Migraine and risk of atrial fibrillation: A 9-year follow-up based on the Trøndelag Health Study.

BACKGROUND: Data from some population-based studies have indicated an increased risk of atrial fibrillation (AF) among patients with migraine, particularly among individuals with migraine with aura. The present study aimed to assess the association between primary headache disorders and AF. METHODS: In a population-based 9-year follow-up design, we evaluated the questionnaire-based headache diagnosis, migraine and tension-type headache (TTH) included, collected in the Trøndelag Health Study (HUNT3) conducted in 2006-2008, and the subsequent risk of AF in the period until December 2015. The population at risk consisted of 39,340 individuals ≥20 years without AF at HUNT3 baseline who answered headache questionnaire during HUNT3. The prospective association was evaluated by multivariable Cox proportional hazard models with 95% confidence intervals (CIs). RESULTS: Among the 39,340 participants, 1524 (3.8%) developed AF during the 9-year follow up, whereof 91% of these were ≥55 years. In the multivariable analyses, adjusting for known confounders, we did not find any association between migraine or TTH and risk of AF. The adjusted hazard ratios (HRs) were respectively 0.84 (95% CI = 0.64-1.11) for migraine, 1.16 (95% CI = 0.86-1.27) for TTH and 1.04 (95% CI = 0.86-1.27) for unclassified headache. However, in sensitivity analyses of individuals aged ≥55 years, a lower risk of AF was found for migraine (HR = 0.53; 95% CI = 0.39-0.73). CONCLUSIONS: In this large population-based study, no increased risk of AF was found among individuals with migraine or TTH at baseline. Indeed, among individuals aged ≥55 years, migraine was associated with a lower risk for AF.

Reference intervals of circulating secretoneurin concentrations determined in a large cohort of community dwellers: the HUNT study.

OBJECTIVES: Secretoneurin (SN) is a novel cardiac biomarker that associates with the risk of mortality and dysfunctional cardiomyocyte Ca2+ handling in heart failure patients. Reference intervals for SN are unknown. METHODS: SN was measured with a CE-marked ELISA in healthy community dwellers from the fourth wave of the Trøndelag Health Study (HUNT4) conducted in 2017-2019. The common, sex and age specific 90th, 95th, 97.5th and 99th percentiles were calculated using the non-parametric method and outlier exclusion according to the Reed test. The applicability of sex and age specific reference intervals were investigated using Harris and Boyd test. We also estimated the percentiles in a subset with normal findings on echocardiographic screening. RESULTS: The total cohort included 887 persons (56.4 % women). After echocardiographic screening 122 persons were excluded, leaving a total of 765 persons (57.8 % women). The 97.5th percentile (95 % CI in brackets) of SN was 59.7 (57.5-62.1) pmol/L in the total population and 58.6 (57.1-62.1) pmol/L after echocardiography screening. In general, slightly higher percentiles were found in women and elderly participants, but less than 4 % in these subgroups had concentrations deviating from the common 97.5th percentile. Low BMI or eGFR was also associated with higher concentrations of SN. CONCLUSIONS: Upper reference limits for SN were similar amongst healthy adult community dwellers regardless of prescreening including cardiac echocardiography or not. Women and elderly showed higher concentrations of SN, but the differences were not sufficiently large to justify age and sex stratified upper reference limits.

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.

Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning.

OBJECTIVE: Echocardiography, a critical tool for assessing left atrial (LA) volume, often relies on manual or semi-automated measurements. This study introduces a fully automated, real-time method for measuring LA volume in both 2-D and 3-D imaging, in the aim of offering accuracy comparable to that of expert assessments while saving time and reducing operator variability. METHODS: We developed an automated pipeline comprising a network to identify the end-systole (ES) time point and robust 2-D and 3-D U-Nets for segmentation. We employed data sets of 789 2-D images and 286 3-D recordings and explored various training regimes, including recurrent networks and pseudo-labeling, to estimate volume curves. RESULTS: Our baseline results revealed an average volume difference of 2.9 mL for 2-D and 7.8 mL for 3-D, respectively, compared with manual methods. The application of pseudo-labeling to all frames in the cine loop generally led to more robust volume curves and notably improved ES measurement in cases with limited data. CONCLUSION: Our results highlight the potential of automated LA volume estimation in clinical practice. The proposed prototype application, capable of processing real-time data from a clinical ultrasound scanner, provides valuable temporal volume curve information in the echo lab.

Systemic embolization in infective endocarditis.

Embolism is a common complication in infective endocarditis which may lead to serious complications, such as stroke, intestinal ischemia, and peripheral embolization. A comprehensive literature search was performed and the registry at our centre, including 390 cases of infective endocarditis, diagnosed between 2010 and 2020, was investigated. Large registries show that 20-40% of patients with infective endocarditis (IE) are affected by embolism. In many instances, embolism is present already at the time of diagnosis. The rate of embolism during the hospital stay in our data was 11%. However, only 2% developed clinical embolism during or following surgery. According to recent guidelines, previous embolism, and the presence of vegetations > 10 mm present an indication for surgical treatment. Routine imaging revealed non-symptomatic cerebral embolism in 8.5% of surgical patients. However, it is not clear whether detection of non-symptomatic embolism and consecutive surgical treatment improves the prognosis of infective endocarditis.

The influence of diagnostic subgroups, patient- and hospital characteristics for the validity of cardiovascular diagnoses-Data from a Norwegian hospital trust.

BACKGROUND: Cardiovascular discharge diagnoses may serve as endpoints in epidemiological studies if they have a high validity. Aim was to study if diagnoses-specific characteristics like type, sub-categories, and position of cardiovascular diagnoses affected diagnostic accuracy. METHODS: Patients (n = 7,164) with a discharge diagnosis of acute myocardial infarction, heart failure or cerebrovascular disease were included. Data were presented as positive predictive values (PPV) and sensitivity. RESULTS: PPV was high (≥88%) for acute myocardial infarction (n = 2,189) (except for outpatients). For heart failure (n = 4,026) PPV was 67% overall, but higher (>99%) when etiology or echocardiography was included. For hemorrhagic (n = 257) and ischemic (n = 1,034) strokes PPVs were 87% and 80%, respectively, with sensitivity of 79% and 75%. Transient ischemic attacks (n = 926) had PPV 56%, but sensitivity 86%. Primary diagnoses showed higher validity than subsequent diagnoses and inpatient diagnoses were more valid than outpatient diagnoses (except for transient ischemic attack). The diagnoses of acute myocardial infarction and heart failure where most valid when placed at cardiology units, while ischemic stroke when discharged from an internal medicine unit. CONCLUSIONS: The diagnoses of acute myocardial infarction and stroke had excellent validity when placed during hospital stays. Similarly, heart failure diagnoses had excellent validity when echocardiography was performed before placing the diagnosis, while overall the diagnoses of heart failure and transient ischemic attack were less valid. In conclusion, the results indicate that cardiovascular diagnoses based on objective findings such as acute myocardial infarction and stroke have excellent validity and may be used as endpoints in clinical epidemiological studies with less rigid validation.

Automated Segmentation and Quantification of the Right Ventricle in 2-D Echocardiography.

OBJECTIVE: The right ventricle receives less attention than its left counterpart in echocardiography research, practice and development of automated solutions. In the work described here, we sought to determine that the deep learning methods for automated segmentation of the left ventricle in 2-D echocardiograms are also valid for the right ventricle. Additionally, here we describe and explore a keypoint detection approach to segmentation that guards against erratic behavior often displayed by segmentation models. METHODS: We used a data set of echo images focused on the right ventricle from 250 participants to train and evaluate several deep learning models for segmentation and keypoint detection. We propose a compact architecture (U-Net KP) employing the latter approach. The architecture is designed to balance high speed with accuracy and robustness. RESULTS: All featured models achieved segmentation accuracy close to the inter-observer variability. When computing the metrics of right ventricular systolic function from contour predictions of U-Net KP, we obtained the bias and 95% limits of agreement of 0.8 ± 10.8% for the right ventricular fractional area change measurements, -0.04 ± 0.54 cm for the tricuspid annular plane systolic excursion measurements and 0.2 ± 6.6% for the right ventricular free wall strain measurements. These results were also comparable to the semi-automatically derived inter-observer discrepancies of 0.4 ± 11.8%, -0.37 ± 0.58 cm and -1.0 ± 7.7% for the aforementioned metrics, respectively. CONCLUSION: Given the appropriate data, automated segmentation and quantification of the right ventricle in 2-D echocardiography are feasible with existing methods. However, keypoint detection architectures may offer higher robustness and information density for the same computational cost.

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.

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.

The importance of patient characteristics, operators, and image quality for the accuracy of heart failure diagnosis by general practitioners using handheld ultrasound devices.

Aims: To evaluate whether the characteristics of patients, operators, and image quality could explain the accuracy of heart failure (HF) diagnostics by general practitioners (GPs) using handheld ultrasound devices (HUDs) with automatic decision-support software and telemedical support. Methods and results: Patients referred to an outpatient cardiac clinic due to symptoms indicating HF were examined by one of five GPs after dedicated training. In total, 166 patients were included [median (inter-quartile range) age 73 (63-78) years; mean ± standard deviation ejection fraction 53 ± 10%]. The GPs considered whether the patients had HF in four diagnostic steps: (i) clinical examination, (ii) adding focused cardiac HUD examination, (iii) adding automatic decision-support software measuring mitral annular plane systolic excursion (autoMAPSE) and ejection fraction (autoEF), and (iv) adding telemedical support. Overall, the characteristics of patients, operators, and image quality explained little of the diagnostic accuracy. Except for atrial fibrillation [lower accuracy for HUD alone and after adding autoEF (P < 0.05)], no patient characteristics influenced the accuracy. Some differences between operators were found after adding autoMAPSE (P < 0.05). Acquisition errors of the four-chamber view and a poor visualization of the mitral plane were associated with reduced accuracy after telemedical support (P < 0.05). Conclusion: The characteristics of patients, operators, and image quality explained just minor parts of the modest accuracy of GPs' HF diagnostics using HUDs with and without decision-support software. Atrial fibrillation and not well-standardized recordings challenged the diagnostic accuracy. However, the accuracy was only modest in well-recorded images, indicating a need for refinement of the technology.

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.

Cardiac Valve Event Timing in Echocardiography Using Deep Learning and Triplane Recordings.

Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely on timing from different cardiac cycles. Recent methods have applied deep learning to cardiac timing, but they have mainly been restricted to only detecting two key time points, namely end-diastole (ED) and end-systole (ES). In this work, we propose a deep learning approach that leverages triplane recordings to enhance detection of valve events in echocardiography. Our method demonstrates improved performance detecting six different events, including valve events conventionally associated with ED and ES. Of all events, we achieve an average absolute frame difference (aFD) of maximum 1.4 frames (29 ms) for start of diastasis, down to 0.6 frames (12 ms) for mitral valve opening when performing a ten-fold cross-validation with test splits on triplane data from 240 patients. On an external independent test consisting of apical long-axis data from 180 other patients, the worst performing event detection had an aFD of 1.8 (30 ms). The proposed approach has the potential to significantly impact clinical practice by enabling more accurate, rapid and comprehensive event detection, leading to improved clinical measurements.

Exercise training and high-sensitivity cardiac troponin-I in patients with heart failure with reduced ejection fraction.

AIMS: The aims of this sub-study of the SMARTEX trial were (1) to evaluate the effects of a 12-week exercise training programme on serum levels of high sensitivity cardiac troponin I (hs-cTnI) in patients with moderate chronic heart failure (CHF), in New York Heart Association class II-III with reduced ejection fraction (HFrEF) and (2) to explore the associations with left ventricular remodelling, functional capacity and filling pressures measured with N-terminal pro brain natriuretic peptide (NT-proBNP). METHODS AND RESULTS: In this sub-study, 196 patients were randomly assigned to high intensity interval training (HIIT, n = 70), moderate continuous training (MCT, n = 59) or recommendation of regular exercise (RRE), (n = 67) for 12 weeks. To reveal potential difference between structured intervention and control, HIIT and MCT groups were merged and named supervised exercise training (SET) group. The RRE group constituted the control group (CG). To avoid contributing factors to myocardial injury, we also evaluated changes in patients without additional co-morbidities (atrial fibrillation, hypertension, diabetes mellitus, and chronic obstructive pulmonary disease). The relationship between hs-cTnI and left ventricular end-diastolic diameter (LVEDD), VO2peak, and NT-proBNP was analysed by linear mixed models. At 12 weeks, Hs-cTnI levels were modestly but significantly reduced in the SET group from median 11.9 ng/L (interquartile ratio, IQR 7.1-21.8) to 11.5 ng/L (IQR 7.0-20.7), P = 0.030. There was no between-group difference (SET vs. CG, P = 0.116). There was a numerical but not significant reduction in hs-cTnI for the whole population (P = 0.067) after 12 weeks. For the sub-group of patients without additional co-morbidities, there was a significant between-group difference: SET group (delta -1.2 ng/L, IQR -2.7 to 0.1) versus CG (delta -0.1 ng/L, IQR -0.4 to 0.7), P = 0.007. In the SET group, hs-cTnI changed from 10.9 ng/L (IQR 6.0-22.7) to 9.2 ng/L (IQR 5.2-20.5) (P = 0.002), whereas there was no change in the CG (6.4 to 5.8 ng/L, P = 0.64). Changes in hs-cTnI (all patients) were significantly associated with changes in; LVEDD, VO2peak, and NT-proBNP, respectively. CONCLUSIONS: In patients with stable HFrEF, 12 weeks of structured exercise intervention was associated with a modest, but significant reduction of hs-cTnI. There was no significant difference between intervention group and control group. In the sub-group of patients without additional co-morbidities, this difference was highly significant. The alterations in hs-cTnI were associated with reduction of LVEDD and natriuretic peptide concentrations as well as improved functional capacity.

Large scale plasma proteomics identifies novel proteins and protein networks associated with heart failure development.

Heart failure (HF) causes substantial morbidity and mortality but its pathobiology is incompletely understood. The proteome is a promising intermediate phenotype for discovery of novel mechanisms. We measured 4877 plasma proteins in 13,900 HF-free individuals across three analysis sets with diverse age, geography, and HF ascertainment to identify circulating proteins and protein networks associated with HF development. Parallel analyses in Atherosclerosis Risk in Communities study participants in mid-life and late-life and in Trøndelag Health Study participants identified 37 proteins consistently associated with incident HF independent of traditional risk factors. Mendelian randomization supported causal effects of 10 on HF, HF risk factors, or left ventricular size and function, including matricellular (e.g. SPON1, MFAP4), senescence-associated (FSTL3, IGFBP7), and inflammatory (SVEP1, CCL15, ITIH3) proteins. Protein co-regulation network analyses identified 5 modules associated with HF risk, two of which were influenced by genetic variants that implicated trans hotspots within the VTN and CFH genes.

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.

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 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.

Moderate continuous or high intensity interval exercise in heart failure with reduced ejection fraction: Differences between ischemic and non-ischemic etiology.

Background: Exercise for heart failure (HF) with reduced ejection fraction (HFrEF) is recommended by guidelines, but exercise mode and intensities are not differentiated between HF etiologies. We, therefore, investigated the effect of moderate or high intensity exercise on left ventricular end-diastolic diameter (LVEDD), left ventricular ejection fraction (LVEF) and maximal exercise capacity (peak VO2) in patients with ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM). Methods: The Study of Myocardial Recovery after Exercise Training in Heart Failure (SMARTEX-HF) consecutively enrolled 231 patients with HFrEF (LVEF ≤ 35 %, NYHA II-III) in a 12-weeks supervised exercise program. Patients were stratified for HFrEF etiology (ICM versus NICM) and randomly assigned (1:1:1) to supervised exercise thrice weekly: a) moderate continuous training (MCT) at 60-70 % of peak heart rate (HR), b) high intensity interval training (HIIIT) at 90-95 % peak HR, or c) recommendation of regular exercise (RRE) according to guidelines. LVEDD, LVEF and peak VO2 were assessed at baseline, after 12 and 52 weeks. Results: 215 patients completed the intervention. ICM (59 %; n = 126) compared to NICM patients (41 %; n = 89) had significantly lower peak VO2 values at baseline and after 12 weeks (difference in peak VO2 2.2 mL/(kg*min); p < 0.0005) without differences between time points (p = 0.11) or training groups (p = 0.15). Etiology did not influence changes of LVEDD or LVEF (p = 0.30; p = 0.12), even when adjusting for sex, age and smoking status (p = 0.54; p = 0.12). Similar findings were observed after 52 weeks. Conclusions: Etiology of HFrEF did not influence the effects of moderate or high intensity exercise on cardiac dimensions, systolic function or exercise capacity. Clinical Trial Registration­URL: http://www.clinicaltrials.gov. Unique identifier: NCT00917046.