The utility of implantable loop recorders in patient management: an age- and indication-stratified study in the outpatient-implant era

INTRODUCTION: Implantable loop recorders (ILR) are now routinely implanted for long-term cardiac monitoring in the clinic setting. This study examined the real-world performance of these devices, focusing on the management decision changes made in response to ILR-recorded data. METHODS AND RESULTS: This was a single centre, prospective observational study of consecutive patients undergoing ILR implantation. All patients who underwent implantation of a Medtronic Reveal LINQ device from September 2017 to June 2019 at Barts Heart Centre were included.501 patients were included. 302 (60%) patients underwent ILR implantation for an indication of pre-syncope/syncope, 96 (19%) for palpitations, 72 (14%) for atrial fibrillation (AF) detection with a history of cryptogenic stroke and 31 (6%) for patients deemed to be high risk of serious cardiac arrhythmia.The primary outcome of this study was that an ILR-derived diagnosis altered management in 110 (22%) of patients. Secondary outcomes concerned sub-group analyses by indication: in patients who presented with syncope/presyncope, a change in management resulting from ILR data was positively associated with age (HR: 1.04 [95%CI 1.02-1.06]; p < 0.001) and negatively associated with a normal ECG at baseline (HR 0.54 [0.31-0.93]; p = 0.03). Few patients (1/57, 2%) aged < 40 years in this group underwent device implantation, compared to 19/62 patients (31%) aged 75 years and over (p = 0.0024). 22/183 (12%) of patients in the 40-74 age range had a device implanted.In patients who underwent ILR insertion following cryptogenic stroke, 13/72 patients (18%) had AF detected leading to a decision to commence anticoagulation. CONCLUSION: These results inform the utility of ILR in the clinical setting. Diagnoses provided by ILR that lead to changes in management are rare in patients under age 40, particularly following syncope, presyncope or palpitations. In older patients new diagnoses are frequently made and trigger important changes in treatment

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Background: Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Methods: Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). Results: By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement was 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric was 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. Conclusions: We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures

Clínica e cirurgia de equinos

O presente relatório pretende descrever as atividades desenvolvidas no âmbito do estágio curricular do Mestrado Integrado em Medicina Veterinária da Universidade de Évora. Este relatório está separado em duas partes. Numa primeira parte apresenta-se a casuística acompanhada nos quatros meses de estágio nas diversas áreas da clínica geral de equinos, descrevendo-se alguns casos clínicos de forma mais específica. Na segunda parte é apresentada uma revisão bibliográfica sobre feridas contendo tecido de granulação, nas extremidades distais dos membros e o seu tratamento. Para terminar discutem-se três casos clínicos com diferente evolução do tecido de granulação. As feridas são das afeções mais comuns na clínica de equinos e, nesta espécie, umas das principais complicações é a formação excessiva de tecido de granulação. Desbridamento cirúrgico, corticosteroides, enxerto de pele e laser são alguns dos tratamentos a que se pode recorrer, embora algumas vezes nenhum deles seja eficaz; Equine clinic and surgery Abstract: The current report prentends to describe the activities developed in the ambit integrated internship of the master's degree in Veterinary Medicine of the University of Evora. This report is separated in two parts. In the first part it will be presented the casuistics followed in the four months of internship in the various areas of general equine practice, with some clinical cases being described more specifically. In the second part is presented a literature review about wounds with granulation tissue in the distal extremities of the limbs and their treatment. To finish, three clinical cases with diferent granulation tissue evolution are discussed. Wounds are the most common affections in the horse clinic, and in this specie, one of the main complications is the excessive formation of granulation tissue. Surgical debridement, corticosteroids, skin grafts and laser are some of the treatments that can be used, although sometimes none of them is effective

Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results

Funding was provided by British Heart Foundation (PG/14/89/31194), and by the National Institutes of Health (USA) 1R01HL121754. SN, SKP acknowledge the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at The Oxford University Hospitals Trust at the University of Oxford, and the British Heart Foundation Centre of Research Excellence. Aaron Lee and Steffen Petersen acknowledge support from the NIHR Biomedical Research Centre at Barts Health NHS Trust and from the “SmartHeart” EPSRC programme grant (EP/ P001009/1)

UK Biobank_SOP_cardiovascular magnetic resonance

Current SOPs for the analysis of cardiovascular magnetic resonance imaging  funded by the BHF PG/14/89/3119

Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to automatically detect when a segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that Reverse classification accuracy has the potential for accurate and fully automatic segmentMRC eMedLab Medical Bioinformatics infrastructure, supported by the Medical Research Council (grant number MR/L016311/1)

Baseline characteristics of study cohort (n = 4,651).

<p>Baseline characteristics of study cohort (n = 4,651).</p

Modifiable risk factors and contribution to adjusted R² as a percentage.

<p>Body mass index, systolic and diastolic blood pressure are the modifiable risk factors which most consistently contribute to alterations in cardiac structure and function.</p