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BackgroundCardiac magnetic resonance (CMR) imaging allows for multiparametric assessment of healthy pulmonary artery (PA) hemodynamics. Gender- and aging-associated PA stiffness and pressure alterations have remained clinically unestablished, however may demonstrate epidemiological differences in disease development. The aim of this study is to evaluate the role of CMR as a surrogate for catheter examinations by providing a comprehensive CMR assessment of sex- and age-related reference values for PA stiffness, flow, and pressure.Methods and ResultsPA hemodynamics were studied between gender and age groups (>/50 years) exhibited reduced PA elasticity (41.7% [31.0; 52.9] vs. 66.4% [47.7; 83.0]; P

Artificial intelligence (AI) is advancing across technology domains including healthcare, commerce, the economy, the environment, cybersecurity, transportation, etc. AI will transform healthcare systems, bringing profound changes to diagnosis, treatment, patient care, data, medicines, devices, etc. However, AI in healthcare introduces entirely new categories of risk for assessment, management, and communication. For this topic, the framing of conventional risk and decision analyses is ongoing. This paper introduces a method to quantify risk as the disruption of the order of AI initiatives in healthcare systems, aiming to find the scenarios that are most and least disruptive to system order. This novel approach addresses scenarios that bring about a re-ordering of initiatives in each of the following three characteristic layers: purpose, structure, and function. In each layer, the following model elements are identified: 1. Typical research and development initiatives in healthcare. 2. The ordering criteria of the initiatives. 3. Emergent conditions and scenarios that could influence the ordering of the AI initiatives. This approach is a manifold accounting of the scenarios that could contribute to the risk associated with AI in healthcare. Recognizing the context-specific nature of risks and highlighting the role of human in the loop, this study identifies scenario s.06—non-interpretable AI and lack of human–AI communications—as the most disruptive across all three layers of healthcare systems. This finding suggests that AI transparency solutions primarily target domain experts, a reasonable inclination given the significance of “high-stakes” AI systems, particularly in healthcare. Future work should connect this approach with decision analysis and quantifying the value of information. Future work will explore the disruptions of system order in additional layers of the healthcare system, including the environment, boundary, interconnections, workforce, facilities, supply chains, and others.

Abstract Left-atrial (LA) strain is the result of complex hemodynamics, which may be better characterized using a multiparametric approach. Cardiovascular magnetic resonance (CMR) feature tracking was used to perform a comprehensive LA strain assessment of 183 enrolled healthy volunteers (11–70 years, 97 females, median 32.9 ± 28.3 years). Novel strain dependencies were assessed using multi-parametric regression (MPR) analyses. LA volumetric data, left ventricular strain, transmitral and pulmonary venous blood flow parameters were utilized to create clusters for MPR of all subjects and a heart rate controlled subgroup (pulse: 60–75/min, N = 106). The LA reservoir(r) and conduit(c) strains of the total cohort were significantly elevated (p ≤ 0.001) in women (r: 49.7 ± 12.9%, c: 32.0 ± 11.0%) compared to men (r: 42.9 ± 11.4%, c: 26.1 IQ 10.5%). In contrast, there were no gender-specific differences (p > 0.05) for subgroup LA reservoir, conduit and booster(b) strains (all, r: 47.3 ± 12.7%; c: 29.0 IQ 15.5%; b: 17.6 ± 5.4%) and strain rates (all, 2.1 IQ 1.0 s−1; − 2.9 IQ 1.5 s−1; − 2.3 IQ 1.0 s−1). MPR found large effect sizes (|R2|≥ 0.26) for correlations between strain and various cardiac functional parameters. Largest effect size was found for the association between LA conduit strain and LA indexed booster volume, LA total ejection fraction, left ventricular global radial strain and E-wave (|R2|= 0.437). In addition to providing normal values for sex-dependent LA strain and strain rate, no gender differences were found with modified heart rate. MPR analyses of LA strain/strain rate and various cardiac functional parameters revealed that heart rate control improved goodness-of-fit for the overall model.

Background: Right ventricular (RV) strain has provided valuable prognostic information for patients with cardiac amyloidosis (CA). However, the extent to which RV strain and strain rate can differentiate CA is not yet clinically established. CA underdiagnosis delays treatment strategies and exacerbates patient prognosis. Aims: Evaluation of cardiac magnetic resonance (CMR) quantified RV global and regional strain of CA and HCM patients along with CA subtypes. Methods: CMR feature tracking attained longitudinal, radial and circumferential global and regional strain in 47 control subjects (CTRL), 43 CA-, 20 hypertrophic cardiomyopathy- (HCM) patients. CA patients were subdivided in 21 transthyretin-related amyloidosis (ATTR) and 20 acquired immunoglobulin light chain (AL) patients. Strain data and baseline clinical parameters were statistically analysed with respect to diagnostic performance and discriminatory power between the different clinical entities. Results: Effective differentiation of CA from HCM patients was achieved utilizing global longitudinal (GLS: 16.5 ± 3.9% vs. −21.3 ± 6.7%, p = 0.032), radial (GRS: 11.7 ± 5.3% vs. 16.5 ± 7.1%, p

Background: Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis. Objective: Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS. Method: Forty-five CMR-negative (CMR(−), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients and forty-four controls (CTRL, 56.5(53.0;63.0)years)) underwent CMR examination. Cardiac parameters were processed using the classifiers of logistic regression, KNN(K-nearest-neighbor), DT (decision tree), RF (random forest), SVM, GBoost, XGBoost, Voting and feature selection. Results: In a three-cluster analysis of CTRL versus vs. CMR(+) vs. CMR(−), RF and Voting classifier yielded the highest prediction rates (81.82%). The two-cluster analysis of CTRL vs. all sarcoidosis (All Sarc.) yielded high prediction rates with the classifiers logistic regression, RF and SVM (96.97%), and low prediction rates for the analysis of CMR(+) vs. CMR(−), which were augmented using feature selection with logistic regression (89.47%). Conclusion: Multi-chamber cardiac function and strain-based supervised machine learning provides a non-contrast approach to accurately differentiate between healthy individuals and sarcoidosis patients. Feature selection overcomes the algorithmically challenging discrimination between CMR(+) and CMR(−) patients, yielding high accuracy predictions. The study findings imply higher prevalence of cardiac involvement than previously anticipated, which may impact clinical disease management.

Myofibrillar myopathies (MFM) are a group of chronic muscle diseases pathophysiologically characterized by accumulation of protein aggregates and structural failure of muscle fibers. A subtype of MFM is caused by heterozygous mutations in the filamin C (FLNC) gene, exhibiting progressive muscle weakness, muscle structural alterations and intracellular protein accumulations. Here, we characterize in depth the pathogenicity of two novel truncating FLNc variants (p.Q1662X and p.Y2704X) and assess their distinct effect on FLNc stability and distribution as well as their impact on protein quality system (PQS) pathways. Both variants cause a slowly progressive myopathy with disease onset in adulthood, chronic myopathic alterations in muscle biopsy including the presence of intracellular protein aggregates. Our analyses revealed that p.Q1662X results in FLNc haploinsufficiency and p.Y2704X in a dominant-negative FLNc accumulation. Moreover, both protein-truncating variants cause different PQS alterations: p.Q1662X leads to an increase in expression of several genes involved in the ubiquitin-proteasome system (UPS) and the chaperone-assisted selective autophagy (CASA) system, whereas p.Y2704X results in increased abundance of proteins involved in UPS activation and autophagic buildup. We conclude that truncating FLNC variants might have different pathogenetic consequences and impair PQS function by diverse mechanisms and to varying extents. Further studies on a larger number of patients are necessary to confirm our observations.

Aims: Cardiac strain parameters are increasingly measured to overcome shortcomings of ejection fraction. For broad clinical use, this study provides reference values for the two strain assessment methods feature tracking (FT) and fast strain-encoded (fSENC) cardiovascular magnetic resonance (CMR) imaging, including the child/adolescent group and systematically evaluates the influence of temporal resolution and muscle mass on strain.Methods and Results: Global longitudinal (GLS), circumferential (GCS), and radial (GRS) strain values in 181 participants (54% women, 11–70 years) without cardiac illness were assessed with FT (CVI42® software). GLS and GCS were also analyzed using fSENC (MyoStrain® software) in a subgroup of 84 participants (60% women). Fourteen patients suffering hypertrophic cardiomyopathy (HCM) were examined with both techniques. CMR examinations were done on a 3.0T MR-system.FT-GLS, FT-GCS, and FT-GRS were −16.9 ± 1.8%, −19.2 ± 2.1% and 34.2 ± 6.1%. fSENC-GLS was higher at −20.3 ± 1.8% (p < 0.001). fSENC-GCS was comparable at−19.7 ± 1.8% (p = 0.06). All values were lower in men (p < 0.001). Cardiac muscle mass correlated (p < 0.001) with FT-GLS (r = 0.433), FT-GCS (r = 0.483) as well as FT-GRS (r = −0.464) and acts as partial mediator for sex differences. FT-GCS, FT-GRS and fSENC-GLS correlated weakly with age. FT strain values were significantly lower at lower cine temporal resolutions, represented by heart rates (r = −0.301, −0.379, 0.385) and 28 or 45 cardiac phases per cardiac cycle (0.3–1.9% differences). All values were lower in HCM patients than in matched controls (p < 0.01). Cut-off values were −15.0% (FT-GLS), −19.3% (FT-GCS), 32.7% (FT-GRS), −17.2% (fSENC-GLS), and −17.7% (fSENC-GCS).Conclusion: The analysis of reference values highlights the influence of gender, temporal resolution, cardiac muscle mass and age on myocardial strain values.

Background: Cardiac amyloidosis (CA) manifests in a hypertrophic phenotype with a poor prognosis, making differentiation from hypertrophic cardiomyopathy (HCM) challenging and delaying early treatment. The extent to which magnetic resonance imaging (MRI) quantifies the right atrial strain (RAS) and strain rate (RASR), providing valuable diagnostic information, is not yet clinically established. Aims: This study assesses diagnostic differences in the longitudinal RAS and RASR between CA and HCM patients, control subjects (CTRL) and CA subtypes in addition to the impact of atrial fibrillation (AF) on the right atrial function in CA patients. The RAS and RASR of tricuspid regurgitation (TR) patients are used to assess the potential for diagnostic overlap. Methods: RAS and RASR quantification was conducted via MRI feature-tracking for biopsy-confirmed CA patients with subtypes identified. Strain parameters were compared for CTRL, HCM and TR patients. Post hoc testing identified intergroup differences. Results: In total, 41 CA patients were compared to 47 CTRL, 20 HCM and 31 TR patients. Reservoir (R), conduit and booster RAS and RASRs allow for significant differentiation (p < 0.001) between CA and HCM patients (R: 10.6 ± 14.3% vs. R: 33.5 ± 16.3%) and CTRL (R: 44.6 ± 15.7%). Booster and reservoir RAS and RASRs qualified as reliable diagnostic tests (AUC > 0.8). CA patients with AF, in contrast to sinus rhythm, demonstrated a significantly impaired reservoir RAS and RASR and booster RASR. The discriminative power of RAS for CA vs. TR was insufficient (R: 10.6% ± 14.3% vs. 7.0% ± 6.0%, p = 0.069). Differentiation between 21 transthyretin and 20 light-chain amyloidosis subtypes was not achievable (R: 0.7% ± 1.0% vs. 0.7% ± 1.0%, p = 0.827). Conclusion: The MRI-derived RAS and RASR are impaired in CA patients and may support noninvasive differentiation between CA, HCM and CTRL.

Background: This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Methods: Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years (±7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Results: Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. Conclusion: SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics.