Dobutamine stress echocardiography after positive CCTA: diagnostic performance using fractional flow reserve and instantaneous wave-free ratio as reference standards.

AIMS: To assess the diagnostic accuracy of dobutamine stress echocardiography (DSE) in symptomatic patients with a low to intermediate pretest probability of obstructive coronary artery disease (CAD) and a positive coronary CT angiography (CCTA). METHODS: We prospectively enrolled 104 consecutive patients undergoing coronary angiography for symptoms of stable CAD and a CCTA indicative of obstructive CAD. The diagnostic performance of DSE was evaluated against two intracoronary pressure indices: (a) fractional flow reserve (FFR) with a cut-off of ≤0.80 and (b) instantaneous wave-free ratio (iFR) with a cut-off of ≤0.89, indicating haemodynamically significant stenoses. RESULTS: Of 102 patients, 46 (45%) had at least one significant lesion as defined by FFR, as did 37 (36%) as defined by iFR. DSE showed positive results in 33% (34/102) of cases. The discriminative power of DSE for detecting significant CAD was moderate, with areas under the curve of 0.63 (p=0.024) compared with FFR and 0.64 (p=0.025) compared with iFR. The accuracy, sensitivity and specificity of DSE were, respectively, 61%, 43%, and 75% against FFR, and 64%, 46% and 74% against iFR. The diagnostic accuracy of DSE did not differ significantly between FFR and iFR as a reference (p=0.549). CONCLUSION: In patients with positive CCTA, DSE has a moderate ability to identify haemodynamically significant CAD, with low sensitivity and moderate specificity. When assessed against FFR and iFR criteria, its additive diagnostic value is limited in patients with low to intermediate pretest probability of obstructive CAD. TRIAL REGISTRATION NUMBER: NCT03045601.

EasyPISA: Automatic Integrated PISA Measurements of Mitral Regurgitation From 2-D Color-Doppler Using Deep Learning.

OBJECTIVE: The proximal isovelocity surface area (PISA) method is a well-established approach for mitral regurgitation (MR) quantification. However, it exhibits high inter-observer variability and inaccuracies in cases of non-hemispherical flow convergence and non-holosystolic MR. To address this, we present EasyPISA, a framework for automated integrated PISA measurements taken directly from 2-D color-Doppler sequences. METHODS: We trained convolutional neural networks (UNet/Attention UNet) on 1171 images from 196 recordings (54 patients) to detect and segment flow convergence zones in 2-D color-Doppler images. Different preprocessing schemes and model architectures were compared. Flow convergence surface areas were estimated, accounting for non-hemispherical convergence, and regurgitant volume (RVol) was computed by integrating the flow rate over time. EasyPISA was retrospectively applied to 26 MR patient examinations, comparing results with reference PISA RVol measurements, severity grades, and cMRI RVol measurements for 13 patients. RESULTS: The UNet trained on duplex images achieved the best results (precision: 0.63, recall: 0.95, dice: 0.58, flow rate error: 10.4 ml/s). Mitigation of false-positive segmentation on the atrial side of the mitral valve was achieved through integration with a mitral valve segmentation network. The intraclass correlation coefficient was 0.83 between EasyPISA and PISA, and 0.66 between EasyPISA and cMRI. Relative standard deviations were 46% and 53%, respectively. Receiver operator characteristics demonstrated a mean area under the curve between 0.90 and 0.97 for EasyPISA RVol estimates and reference severity grades. CONCLUSION: EasyPISA demonstrates promising results for fully automated integrated PISA measurements in MR, offering potential benefits in workload reduction and mitigating inter-observer variability in MR assessment.

Five-year outcomes of mitral valve repair for leaflet prolapse at a medium-sized Norwegian university hospital.

Objective. To evaluate patient characteristics and 5-year outcomes after surgical mitral valve (MV) repair for leaflet prolapse at a medium-sized cardiothoracic center. Background. Contemporary reports on the outcome of MV repair at medium-sized cardiothoracic centers are sparse. Methods. Patients receiving open-heart surgery with MV repair due to primary mitral regurgitation caused by leaflet prolapse between 2015 and 2021, without active endocarditis, were included. Clinical data, complications, re-interventions, mortality, and echocardiographic data were retrospectively registered from electronical patient charts, both pre-operatively and from post-operative follow-ups. Results. One hundred and three patients were included, 83% male, with a mean age of 62 years. All-cause mortality was 9% during a median follow-up time of 4.9 years. Re-intervention rate on the MV was 4%. Post-operative complications before last available follow-up visit at median 3.0 years were infrequent, with new-onset atrial fibrillation/flutter in 16%, post-operative MV regurgitation grade II or above in 17% and post-operative tricuspid regurgitation grade II or above in 14%. Conclusions. These data demonstrate that surgical MV repair for leaflet prolapse at a medium-sized cardiothoracic center was associated with low re-intervention rate and few severe complications. The presented results are comparable to data from surgical high-volume centers, indicating that surgical MV repair can be safely performed at selected medium-sized cardiothoracic centers.

Long-term cardiac effects of modern treatment for Hodgkin's lymphoma.

BACKGROUND: Hodgkin's lymphoma (HL) is a hematological malignancy that affects both children and young adults. Traditional treatment is associated with a life-time prevalence of cardiac disease exceeding 50%. In the late 1990s protocols were modified to reduce cancer therapy-related adverse cardiac effects. This study aimed to assess the long-term impact of advances in treatment protocols on the cardiac health of HL survivors (HLS). METHODS: HLS (n = 246) treated between 1997 and 2007 with anthracycline-based chemotherapy in three centers in Norway were included. Of these, 132 (53%) had also received mediastinal radiotherapy. HLS were compared to controls (n = 58) recruited from the general population and matched for sex, age, smoking status, and heredity for coronary artery disease. All subjects underwent echocardiography, clinical assessment, and blood sampling. RESULTS: The HLS were 46 ± 9 years old and had been treated 17 ± 3 years before inclusion in the study. There was no significant difference between HLS and controls in ejection fraction (EF) (58%±5 vs. 59%±4, p = 0.08) or prevalence of heart failure. HLS treated with both anthracyclines and mediastinal radiotherapy (AC + MRT) had slightly worse left ventricular global longitudinal strain than controls (-19.3 ± 2.5% vs. -20.8 ± 2.0%, p < 0.001), but those treated with only anthracyclines did not. HLS treated with AC + MRT had a higher prevalence of valve disease than those treated only with anthracyclines (12% vs. 4%, p < 0.05). CONCLUSIONS: HLS treated with anthracyclines after the late 1990s have similar cardiac function and morphology as age-matched controls, apart from higher rates of valvular disease in those who also underwent mediastinal radiotherapy.

Automatic Detection and Tracking of Anatomical Landmarks in Transesophageal Echocardiography for Quantification of Left Ventricular Function.

OBJECTIVE: Evaluation of left ventricular (LV) function in critical care patients is useful for guidance of therapy and early detection of LV dysfunction, but the tools currently available are too time-consuming. To resolve this issue, we previously proposed a method for the continuous and automatic quantification of global LV function in critical care patients based on the detection and tracking of anatomical landmarks on transesophageal heart ultrasound. In the present study, our aim was to improve the performance of mitral annulus detection in transesophageal echocardiography (TEE). METHODS: We investigated several state-of-the-art networks for both the detection and tracking of the mitral annulus in TEE. We integrated the networks into a pipeline for automatic assessment of LV function through estimation of the mitral annular plane systolic excursion (MAPSE), called autoMAPSE. TEE recordings from a total of 245 patients were collected from St. Olav's University Hospital and used to train and test the respective networks. We evaluated the agreement between autoMAPSE estimates and manual references annotated by expert echocardiographers in 30 Echolab patients and 50 critical care patients. Furthermore, we proposed a prototype of autoMAPSE for clinical integration and tested it in critical care patients in the intensive care unit. RESULTS: Compared with manual references, we achieved a mean difference of 0.8 (95% limits of agreement: -2.9 to 4.7) mm in Echolab patients, with a feasibility of 85.7%. In critical care patients, we reached a mean difference of 0.6 (95% limits of agreement: -2.3 to 3.5) mm and a feasibility of 88.1%. The clinical prototype of autoMAPSE achieved real-time performance. CONCLUSION: Automatic quantification of LV function had high feasibility in clinical settings. The agreement with manual references was comparable to inter-observer variability of clinical experts.

Mitral Valve Segmentation and Tracking from Transthoracic Echocardiography Using Deep Learning.

OBJECTIVE: Valvular heart diseases (VHDs) pose a significant public health burden, and deciding the best treatment strategy necessitates accurate assessment of heart valve function. Transthoracic echocardiography (TTE) is the key modality to evaluate VHDs, but the lack of standardized quantitative measurements leads to subjective and time-consuming assessments. We aimed to use deep learning to automate the extraction of mitral valve (MV) leaflets and annular hinge points from echocardiograms of the MV, improving standardization and reducing workload in quantitative assessment of MV disease. METHODS: We annotated the MV leaflets and annulus points in 2931 images from 127 patients. We propose an approach for segmenting the annotated features using Attention UNet with deep supervision and weight scheduling of the attention coefficients to enforce saliency surrounding the MV. The derived segmentation masks were used to extract quantitative biomarkers for specific MV leaflet scallops throughout the heart cycle. RESULTS: Evaluation performance was summarized using a Dice score of 0.63 ± 0.14, annulus error of 3.64 ± 2.53 and leaflet angle error of 8.7 ± 8.3°. Leveraging Attention UNet with deep supervision robustness of clinically relevant metrics was improved compared with UNet, reducing standard deviations by 2.7° (angle error) and 0.73 mm (annulus error). We correctly identified cases of MV prolapse, cases of stenosis and healthy references from a clinical material using the derived biomarkers. CONCLUSION: Robust deep learning segmentation and tracking of MV morphology and motion is possible by leveraging attention gates and deep supervision, and holds promise for enhancing VHD diagnosis and treatment monitoring.

European Association of Cardiovascular Imaging survey on the evaluation of mitral regurgitation.

AIMS: To evaluate the diagnosis and imaging of patients with mitral regurgitation (MR) and the management in routine clinical practice across Europe, the European Association of Cardiovascular Imaging Scientific Initiatives Committee performed a survey across European centres. In particular, the routine use of echocardiography, advanced imaging modalities, heart valve clinics, and heart valve teams was explored. METHODS AND RESULTS: A total of 61 responders, mainly from tertiary centres or university hospitals, from 26 different countries responded to the survey, which consisted of 22 questions. For most questions related to echocardiography and advanced imaging, the answers were relatively homogeneous and demonstrated good adherence to current recommendations. In particular, the centres used a multi-parametric echocardiographic approach and selected the effective regurgitant orifice and vena contracta width as their preferred assessments. 2D measurements are still the most widely used parameters to assess left ventricular structure; however, the majority use 3D trans-oesophageal echocardiography (TOE) to evaluate valve morphology in severe MR. The majority of centres reported the onsite availability and clinical use of ergometric stress echocardiography, cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR) imaging. Heart valve clinics and heart valve teams were also widely prevalent. CONCLUSION: Consistent with current guidelines, echocardiography (transthoracic echocardiography and TOE) remains the first-line and central imaging modality for the assessment of MR although the complementary use of 3D TOE, CCT, and CMR appears to be growing. Heart valve clinics and heart valve teams are now widely prevalent.

Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases.

AIMS: Echocardiography is a cornerstone in cardiac imaging, and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully automatic measurements of LV volumes and EF both during scanning and in stored recordings. The aim of this study was to evaluate the impact of implementing AI measurements on acquisition and processing time and test-retest reproducibility compared with standard clinical workflow, as well as to study the agreement with reference in large internal and external databases. METHODS AND RESULTS: Fully automatic measurements of LV volumes and EF by a novel AI software were compared with manual measurements in the following clinical scenarios: (i) in real time use during scanning of 50 consecutive patients, (ii) in 40 subjects with repeated echocardiographic examinations and manual measurements by 4 readers, and (iii) in large internal and external research databases of 1881 and 849 subjects, respectively. Real-time AI measurements significantly reduced the total acquisition and processing time by 77% (median 5.3 min, P < 0.001) compared with standard clinical workflow. Test-retest reproducibility of AI measurements was superior in inter-observer scenarios and non-inferior in intra-observer scenarios. AI measurements showed good agreement with reference measurements both in real time and in large research databases. CONCLUSION: The software reduced the time taken to perform and volumetrically analyse routine echocardiograms without a decrease in accuracy compared with experts.

Echocardiographic Reference Ranges of Global Longitudinal Strain for All Cardiac Chambers Using Guideline-Directed Dedicated Views.

BACKGROUND: Myocardial deformation by echocardiographic strain imaging is a key measurement in cardiology, providing valuable diagnostic and prognostic information. Reference ranges for strain should be established from large healthy populations with minimal methodologic biases and variability. OBJECTIVES: The aim of this study was to establish echocardiographic reference ranges, including lower normal limits of global strains for all 4 cardiac chambers, by guideline-directed dedicated views from a large healthy population and to evaluate the influence of subject-specific characteristics on strain. METHODS: In total, 1,329 healthy participants from HUNT4Echo, the echocardiographic substudy of the 4th wave of the Trøndelag Health Study, were included. Echocardiographic recordings specific for each chamber were optimized according to current recommendations. Two experienced sonographers recorded all echocardiograms using GE HealthCare Vivid E95 scanners. Analyses were performed by experts using GE HealthCare EchoPAC. RESULTS: The reference ranges for left ventricular (LV) global longitudinal strain and right ventricular free-wall strain were -24% to -16% and -35% to -17%, respectively. Correspondingly, left atrial (LA) and right atrial (RA) reservoir strains were 17% to 49% and 17% to 59%. All strains showed lower absolute values with higher age, except for LA and RA contractile strains, which were higher. The feasibility for strain was overall good (LV 96%, right ventricular 83%, LA 94%, and RA 87%). All chamber-specific strains were associated with age, and LV strain was associated with sex. CONCLUSIONS: Reference ranges of strain for all cardiac chambers were established based on guideline-directed chamber-specific recordings. Age and sex were the most important factors influencing reference ranges and should be considered when using strain echocardiography.

Automated estimation of mitral annular plane systolic excursion by artificial intelligence from 3D ultrasound recordings.

Perioperative monitoring of cardiac function is beneficial for early detection of cardiovascular complications. The standard of care for cardiac monitoring performed by trained cardiologists and anesthesiologists involves a manual and qualitative evaluation of ultrasound imaging, which is a time-demanding and resource-intensive process with intraobserver- and interobserver variability. In practice, such measures can only be performed a limited number of times during the intervention. To overcome these difficulties, this study presents a robust method for automatic and quantitative monitoring of cardiac function based on 3D transesophageal echocardiography (TEE) B-mode ultrasound recordings of the left ventricle (LV). Such an assessment obtains consistent measurements and can produce a near real-time evaluation of ultrasound imagery. Hence, the presented method is time-saving and results in increased accessibility. The mitral annular plane systolic excursion (MAPSE), characterizing global LV function, is estimated by landmark detection and cardiac view classification of two-dimensional images extracted along the long-axis of the ultrasound volume. MAPSE estimation directly from 3D TEE recordings is beneficial since it removes the need for manual acquisition of cardiac views, hence decreasing the need for interference by physicians. Two convolutional neural networks (CNNs) were trained and tested on acquired ultrasound data of 107 patients, and MAPSE estimates were compared to clinically obtained references in a blinded study including 31 patients. The proposed method for automatic MAPSE estimation had low bias and low variability in comparison to clinical reference measures. The method accomplished a mean difference for MAPSE estimates of (-0.16±1.06) mm. Thus, the results did not show significant systematic errors. The obtained bias and variance of the method were comparable to inter-observer variability of clinically obtained MAPSE measures on 2D TTE echocardiography. The novel pipeline proposed in this study has the potential to enhance cardiac monitoring in perioperative- and intensive care settings.

Aberration correction in 2D echocardiography.

Background: An aberration correction algorithm has been implemented and demonstrated in an echocardiographic clinical trial using two-dimensional (2D) imaging. The method estimates and compensates arrival time errors between different sub-aperture processor (SAP) signals in a matrix array probe. Methods: Five standard views of channel data cineloops were recorded from 22 patients (11 male and 11 female) resulting in a total of 116 cineloops. The channel data were processed with and without the aberration correction algorithm, allowing for side-by-side comparison of images processed from the same channel data cineloops. Results: The aberration correction algorithm improved image quality, as quantified by a coherence metric, in all 7,380 processed frames. In a blinded and left-right-randomized side-by-side evaluation, four cardiologists (two experienced and two in training) preferred the aberration corrected cineloops in 97% of the cases. The clinicians reported that the corrected cineloops appeared sharper with better contrast and less noise. Many structures like valve leaflets, chordae, endocardium, and endocardial borders appeared narrower and more clearly defined in the aberration corrected images. An important finding is that aberration correction improves contrast between the endocardium and ventricle cavities for every processed image. The gain difference was confirmed by the cardiologists in their feedback and quantified with a median global gain difference estimate between the aberration-corrected and non-corrected images of 1.2 dB. Conclusions: The study shows the potential value of aberration correction in clinical echocardiography. Systematic improvement of images acquired with state-of-art equipment was observed both with quantitative metrics of image quality and clinician preference.

Real-Time Echocardiography Guidance for Optimized Apical Standard Views.

Measurements of cardiac function such as left ventricular ejection fraction and myocardial strain are typically based on 2-D ultrasound imaging. The reliability of these measurements depends on the correct pose of the transducer such that the 2-D imaging plane properly aligns with the heart for standard measurement views and is thus dependent on the operator's skills. We propose a deep learning tool that suggests transducer movements to help users navigate toward the required standard views while scanning. The tool can simplify echocardiography for less experienced users and improve image standardization for more experienced users. Training data were generated by slicing 3-D ultrasound volumes, which permits simulation of the movements of a 2-D transducer. Neural networks were further trained to calculate the transducer position in a regression fashion. The method was validated and tested on 2-D images from several data sets representative of a prospective clinical setting. The method proposed the adequate transducer movement 75% of the time when averaging over all degrees of freedom and 95% of the time when considering transducer rotation solely. Real-time application examples illustrate the direct relation between the transducer movements, the ultrasound image and the provided feedback.

Fully automatic estimation of global left ventricular systolic function using deep learning in transoesophageal echocardiography.

Aims: To improve monitoring of cardiac function during major surgery and intensive care, we have developed a method for fully automatic estimation of mitral annular plane systolic excursion (auto-MAPSE) using deep learning in transoesophageal echocardiography (TOE). The aim of this study was a clinical validation of auto-MAPSE in patients with heart disease. Methods and results: TOE recordings were collected from 185 consecutive patients without selection on image quality. Deep-learning-based auto-MAPSE was trained and optimized from 105 patient recordings. We assessed auto-MAPSE feasibility, and agreement and inter-rater reliability with manual reference in 80 patients with and without electrocardiogram (ECG) tracings. Mean processing time for auto-MAPSE was 0.3 s per cardiac cycle/view. Overall feasibility was >90% for manual MAPSE and ECG-enabled auto-MAPSE and 82% for ECG-disabled auto-MAPSE. Feasibility in at least two walls was ≥95% for all methods. Compared with manual reference, bias [95% limits of agreement (LoA)] was -0.5 [-4.0, 3.1] mm for ECG-enabled auto-MAPSE and -0.2 [-4.2, 3.6] mm for ECG-disabled auto-MAPSE. Intra-class correlation coefficient (ICC) for consistency was 0.90 and 0.88, respectively. Manual inter-observer bias [95% LoA] was -0.9 [-4.7, 3.0] mm, and ICC was 0.86. Conclusion: Auto-MAPSE was fast and highly feasible. Inter-rater reliability between auto-MAPSE and manual reference was good. Agreement between auto-MAPSE and manual reference did not differ from manual inter-observer agreement. As the principal advantages of deep-learning-based assessment are speed and reproducibility, auto-MAPSE has the potential to improve real-time monitoring of left ventricular function. This should be investigated in relevant clinical settings.

Real-time guiding by deep learning during echocardiography to reduce left ventricular foreshortening and measurement variability.

Aims: Apical foreshortening leads to an underestimation of left ventricular (LV) volumes and an overestimation of LV ejection fraction and global longitudinal strain. Real-time guiding using deep learning (DL) during echocardiography to reduce foreshortening could improve standardization and reduce variability. We aimed to study the effect of real-time DL guiding during echocardiography on measures of LV foreshortening and inter-observer variability. Methods and results: Patients (n = 88) in sinus rhythm referred for echocardiography without indication for contrast were included. All participants underwent three echocardiograms. The first two examinations were performed by sonographers, and the third by cardiologists. In Period 1, the sonographers were instructed to provide high-quality echocardiograms. In Period 2, the DL guiding was used by the second sonographer. One blinded expert measured LV length in all recordings. Tri-plane recordings by cardiologists were used as reference. Apical foreshortening was calculated at the end-diastole. Both sonographer groups significantly foreshortened the LV in Period 1 (mean foreshortening: Sonographer 1: 4 mm; Sonographer 2: 3 mm, both P < 0.001 vs. reference) and reduced foreshortening in Period 2 (2 and 0 mm, respectively. Period 1 vs. Period 2, P < 0.05). Sonographers using DL guiding did not foreshorten more than cardiologists (P ≥ 0.409). Real-time guiding did not improve intra-class correlation (ICC) [LV end-diastolic volume ICC, (95% confidence interval): DL guiding 0.87 (0.77-0.93) vs. no guiding 0.92 (0.88-0.95)]. Conclusion: Real-time guiding reduced foreshortening among experienced operators and has the potential to improve image standardization. Even though the effect on inter-operator variability was minimal among experienced users, real-time guiding may improve test-retest variability among less experienced users. Clinical trial registration: ClinicalTrials.gov, Identifier: NCT04580095.

Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions.

Aims: Impaired standardization of echocardiograms may increase inter-operator variability. This study aimed to determine whether the real-time guidance of experienced sonographers by deep learning (DL) could improve the standardization of apical recordings. Methods and results: Patients (n = 88) in sinus rhythm referred for echocardiography were included. All participants underwent three examinations, whereof two were performed by sonographers and the third by cardiologists. In the first study period (Period 1), the sonographers were instructed to provide echocardiograms for the analyses of the left ventricular function. Subsequently, after brief training, the DL guidance was used in Period 2 by the sonographer performing the second examination. View standardization was quantified retrospectively by a human expert as the primary endpoint and the DL algorithm as the secondary endpoint. All recordings were scored in rotation and tilt both separately and combined and were categorized as standardized or non-standardized. Sonographers using DL guidance had more standardized acquisitions for the combination of rotation and tilt than sonographers without guidance in both periods (all P ≤ 0.05) when evaluated by the human expert and DL [except for the apical two-chamber (A2C) view by DL evaluation]. When rotation and tilt were analysed individually, A2C and apical long-axis rotation and A2C tilt were significantly improved, and the others were numerically improved when evaluated by the echocardiography expert. Furthermore, all, except for A2C rotation, were significantly improved when evaluated by DL (P < 0.01). Conclusion: Real-time guidance by DL improved the standardization of echocardiographic acquisitions by experienced sonographers. Future studies should evaluate the impact with respect to variability of measurements and when used by less-experienced operators. ClinicalTrialsgov Identifier: NCT04580095.

Safety and efficacy of plasma transfusion from exercise-trained donors in patients with early Alzheimer's disease: protocol for the ExPlas study.

INTRODUCTION: Given that exercise training reduces the risk of developing Alzheimer's disease (AD), induces changes in the blood composition and has widespread systemic benefits, it is reasonable to hypothesise that exercised plasma (ExPlas) may have rejuvenative properties. The main objective is to test safety and tolerability of transfusing ExPlas from young, healthy, fit adults to patients with mild cognitive impairment (MCI) or early AD. The study is a pilot for a future efficacy study. The key secondary objectives are examining the effect of plasma transfusions on cognitive function, fitness level, vascular risk profile, assessment of cerebral blood flow and hippocampal volume, quality of life, functional connectivity assessed by resting state functional MRI and biomarkers in blood and cerebrospinal fluid. METHODS AND ANALYSIS: ExPlas is a double-blinded, randomised controlled clinical single-centre trial. Patients up to 75 years of age with diagnosis early symptomatic phase AD will be recruited from two Norwegian hospitals. ExPlas is plasma drawn by plasmapheresis once a month for 4 months, from a total of 30 fit male donors (aged 18-40, BMI≤27 kg/m2 and maximal oxygen uptake>55 mL/kg/min). All units will be virus inactivated by the Intercept method in accordance with procedures at St. Olavs University Hospital. Comparison with isotonic saline allows differentiation from a non-blood product. The main study consists of 6 rounds of examinations in addition to 12 plasma transfusions divided over three 4-week periods during study year-1. It is also planned to conduct follow-up examinations 2 and 5 years after baseline ETHICS AND DISSEMINATION: Written informed consent will be obtained from all participants and participation is voluntary. All participants have a next of kin who will follow them throughout the study to represent the patient's interest. The study is approved by the Regional Committee for Medical and Health Research Ethics (REK 2018/702) and the Norwegian Medicines Agency (EudraCT No. 2018-000148-24). The study will be published in an open access journal and results will be presented at numerous national and international meetings as well as on social media platforms. TRIAL REGISTRATION NUMBER: EudraCT No. 2018-000148-24. CLINICALTRIALS: gov, NCT05068830.

Complete embolization of a mechanical aortic valve during trail running-a case report with a lucky ending.

Background: Complete embolization of a prosthetic heart valve is extremely rare and dangerous. This case reports a total embolization of a mechanical aortic valve and contributes to the literature regarding the diagnostic challenges related to infective endocarditis and follow-up after valvular surgery. Case summary: A 28-year-old male 11.5 years status-post a mechanical aortic valve replacement presented with acute onset of chest pain and dyspnoea while jogging. The patient lost consciousness and went into cardiopulmonary arrest with acute pulmonary oedema and circulatory shock. An echocardiogram revealed an empty aortic annulus, and a chest radiograph showed an embolized valve in the aortic arch. The patient underwent emergent removal of the embolized valve and replacement with a new mechanical aortic valve. The patient survived with minimal sequelae. At a 3-month follow-up, he had resumed work, and the only sequelae were mild left ventricular dysfunction and minor vision loss. Although he experienced no warning signs or symptoms, the most likely aetiology for embolization of the valvular prosthesis was infective endocarditis, which was revealed by re-evaluation of an echocardiogram recorded 1 month before the presentation which demonstrated a subtle motion abnormality of the valve. Conclusions: We present a case of a late complete embolization of a mechanical aortic valve most likely caused by asymptomatic infective endocarditis. The case illustrates the challenges in follow-up after valvular surgery and highlights the ultimate benefit of a well-functioning pre-hospital to hospital chain.

High-intensity interval training and pulmonary hemodynamics in COPD with hypoxemia.

BACKGROUND: Exercise is recommended for all patients with COPD. Evidence for its benefit is considerably weaker in the more severe stages of the disease. The aim of this study was to investigate whether high-intensity interval training could improve exercise capacity, pulmonary hemodynamics and cardiac function in patients with severe COPD and hypoxemia. METHODS: Stable patients with COPD GOLD stage III or IV and hypoxemia were included. They underwent extensive cardiopulmonary testing including right heart catheterization, lung function tests, echocardiography and 6-minute walk test before and after completion of 10 weeks of high-intensity interval training performed with supplemental oxygen. Primary endpoint was change in pulmonary artery pressure measured by right heart catheterization. RESULTS: Ten patients with very severe airflow obstruction, mean FEV1 28.7% predicted and mean FEV1/VC 0.39 completed the exercise programme. Pulmonary artery pressure remained unchanged following the intervention (26,3 mmHg vs. 25,8 mmHg at baseline, p 0.673). Six-minute walk distance improved by a mean of44.8 m (p 0.010), which is also clinically significant. We found marginally improved left ventricular ejection fraction on echocardiography (54.6% vs 59.5%, p 0.046). CONCLUSION: High-intensity interval training significantly improved exercise capacity while pulmonary hemodynamics remained unchanged. The improvement may therefore be due to mechanisms other than altered pulmonary artery pressure. The increase in ejection fraction is of uncertain clinical significance. The low number of patients precludes firm conclusions.

Corynebacterium freneyi as a cause of early prosthetic valve endocarditis.

Infective endocarditis (IE) is associated with severe complications and a high mortality rate. Identification of the causative pathogen is crucial to optimise treatment. We present a case of prosthetic valve endocarditis caused by Corynebacterium freneyi, a very rare cause of human infection and not previously reported as a cause of IE. Despite proper antibiotic therapy, the patient eventually needed surgery after progression of the infection. After surgery, he quickly recovered without evidence of relapse during an 8-month follow-up period. This report highlights critical decision making in a complex and potentially life-threatening situation, where neither guidelines nor previous clinical or microbiological experience were able to give clear treatment recommendations.

EACVI survey on the evaluation of left ventricular diastolic function.

AIMS: The aim of this study is to analyse how current recommendations on left ventricular (LV) diastolic function assessment have been adopted. Identifying potential discrepancies between recommendations and everyday clinical practice would enable us to better understand and address the remaining challenges in this controversial and complex field. METHODS AND RESULTS: A total of 93 centres, mainly from tertiary care settings, responded to the survey. More than three-quarters (77%) of centres follow the 2016 ASE/EACVI recommendations for LV diastolic function evaluation in patients with preserved ejection fraction based upon e', E/e', tricuspid regurgitation velocity, and left atrial (LA) volume. These recommendations were generally preferred to the previous 2009 version. Many centres also consider strain assessments in the LV (48%) and left atrium (53%) as well as diastolic stress echocardiography (33%) to be useful as additional assessments of LV diastolic function. Echocardiographic assessments of LV diastolic function were used frequently to guide therapy in 72% of centres. CONCLUSION: There is widespread adoption of current recommendation on the evaluation of LV diastolic function and these are frequently used to guide patient management. Many centres now also consider LV and LA strain assessments useful in the clinical assessment of diastolic function. These may be considered in future recommendations.