Imperial College London

ProfessorDeclanO'Regan

Faculty of MedicineInstitute of Clinical Sciences

Professor of Imaging Sciences
 
 
 
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Contact

 

+44 (0)20 3313 1510declan.oregan

 
 
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Location

 

Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Publication Type
Year
to

183 results found

McGurk K, Santofimio V, Clement A, ORegan D, Ware Jet al., 2023, Identification of an increased lifetime risk of major adverse cardiovascular events in UK Biobank participants with scoliosis, Open Heart, Vol: 10, Pages: 1-8, ISSN: 2053-3624

Background Structural changes caused by spinal curvature may impact the organs within the thoracic cage, including the heart. Cardiac abnormalities in patients with idiopathic scoliosis are often studied post-corrective surgery or secondary to diseases. To investigate cardiac structure, function and outcomes in participants with scoliosis, phenotype and imaging data of the UK Biobank (UKB) adult population cohort were analysed.Methods Hospital episode statistics of 502 324 adults were analysed to identify participants with scoliosis. Summary 2D cardiac phenotypes from 39 559 cardiac MRI (CMR) scans were analysed alongside a 3D surface-to-surface (S2S) analysis.Results A total of 4095 (0.8%, 1 in 120) UKB participants were identified to have all-cause scoliosis. These participants had an increased lifetime risk of major adverse cardiovascular events (MACEs) (HR=1.45, p<0.001), driven by heart failure (HR=1.58, p<0.001) and atrial fibrillation (HR=1.54, p<0.001). Increased radial and decreased longitudinal peak diastolic strain rates were identified in participants with scoliosis (+0.29, Padj <0.05; −0.25, Padj <0.05; respectively). Cardiac compression of the top and bottom of the heart and decompression of the sides was observed through S2S analysis. Additionally, associations between scoliosis and older age, female sex, heart failure, valve disease, hypercholesterolemia, hypertension and decreased enrolment for CMR were identified.Conclusion The spinal curvature observed in participants with scoliosis alters the movement of the heart. The association with increased MACE may have clinical implications for whether to undertake surgical correction. This work identifies, in an adult population, evidence for altered cardiac function and an increased lifetime risk of MACE in participants with scoliosis.

Journal article

Saitta S, Maga L, Armour C, Votta E, O'Regan DP, Salmasi MY, Athanasiou T, Weinsaft JW, Xu XY, Pirola S, Redaelli Aet al., 2023, Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta., Computer Methods and Programs in Biomedicine, Vol: 233, Pages: 1-8, ISSN: 0169-2607

BACKGROUND AND OBJECTIVE: Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. METHODS: Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. RESULTS: Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. CONCLUSIONS: We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational stu

Journal article

Curran L, de Marvao A, Inglese P, McGurk KA, Schiratti P-R, Clement A, Zheng SL, Li S, Pua CJ, Shah M, Jafari M, Theotokis P, Buchan RJ, Jurgens SJ, Raphael CE, Baksi AJ, Pantazis A, Halliday BP, Pennell DJ, Bai W, Chin CWL, Tadros R, Bezzina CR, Watkins H, Cook SA, Prasad SK, Ware JS, ORegan DPet al., 2023, A genotype-phenotype taxonomy of hypertrophic cardiomyopathy

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes but there is no systematic framework for classifying morphology or assessing associated risks. Here we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We enrolled 436 HCM patients (median age 60 years; 28.8% women) with clinical, genetic and imaging data. An independent cohort of 60 HCM patients from Singapore (median age 59 years; 11% women) and a reference population from UK Biobank (n = 16,691, mean age 55 years; 52.5% women) were also recruited. We used machine learning to analyse the three-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Carriers of pathogenic or likely pathogenic variants for HCM (P/LP) variants had lower left ventricular mass, but greater basal septal hypertrophy, with reduced lifespan (mean follow-up 9.9 years) compared to genotype negative individuals (hazard ratio: 2.66; 95% confidence interval [CI]: 1.42-4.96;<jats:italic>P</jats:italic>&lt; 0.002). Four main phenotypic branches were identified using unsupervised learning of three-dimensional shape: 1) non-sarcomeric hypertrophy with co-existing hypertension; 2) diffuse and basal asymmetric hypertrophy associated with outflow tract obstruction; 3) isolated basal hypertrophy; 4) milder non-obstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for P/LP variants: 2.18 [95% CI: 1.93-2.28

Journal article

Salmasi MY, Alwis S, Cyclewala S, Jarral OA, Mohamed H, Mozalbat D, Nienaber CA, Athanasiou T, Morris-Rosendahl D, Members of the London Aortic Mechanobiology Working Groupet al., 2023, The genetic basis of thoracic aortic disease: The future of aneurysm classification?, Hellenic Journal of Cardiology, Vol: 69, Pages: 41-50, ISSN: 1109-9666

The expansion in the repertoire of genes linked to thoracic aortic aneurysms (TAA) has revolutionised our understanding of the disease process. The clinical benefits of such progress are numerous, particularly helping our understanding of non-syndromic hereditary causes of TAA (HTAAD) and further refinement in the subclassification of disease. Furthermore, the understanding of aortic biomechanics and mechanical homeostasis has been significantly informed by the discovery of deleterious mutations and their effect on aortic phenotype. The drawbacks in genetic testing in TAA lie with the inability to translate genotype to accurate prognostication in the risk of thoracic aortic dissection (TAD), which is a life-threatening condition. Under current guidelines, there are no metrics by which those at risk for dissection with normal aortic diameters may undergo preventive surgery. Future research lies with more advanced genetic diagnosis of HTAAD and investigation of the diverse pathways involved in its pathophysiology, which will i) serve to improve our understanding of the underlying mechanisms, ii) improve guidelines for treatment and iii) prevent complications for HTAAD and sporadic aortopathies.

Journal article

Siguero-Álvarez M, Salguero-Jiménez A, Grego-Bessa J, de la Barrera J, MacGrogan D, Prados B, Sánchez-Sáez F, Piñeiro-Sabarís R, Felipe-Medina N, Torroja C, José Gómez M, Sabater-Molina M, Escribá R, Richaud-Patin I, Iglesias-García O, Sbroggio M, Callejas S, O'Regan DP, McGurk KA, Dopazo A, Giovinazzo G, Ibañez B, Monserrat L, María Pérez-Pomares J, Sánchez-Cabo F, Pendas AM, Raya A, Gimeno-Blanes JR, de la Pompa JLet al., 2023, Human hereditary cardiomyopathy shares a genetic substrate with bicuspid aortic valve., Circulation, Vol: 147, Pages: 47-65, ISSN: 0009-7322

BACKGROUND: The complex genetics underlying human cardiac disease is evidenced by its heterogenous manifestation, multigenic basis, and sporadic occurrence. These features have hampered disease modeling and mechanistic understanding. Here, we show that 2 structural cardiac diseases, left ventricular noncompaction (LVNC) and bicuspid aortic valve, can be caused by a set of inherited heterozygous gene mutations affecting the NOTCH ligand regulator MIB1 (MINDBOMB1) and cosegregating genes. METHODS: We used CRISPR-Cas9 gene editing to generate mice harboring a nonsense or a missense MIB1 mutation that are both found in LVNC families. We also generated mice separately carrying these MIB1 mutations plus 5 additional cosegregating variants in the ASXL3, APCDD1, TMX3, CEP192, and BCL7A genes identified in these LVNC families by whole exome sequencing. Histological, developmental, and functional analyses of these mouse models were carried out by echocardiography and cardiac magnetic resonance imaging, together with gene expression profiling by RNA sequencing of both selected engineered mouse models and human induced pluripotent stem cell-derived cardiomyocytes. Potential biochemical interactions were assayed in vitro by coimmunoprecipitation and Western blot. RESULTS: Mice homozygous for the MIB1 nonsense mutation did not survive, and the mutation caused LVNC only in heteroallelic combination with a conditional allele inactivated in the myocardium. The heterozygous MIB1 missense allele leads to bicuspid aortic valve in a NOTCH-sensitized genetic background. These data suggest that development of LVNC is influenced by genetic modifiers present in affected families, whereas valve defects are highly sensitive to NOTCH haploinsufficiency. Whole exome sequencing of LVNC families revealed single-nucleotide gene variants of ASXL3, APCDD1, TMX3, CEP192, and BCL7A cosegregating with the MIB1 mutations and LVNC. In experiments with mice harboring the orthologous variants on the corres

Journal article

Shah M, Inácio MHDA, Lu C, Schiratti P-R, Zheng SL, Clement A, Bai W, King AP, Ware JS, Wilkins MR, Mielke J, Elci E, Kryukov I, McGurk KA, Bender C, Freitag DF, ORegan DPet al., 2022, Environmental and genetic predictors of human cardiovascular ageing

<jats:title>ABSTRACT</jats:title><jats:p>Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we used machine learning approaches to quantify cardiovascular age from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing was found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identified prescribed medications that were potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.</jats:p>

Journal article

Patel K, Bajaj N, Statton B, Li X, Herath NS, Nyamakope K, Davidson R, Stoks J, Purkayastha S, Ware JS, O'Regan DP, Lambiase PD, Cluitmans M, Peters NS, Ng FSet al., 2022, Bariatric surgery reverses ventricular repolarisation heterogeneity in obesity: mechanistic insights into fat-related arrhythmic risk, Publisher: OXFORD UNIV PRESS, Pages: 658-658, ISSN: 0195-668X

Conference paper

Mallon DH, McNamara CD, Rahmani GS, O'Regan DP, Amiras DGet al., 2022, Automated detection of enteric tubes misplaced in the respiratory tract on chest radiographs using deep learning with two centre validation, Clinical Radiology, Vol: 77, Pages: e758-e764, ISSN: 0009-9260

AIM: To develop and test a model based on a convolutional neural network that can identify enteric tube position accurately on chest radiography. MATERIALS AND METHODS: The chest radiographs of adult patients were classified by radiologists based on enteric tube position as either critically misplaced (within the respiratory tract) or not critically misplaced (misplaced within the oesophagus or safely positioned below the diaphragm). A deep-learning model based on the 121-layer DenseNet architecture was developed using a training and validation set of 4,693 chest radiographs. The model was evaluated on an external test data set from a separate institution that consisted of 1,514 consecutive radiographs with a real-world incidence of critically misplaced enteric tubes. RESULTS: The receiver operator characteristic area under the curve was 0.90 and 0.92 for the internal validation and external test data sets, respectively. For the external data set with a prevalence of 4.4% of critically misplaced enteric tubes, the model achieved high accuracy (92%), sensitivity (80%), and specificity (92%) for identifying a critically misplaced enteric tube. The negative predictive value (99%) was higher than the positive predictive value (32%). CONCLUSION: The present study describes the development and external testing of a model that accurately identifies an enteric tube misplaced within the respiratory tract. This model could help reduce the risk of the catastrophic consequences of feeding through a misplaced enteric tube.

Journal article

Alabed S, Alandejani F, Dwivedi K, Karunasaagarar K, Sharkey M, Garg P, de Koning PJH, Tóth A, Shahin Y, Johns C, Mamalakis M, Stott S, Capener D, Wood S, Metherall P, Rothman AMK, Condliffe R, Hamilton N, Wild JM, O'Regan DP, Lu H, Kiely DG, van der Geest RJ, Swift AJet al., 2022, Validation of artificial intelligence cardiac MRI measurements: relationship to heart catheterization and mortality prediction, Radiology, Vol: 305, ISSN: 0033-8419

Background Cardiac MRI measurements have diagnostic and prognostic value in the evaluation of cardiopulmonary disease. Artificial intelligence approaches to automate cardiac MRI segmentation are emerging but require clinical testing. Purpose To develop and evaluate a deep learning tool for quantitative evaluation of cardiac MRI functional studies and assess its use for prognosis in patients suspected of having pulmonary hypertension. Materials and Methods A retrospective multicenter and multivendor data set was used to develop a deep learning-based cardiac MRI contouring model using a cohort of patients suspected of having cardiopulmonary disease from multiple pathologic causes. Correlation with same-day right heart catheterization (RHC) and scan-rescan repeatability was assessed in prospectively recruited participants. Prognostic impact was assessed using Cox proportional hazard regression analysis of 3487 patients from the ASPIRE (Assessing the Severity of Pulmonary Hypertension In a Pulmonary Hypertension Referral Centre) registry, including a subset of 920 patients with pulmonary arterial hypertension. The generalizability of the automatic assessment was evaluated in 40 multivendor studies from 32 centers. Results The training data set included 539 patients (mean age, 54 years ± 20 [SD]; 315 women). Automatic cardiac MRI measurements were better correlated with RHC parameters than were manual measurements, including left ventricular stroke volume (r = 0.72 vs 0.68; P = .03). Interstudy repeatability of cardiac MRI measurements was high for all automatic measurements (intraclass correlation coefficient range, 0.79-0.99) and similarly repeatable to manual measurements (all paired t test P > .05). Automated right ventricle and left ventricle cardiac MRI measurements were associated with mortality in patients suspected of having pulmonary hypertension. Conclusion An automatic cardiac MRI measurement approach was developed and tested in a large cohort of pa

Journal article

Sharkey MJ, Taylor JC, Alabed S, Dwivedi K, Karunasaagarar K, Johns CS, Rajaram S, Garg P, Alkhanfar D, Metherall P, O'Regan DP, van der Geest RJ, Condliffe R, Kiely DG, Mamalakis M, Swift AJet al., 2022, Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning, Frontiers in Cardiovascular Medicine, Vol: 9, Pages: 1-18, ISSN: 2297-055X

Introduction: Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA.Methods: A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort.Results: Dice similarity coefficients (DSC) for segmented structures were in the range 0.58–0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785–0.801) and 0.520 (0.482–0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar

Journal article

Alabed S, Alandejani F, Dwivedi K, Karunasaagarar K, Sharkey M, Garg P, de Koning PJH, Tóth A, Shahin Y, Johns C, Mamalakis M, Stott S, Capener D, Wood S, Metherall P, Rothman AMK, Condliffe R, Hamilton N, Wild JM, O'Regan DP, Lu H, Kiely DG, van der Geest RJ, Swift AJet al., 2022, Validation of artificial intelligence cardiac MRI measurements: relationship to heart catheterization and mortality prediction., Radiology, Vol: 304, Pages: 1-1, ISSN: 0033-8419

Journal article

Meng Q, Bai W, Liu T, Simoes Monteiro de Marvao A, O'Regan D, Rueckert Det al., 2022, MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI, IEEE Transactions on Medical Imaging, Vol: 41, Pages: 1961-1974, ISSN: 0278-0062

Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.

Journal article

Alabed S, Maiter A, Salehi M, Mahmood A, Daniel S, Jenkins S, Goodlad M, Sharkey M, Mamalakis M, Rakocevic V, Dwivedi K, Assadi H, Wild JMM, Lu H, O'Regan DPP, van der Geest RJJ, Garg P, Swift AJJet al., 2022, Quality of reporting in AI cardiac MRI segmentation studies - A systematic review and recommendations for future studies, Frontiers in Cardiovascular Medicine, Vol: 9, Pages: 1-10, ISSN: 2297-055X

Background: There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation.Methods: MEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains.Results: 209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59–73%). Median compliance was highest for the model description domain (100%, IQR 80–100%) and lower for the study (71%, IQR 63–86%), dataset (63%, IQR 50–67%) and performance (60%, IQR 50–70%) description domains.Conclusion: This systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing—most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis—that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence

Journal article

Tayal U, Verdonschot JAJ, Hazebroek MR, Howard J, Gregson J, Newsome S, Gulati A, Pua CJ, Halliday BP, Lota AS, Buchan RJ, Whiffin N, Kanapeckaite L, Baruah R, Jarman JWE, O'Regan DP, Barton PJR, Ware JS, Pennell DJ, Adriaans BP, Bekkers SCAM, Donovan J, Frenneaux M, Cooper LT, Januzzi JL, Cleland JGF, Cook SA, Deo RC, Heymans SRB, Prasad SKet al., 2022, Precision phenotyping of dilated cardiomyopathy using multidimensional data., Journal of the American College of Cardiology, Vol: 79, Pages: 2219-2232, ISSN: 0735-1097

BACKGROUND: Dilated cardiomyopathy (DCM) is a final common manifestation of heterogenous etiologies. Adverse outcomes highlight the need for disease stratification beyond ejection fraction. OBJECTIVES: The purpose of this study was to identify novel, reproducible subphenotypes of DCM using multiparametric data for improved patient stratification. METHODS: Longitudinal, observational UK-derivation (n = 426; median age 54 years; 67% men) and Dutch-validation (n = 239; median age 56 years; 64% men) cohorts of DCM patients (enrolled 2009-2016) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments. Machine learning with profile regression identified novel disease subtypes. Penalized multinomial logistic regression was used for validation. Nested Cox models compared novel groupings to conventional risk measures. Primary composite outcome was cardiovascular death, heart failure, or arrhythmia events (median follow-up 4 years). RESULTS: In total, 3 novel DCM subtypes were identified: profibrotic metabolic, mild nonfibrotic, and biventricular impairment. Prognosis differed between subtypes in both the derivation (P < 0.0001) and validation cohorts. The novel profibrotic metabolic subtype had more diabetes, universal myocardial fibrosis, preserved right ventricular function, and elevated creatinine. For clinical application, 5 variables were sufficient for classification (left and right ventricular end-systolic volumes, left atrial volume, myocardial fibrosis, and creatinine). Adding the novel DCM subtype improved the C-statistic from 0.60 to 0.76. Interleukin-4 receptor-alpha was identified as a novel prognostic biomarker in derivation (HR: 3.6; 95% CI: 1.9-6.5; P = 0.00002) and validation cohorts (HR: 1.94; 95% CI: 1.3-2.8; P = 0.00005). CONCLUSIONS: Three reproducible, mechanistically distinct DCM subtypes were identified using widely available clinical and biological data, adding prognostic value to trad

Journal article

Patel K, Bajaj N, Statton B, Herath N, Li X, Davidson R, Savvidou S, Coghlin J, Stoks J, Purkayastha S, Cousins J, Ware J, O'Regan D, Lambiase P, Cluitmans M, Peters N, Ng FSet al., 2022, Bariatric surgery reverses ventricular repolarisation heterogeneity – mechanistic insights into fat-related arrhythmic risk, British Cardiovascular Society Annual Conference, ‘100 years of Cardiology’, 6–8 June 2022, Publisher: BMJ Publishing Group, Pages: A60-A61, ISSN: 1355-6037

Conference paper

Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P, DECIDE-AI expert groupet al., 2022, Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI, BMJ: British Medical Journal, Vol: 377, ISSN: 0959-535X

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.

Journal article

Thanaj M, Mielke J, McGurk K, Bai W, Savioli N, Simoes Monteiro de Marvao A, Meyer H, Zeng L, Sohler F, Lumbers T, Wilkins M, Ware J, Bender C, Rueckert D, MacNamara A, Freitag D, O'Regan Det al., 2022, Genetic and environmental determinants of diastolic heart function, Nature Cardiovascular Research, Vol: 1, Pages: 361-371, ISSN: 2731-0590

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends onmyocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processesand is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiacmotion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wideassociation study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomericfunction under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes wereindependent predictors of diastolic function and we found a causal relationship between genetically-determined ventricularstiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolicfunction that are relevant for identifying causal relationships and potential tractable targets.

Journal article

Manchester E, Pirola S, Salmasi MY, O'Regan D, Athanasiou T, Xu Xet al., 2022, Evaluation of computational methodologies for accurate prediction of wall shear stress and turbulence parameters in a patient-specific aorta, Frontiers in Bioengineering and Biotechnology, Vol: 10, ISSN: 2296-4185

Background: Recent studies suggest that blood flow in main arteries is intrinsically disturbed, even under healthy conditions. Despite this, many computational fluid dynamics (CFD) analyses of aortic haemodynamics make the assumption of laminar flow, and best practices surroundingappropriate modelling choices are lacking. This study aims to address this gap by evaluating different modelling and post-processing approaches in simulations of a patient-specific aorta. Methods: Magnetic resonance imaging (MRI) and 4D flow MRI from a patient with aortic valve stenosis were used to reconstruct the aortic geometry and derive patient-specific inlet and outlet boundary conditions. Three different computational approaches were considered based on assumed laminar or assumed disturbed flow states including low-resolution laminar (LR-laminar),high-resolution laminar (HR-Laminar) and large-eddy simulation (LES). Each simulation was ran for 30 cardiac cycles and post-processing was conducted on either the final cardiac cycle, or using a phase-averaged approach which utilised all 30 simulated cycles. Model capabilities were evaluated in terms of mean and turbulence-based parameters. Results: All simulation types, regardless of post-processing approach could correctly predict velocity values and flow patterns throughout the aorta. Lower resolution simulations could not accurately predict gradient-derived parameters including wall shear stress and viscous energy loss (largest differences up to 44.6% and 130.3%, respectively), although phase-averagingthese parameters improved predictions. The HR-Laminar simulation produced more comparable results to LES with largest differences in wall shear stress and viscous energy loss parameters up to 5.1% and 11.6%, respectively. Laminar-based parameters were better estimated thanturbulence-based parameters.Conclusions: Our findings suggest that well-resolved laminar simulations can accurately predict many laminar-based parameters in disturbed flo

Journal article

McGurk KA, Zheng SL, Henry A, Josephs K, Edwards M, de Marvao A, Whiffin N, Roberts A, Lumbers TR, O'Regan DP, Ware JSet al., 2022, Correspondence on "ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG)" by Miller et al, Genetics in Medicine, Vol: 24, Pages: 744-746, ISSN: 1098-3600

Journal article

Meng Q, Bai W, Liu T, Simoes Monteiro de Marvao A, O'Regan D, Rueckert Det al., 2022, Multiview Motion Estimation for 3D cardiac motion tracking

Code for paper ''MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI''

Software

Jia X, Thorley A, Chen W, Qiu H, Shen L, Styles IB, Chang HJ, Leonardis A, de Marvao A, O'Regan DP, Rueckert D, Duan Jet al., 2022, Learning a Model-Driven Variational Network for Deformable Image Registration, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 41, Pages: 199-212, ISSN: 0278-0062

Journal article

Meng Q, Bai W, Liu T, O'Regan DP, Rueckert Det al., 2022, Mesh-Based 3D Motion Tracking in Cardiac MRI Using Deep Learning, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 248-258, ISSN: 0302-9743

Conference paper

Osimo E, Sweeney M, De Marvao A, Berry A, Statton B, Perry BI, Pillinger T, Whitehurst T, Cook S, ORegan D, Thomas EL, Howes ODet al., 2021, Adipose tissue dysfunction, inflammation, and insulin resistance: alternative pathways to cardiac remodelling in schizophrenia. A multimodal, case-control study, Translational Psychiatry, Vol: 11, Pages: 1-9, ISSN: 2158-3188

Cardiovascular diseases are the leading cause of death in schizophrenia. Patients with schizophrenia show evidence of concentric cardiac remodelling (CCR), defined as an increase in left-ventricular mass over end-diastolic volumes. CCR is a predictor of cardiac disease, but the molecular pathways leading to this in schizophrenia are unknown. We aimed to explore the relevance of hypertensive and non-hypertensive pathways to CCR and their potential molecular underpinnings in schizophrenia. In this multimodal case–control study, we collected cardiac and whole-body fat magnetic resonance imaging (MRI), clinical measures, and blood levels of several cardiometabolic biomarkers known to potentially cause CCR from individuals with schizophrenia, alongside healthy controls (HCs) matched for age, sex, ethnicity, and body surface area. Of the 50 participants, 34 (68%) were male. Participants with schizophrenia showed increases in cardiac concentricity (d = 0.71, 95% CI: 0.12, 1.30; p = 0.01), indicative of CCR, but showed no differences in overall content or regional distribution of adipose tissue compared to HCs. Despite the cardiac changes, participants with schizophrenia did not demonstrate activation of the hypertensive CCR pathway; however, they showed evidence of adipose dysfunction: adiponectin was reduced (d = −0.69, 95% CI: −1.28, −0.10; p = 0.02), with evidence of activation of downstream pathways, including hypertriglyceridemia, elevated C-reactive protein, fasting glucose, and alkaline phosphatase. In conclusion, people with schizophrenia showed adipose tissue dysfunction compared to body mass-matched HCs. The presence of non-hypertensive CCR and a dysmetabolic phenotype may contribute to excess cardiovascular risk in schizophrenia. If our results are confirmed, acting on this pathway could reduce cardiovascular risk and resultant life-years lost in people with schizophrenia.

Journal article

Keenan NG, Captur G, McCann GP, Berry C, Myerson SG, Fairbairn T, Hudsmith L, O'Regan DP, Westwood M, Greenwood JPet al., 2021, Regional variation in cardiovascular magnetic resonance service delivery across the UK, Heart, Vol: 107, Pages: 1974-1979, ISSN: 1355-6037

OBJECTIVES: To examine service provision in cardiovascular magnetic resonance (CMR) in the UK. Equitable access to diagnostic imaging is important in healthcare. CMR is widely available in the UK, but there may be regional variations. METHODS: An electronic survey was sent by the British Society of CMR to the service leads of all CMR units in the UK in 2019 requesting data from 2017 and 2018. Responses were analysed by region and interpreted alongside population statistics. RESULTS: The survey response rate was 100% (82 units). 100 386 clinical scans were performed in 2017 and 114 967 in 2018 (15% 1-year increase; 5-fold 10-year increase compared with 2008 data). In 2018, there were 1731 CMR scans/million population overall, with significant regional variation, for example, 4256 scans/million in London vs 396 scans/million in Wales. Median number of clinical scans per unit was 780, IQR 373-1951, range 98-10 000, with wide variation in mean waiting times (median 41 days, IQR 30-49, range 5-180); median 25 days in London vs 180 days in Northern Ireland). Twenty-five units (30%) reported mean elective waiting times in excess of 6 weeks, and 8 (10%) ≥3 months. There were 351 consultants reporting CMR, of whom 230 (66%) were cardiologists and 121 (34%) radiologists; 81% of units offered a CMR service for patients with pacemakers and defibrillators. CONCLUSIONS: This survey provides a unique, contemporary insight into national CMR delivery with 100% centre engagement. The 10-year growth in CMR usage at fivefold has been remarkable but heterogeneous across the UK, with some regions still reporting low usage or long waiting times which may be of clinical concern.

Journal article

Zekavat SM, Raghu VK, Trinder M, Ye Y, Koyama S, Honigberg MC, Yu Z, Pampana A, Urbut S, Haidermota S, O'Regan DP, Zhao H, Ellinor PT, Segrè AV, Elze T, Wiggs JL, Martone J, Adelman RA, Zebardast N, Del Priore L, Wang JC, Natarajan Pet al., 2021, Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature., Circulation, Vol: 145, Pages: 134-150, ISSN: 0009-7322

Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health as well as tumorigenesis. The retinal fundus is a window for human in vivo non-invasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We utilized 97,895 retinal fundus images from 54,813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated fractal dimension (FD) as a measure of vascular branching complexity, and vascular density. We associated these indices with 1,866 incident ICD-based conditions (median 10y follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular FD and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular FD and density identified 7 and 13 novel loci respectively, which were enriched for pathways linked to angiogenesis (e.g., VEGF, PDGFR, angiopoietin, and WNT signaling pathways) and inflammation (e.g., interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights on genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health records, biomarker, and genetic data to inform risk prediction and risk modification.

Journal article

Salmasi MYB, Pirola S, Sasidharan S, Fisichella S, Redaelli A, Jarral O, O'Regan D, Oo A, Moore Jr J, Xu XY, Athanasiou Tet al., 2021, High wall shear stress can predict wall degradation in ascending aorticaneurysms: an integrated biomechanics study, Frontiers in Bioengineering and Biotechnology, Vol: 9, Pages: 1-13, ISSN: 2296-4185

Background: Blood flow patterns can alter material properties of ascending thoracic aortic aneurysms (ATAA) via vascular wall remodeling. This study examines the relationship between wall shear stress (WSS) obtained from image-based computational modelling with tissue-derived mechanical and microstructural properties of the ATAA wall using segmental analysis.Methods: Ten patients undergoing surgery for ATAA were recruited. Exclusions: bicuspid aortopathy, connective tissue disease. All patients had pre-operative 4-dimensional flow magnetic resonance imaging (4D-MRI), allowing for patient-specific computational fluid dynamics (CFD) analysis and anatomically precise WSS mapping of ATAA regions (6–12 segments per patient). ATAA samples were obtained from surgery and subjected to region-specific tensile and peel testing (matched to WSS segments). Computational pathology was used to characterize elastin/collagen abundance and smooth muscle cell (SMC) count.Results: Elevated values of WSS were predictive of: reduced wall thickness [coef −0.0489, 95% CI (−0.0905, −0.00727), p = 0.022] and dissection energy function (longitudinal) [−15,0, 95% CI (−33.00, −2.98), p = 0.048]. High WSS values also predicted higher ultimate tensile strength [coef 0.136, 95% CI (0 0.001, 0.270), p = 0.048]. Additionally, elevated WSS also predicted a reduction in elastin levels [coef −0.276, 95% (CI −0.531, −0.020), p = 0.035] and lower SMC count ([oef −6.19, 95% CI (−11.41, −0.98), p = 0.021]. WSS was found to have no effect on collagen abundance or circumferential mechanical properties.Conclusions: Our study suggests an association between elevated WSS values and aortic wall degradation in ATAA disease. Further studies might help identify threshold values to predict acute aortic events.

Journal article

De Marvao A, McGurk K, Zheng S, Thanaj M, Bai W, Duan J, Halliday B, Pantazis A, Prasad S, Rueckert D, Walsh R, Ho C, Cook S, Ware J, O'Regan Det al., 2021, Outcomes and phenotypic expression of rare variants in hypertrophic cardiomyopathy genes in over 200,000 adults, ESC Congress 2021, Publisher: European Society of Cardiology, Pages: 1731-1731, ISSN: 0195-668X

BackgroundHypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population.PurposeTo determine the population prevalence of HCM-associated sarcomeric variants, characterise their phenotypic manifestations, estimate penetrance, and identify associations between sarcomeric variants and clinical outcomes, we performed an observational study of 218,813 adults in the UK Biobank (UKBB), of whom 200,584 have whole exome sequencing (WES).MethodsWe carried out an integrated analysis of WES and cardiac magnetic resonance (CMR) imaging in UK Biobank participants stratified by sarcomere-encoding variant status. Computer vision techniques were used to automatically segment the four chambers of the heart (Figure 1). Cardiac motion analysis was used to derive strain and strain rates. Regional analysis of left ventricular wall thickness was performed using three-dimensional modelling of these segmentations.ResultsMedian age at recruitment was 58 (IQR 50–63 years), and participants were followed up for a median of 10.8 years (IQR 9.9–11.6 years) with a total of 19,507 primary clinical events reported.The prevalence of rare variants (allele frequency <0.ehab724.17314) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n=5,727; 1 in 35), and the prevalence of pathogenic or likely pathogenic variants (SARC-P/LP) was 0.24% (n=474, 1 in 423).SARC-P/LP variants were associated with increased risk of death or major adverse cardiac events (MACE) compared to controls (HR 1.68, 95% CI 1.37–2.06, p<0.001), mainly due to heart failure endpoints (Figure 2: cumulative hazard curves with zoomed plots for lifetime risk of A) death and MACE or B) heart failure, stratified by genotype; genotype negative (SARC-NEG), carriers of indeterminate sarcomeric variants (SARC-IND) or SARC-P/LP; C) Forest plot of comparative lifetime risk of c

Conference paper

Wang S, Qin C, Savioli N, Chen C, O'Regan D, Cook S, Guo Y, Rueckert D, Bai Wet al., 2021, Joint motion correction and super resolution for cardiac segmentationvia latent optimisation, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer, Pages: 14-24

In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration andrespiratory/cardiac motion, stacks of multi-slice 2D images are acquired inclinical routine. The segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Given a low-resolution segmentation as input, the framework accounts for inter-slice motion in cardiac MR imaging and super-resolves the input into a high-resolution segmentation consistent with input. A multi-view loss is incorporated to leverage information from both short-axis view and long-axis view of cardiac imaging. To solve the inverse problem, iterative optimisation is performed in a latent space, which ensures the anatomical plausibility. This alleviates the need of paired low-resolution and high-resolution images for supervised learning. Experiments on two cardiac MR datasets show that the proposed framework achieves high performance, comparable to state-of-the-art super-resolution approaches and with better cross-domain generalisability and anatomical plausibility.

Conference paper

Thorley A, Jia X, Chang HJ, Liu B, Bunting K, Stoll V, de Marvao A, O'Regan DP, Gkoutos G, Kotecha D, Duan Jet al., 2021, Nesterov accelerated ADMM for fast diffeomorphic image registration, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 150-160, ISSN: 0302-9743

Deterministic approaches using iterative optimisation have been historically successful in diffeomorphic image registration (DiffIR). Although these approaches are highly accurate, they typically carry a significant computational burden. Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods. In this paper, we attempt to reduce this difference in speed whilst retaining the performance advantage of iterative approaches in DiffIR. We first propose a simple iterative scheme that functionally composes intermediate non-stationary velocity fields to handle large deformations in images whilst guaranteeing diffeomorphisms in the resultant deformation. We then propose a convex optimisation model that uses a regularisation term of arbitrary order to impose smoothness on these velocity fields and solve this model with a fast algorithm that combines Nesterov gradient descent and the alternating direction method of multipliers (ADMM). Finally, we leverage the computational power of GPU to implement this accelerated ADMM solver on a 3D cardiac MRI dataset, further reducing runtime to less than 2 s. In addition to producing strictly diffeomorphic deformations, our methods outperform both state-of-the-art deep learning-based and iterative DiffIR approaches in terms of dice and Hausdorff scores, with speed approaching the inference time of deep learning-based methods.

Conference paper

Simoes Monteiro de Marvao A, McGurk K, Zheng S, Thanaj M, Bai W, Duan J, Biffi C, Mazzarotto F, Statton B, Dawes T, Savioli N, Halliday B, Xu X, Buchan R, Baksi A, Quinlan M, Tokarczuk P, Tayal U, Francis C, Whiffin N, Theotokis A, Zhang X, Jang M, Berry A, Pantazis A, Barton P, Rueckert D, Prasad S, Walsh R, Ho C, Cook S, Ware J, O'Regan Det al., 2021, Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy, Journal of the American College of Cardiology, Vol: 78, Pages: 1097-1110, ISSN: 0735-1097

Background: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomereencoding genes, but little is known about the clinical significance of these variants in thegeneral population.Objectives: To compare lifetime outcomes and cardiovascular phenotypes according to thepresence of rare variants in sarcomere-encoding genes amongst middle-aged adults.Methods: We analysed whole exome sequencing and cardiac magnetic resonance (CMR)imaging in UK Biobank participants stratified by sarcomere-encoding variant status.Results: The prevalence of rare variants (allele frequency <0.00004) in HCM-associatedsarcomere-encoding genes in 200,584 participants was 2.9% (n=5,712; 1 in 35), and theprevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was0.25% (n=493, 1 in 407). SARC-HCM-P/LP variants were associated with increased risk ofdeath or major adverse cardiac events compared to controls (HR 1.69, 95% CI 1.38 to 2.07,p<0.001), mainly due to heart failure endpoints (HR 4.23, 95% CI 3.07 to 5.83, p<0.001). In21,322 participants with CMR, SARC-HCM-P/LP were associated with asymmetric increasein left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p<0.001) buthypertrophy (≥13mm) was only present in 18.4% (n=9/49, 95% CI 9 to 32%). SARC-HCMP/LP were still associated with heart failure after adjustment for wall thickness (HR 6.74,95% CI 2.43 to 18.7, p<0.001).Conclusions: In this population of middle-aged adults, SARC-HCM-P/LP variants have lowaggregate penetrance for overt HCM but are associated with increased risk of adversecardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absoluteevent rates are low, identification of these variants may enhance risk stratification beyondfamilial disease.

Journal article

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