Multi-parametric analyses to investigate dependencies of normal left atrial strain by cardiovascular magnetic resonance feature tracking

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

A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function

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

Risk Analysis of Artificial Intelligence in Medicine with a Multilayer Concept of System Order

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

Impact of left atrial appendage fibrosis on atrial fibrillation in patients following coronary bypass surgery

Eckstein J, Renner A, Zittermann A, et al. Impact of left atrial appendage fibrosis on atrial fibrillation in patients following coronary bypass surgery. Clinical Cardiology . 2022.OBJECTIVES: We aimed to assess the relationship of left atrial appendage (LAA) fibrosis with atrial fibrillation (AF) and postoperative events in patients receiving coronary artery bypass graft surgery (CABG).; BACKGROUND: Increased atrial fibrosis has been associated with AF and worse outcome following catheter ablation. Only limited data exists focusing on the impact of LAA fibrosis on AF after CABG.; METHODS: LAA tissue from 164 CABG-patients was stained with Masson-Goldner trichrome. The histological landscape was scanned and segmented into superpixels for software analysis (QuPath). A classification algorithm was extensively trained to detect fibrotic superpixels for quantification. In 43 propensity score matched pairs with AF or sinus rhythm (SR), LAA fibrosis was compared. Moreover, subgroups of mitral valve regurgitation (MR) were analyzed as follows: SR, SR+MR, AF and AF+MR. The predictive value of LAA fibrosis postoperative stroke, postoperative AF and mortality was assessed.; RESULTS: Fibrotic remodeling (%) showed no significant difference for the total cohort between the SR and AF group (SR: 30.8±11.4% and AF: 33.8±16.0%, respectively, p=.32). However, significant fibrotic remodeling was observed for SR and AF subgroups (SR: 27.2±12.2% vs. AF: 35.3±13.7%; respectively, p=.049) and between SR and SR+MR subgroups (SR: 27.2±12.2% vs. SR+MR: 34.9±9.1%, respectively, p=.027). LAA fibrosis was not significantly associated with postoperative stroke, postoperative AF or overall mortality (all p>.05).; CONCLUSION: LAA fibrosis may contribute to an individual arrhythmia substrate for AF in patients with AF but also in those with SR and coincidence of MR. LAA fibrosis was not found to be predictive for clinical events in patients after CABG. © 2022 The Authors. Clinical Cardiology published by Wiley Periodicals LLC

Cardiovascular Magnetic Resonance Imaging-Based Right Atrial Strain Analysis of Cardiac Amyloidosis

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

Left-ventricular reference myocardial strain assessed by cardiovascular magnetic resonance feature tracking and fSENC

$\bf Aims:$ 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. $\textbf {Methods and Results:}$ 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 $\pm$ 1.8%, −19.2 $\pm$ 2.1% and 34.2 $\pm$ 6.1%. fSENC-GLS was higher at −20.3 $\pm$ 1.8% ($\it p$ < 0.001). fSENC-GCS was comparable at−19.7 $\pm$ 1.8% ($\it p$ = 0.06). All values were lower in men ($\it p$ < 0.001). Cardiac muscle mass correlated ($\it p$ < 0.001) with FT-GLS ($\it r$ = 0.433), FT-GCS ($\it r$ = 0.483) as well as FT-GRS ($\it r$ = −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 ($\it r$ = −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 ($\it p$ < 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). $\bf Conclusion:$ The analysis of reference values highlights the influence of gender, temporal resolution, cardiac muscle mass and age on myocardial strain values

Multi-parametric analyses to investigate dependencies of normal left atrial strain by cardiovascular magnetic resonance feature tracking

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 $\pm$ 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 $\leq$ 0.001) in women (r: 49.7 $\pm$ 12.9%, c: 32.0 $\pm$ 11.0%) compared to men (r: 42.9 $\pm$ 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 $\pm$ 12.7%; c: 29.0 IQ 15.5%; b: 17.6 $\pm$ 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 (|$R^{2}$|$\geq$ 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 (|$R^{2}$|= 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

A machine learning challenge

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 ($\pm$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

Magnetic-Resonance-Imaging-Based Left Atrial Strain and Left Atrial Strain Rate as Diagnostic Parameters in Cardiac Amyloidosis

Aims: The present study aims to evaluate magnetic-resonance-imaging (MRI)-assessed left atrial strain (LAS) and left atrial strain rate (LASR) as potential parameters for the diagnosis of cardiac amyloidosis (CA), the distinction of clinical subtypes and differentiation from other cardiomyopathies. Methods and results: LAS and LASR were assessed by MRI feature tracking in patients with biopsy-proven CA. LAS and LASR of patients with CA were compared to healthy subjects and patients with hypertrophic cardiomyopathy. LAS and LASR were also analyzed concerning differences between patients with transthyretin (ATTR) and light chain amyloidosis (AL). A total of 44 patients with biopsy-proven CA, 19 patients with hypertrophic cardiomyopathy and 24 healthy subjects were included. In 22 CA patients (50%), histological examination identified ATTR as CA subtype and AL in the remaining patients. No significant difference was observed for reservoir, conduit or booster LAS in patients with AL or ATTR. Reservoir LAS, conduit LAS and booster LAS were significantly reduced in patients with CA and HCM as compared to healthy subjects (p &lt; 0.001). Reservoir LAS and booster LAS were significantly reduced in CA as compared to HCM patients (p &lt; 0.001). A linear correlation was observed between LA global reservoir strain and LA-EF (p &lt; 0.001, r = 0.5), conduit strain and global longitudinal LV strain (p &lt; 0.001, r = 0.5), global booster strain rate and LA-EF (p &lt; 0.001, r = 0.6) and between global booster strain rate and LA area at LVED (p &lt; 0.0001, 0.5). Conclusions: LAS and LASR are severely impaired in patients with CA. The MRI-based assessment of LAS and LASR might allow non-invasive diagnosis and categorization of CA and its distinct differentiation from other hypertrophic phenotypes

CMR-based right ventricular strain analysis in cardiac amyloidosis and its potential as a supportive diagnostic feature

$\it Background:$ Right ventricular (RV) strain has provided valuable prognostic information for patients with $\underline {cardiac amyloidosis}$ (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. $\it Aims:$ Evaluation of $\underline {cardiac magnetic resonance}$ (CMR) quantified RV global and regional strain of CA and $\underline {HCM}$ patients along with CA subtypes. $\it Methods:$ 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 $\underline {diagnostic performance}$ and discriminatory power between the different clinical entities. $\it Results:$ Effective differentiation of CA from HCM patients was achieved utilizing global longitudinal (GLS: 16.5 $\pm$ 3.9% vs. −21.3 $\pm$ 6.7%, p = 0.032), radial (GRS: 11.7 $\pm$ 5.3% vs. 16.5 $\pm$ 7.1%, p < 0.001) and circumferential (GCS: -7.6 $\pm$ 4.0% vs. −9.4 $\pm$ 4.4%, p = 0.015) right ventricular strain. Highest strain-based hypertrophic phenotype differentiation was attained using GRS (AUC = 0.86). Binomial regression found right $\underline {ventricular ejection fraction}$ (RV-EF) (p = 0.017) to be a significant predictor of CA-HCM differentiation. CA subtypes had comparable cardiac strains. $\it Conclusion:$ CMR-derived RV global strains and various regional longitudinal strains provide discriminative radiological features for CA-HCM differentiation. However, in terms of feasibility, cine-derived RV-EF quantification may suffice for efficient differential diagnostic support