Browsing by Author "White, James A"

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    Layer-specific strain in patients with heart failure using cardiovascular magnetic resonance: not all layers are the same
    (2020-12-03) Xu, Lingyu; Pagano, Joseph J; Haykowksy, Mark J; Ezekowitz, Justin A; Oudit, Gavin Y; Mikami, Yoko; Howarth, Andrew; White, James A; Dyck, Jason R B; Anderson, Todd; Paterson, D. I; Thompson, Richard B
    Abstract Background Global longitudinal strain (GLS), most commonly measured at the endocardium, has been shown to be superior to left ventricular (LV) ejection fraction (LVEF) for the identification of systolic dysfunction and prediction of outcomes in heart failure (HF). We hypothesized that strains measured at different myocardial layers (endocardium = ENDO, epicardium = EPI, average = AVE) will have distinct diagnostic and predictive performance for patients with HF. Methods Layer-specific GLS, layer-specific global circumferential strain (GCS) and global radial strain (GRS) were evaluated by cardiovascular magnetic resonance imaging (CMR) feature tracking in the Alberta HEART study. A total of 453 subjects consisted of healthy controls (controls, n = 77), at-risk for HF (at-risk, n = 143), HF with preserved ejection fraction (HFpEF, n = 87), HF with mid-range ejection fraction (HFmrEF, n = 88) and HF with reduced ejection fraction (HFrEF, n = 58). For outcomes analysis, CMR-derived imaging parameters were adjusted with a base model that included age and N-terminal prohormone of b-type natriuretic peptide (NT-proBNP) to test their independent association with 5-year all-cause mortality. Results GLS_EPI distinguished all groups with preserved LVEF (controls − 16.5 ± 2.4% vs. at-risk − 15.5 ± 2.7% vs. HFpEF − 14.1 ± 3.0%, p < 0.001) while GLS_ENDO and all GCS (all layers) were similar among these groups. GRS was reduced in HFpEF (41.1 ± 13.8% versus 48.9 ± 10.7% in controls, p < 0.001) and the difference between GLS_EPI and GLS_ENDO were significantly larger in HFpEF as compared to controls. Within the preserved LVEF groups, reduced GRS and GLS_EPI were significantly associated with increased LV mass (LVM) and LVM/LV end-diastolic volume EDV (concentricity). In multivariable analysis, only GLS_AVE and GRS predicted 5-year all-cause mortality (all ps < 0.05), with the strongest association with 5-year all-cause mortality by Akaike Information Criterion analysis and significant incremental value for outcomes prediction beyond LVEF or GLS_ENDO by the likelihood ratio test. Conclusion Global strains measured on endocardium, epicardium or averaged across the wall thickness are not equivalent for the identification of systolic dysfunction or outcomes prediction in HF. The endocardium-specific strains were shown to have poorest all-around performance. GLS_AVE and GRS were the only CMR parameters to be significantly associated with 5-year all-cause mortality in multivariable analysis. GLS_EPI and GRS, as well as the difference in endocardial and epicardial strains, were sensitive to systolic dysfunction among HF patients with normal LVEF (> 55%), in whom lower strains were associated with increased concentricity.
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    Phenotype-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.
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    Three-dimensional thoracic aorta principal strain analysis from routine ECG-gated computerized tomography: feasibility in patients undergoing transcatheter aortic valve replacement
    (2018-05-02) Satriano, Alessandro; Guenther, Zachary; White, James A; Merchant, Naeem; Di Martino, Elena S; Al-Qoofi, Faisal; Lydell, Carmen P; Fine, Nowell M
    Abstract Background Functional impairment of the aorta is a recognized complication of aortic and aortic valve disease. Aortic strain measurement provides effective quantification of mechanical aortic function, and 3-dimenional (3D) approaches may be desirable for serial evaluation. Computerized tomographic angiography (CTA) is routinely performed for various clinical indications, and offers the unique potential to study 3D aortic deformation. We sought to investigate the feasibility of performing 3D aortic strain analysis in a candidate population of patients undergoing transcatheter aortic valve replacement (TAVR). Methods Twenty-one patients with severe aortic valve stenosis (AS) referred for TAVR underwent ECG-gated CTA and echocardiography. CTA images were analyzed using a 3D feature-tracking based technique to construct a dynamic aortic mesh model to perform peak principal strain amplitude (PPSA) analysis. Segmental strain values were correlated against clinical, hemodynamic and echocardiographic variables. Reproducibility analysis was performed. Results The mean patient age was 81±6 years. Mean left ventricular ejection fraction was 52±14%, aortic valve area (AVA) 0.6±0.3 cm2 and mean AS pressure gradient (MG) 44±11 mmHg. CTA-based 3D PPSA analysis was feasible in all subjects. Mean PPSA values for the global thoracic aorta, ascending aorta, aortic arch and descending aorta segments were 6.5±3.0, 10.2±6.0, 6.1±2.9 and 3.3±1.7%, respectively. 3D PSSA values demonstrated significantly more impairment with measures of worsening AS severity, including AVA and MG for the global thoracic aorta and ascending segment (p<0.001 for all). 3D PSSA was independently associated with AVA by multivariable modelling. Coefficients of variation for intra- and inter-observer variability were 5.8 and 7.2%, respectively. Conclusions Three-dimensional aortic PPSA analysis is clinically feasible from routine ECG-gated CTA. Appropriate reductions in PSSA were identified with increasing AS hemodynamic severity. Expanded study of 3D aortic PSSA for patients with various forms of aortic disease is warranted.