics. However, both baseline CRF, 12-week CRF, and CRF changes following CR completion had inverse dose-dependent relationships with the risk of incident AF. Furthermore, we developed a risk prediction model for incident AF in patients completing a CR program, which showed good discrimination and was well calibrated in predicting the risk of AF at 5-years follow-up. Conclusions: These findings have enhanced the importance of multifactorial risk factor intervention in managing patients with AF, and added to the current state of knowledge of CR in improving the prognosis of patients with CAD, thereby providing further support for the promotion of CR. Furthermore, the risk prediction model will help prioritize resources for patients who are at high risk of developing AF and can benefit the most from screening for AF and participating in personalized CR services.
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    Design and Feasibility of a Behavioural Weight Loss Treatment for Patients with Atrial Fibrillation and Obesity referred to Cardiac Rehabilitation
    (2024-09-17) Williamson, Tamara Marie; Campbell, Tavis; Rouleau, Codie; Wilton, Stephen; Tomfohr-Madsen, Lianne; Alberga, Angela; McDonough, Megan
    Background: Moderate weight loss (i.e., ≥10%) reduces symptom burden and disease progression among patients with atrial fibrillation (AF) and co-morbid obesity (BMI ≥ 30 kg/m2). Cardiac rehabilitation (CR) improves AF risk factors (e.g., hypertension, cardiorespiratory fitness, lipids) and thus represents an ideal multidisciplinary setting for AF management. Yet, few CR programs include targeted behavioural weight loss treatment (BWLT) for patients with cooccurring obesity. Evidence-based approaches to integrating BWLT into CR are needed to encourage sustainable weight loss in this patient group. Purpose: The purpose of this dissertation was to adapt and establish the acceptability of a Small Change BWLT that could be integrated into an existing CR program for patients with AF and obesity prior to efficacy testing in a randomized clinical trial (RCT). Methods: Chapter 2 was a qualitative descriptive exploration of patients’ preferences, barriers, and enablers to participating in a Small Change BWLT in CR. Chapter 3 was a pre-post feasibility study to adapt the BWLT to patients with AF and obesity, and establish the acceptability of the modified program. Chapter 4 was a protocol for an RCT comparing the efficacy of the BWLT + CR in terms of 12-month weight loss relative to the traditional CR program. Results: Barriers and enablers identified in Chapter 2 were used to adapt the BWLT program and included changes to patient education materials and discussion of AF risk factors. In Chapter 3, patients reported strong liking of the adapted program, however weight loss at 6 months was not clinically or statistically significant. The BWLT was further refined using the findings from Chapter 3 to inform the protocol in Chapter 4, which is currently recruiting patients (NCT05230823). Conclusion: While the benefits of a Small Changes BWLT + CR for weight loss among patients with AF and obesity relative to CR-only remain to be seen, the results of this dissertation provide important acceptability and feasibility evidence suggesting that program uptake, adherence, and completion may be favourable in the RCT. Future directions including the potential addition of adjunct pharmacotherapy to BWLT and additional therapeutic BWLT components (e.g., values-based behaviour change, self-compassion) are discussed.
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    Evaluating the Association between Estradiol and Quality of Life and Cardiovascular Risk and Mortality in Healthy Women and Women with Chronic Kidney Disease
    (2016) Ramesh, Sharanya; Ahmed, Sofia; Wilton, Stephen; James, Matthew; Holroyd-Leduc, Jayna
    Chronic kidney disease (CKD) is associated with a poor quality of life and high risk of cardiovascular (CV) mortality, specifically sudden cardiac death (SCD), and an upregulated renin angiotensin system. Women with end stage kidney disease (ESKD) experience premature menopause, and in healthy women menopause is correlated with a poor quality of life and higher CV mortality. A series of studies was conducted in healthy women and women with CKD to determine the associations between menopause status, serum estradiol and 1) cardiac autonomic tone (CAT), a surrogate marker for SCD, in a high Angiotensin II (AngII) state 2) mortality in women with ESKD and 3) quality of life(QoL) in women with CKD. We also summarized the impressions of healthcare workers and patients on the discussion of symptoms of low sex hormones in a clinical setting. In healthy men and women, sex hormones did not correlate with baseline CAT; however, men with lower testosterone levels were unable to maintain CAT in response to AngII. At baseline, postmenopausal women had a lower CAT in comparison to premenopausal women. In response to AngII postmenopausal women and premenopausal women in the luteal phase were unable to maintain their CAT. Through a survey of nephrologists we found that nephrologists recognize the impact of CKD on sex hormones in women but report infrequently discussing sex hormone related issues with patients. In a systematic review of studies examining the effect of postmenopausal hormone therapy on CV outcomes in women with ESKD, hormone therapy was associated with a favourable lipid profile. However, we found that peri- and premenopausal women with ESKD on hemodialysis had a higher risk of all-cause, cardiovascular and non-cardiovascular mortality compared to postmenopausal women. Furthermore menopause specific QoL scores did not correlate with kidney function in CKD women. We found that associations between menopause status and CV risk and QoL in the CKD population are complex. This body of work can be used for hypothesis generation for future studies and trials aimed to determine the mediators of cardiovascular risk and poor quality of life in this population.
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    Integrating Multi-Domain Electronic Health Data, Machine Learning, and Automated Cardiac Phenomics for Personalized Cardiovascular Care
    (2024-04-19) Dykstra, Steven; White, James; Gavrilova, Marina; Wilton, Stephen; Alim, Usman
    This thesis aims to address core challenges surrounding the integration of multi-domain cardiovascular data, inclusive of patient reported health, electronic health information, and diagnostic imaging, to support artificial intelligence (AI) based risk prediction modelling. Despite inaugural success surrounding the use of AI-driven approaches to leverage granular features from each respective data source, the lack of integration continues to limit a comprehensive representation of patient health critical to the implementation of AI-augmented clinical decision support (AI-CDS). Central to this thesis was the primary hypothesis that patient-consented migration, integration, and curation of disparate data sources can be achieved in real-world clinical environments, permitting longitudinal accumulation of standardized resources for machine learning-based risk modelling. To test this hypothesis, my first aim was to develop a software infrastructure to establish and maintain a precision health data model for cardiovascular care. This data model forms the foundation of the Cardiovascular Imaging Registry of Calgary (CIROC), a platform which to date has generated structured data resources for over 20,000 unique patients with cardiovascular disease. The success of this robust data model has led to the expansion of this infrastructure to support all clinics of the Libin Cardiovascular Institute. The design of this initiative, called the PULSE program, was established as an objective of Aim 1, delivering a structured manuscript describing methods and recommendations for implementing a scalable institutional personalized medicine program for the ethical, fair, and equitable introduction of AI-CDS. Subsequently, the second aim demonstrates the value of the established data model, highlighting how it can be used for the development and validation of machine-learning based prediction models for cardiovascular outcomes. Utilizing multi-domain features of the CIROC data model, I demonstrated superiority of machine learning-based approaches over traditional risk prediction methods to predict new-onset atrial fibrillation, a leading cause of stroke. This study highlighted the value of integrating patient-reported health, electronic health record, and cardiac diagnostic data to forecast future cardiovascular events with improved accuracy. Further, my third aim targeted an expansion of disease features from source diagnostic testing data to improve risk modelling. To achieve this, I developed deep learning-based models for the automated analysis (segmentation and fiducial labelling) of the left ventricle from cine cardiac MRI imaging, enabling the delivery of 3D shape phenomics. This work showcases the capacity for deep learning techniques to further enhance the developed data models for patient-specific risk modelling by supporting advanced analyses of unique disease characteristics including shape and deformation. This novel solution is now planned for external validation by a large, international clinical study assessing the incremental value of 3D shape phenomics to improve prediction accuracy across a broad range of diseases. Overall, this thesis presents a comprehensive exploration of technical development required for, and value generated by multi-domain data integration for AI-CDS in cardiovascular care. Incremental to demonstrating feasibility, the deliverables of this thesis serve as a foundation for growth of an emerging institutional precision medicine initiative and for the development of future advanced multi-domain machine learning models relevant to cardiovascular care.
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    Patient preferences and individualized risk prediction for management of acute coronary syndrome in chronic kidney disease
    (2021-09-22) Wilson, Todd Allen; James, Matthew; Sajobi, Tolulope; Wilton, Stephen; Hazlewood, Glen
    Chronic kidney disease (CKD) affects over 10% of adult Canadians and is associated with high risks of morbidity and mortality following non-ST-elevation acute coronary syndrome (ACS). ACS is managed invasively or conservatively with medication, however, people with CKD are 20-50% less likely to receive invasive management than patients without CKD. There are important knowledge gaps to support shared decision-making for ACS in this patient population related to risk prediction of clinical outcomes and patient preferences for treatment options. This thesis’ objectives are to: 1) understand patient preferences towards invasive versus conservative management of ACS, 2) develop tools for predicting long-term adverse outcomes following ACS, and 3) estimate net effects of invasive management based on trade-offs in reducing risks of mortality or readmission for myocardial infarction (MI) versus increasing risk of end-stage kidney disease (ESKD). To achieve these objectives, we conducted a discrete choice experiment (DCE) to quantify patient preferences toward treatment decisions for ACS and developed risk prediction models for mortality, readmission for MI, the composite of mortality and readmission for MI, and ESKD. Further, we synthesized patient preference and absolute risk estimates, while incorporating treatment effects from randomized control trials, to conduct a net effect analysis. The DCE found most patients preferred treatment options that lowered their risk of mortality; however, a subgroup of patients was identified with strong preferences toward conservative treatment for ACS. Risk prediction model performance varied. Model calibration was very good; however, discrimination ranged from excellent for predicting ESKD to poor for predicting readmission for MI. The net effect analysis estimated 87% of all patients with CKD were expected to experience net benefit from invasive management with reductions in risks of mortality or readmission for MI outweighing increases in risk of progression to ESKD. This work has addressed knowledge gaps for understanding preferences of patients with CKD toward key attributes of treatment options for ACS and for providing individualized estimates of long-term outcomes according to treatment strategy for ACS for patients with CKD. This work provides knowledge to individualize benefit-harm information and support shared decision-making approaches for ACS treatment for patients with CKD.
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    Refined Prognostication in Coronary Artery Disease Using Routine Laboratory Test Data
    (2016) Gerling, Michael; de Koning, Lawrence; James, Matthew; Naugler, Christopher; Wilton, Stephen
    Coronary artery disease (CAD) is a leading cause of morbidity and mortality. Numerous prognostic scores have been developed that rely on clinical information to predict risk of adverse outcomes and subsequently aid clinicians in determining appropriate intervention strategies. This thesis examines the ability of laboratory test data, including the complete blood count (CBC), electrolytes, estimated glomerular filtration rate (eGFR), and 25-OH vitamin D, to improve prognostic assessment in CAD patients beyond existing clinical risk factors. Although 25-OH vitamin D status was found to be inversely associated with mortality, it was neither associated with nor predictive of hospital readmission, and provided little additional prognostic information beyond existing risk factors. Conversely, a risk score derived from components of the CBC, electrolytes, and eGFR, in conjunction with age and sex, was strongly predictive of mortality, and led to considerable improvement in the ability to identify high-risk patients beyond existing risk factors.