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.

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.

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.

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.

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.

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.

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.

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.

The importance of concomitant mitral regurgitation for estimates of mitral valve area by pressure half time in patients with chronic rheumatic heart disease.

AIMS: Aim was to study how concomitant mitral regurgitation (MR) assessed by qualitative and quantitative methods influence mitral valve area (MVA) calculations by the pressure half time method (MVAPHT) compared to reference MVA (planimetry) in patients with rheumatic heart disease. METHODS AND RESULTS: In 72 patients with chronic rheumatic heart disease, MVAPHT was calculated as 220 divided by the pressure half time of the mitral early inflow Doppler spectrum. Direct measurement by planimetry was used as reference MVA and was mean (SD) 0.99 (0.69-1.99) cm2. Concomitant MR was present in 82%. MR severity was assessed qualitatively in all, semi-quantitatively by measuring the vena contracta width in 58 (81%), and quantitatively by calculation of the regurgitant volume in 28 (39%). MVA was significantly underestimated by MVAPHT, with increasing MR. In regression analyses MVAPHT underestimated MVA by 0.19 cm2 per higher grade of MR severity in qualitative assessment, and by 0.12-0.13 cm2 per mm larger vena contracta width and 10 ml larger regurgitant volume, respectively. The presented associations were more evident when i) MR severity was quantified compared to qualitative assessment and ii) reference measurements were made by three-dimensional transoesophageal recordings compared to transthoracic recordings. CONCLUSION: MVAPHT underestimated mitral valve area compared to planimetry in patients with MS and concomitant MR. This study highlights the importance of taking the MR severity into account when evaluating MVA based on the PHT method. Direct measurements should be included in clinical decision making.

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.

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.

Investigating the causal interplay between sleep traits and risk of acute myocardial infarction: a Mendelian randomization study.

BACKGROUND: Few studies have investigated the joint effects of sleep traits on the risk of acute myocardial infarction (AMI). No previous study has used factorial Mendelian randomization (MR) which may reduce confounding, reverse causation, and measurement error. Thus, it is prudent to study joint effects using robust methods to propose sleep-targeted interventions which lower the risk of AMI. METHODS: The causal interplay between combinations of two sleep traits (including insomnia symptoms, sleep duration, or chronotype) on the risk of AMI was investigated using factorial MR. Genetic risk scores for each sleep trait were dichotomized at their median in UK Biobank (UKBB) and the second survey of the Trøndelag Health Study (HUNT2). A combination of two sleep traits constituting 4 groups were analyzed to estimate the risk of AMI in each group using a 2×2 factorial MR design. RESULTS: In UKBB, participants with high genetic risk for both insomnia symptoms and short sleep had the highest risk of AMI (hazard ratio (HR) 1.10; 95% confidence interval (CI) 1.03, 1.18), although there was no evidence of interaction (relative excess risk due to interaction (RERI) 0.03; 95% CI -0.07, 0.12). These estimates were less precise in HUNT2 (HR 1.02; 95% CI 0.93, 1.13), possibly due to weak instruments and/or small sample size. Participants with high genetic risk for both a morning chronotype and insomnia symptoms (HR 1.09; 95% CI 1.03, 1.17) and a morning chronotype and short sleep (HR 1.11; 95% CI 1.04, 1.19) had the highest risk of AMI in UKBB, although there was no evidence of interaction (RERI 0.03; 95% CI -0.06, 0.12; and RERI 0.05; 95% CI -0.05, 0.14, respectively). Chronotype was not available in HUNT2. CONCLUSIONS: This study reveals no interaction effects between sleep traits on the risk of AMI, but all combinations of sleep traits increased the risk of AMI except those with long sleep. This indicates that the main effects of sleep traits on AMI are likely to be independent of each other.

Regional motion of the AV-plane is related to the cardiac anatomy and deformation of the AV-plane. Data from the HUNT study.

The study examines global and regional systolic shortening of the left (LV) and right ventricle (RV) in 1266 individuals without evidence of heart disease in the third wave of the HUNT study. Regional mitral annular systolic displacement (mitral annular plane systolic excursion [MAPSE]) was 1.5 cm in the septum and anterior walls, 1.6 cm in the lateral wall and 1.7 cm in the inferior wall, global mean 1.6 cm. Peak systolic velocity S' was 8.0, 8.3, 8.8 and 8.6 cm/s in the same walls (global mean 8.7 cm/s). All measures of LV longitudinal shortening correlated, mean MAPSE and S' also correlated with stroke volume (SV) and ejection fraction (EF). Global longitudinal strain by either method correlated with MAPSE, S' and EF, but not with SV, reflecting a systematic difference. S' and MAPSE correlated with early annular diastolic velocity (e'), reflecting that e' is the recoil from systole. Mean displacement was 2.8 (0.5) cm in the tricuspid annulus (tricuspid annular plane systolic excursion [TAPSE]). Normal values by age and sex are provided. Both TAPSE and S' were lower in women, where body size explained the sex difference. Normalisation of MAPSE and S' for wall length reduced intra-individual variation of displacement and velocity by 80%-90%, showing regional MAPSE to be related to LV wall length, and that longitudinal wall strain was relatively uniform. Displacement and S' were lowest in the septum and highest in the left and right free walls, shows systolic bending of the AV-plane into a U-shape, relating to the total cardiac volume changes during the heart cycle.

Lipoprotein subfraction profiling in the search of new risk markers for myocardial infarction: The HUNT study.

BACKGROUND: Traditional biomarkers used to measure risk of myocardial infarction (MI) only explain a modest proportion of the incidence. Lipoprotein subfractions have the potential to improve risk prediction of MI. AIM: We aimed to identify lipoprotein subfractions that were associated with imminent MI risk. METHODS: We identified apparently healthy participants with a predicted low 10-year risk of MI from The Trøndelag Health Survey 3 (HUNT3) that developed MI within 5 years after inclusion (cases, n = 50) and 100 matched controls. Lipoprotein subfractions were analyzed in serum by nuclear magnetic resonance spectroscopy at time of inclusion in HUNT3. Lipoprotein subfractions were compared between cases and controls in the full population (N = 150), and in subgroups of males (n = 90) and females (n = 60). In addition, a sub analysis was performed in participants that experienced MI within two years and their matched controls (n = 56). RESULTS: None of the lipoprotein subfractions were significantly associated with future MI when adjusting for multiple testing (p<0.002). At nominal significance level (p<0.05), the concentration of apolipoprotein A1 in the smallest high-density lipoprotein (HDL) subfractions was higher in cases compared to controls. Further, in sub analyses based on sex, male cases had lower lipid concentration within the large HDL subfractions and higher lipid concentration within the small HDL subfractions compared to male controls (p<0.05). No differences were found in lipoprotein subfractions between female cases and controls. In sub analysis of individuals suffering from MI within two years, triglycerides in low-density lipoprotein were higher among cases (p<0.05). CONCLUSION: None of the investigated lipoprotein subfractions were associated with future MI after adjustment for multiple testing. However, our findings suggests that HDL subfractions may be of interest in relation to risk prediction for MI, especially in males. This need to be further investigated in future studies.

Development of a tool to predict the risk of incident heart failure in a general population: the HUNT for HF risk score.

AIMS: Currently, no incident heart failure (HF) risk score that is in regular use in a general population is available. We aimed to develop this and compare with existing HF risk scores. METHODS AND RESULTS: Participants in the third wave (2006-08) of the population-based Trøndelag Health Study 3 (HUNT3) were included if they reported no previous HF. Any hospital diagnoses captured during follow-up (until the end of 2018) of HF, cardiomyopathy, or hypertensive heart disease were assessed by an experienced cardiologist. Valid HF events were defined as symptoms/signs of HF and objective evidence of structural/functional abnormality of the heart at rest. The model was compared with slightly modified HF risk scores (the Health Aging and Body Composition HF risk score, the Framingham HF risk score, the Pooled Cohort equations to Prevent HF risk score, and NORRISK 2). Among 36 511 participants (mean ± SD age of 57.9 ± 13.3 years, 55.4% female), with a mean follow-up of 10.2 ± 1.3 years, 1366 developed HF (incidence rate of 3.66 per 1000 participant years). Out of the 38 relevant clinical variables assessed, we identified 12 (atrial fibrillation being the strongest) that independently predicted an HF event. The final model demonstrated good discrimination (C statistics = 0.904) and calibration, was stable in internal validation, and performed well compared with existing risk scores. The model identified that, at enrolment, 31 391 (86%), 2386 (7%), 1246 (3%), and 1488 (4%) had low, low-intermediate, high-intermediate, and high 10-year HF risk, respectively. CONCLUSIONS: Twelve clinical variables independently predicted 10-year HF risk. The model may serve well as the foundation of a practical, online risk score for HF in general practice. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04648852.