Improving stroke risk prediction and individualised treatment in carotid atherosclerosis

Background: Unstable carotid atherosclerosis causes stroke, but methods to identify patients and lesions at risk are lacking. Currently, this risk estimation is based on measurements of stenosis and neurological symptoms, which determines the therapy of either medical treatment with or without carotid endarterectomy. The efficacy of this therapy is low and higher accuracy of diagnosis and therapy is warranted. Imaging of carotid plaque morphology using software for visualisation of plaque components may improve assessment of plaque phenotype and stroke risk. These studies aimed firstly to investigate if, and if yes, how, the carotid plaque morphology with image analysis of CTA associated with on-going biology in the corresponding specimen. Secondly, if risk stratification in clinical risk scores can be linked to the aforementioned associations. Finally, if the on-going biological processes can be specifically predicted out of the CTA imaging analysis. Methods: Plaque features were analysed in pre-operative CTA with dedicated software. In study I and II, the plaques were stratified according to quantified high and low of each feature, profiled with microarrays, followed by bioinformatic analyses. Immunohistochemistry was performed to evaluate the findings in plaques. In study III, patient phenotype, according to clinical stroke risk scores of CAR and ABCD2 stratified the cohorts of high vs low scores which were subsequently profiled with microarrays, followed by bioinformatic analyses and correlation analyses of plaque morphology in CTA. In study IV, the microarray transcriptomes were individually coupled to morphological data from the CTA analysis, developing models with machine intelligence to predict the gene expression from a CTA image. The models were then tested in unseen patients. Results: In study I, stabilising markers and processes related to SMCs and ECM organisation were associated with highly calcified plaques, while inflammatory and lipid related processes were repressed. PRG4, a novel marker for atherosclerosis, was identified as the most up-regulated gene in highly calcified plaques. Study II showed that carotid lesions with large lipid rich necrotic core, intraplaque haemorrhage or plaque burden were characterized by molecular signatures coupled with inflammation and extracellular matrix degradation, typically linked with instability. Symptomatology associated with large lipid rich necrotic core and plaque burden. Cross-validated prediction model for symptoms, showed that plaque morphology by CTA alone was superior to stenosis degree. Study III revealed that a high clinical risk score in CAR and ABCD2, reflect a plaque phenotype linked to immune response and coagulation, where the novel ABCB5, was one of the most up-regulated genes. The high risk scores correlated with the plaque components matrix and calcification but no positive association with stenosis degree. Study IV resulted in 414 robustly predicted transcripts from the CTA image analysis, of which pathway analysis showed biological processes associated with typical pathophysiology of atherosclerosis and plaque instability. The model testing demonstrated a good correlation between predicted and observed transcript expression levels and pathway analysis revealed a unique dominant mechanism for each individual. Conclusions: Biological processes in carotid plaques associated to vulnerability, can be linked to plaque morphology analysed with CTA image analysis. Patient phenotype classified with clinical risk scores associates to plaque phenotype and morphology in CTA. The biological processes in the atherosclerotic plaque can be predicted with plaque morphology CTA analysis in this small pilot study, providing a possibility to precision medicine after validation in larger scale studie

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Radiomics and Machine Learning in the Prediction of Cardiovascular Disease

Carotid atherosclerosis is a major risk factor for ischaemic stroke which is a leading cause of death worldwide. For stroke survivors, 1 in 4 will have another stroke within five years. Carotid CT angiography (CTA) is commonly performed following an ischaemic stroke or transient ischemic attack to help guide patient management in the secondary prevention of stroke. For example, carotid endarterectomy surgery plus medical therapy or medical therapy alone. The degree of carotid stenosis is the mainstay in making this decision and uses only one aspect of anatomical information that can be obtained from a carotid CTA scan. Radiomics, sometimes called ‘texture analysis’, is the extraction of quantitative data from medical images that may not be apparent to the naked eye and has already demonstrated clinical utility in oncology for applications ranging from lesion characterisation to tumour grading and prognostication. Machine learning refers to the process of learning from experience (in this case data), rather than following pre-programmed rules. This thesis presents the findings of a proof-of-principle study to assess the value of radiomics in identifying the ‘vulnerable plaque’ and the ‘vulnerable patient’ within the context of cerebrovascular events. To evaluate the potential of radiomic features as imaging biomarkers, their reproducibility and robustness to morphological perturbations were assessed, as well as their biological associations with both PET and immunohistochemistry data. The ability of radiomic features to classify different carotid artery types, namely, culprit, non-culprit and asymptomatic carotid arteries was assessed using several machine learning classifiers. This was subsequently compared with a deep learning approach, which has greater capacity for data mining than feature-based machine learning approaches. Overall, radiomics could extract further useful information from carotid CTA scans. Culprit versus non-culprit carotid arteries in symptomatic patients and asymptomatic carotid arteries from asymptomatic patients had different radiomic profiles that could be leveraged using machine learning for better classification performance than carotid calcification or carotid PET imaging alone. Reliable and robust CT-based carotid radiomic features were identified that were associated with the degree of inflammation underlying the carotid artery. If validated with future prospective studies, this has the potential to improve personalised patient care in stroke management and advance clinical decision-making.Cambridge School of Clinical Medicine, the Medical Research Council's Doctoral Training Partnership and the Frank Edward Elmore Fun

Prediction of coronary artery disease using urinary proteomics

Aims: Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD. Methods and results: Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78–0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66–0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47–0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80–0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26–1.89, P \u3c 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25–0.95, P = 0.001; 0.64, 95% CI: 0.28–0.98, P = 0.001, correspondingly). Conclusion: A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention

Chronic kidney disease : a clinical model of premature vascular aging

Patients with chronic kidney disease (CKD) are prone to develop an accelerated vascular aging phenotype characterized by vascular calcification (VC), a major culprit of cardiovascular complications and premature death. While VC has been recognized as an active pathophysiologic process with involvement of specific mediators and effectors, the coexistence of traditional risk factors (i.e., high age, diabetes, hypertension, dyslipidemia), inflammaging stimuli and pharmacological interventions (e.g., phosphate binders, warfarin and statin therapy) adds to the complexity of the course and consequences of different types of VC (e.g., intima and media VC, micro- and macrocalcification) in the context of CKD. This work attempts to further explore the prognostic value, predictive markers as well as collateral therapeutic consequence of VC in uremic milieu. Study I explores the associations of the composites of coronary artery calcium (CAC) score, i.e., CAC density and CAC volume, with mortality risk in patients with CKD stage 5 (CKD G5). We found that while mortality risk increases with higher CAC score and CAC volume, CAC density shows an inverse-J shaped pattern, with the crude mortality rate being highest in the middle tertile of CAC density. Study II evaluates the overlapping presence of aortic valve calcium (AVC) and CAC and the prognostic value of AVC in CKD5 patients. We found a more common overlap of AVC and CAC in CKD G5 than that observed in general population. High AVC score is associated with increased all-cause mortality independent of presence of CAC, traditional risk factors and inflammation. Study III investigates phenotypic factors associated with the presence of biopsy-verified media VC in CKD G5 patients using the relaxed linear separability feature selection model. We identified through a mapping and ranking process, 17 features including novel biomarkers and traditional risk factors that can differentiate patients with media VC from those without. These results, if confirmed, may inform future investigations on media VC without the need of arterial biopsies. Study IV assesses the association of commonly prescribed phosphate binder sevelamer with gut microbial metabolites in CKD G5 patients. We found that sevelamer therapy associates with increased gut-derived uremic toxins and poor vitamin K status, suggesting potential tradeoffs of sevelamer therapy in CKD. Study V explores the plausible association between plasma dephosphorylated-uncarboxylated matrix Gla-protein (dp-ucMGP, a circulating marker of functional vitamin K deficiency), VC and mortality in CKD G5 patients. We found an independent association between high dpucMGP levels and increased mortality risk that is not modified by presence of CAC and AVC in CKD G5

Pathways linking atherosclerosis to aortic stenosis

Cardiovascular disease is the most common cause of death world-wide where atherosclerosis is the main culprit and aortic valve disease accounts about two percent of all CVD deaths. Atherosclerosis is a lipid and inflammation driven disease that share many features with aortic valve stenosis (AVS). Globally, the prevalence of AVS has been estimated to over 10 million patients and the incidence to over 12 500 new cases annually which is likely increasing due to increased longevity, yet no medical treatment is available. A link between atherosclerosis and AVS has previously been established by overlapping prevalence and common pathobiological hallmarks including lipid infiltration, inflammation, and calcification. Recent genetic studies have demonstrated several loci in which single nucleotide polymorphisms are associated with both diseases. However, there is also evidence pointing to separate etiologies including disease specific genetic risk factors, histopathological differences, and isolated clinical presentation. The aim of this thesis was to establish the interplay between atherosclerosis and AVS. A physiologic part was covered in Article I, specific mechanisms in Article II-IV and molecular epidemiology in Article IV. In Article I, arterial stiffness was determined in a cohort with ascending aortic dilatation and/or aortic valve disease before and after cardiac surgery. Arterial stiffness correlates with atherosclerotic cardiovascular disease and aggravates the increased left ventricular stress in AVS. Cardio-ankle vascular index (CAVI) measures arterial stiffness from the heart to the ankle and was lower in subjects with AVS compared with aortic regurgitation and ascending aortic dilation, before surgery, despite being older. In contrast, aortic stiffness assessed by carotid femoral pulse wave velocity (cfPWV) was not different between the groups. After surgery, CAVI but not cfPWV increased in patients with AVS but remained unchanged in patients undergoing aortic surgery. Age, diabetes, lower body mass index, decreased ejection time and lower preoperative CAVI was associated with an increased CAVI after surgery. The results suggest that AVS may mask an increased arterial stiffness if peripheral arteries are included in the measurement. Also, ejection time emerged as an important variable to account for when measuring arterial stiffness in aortic valve disease patients. Future work should aim to establish if arterial stiffness may be used to risk-stratify AVS patients. In Article II, the impact of a single nucleotide polymorphism (SNP) within FADS1 on aortic valve gene expression and fatty acid composition was identified. Fatty acid desaturase (FADS)1 and FADS2 encode rate limiting enzymes in the metabolism of omega-3 and omega-6 polyunsaturated fatty acids (PUFAs) and the SNP within this locus is associated with lower risk of both AVS and CAD. The SNP rs17547 was associated with FADS2 mRNA expression in calcified aortic valve tissue and the enzymatic activity of both FADS1 and FADS2. In addition, the aortic valve omega-3 PUFA docosahexaenoic acid proportion was higher in non-calcified compared with calcified tissue and positively correlated with the SNP. The results indicate that the protective effects of the SNP might be mediated via an increased DHA proportion in the aortic valve and/or possibly via downstream mediators from DHA such as specialized pro-resolving mediators which have been shown to dampen inflammation. Further pathophysiological evidence of shared pathways between CAD and AVS was obtained in Article III. The presence of antiphospholipid antibodies (aPL) in the general population is higher in patients with a recent myocardial infarction. Positivity for antibodies against β2-glycoprotein I and/or cardiolipin of IgG isotype was identified to be 8-fold higher in AVS patients compared with matched controls. In aortic valve tissue, aPL positivity was associated with downregulated interferon pathways and upregulated pathways related to mechanosensory signaling. Importantly, the differentially expressed genes could predict resilient (healthy), thickened (fibrotic) and calcified aortic valve tissue with high accuracy using supervised machine learning models suggesting a tight relationship between aPL related genes and local disease progression. The overall results imply that aPL IgG in the general population (without rheumatic disease) could be a risk factor for AVS and may potentially be used guide AVS precision medicine. In Article IV, CAD associated gene expression in aortic valve tissue was identified. First, the prevalence of CAD in a contemporary surgical tricuspid AVS cohort was established at 49% and was associated with claudication, smoking, male sex, and diabetes. An exploratory analysis of aortic valve transcriptomic data from 74 patients revealed that severe CAD, affecting 2 or 3 vessel territories, was associated with the most prominent difference in gene expression. The differentially expressed genes were primarily found in non-calcified tissue and were enriched in pathways related to oxidative stress, inflammation, and lipids. Furthermore, a supervised machine learning model could predict if aortic valve tissue stemmed from patients with severe CAD, at high accuracy. The most important gene predictors of severe CAD could further be used to predict atherosclerotic or macroscopically normal carotid artery tissue. The results suggest that AVS patients with concomitant severe CAD exhibit more atherosclerosis related mechanisms in non-calcified tissue, ultimately leading to a common end-stage disease with severe AVS. In summary, the results in this thesis demonstrate that AVS may be a cause of masked systemic arterial stiffness. Furthermore, pathways related to fatty acid metabolism and aPL are implicated in the pathophysiology of AVS and patients with severe CAD exhibit upregulated pathways related to atherosclerosis in the aortic valve. Collectively, pathways linking and differentiating aortic valve and vascular atherosclerotic disease were unraveled which open up for novel precision treatment regiments to halt AVS

Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT:A validation study

Purpose: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. Methods: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. Results: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R-2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R-2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. Conclusion: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions