cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification

Background\ud Pediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity.\ud \ud Methods\ud We develop and test a novel informatics framework called cMRI-BED for biomarker extraction and discovery from such complex pediatric cMRI data that includes the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied our workflow to obtain and analyze a dataset of 83 de-identified cases and controls containing cMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Bayesian rule learning (BRL) methods were applied to derive understandable models in the form of propositional rules with posterior probabilities pertaining to their validity. Popular machine learning methods in the WEKA data mining toolkit were applied using default parameters to assess cross-validation performance of this dataset using accuracy and percentage area under ROC curve (AUC) measures.\ud \ud Results\ud The best 10-fold cross validation predictive performance obtained on this cMRI-derived biomarker dataset was 80.72% accuracy and 79.6% AUC by a BRL decision tree model, which is promising from this type of rare data. Moreover, we were able to verify that mycocardial delayed enhancement (MDE) status, which is known to be an important qualitative factor in the classification of cardiomyopathies, is picked up by our rule models as an important variable for prediction.\ud \ud Conclusions\ud Preliminary results show the feasibility of our framework for processing such data while also yielding actionable predictive classification rules that can augment knowledge conveyed in cardiac radiology outcome reports. Interactions between MDE status and other cMRI parameters that are depicted in our rules warrant further investigation and validation. Predictive rules learned from cMRI data to classify positive and negative findings of cardiomyopathy can enhance scientific understanding of the underlying interactions among imaging-derived parameters

Western Diet-Fed, Aortic-Banded Ossabaw Swine: A Preclinical Model of Cardio-Metabolic Heart Failure.

The development of new treatments for heart failure lack animal models that encompass the increasingly heterogeneous disease profile of this patient population. This report provides evidence supporting the hypothesis that Western Diet-fed, aortic-banded Ossabaw swine display an integrated physiological, morphological, and genetic phenotype evocative of cardio-metabolic heart failure. This new preclinical animal model displays a distinctive constellation of findings that are conceivably useful to extending the understanding of how pre-existing cardio-metabolic syndrome can contribute to developing HF

Image processing techniques in nuclear medicine

The application of image processing techniques to radionuclide images acquired on a gamma camera - computer system has been investigated. Hepatic perfusion imaging studies with 99Tcm-tin colloid were performed in patients with primary colorectal carcimma. The hepatic perfusion index performed poorly in the detection of those patients with occult or overt hepatic metastastes, as did mean transit times of liver colloid flow derived from deconvolution analysis. A discriminant function was developed which separated those patients with occult metastases from those without liver disease. A fully automatic algorithm to derive a left ventricular edge from each frame of an ECG gated cardiac blood pool study was developed and validated in patient studies. Left ventricular ejection fractions calculated from count rates within the edge were reproducible and correlated well with ejection fractions derived from the same images by a manual technique, and with ejection fractions derived from left ventricular cineangiography. Studies were performed in patients to evaluate the effectiveness of tomographic imaging of the myocardial perfusion imaging agent 99Tcm-tBIN for detection of ischaemic heart disease. Tomographic reconstructions in the planes of the axes of the left ventricle gave better results than transaxial reconstructions or planar imaging. Choice of the optimum reconstruction filter for use in tomography using 99Tcm-tBIN was examined by means of patient am phantom studies. These showed that more accurate diagnoses and better reconstructions were obtained with smoothing filters than by the use of sharp reconstruction filters. This work shows that optimum image processing techniques must be established to attain the best possible results with new radiopharmaceuticals for nuclear medicine investigations

Doctor of Philosophy

DissertationHealth information technology (HIT) in conjunction with quality improvement (QI) methodologies can promote higher quality care at lower costs. Unfortunately, most inpatient hospital settings have been slow to adopt HIT and QI methodologies. Successful adoption requires close attention to workflow. Workflow is the sequence of tasks, processes, and the set of people or resources needed for those tasks that are necessary to accomplish a given goal. Assessing the impact on workflow is an important component of determining whether a HIT implementation will be successful, but little research has been conducted on the impact of eMeasure (electronic performance measure) implementation on workflow. One solution to addressing implementation challenges such as the lack of attention to workflow is an implementation toolkit. An implementation toolkit is an assembly of instruments such as checklists, forms, and planning documents. We developed an initial eMeasure Implementation Toolkit for the heart failure (HF) eMeasure to allow QI and information technology (IT) professionals and their team to assess the impact of implementation on workflow. During the development phase of the toolkit, we undertook a literature review to determine the components of the toolkit. We conducted stakeholder interviews with HIT and QI key informants and subject matter experts (SMEs) at the US Department of Veteran Affairs (VA). Key informants provided a broad understanding about the context of workflow during eMeasure implementation. Based on snowball sampling, we also interviewed other SMEs based on the recommendations of the key informants who suggested tools and provided information essential to the toolkit development. The second phase involved evaluation of the toolkit for relevance and clarity, by experts in non-VA settings. The experts evaluated the sections of the toolkit that contained the tools, via a survey. The final toolkit provides a distinct set of resources and tools, which were iteratively developed during the research and available to users in a single source document. The research methodology provided a strong unified overarching implementation framework in the form of the Promoting Action on Research Implementation in Health Services (PARIHS) model in combination with a sociotechnical model of HIT that strengthened the overall design of the study

Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning

A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamber view and 2-chamber view) and 10 images in a cycle, used for training a convolutional neural network to classify views (total 17,000 labeled images). All DICOM images were rigidly registered and rescaled into a reference image to fit the size of echocardiographic images. We employed 5-fold cross validation to examine model performance. We tested models trained by two types of data, averaged images and 10 selected images. Our best model (from 10 selected images) classified video views with 98.1% overall test accuracy in the independent cohort. In our view classification model, 1.9% of the images were mislabeled. To determine if this 98.1% accuracy was acceptable for creating the clinical prediction model using echocardiographic data, we tested the prediction model for EF using learning data with a 1.9% error rate. The accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images. The CNN algorithm can classify images into five standard views in a clinical setting. Our results suggest that this approach may provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data

Cardiovasular risk factors and their association with biomarkers in children with chronic kidney disease in Johannesburg, South Africa

A thesis submitted to the Faculty of Health Sciences, University of the Witwatersrand, in fulfilment of the requirements for the degree of Doctor of Philosophy Johannesburg, 2017.Background: In spite of the contributions of cardiovascular disease (CVD) to morbidity and mortality in chronic kidney disease (CKD) worldwide, there are no studies that have looked at cardiovascular risk factors (CVRFs) and their association with cardiovascular changes in African children with CKD. Several CVRFs have been implicated in the initiation and progression of cardiovascular changes in children with CKD, and these changes have been reported even in early CKD. This study investigated CVRFs and their association with cardiovascular changes in South African children with CKD. Method: This comparative cross sectional study recruited children (5-18 years) with CKD being followed up at the Division of Paediatric Nephrology of the Charlotte Maxeke Johannesburg Hospital and the Chris Hani Baragwanath Academic Hospital. One hundred and six children with a spectrum of CKD including those on chronic dialysis (34 CKD I, 36 CKD II-IV and 36 CKD V-dialysis) were enrolled over a 12 month study period. All patients had a short history taken along with a physical examination. Blood samples for serum creatinine, urea, albumin, calcium, phosphorus, parathyroid hormone (PTH), alkaline phosphatase, total cholesterol, haemoglobin and C-reactive protein, Vitamin D, Fibroblast growth factor-23 (FGF-23), Fetuin-A and genomic DNA studies were taken. Where feasible, transthoracic echocardiography and high resolution ultrasonography of the common carotid artery was performed. Results: The overall median age of the patients was 11 years (8-14 years), with a male female ratio of 2.1:1. Several CVRFs detected include hypertension, proteinuria, anaemia, hypercholesterolaemia and dysregulated mineral bone metabolism. The most common CVRF detected was anaemia (39.6%) and its prevalence was highest in the dialysis group when compared with the other CKD groups. The overall median (range) cIMT was 0.505mm (0.380-0.675), and was highest in patients with dialysis dependant CKD (p=0.003). The distribution of left atrial diameter (LAD) and left ventricular mass (LVM) differed significantly (p<0.05) across the different CKD groups. Abnormal LAD was seen in 10% of patients; left ventricular hypertrophy (LVH) in 27%; left ventricular systolic dysfunction in 6% and diastolic dysfunction in one patient. Mean arterial pressure and haemoglobin levels were independently associated with cIMT; hypertension was independently associated with concentric LVH; and age and hypoalbuminaemia were independently associated with eccentric LVH. Overall, the dialysis group had the highest prevalence of vascular changes, cardiac changes and associated risk factors. A skewed pattern of Fetuin-A and FGF-23 levels with medians (range) of 57.7 (0.9-225.2) mg/dL and 28.9 (0-3893.0) pg/ml respectively, were observed. The levels of these two biomarkers varied significantly between the different CKD groups (p<0.05). Fetuin-A was independently associated with abnormal LAD but no similar relationship with other cardiovascular changes and plasma levels of Fetuin-A and FGF-23 was found. Plasma FGF-23 levels correlated better with markers of bone mineralization than Fetuin-A. Eight Fetuin-A SNPs were analysed; rs2248690, rs6787344, rs4831, rs4917, rs4918, rs2070633, rs2070634 and rs2070635. We found an association between log-transformed Fetuin-A levels and the SNP rs4918 G-allele compared to the rs4918 C-allele (p=0.046) and the rs2070633 T-allele when compared to the rs2070633 C-allele (p=0.015). Markers of MBD such as phosphate and PTH levels were associated with Fetuin-A SNPs. The rs6787344 G-allele was significantly associated with phosphate levels (0.042), and the rs4918 G-allele with PTH (p=0.044). Seven deaths were recorded in the dialysis group during the study period and severe hypertension and intracranial bleed were the most common causes of death. Modifiable risk factors such as increased total cholesterol (TC) and decreased albumin levels were more commonly seen among the deceased dialysis patients. Conclusion: A high prevalence of CVRFs and cardiovascular changes were observed in the study groups, even in those with mild to moderate disease. Information obtained from the study highlights the need to address modifiable CVRFs such as hypertension, anaemia and hypoalbuminaemia in children with CKD and also the need to determine new, population specific, paediatric reference values for cIMT in healthy African children. Finally, the study was able to demonstrate differences in the relationship between Fetuin A SNPs and Fetuin-A levels and cardiovascular changes in our study population when compared with previously published data. We postulate that these differences may be due to genetic differences between our population and other population groups previously studied.LG201

Label-free segmentation from cardiac ultrasound using self-supervised learning

Segmentation and measurement of cardiac chambers is critical in cardiac ultrasound but is laborious and poorly reproducible. Neural networks can assist, but supervised approaches require the same laborious manual annotations. We built a pipeline for self-supervised (no manual labels) segmentation combining computer vision, clinical domain knowledge, and deep learning. We trained on 450 echocardiograms (93,000 images) and tested on 8,393 echocardiograms (4,476,266 images; mean 61 years, 51% female), using the resulting segmentations to calculate biometrics. We also tested against external images from an additional 10,030 patients with available manual tracings of the left ventricle. r2 between clinically measured and pipeline-predicted measurements were similar to reported inter-clinician variation and comparable to supervised learning across several different measurements (r2 0.56-0.84). Average accuracy for detecting abnormal chamber size and function was 0.85 (range 0.71-0.97) compared to clinical measurements. A subset of test echocardiograms (n=553) had corresponding cardiac MRIs, where MRI is the gold standard. Correlation between pipeline and MRI measurements was similar to that between clinical echocardiogram and MRI. Finally, the pipeline accurately segments the left ventricle with an average Dice score of 0.89 (95% CI [0.89]) in the external, manually labeled dataset. Our results demonstrate a manual-label free, clinically valid, and highly scalable method for segmentation from ultrasound, a noisy but globally important imaging modality.Comment: 37 pages, 3 Tables, 7 Figure

Cell-based gene therapy for mending infarcted hearts

The goal of this study was to analyse the efficiency of a combinatorial cell/growth factor therapy to improve function of infarcted murine hearts. The Insulin-like Growth Factor-1 (IGF-1) isoform, IGF-1Ea, has been shown to reduce scar formation and decrease cell death after MI. The present study utilized P19Cl6-derived, IGF-1Ea over-expressing cardiomyocytes to achieve its goal. The P19Cl6 cells were stably transduced with IGF-1Ea using a lentiviral vector and investigated first in vitro for their feasibility for in vivo cell therapy. The engineered pluripotent cells over-expressing IGF-1Ea survived better to hypoxia-induced injury than the control cells. The cells maintained their pluripotency and efficient differentiation capacity towards ventricular cardiomyocyte lineage, generating large quantities of cardiomyocytes optimal for the transplantation study. The generated cardiomyocytes were functionally active and exhibited a mature phenotype. Transplantation of the cardiomyocytes into allogeneic wild type murine infarcted hearts conferred a tendency for maintenance of function at short-term time point. At long-term however, this effect was lost, returning to the level of the control infarcted hearts. Cell tracing assessment revealed engraftment of both IGF-1Ea- and empty-cells, although the cells failed to couple with the recipient tissue. Scar size and capillary density analyses revealed no significant difference between the cells transplanted compared to the saline treated hearts, corroborating with the long-term functional data. Interestingly, the IGF- 1Ea-cell transplanted hearts expressed significantly higher amount of VEGFa compared to the controls, albeit no change in capillary density. Further investigation revealed that the enhanced VEGFa expression in IGF-1Ea-cells transplanted hearts was associated with reduced hypertrophy, marked by reduced cell cross-sectional area at the border-zone, aSK and bMHC expression compared to the control hearts. Nonetheless, modulation of hypertrophic response and transplantation of IGF-1Ea-cells were not able to confer lasting functional preservation, possibly due to lack of sufficient engraftment and coupling of the transplanted cells

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

Review Purpose: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. Findings: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. Summary: The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. Graphical Abstract: HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease