Increased Complement Factor B and Bb Levels Are Associated with Mortality in Patients with Severe Aortic Stenosis.

Inflammation is involved in initiation and progression of aortic stenosis (AS). However, the role of the complement system, a crucial component of innate immunity in AS, is unclear. We hypothesized that circulating levels of complement factor B (FB), an important component of the alternative pathway, are upregulated and could predict outcome in patients with severe symptomatic AS. Therefore, plasma levels of FB, Bb, and terminal complement complex were analyzed in three cohorts of patients with severe symptomatic AS and mild-to-moderate or severe asymptomatic AS (population 1, n = 123; population 2, n = 436; population 3, n = 61) and in healthy controls by enzyme immunoassays. Compared with controls, symptomatic AS patients had significantly elevated levels of FB (2.9- and 2.8-fold increase in population 1 and 2, respectively). FB levels in symptomatic and asymptomatic AS patients were comparable (population 2 and 3), and in asymptomatic patients FB correlated inversely with valve area. FB levels in population 1 and 2 correlated with terminal complement complex levels and measures of systemic inflammation (i.e., CRP), cardiac function (i.e., NT-proBNP), and cardiac necrosis (i.e., Troponin T). High FB levels were significantly associated with mortality also after adjusting for clinical and biochemical covariates (hazard ratio 1.37; p = 0.028, population 2). Plasma levels of the Bb fragment showed a similar pattern in relation to mortality. We concluded that elevated levels of FB and Bb are associated with adverse outcome in patients with symptomatic AS. Increased levels of FB in asymptomatic patients suggest the involvement of FB from the early phase of the disease.

Examining the educational impact of the mini-CEX: a randomised controlled study.

BACKGROUND: The purpose of this study is to evaluate the mini-Clinical Evaluation Exercise (mini-CEX) as a formative assessment tool among undergraduate medical students, in terms of student perceptions, effects on direct observation and feedback, and educational impact. METHODS: Cluster randomised study of 38 fifth-year medical students during a 16-week clinical placement. Hospitals were randomised to provide a minimum of 8 mini-CEXs per student (intervention arm) or continue with ad-hoc feedback (control arm). After finishing their clinical placement, students completed an Objective Structured Clinical Examination (OSCE), a written test and a survey. RESULTS: All participants in the intervention group completed the pre-planned number of assessments, and 60% found them to be useful during their clinical placement. Overall, there were no statistically significant differences between groups in reported quantity or quality of direct observation and feedback. Observed mean scores were marginally higher on the OSCE and written test in the intervention group, but not statistically significant. CONCLUSIONS: There is considerable potential in assessing medical students during clinical placements and routine practice, but the educational impact of formative assessments remains mostly unknown. This study contributes with a robust study design, and may serve as a basis for future research.

Implantation of cardioverter-defibrillators at St Olav's Hospital 2006­15. / Implantasjon av hjertestartere ved St. Olavs hospital 2006­15.

BAKGRUNN: Implantasjon av hjertestarter (implantable cardioverter defibrillator, ICD) er etablert behandling hos pasienter med høy risiko for plutselig hjertedød. Studiens formål var å kartlegge pasientkarakteristika, indikasjoner, hyppigheten av ICD-støt, komplikasjoner, reoperasjoner samt endringer over tid i ICD-behandlingen ved St. Olavs hospital. MATERIALE OG METODE: Alle pasienter som fikk implantert hjertestarter ved St. Olavs hospital i perioden 2006-15 ble inkludert. Pasientene ble identifisert i pacemakerregisteret. Data ble hentet fra pacemakerregisteret og elektronisk pasientjournal. RESULTATER: Studien inkluderte 598 pasienter (82 % menn, medianalder 65 år). Tidligere hjertestans eller alvorlig arytmi forelå hos 401 (67 %) av dem som fikk implantert hjertestarter. Koronarsykdom (n = 383) var vanligste underliggende årsak. I oppfølgingstiden (median 3,6 år) fikk 203 (34 %) av pasientene ICD-støt, 154 (26 %) fikk berettigede og 65 (11 %) fikk uberettigede støt. Hos 139 (23 %) pasienter oppstod komplikasjoner. 101 (17 %) pasienter døde i oppfølgingsperioden. FORTOLKNING: Studien gir et godt grunnlag for kvalitetssikring av implantasjonsvirksomheten ved St. Olavs hospital. Kjønns- og aldersfordeling, indikasjon og underliggende årsaker for implantasjon samt hyppighet av støt og komplikasjoner samsvarer godt med tidligere publiserte data.

Myocardial fibrosis. / Myokardfibrose.

BAKGRUNN: Myokardfibrose oppstår sekundært til kardial belastning eller skade. I denne oversiktsartikkelen presenteres sentrale aspekter ved myokardfibrose. KUNNSKAPSGRUNNLAG: Vi foretok 2 søk i PubMed som til sammen ga 417 treff. Artiklenes relevans ble vurdert på grunnlag av tittel, sammendrag og eventuell fulltekst. 44 sentrale artikler ble inkludert. RESULTATER: Myokardfibrose klassifiseres som interstitiell fibrose og erstatningsfibrose. Fibrose kan forårsake ugunstige endringer i hjertets elektriske og mekaniske funksjon, og forverrer prognosen ved mange hjertesykdommer. Bildediagnostikk og forskning på biomarkører har forbedret mulighetene for å påvise fibrose. Det ultimate målet er å utvikle medikamenter som kan bremse eller reversere myokardfibrose. FORTOLKNING: Moderne diagnostikk har forbedret mulighetene for å påvise myokardfibrose og økt forståelsen av fibrosens betydning ved hjertesykdommer. Utvikling av medikamenter som hemmer fibroseutviklingen, vil kunne få stor betydning for moderne hjertemedisin.

Mechanical Wave Velocities in Left Ventricular Walls in Healthy Subjects and Patients With Aortic Stenosis.

BACKGROUND: Mechanical wave velocity (MWV) measurement is a promising method for evaluating myocardial stiffness, because these velocities are higher in patients with myocardial disease. OBJECTIVES: Using high frame rate echocardiography and a novel method for detection of myocardial mechanical waves, this study aimed to estimate the MWVs for different left ventricular walls and events in healthy subjects and patients with aortic stenosis (AS). Feasibility and reproducibility were evaluated. METHODS: This study included 63 healthy subjects and 13 patients with severe AS. All participants underwent echocardiographic examination including 2-dimensional high frame rate recordings using a clinical scanner. Cardiac magnetic resonance was performed in 42 subjects. The authors estimated the MWVs at atrial kick and aortic valve closure in different left ventricular walls using the clutter filter wave imaging method. RESULTS: Mechanical wave imaging in healthy subjects demonstrated the highest feasibility for the atrial kick wave reaching >93% for all 4 examined left ventricular walls. The MWVs were higher for the inferolateral and anterolateral walls (2.2 and 2.6 m/s) compared with inferoseptal and anteroseptal walls (1.3 and 1.6 m/s) (P < 0.05) among healthy subjects. The septal MWVs at aortic valve closure were significantly higher for patients with severe AS than for healthy subjects. CONCLUSIONS: MWV estimation during atrial kick is feasible and demonstrates higher velocities in the lateral walls, compared with septal walls. The authors propose indicators for quality assessment of the mechanical wave slope as an aid for achieving consistent measurements. The discrimination between healthy subjects and patients with AS was best for the aortic valve closure mechanical waves. (Ultrasonic Markers for Myocardial Fibrosis and Prognosis in Aortic Stenosis; NCT03422770).

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.

A Very Large Cardiac Channel Data Database (VLCD) Used to Evaluate Global Image Coherence (GIC) as an In Vivo Image Quality Metric.

Ultrasound image quality is of utmost importance for a clinician to reach a correct diagnosis. Conventionally, image quality is evaluated using metrics to determine the contrast and resolution. These metrics require localization of specific regions and targets in the image such as a region of interest (ROI), a background region, and/or a point scatterer. Such objects can all be difficult to identify in in-vivo images, especially for automatic evaluation of image quality in large amounts of data. Using a matrix array probe, we have recorded a Very Large cardiac Channel data Database (VLCD) to evaluate coherence as an in vivo image quality metric. The VLCD consists of 33280 individual image frames from 538 recordings of 106 patients. We also introduce a global image coherence (GIC), an in vivo image quality metric that does not require any identified ROI since it is defined as an average coherence value calculated from all the data pixels used to form the image, below a preselected range. The GIC is shown to be a quantitative metric for in vivo image quality when applied to the VLCD. We demonstrate, on a subset of the dataset, that the GIC correlates well with the conventional metrics contrast ratio (CR) and the generalized contrast-to-noise ratio (gCNR) with R = 0.74 ( ) and R = 0.62 ( ), respectively. There exist multiple methods to estimate the coherence of the received signal across the ultrasound array. We further show that all coherence measures investigated in this study are highly correlated ( 0.9 and ) when applied to the VLCD. Thus, even though there are differences in the implementation of coherence measures, all quantify the similarity of the signal across the array and can be averaged into a GIC to evaluate image quality automatically and quantitatively.

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.

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.

3D Myocardial Mechanical Wave Measurements: Toward In Vivo 3D Myocardial Elasticity Mapping.

OBJECTIVES: This study aimed to investigate the potential of a novel 3-dimensional (3D) mechanical wave velocity mapping technique, based on the natural mechanical waves produced by the heart itself, to approach a noninvasive 3D stiffness mapping of the left ventricle. BACKGROUND: Myocardial fibrosis is recognized as a pathophysiological substrate of major cardiovascular disorders such as cardiomyopathies and valvular heart disease. As fibrosis leads to increased myocardial stiffness, ultrasound elastography measurements could provide important clinical information. METHODS: A 3D high frame rate imaging sequence was implemented on a high-end clinical ultrasound scanner to achieve 820 volumes/s when gating over 4 consecutive cardiac cycles. Five healthy volunteers and 10 patients with various degrees of aortic stenosis were included to evaluate feasibility and reproducibility. Mechanical waves were detected using the novel Clutter Filter Wave Imaging approach, shown to be highly sensitive to the weak tissue displacements caused by natural mechanical waves. RESULTS: 3D spatiotemporal maps of mechanical wave velocities were produced for all subjects. Only the specific mechanical wave at atrial contraction provided a full 3D coverage of the left ventricle (LV). The average atrial kick propagation velocity was 1.6 ± 0.2 m/s in healthy volunteers and 2.8 ± 0.8 m/s in patients (p = 0.0016). A high correlation was found between mechanical wave velocity and age (R2 = 0.88, healthy group), septal wall thickness (R2 = 0.73, entire group), and peak jet velocity across the aortic valve (R2 = 0.70). For 3 of the patients, the higher mechanical wave velocity coexisted with the presence of late gadolinium enhancement on cardiac magnetic resonance. CONCLUSIONS: In this study, 3D LV mechanical wave velocities were visualized and measured in healthy volunteers and patients with aortic stenosis. The proposed imaging sequence and measurement technique allowed, for the first time, the measurement of full spatiotemporal 3D elasticity maps of the LV using ultrasound. (Ultrasonic markers for myocardial fibrosis and prognosis in aortic stenosis; NCT03422770).

LU-Net: A Multistage Attention Network to Improve the Robustness of Segmentation of Left Ventricular Structures in 2-D Echocardiography.

Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semiautomatically in clinical routine and is, thus, prone to interobserver and intraobserver variabilities. Recent studies have shown that deep learning has the potential to perform fully automatic segmentation. However, the current best solutions still suffer from a lack of robustness in terms of accuracy and number of outliers. The goal of this work is to introduce a novel network designed to improve the overall segmentation accuracy of left ventricular structures (endocardial and epicardial borders) while enhancing the estimation of the corresponding clinical indices and reducing the number of outliers. This network is based on a multistage framework where both the localization and segmentation steps are optimized jointly through an end-to-end scheme. Results obtained on a large open access data set show that our method outperforms the current best-performing deep learning solution with a lighter architecture and achieved an overall segmentation accuracy lower than the intraobserver variability for the epicardial border (i.e., on average a mean absolute error of 1.5 mm and a Hausdorff distance of 5.1mm) with 11% of outliers. Moreover, we demonstrate that our method can closely reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.96 and a mean absolute error of 7.6 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.83 and an absolute mean error of 5.0%, producing scores that are slightly below the intraobserver margin. Based on this observation, areas for improvement are suggested.

YKL-40 (Chitinase-3-Like Protein 1) Serum Levels in Aortic Stenosis.

BACKGROUND: Identification of novel biomarkers could provide prognostic information and improve risk stratification in patients with aortic stenosis (AS). YKL-40 (chitinase-3-like protein 1), a protein involved in atherogenesis, is upregulated in human calcific aortic valves. We hypothesized that circulating YKL-40 would be elevated and associated with the degree of AS severity and outcome in patients with symptomatic AS. METHODS: Plasma YKL-40 was analyzed in 2 AS populations, one severe AS (n=572) with outcome measures and one with mixed severity (n=67). YKL-40 expression in calcified valves and in an experimental pressure overload model was assessed. RESULTS: We found (1) patients with AS had upregulated circulating YKL-40 compared with healthy controls (median 109 versus 34 ng/mL, P<0.001), but levels were not related to the degree of AS severity. (2) High YKL-40 levels (quartile 4) were associated with long-term (median follow-up 4.7 years) all-cause mortality (adjusted hazard ratio, 1.93 [95% CI, 1.37-2.73], P<0.001). (3) YKL-40 protein expression in human calcific valves co-localized with its putative receptor IL-13rα2 in close proximity to valve interstitial cells. (4) Myocardial YKL-40 increased in experimental pressure overload (6-fold in decompensated versus sham mice). CONCLUSIONS: YKL-40 levels were elevated in AS and associated with mortality but not with other metrics of disease severity including the degree of AS severity. Despite scientific rationale for its role in AS, the clinical utility of circulating YKL-40 as a biomarker is limited. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01794832.

Combining Automatic Angle Correction and 3-D Tracking Doppler for the Assessment of Aortic Stenosis Severity.

Aortic valve stenosis (AS) is a narrowing of the aortic valve opening, which causes increased load on the left ventricle. Untreated, this condition can eventually lead to heart failure and death. According to current recommendations, an accurate diagnosis of AS mandates the use of multiple acoustic windows to determine the highest velocity. Furthermore, the optimal positioning of both patient and transducer to reduce the beam-to-flow angle is emphasized. Being operator dependent, the beam alignment is a potential source of uncertainty. In this work, we perform noncompounded 3-D plane wave imaging for retrospective estimation of maximum velocities in aortic jets with automatic angle correction. This is achieved by combining a hybrid 3-D speckle tracking method to estimate the jet direction and 3-D tracking Doppler to generate angle-corrected sonograms, using the direction from speckle tracking as input. Results from simulations of flow through an orifice show that 3-D speckle tracking can estimate the jet orientation with acceptable accuracy for signal-to-noise ratios above 10 dB. Results from 12 subjects show that sonograms recorded from a standard apical view using the proposed method yield a maximum velocity that matches continuous wave (CW) Doppler sonograms recorded from the acoustic window with the lowest angle within a ±10% margin, provided that a high enough pulse repetition frequency could be achieved. These results motivate further validation and optimization studies.

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography.

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.