Echocardiographic assessment of a cardiac lymphoma: beyond two-dimensional imaging

Lymphoma is usually recognized as the third most frequent metastatic malignancy involving the heart. In recent years, the incidence of cardiac lymphoma has increased, mainly because of HIV-infected patients. We present a case of secondary cardiac lymphoma in an HIV patient presenting with heart failure. Transthoracic echocardiography showed increased left ventricular (LV) wall thickness and an extensive mass in the right cavities with involvement of the tricuspid annulus (Figure 1). Doppler tissue imaging (DTI) showed reduced systolic and diastolic velocities at mitral and tricuspid annulus, compatible with systolic and diastolic myocardial dysfunction, likely owing to infiltration. After 2 weeks of chemotherapy, repeated exam showed significant reduction of the tumour mass and of the LV wall thickness, as well as normalized systolic and diastolic velocities at mitral and tricuspid annulus, as assessed by DTI. Use of transthoracic echocardiography, mostly two-dimensional imaging, has been described for several years for the diagnosis of cardiac involvement as well as for the assessment of tumour regression in response to chemotherapy. The present case report highlights the potential utility of other echocardiographic modalities, particularly DTI, for the assessment of cardiac lymphoma but also for monitoring the tumour response to adequate therapy

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

Late-Onset Prosthetic Endocarditis with Paraaortic Abscess Caused by Cutibacterium acnes

Cutibacterium acnes, an integral component of the skin’s customary bacterial flora, represents a Gram-positive anaerobic bacterium characterized by its low virulence. Despite its low virulence, the pathogen can cause profound-seated infections as well as infections linked to medical devices. We report a case study of a prosthesis endocarditis accompanied by a paraaortic abscess caused by C. acnes, a development occurring five years prior to composite aortic root and valve replacement. At the point of admission, the patient presented with a combination of symptoms hinting at a subacute progression, such as weight loss, chest pain, and limitations of cardiopulmonary functionality. An anaerobic pathogen, namely C. acnes, was detected in a singular blood culture vial. Since first-line imaging modalities such as echocardiography did not reveal any signs of inflammation, and in the case of a suspected diagnosis for IE, did not show high pretest probability, further diagnostic imaging such as 18F-FDG PET CT was put to use. Here, a highly elevated glucose metabolism around the aortic valve ring was detected, pointing to an inflammatory process. The patient received adjusted intravenous antibiotic therapy over a course of six weeks; he then underwent surgical therapy via re-replacement of the aortic root and valve using a composite conduit. Advanced microbiological analyses, including the amplification of PCR and valve sequencing via 16S rDNA, mainly detected one pathogen: C. acnes. Delayed onset with mild symptoms and laboratory findings is characteristic of infective endocarditis by C. acnes. Due to its high rate of complications, mortality, and morbidity, an infection should not be disregarded as contamination. Recommendations from different studies underline a combination of a positive blood culture and microbiological evidence to differentiate between contamination and true infection in the case of an infection involving C. acnes. Serial blood cultures with prolonged incubation, advanced microbiological analyses, and modified Duke criteria including second-line imaging techniques should be utilized for further evaluation

Cardiac Masses on Cardiac CT: A Review

Cardiac masses are rare entities that can be broadly categorized as either neoplastic or non-neoplastic. Neoplastic masses include benign and malignant tumors. In the heart, metastatic tumors are more common than primary malignant tumors. Whether incidentally found or diagnosed as a result of patients’ symptoms, cardiac masses can be identified and further characterized by a range of cardiovascular imaging options. While echocardiography remains the first-line imaging modality, cardiac computed tomography (cardiac CT) has become an increasingly utilized modality for the assessment of cardiac masses, especially when other imaging modalities are non-diagnostic or contraindicated. With high isotropic spatial and temporal resolution, fast acquisition times, and multiplanar image reconstruction capabilities, cardiac CT offers an alternative to cardiovascular magnetic resonance imaging in many patients. Additionally, cardiac masses may be incidentally discovered during cardiac CT for other reasons, requiring imagers to understand the unique features of a diverse range of cardiac masses. Herein, we define the characteristic imaging features of commonly encountered and selected cardiac masses and define the role of cardiac CT among noninvasive imaging options

Arquiteturas federadas para integração de dados biomédicos

Doutoramento Ciências da ComputaçãoThe last decades have been characterized by a continuous adoption of IT solutions in the healthcare sector, which resulted in the proliferation of tremendous amounts of data over heterogeneous systems. Distinct data types are currently generated, manipulated, and stored, in the several institutions where patients are treated. The data sharing and an integrated access to this information will allow extracting relevant knowledge that can lead to better diagnostics and treatments. This thesis proposes new integration models for gathering information and extracting knowledge from multiple and heterogeneous biomedical sources. The scenario complexity led us to split the integration problem according to the data type and to the usage specificity. The first contribution is a cloud-based architecture for exchanging medical imaging services. It offers a simplified registration mechanism for providers and services, promotes remote data access, and facilitates the integration of distributed data sources. Moreover, it is compliant with international standards, ensuring the platform interoperability with current medical imaging devices. The second proposal is a sensor-based architecture for integration of electronic health records. It follows a federated integration model and aims to provide a scalable solution to search and retrieve data from multiple information systems. The last contribution is an open architecture for gathering patient-level data from disperse and heterogeneous databases. All the proposed solutions were deployed and validated in real world use cases.A adoção sucessiva das tecnologias de comunicação e de informação na área da saúde tem permitido um aumento na diversidade e na qualidade dos serviços prestados, mas, ao mesmo tempo, tem gerado uma enorme quantidade de dados, cujo valor científico está ainda por explorar. A partilha e o acesso integrado a esta informação poderá permitir a identificação de novas descobertas que possam conduzir a melhores diagnósticos e a melhores tratamentos clínicos. Esta tese propõe novos modelos de integração e de exploração de dados com vista à extração de conhecimento biomédico a partir de múltiplas fontes de dados. A primeira contribuição é uma arquitetura baseada em nuvem para partilha de serviços de imagem médica. Esta solução oferece um mecanismo de registo simplificado para fornecedores e serviços, permitindo o acesso remoto e facilitando a integração de diferentes fontes de dados. A segunda proposta é uma arquitetura baseada em sensores para integração de registos electrónicos de pacientes. Esta estratégia segue um modelo de integração federado e tem como objetivo fornecer uma solução escalável que permita a pesquisa em múltiplos sistemas de informação. Finalmente, o terceiro contributo é um sistema aberto para disponibilizar dados de pacientes num contexto europeu. Todas as soluções foram implementadas e validadas em cenários reais

Right ventricular outflow tract velocity time integral-to-pulmonary artery systolic pressure ratio: a non-invasive metric of pulmonary arterial compliance differs across the spectrum of pulmonary hypertension.

Pulmonary arterial compliance (PAC), invasively assessed by the ratio of stroke volume to pulmonary arterial (PA) pulse pressure, is a sensitive marker of right ventricular (RV)-PA coupling that differs across the spectrum of pulmonary hypertension (PH) and is predictive of outcomes. We assessed whether the echocardiographically derived ratio of RV outflow tract velocity time integral to PA systolic pressure (RVOT-VTI/PASP) (a) correlates with invasive PAC, (b) discriminates heart failure with preserved ejection-associated PH (HFpEF-PH) from pulmonary arterial hypertension (PAH), and (c) is associated with functional capacity. We performed a retrospective cohort study of patients with PAH (n = 70) and HFpEF-PH (n = 86), which was further dichotomized by diastolic pressure gradient (DPG) into isolated post-capillary PH (DPG \u3c 7 mmHg; Ipc-PH, n = 54), and combined post- and pre-capillary PH (DPG ≥ 7 mm Hg; Cpc-PH, n = 32). Of the 156 patients, 146 had measurable RVOT-VTI or PASP and were included in further analysis. RVOT-VTI/PASP correlated with invasive PAC overall (ρ = 0.61, P \u3c 0.001) and for the PAH (ρ = 0.38, P = 0.002) and HFpEF-PH (ρ = 0.63, P \u3c 0.001) groups individually. RVOT-VTI/PASP differed significantly across the PH spectrum (PAH: 0.13 [0.010-0.25] vs. Cpc-PH: 0.20 [0.12-0.25] vs. Ipc-PH: 0.35 [0.22-0.44]; P \u3c 0.001), distinguished HFpEF-PH from PAH (AUC = 0.72, 95% CI = 0.63-0.81) and Cpc-PH from Ipc-PH (AUC = 0.78, 95% CI = 0.68-0.88), and remained independently predictive of 6-min walk distance after multivariate analysis (standardized β-coefficient = 27.7, 95% CI = 9.2-46.3; P = 0.004). Echocardiographic RVOT-VTI/PASP is a novel non-invasive metric of PAC that differs across the spectrum of PH. It distinguishes the degree of pre-capillary disease within HFpEF-PH and is predictive of functional capacity

Narrative review of the role of artificial intelligence to improve aortic valve disease management

Valvular heart disease (VHD) is a chronic progressive condition with an increasing prevalence in the Western world due to aging populations. VHD is often diagnosed at a late stage when patients are symptomatic and the outcomes of therapy, including valve replacement, may be sub-optimal due the development of secondary complications, including left ventricular (LV) dysfunction. The clinical application of artificial intelligence (AI), including machine learning (ML), has promise in supporting not only early and more timely diagnosis, but also hastening patient referral and ensuring optimal treatment of VHD. As physician auscultation lacks accuracy in diagnosis of significant VHD, computer-aided auscultation (CAA) with the help of a commercially available digital stethoscopes improves the detection and classification of heart murmurs. Although used little in current clinical practice, CAA can screen large populations at low cost with high accuracy for VHD and faciliate appropriate patient referral. Echocardiography remains the next step in assessment and planning management and AI is delivering major changes in speeding training, improving image quality by pattern recognition and image sorting, as well as automated measurement of multiple variables, thereby improving accuracy. Furthermore, AI then has the potential to hasten patient disposal, by automated alerts for red-flag findings, as well as decision support in dealing with results. In management, there is great potential in ML-enabled tools to support comprehensive disease monitoring and individualized treatment decisions. Using data from multiple sources, including demographic and clinical risk data to image variables and electronic reports from electronic medical records, specific patient phenotypes may be identified that are associated with greater risk or modeled to the estimate trajectory of VHD progression. Finally, AI algorithms are of proven value in planning intervention, facilitating transcatheter valve replacement by automated measurements of anatomical dimensions derived from imaging data to improve valve selection, valve size and method of delivery

Pulmonary sarcoidosis : a clinical update

Peer reviewedPublisher PD