Browsing by Author "Dykstra, Steven"

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    Inhibitory Neural Coding in the Deep Cerebellar Nuclei
    (2015-06-03) Dykstra, Steven; Turner, Raymond
    This study examines the response of Weak and Transient burst deep cerebellar nuclear (DCN) neurons when they are stimulated by physiological patterns of Purkinje cell inhibitory input in vitro. We also report the DCN neurons response to regular patterns of Purkinje cell firing defined by a CV2 analysis, as well as complex spike discharge. Currently the functional significance, and input parameters that generate rebound bursts in the DCN are unknown. Here we report that there are subtle differences in the Weak and Transient cell burst response suggesting the presence of differential coding by DCN neurons. Reverse correlation between DCN cell bursts and Purkinje cell input revealed an elevation-pause pattern of Purkinje cell firing triggers a rebound burst, while CV2-defined patterns and complex spikes failed to reliably trigger rebound responses. This work identifies a framework for future studies to investigate the relationship between Purkinje cell input and DCN output.
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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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    Right ventricular insertion site fibrosis in a dilated cardiomyopathy referral population: phenotypic associations and value for the prediction of heart failure admission or death
    (2021-06-17) Mikami, Yoko; Cornhill, Aidan; Dykstra, Steven; Satriano, Alessandro; Hansen, Reis; Flewitt, Jacqueline; Seib, Michelle; Rivest, Sandra; Sandonato, Rosa; Lydell, Carmen P.; Howarth, Andrew G.; Heydari, Bobak; Merchant, Naeem; Fine, Nowell; White, James A.
    Abstract Background Dilated cardiomyopathy (DCM) is increasingly recognized as a heterogenous disease with distinct phenotypes on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging. While mid-wall striae (MWS) fibrosis is a widely recognized phenotypic risk marker, other fibrosis patterns are prevalent but poorly defined. Right ventricular (RV) insertion (RVI) site fibrosis is commonly seen, but without objective criteria has been considered a non-specific finding. In this study we developed objective criteria for RVI fibrosis and studied its clinical relevance in a large cohort of patients with DCM. Methods We prospectively enrolled 645 DCM patients referred for LGE-CMR. All underwent standardized imaging protocols and baseline health evaluations. LGE images were blindly scored using objective criteria, inclusive of RVI site and MWS fibrosis. Associations between LGE patterns and CMR-based markers of adverse chamber remodeling were evaluated. Independent associations of LGE fibrosis patterns with the primary composite clinical outcome of heart failure admission or death were determined by multivariable analysis. Results The mean age was 56 ± 14 (28% female) with a mean left ventricular (LV) ejection fraction (LVEF) of 37%. At a median of 1061 days, 129 patients (20%) experienced the primary outcome. Any abnormal LGE was present in 306 patients (47%), inclusive of 274 (42%) meeting criteria for RVI site fibrosis and 167 (26%) for MWS fibrosis. All with MWS fibrosis showed RVI site fibrosis. Solitary RVI site fibrosis was associated with higher bi-ventricular volumes [LV end-systolic volume index (78 ± 39 vs. 66 ± 33 ml/m2, p = 0.01), RV end-diastolic volume index (94 ± 28 vs. 84 ± 22 ml/m2 (p < 0.01), RV end-systolic volume index (56 ± 26 vs. 45 ± 17 ml/m2, p < 0.01)], lower bi-ventricular function [LVEF 35 ± 12 vs. 39 ± 10% (p < 0.01), RV ejection fraction (RVEF) 43 ± 12 vs. 48 ± 10% (p < 0.01)], and higher extracellular volume (ECV). Patient with solitary RVI site fibrosis experienced a non-significant 1.4-fold risk of the primary outcome, increasing to a significant 2.6-fold risk when accompanied by MWS fibrosis. Conclusions RVI site fibrosis in the absence of MWS fibrosis is associated with bi-ventricular remodelling and intermediate risk of heart failure admission or death. Our study findings suggest RVI site fibrosis to be pre-requisite for the incremental development of MWS fibrosis, a more advanced phenotype associated with greater LV remodeling and risk of clinical events.