Tafamidis improves outcomes in patients with transthyretin amyloid cardiomyopathy (ATTR-CM). However, it is not yet known whether tafamidis affects cardiac amyloid deposition and structural changes in the myocardium. We aimed to determine disease-modifying effects on myocardial amyloid progression and to identify imaging parameters that could be applied for specific therapy monitoring. ATTR-CM patients underwent serial cardiac magnetic resonance (CMR) imaging using T1 mapping techniques to derive extracellular volume (ECV). Patients receiving tafamidis 61 mg (n = 35) or 20 mg (n = 15) once daily showed stable measurements at follow-up (FU) {61 mg: 9.0 [interquartile range (IQR) 7.0–11.0] months, 20 mg: 11.0 (IQR 8.0–18.0) months} in left ventricular (LV) ejection fraction (LVEF; 61 mg: 47.6% vs. 47.5%, P = 0.935; 20 mg: 52.4% vs. 52.1%, P = 0.930), LV mass index (LVMI; 61 mg: 110.2 vs. 106.2 g/m², P = 0.304; 20 mg: 114.5 vs. 115.4 g/m², P = 0.900), and ECV (61 mg: 47.5% vs. 47.7%, P = 0.861; 20 mg: 56.7% vs. 57.5%, P = 0.759), whereas treatment-naïve ATTR-CM patients (n = 19) had clear signs of disease progression at the end of the observation period [12.0 (IQR 10.0–21.0) months; LVEF: 53.3% vs. 45.7%, P = 0.031; LVMI: 98.9 vs. 106.9 g/m², P = 0.027; ECV: 49.3% vs. 54.6%, P = 0.023]. Between-group comparison at FU revealed positive effects in tafamidis 61 mg-treated compared to treatment-naïve patients (LVEF: P = 0.035, LVMI: P = 0.036, ECV: P = 0.030), while those treated with 20 mg showed no difference in the above LV measurements when compared with treatment-naïve (P = 0.120, P = 0.287, P = 0.158). However, both treatment groups showed clinically beneficial effects compared to the natural course [61 mg, 6-min walk distance (6-MWD): P = 0.005, N-terminal prohormone of brain natriuretic peptide (NT-proBNP): P = 0.002; 20 mg, 6-MWD: P = 0.023, NT-proBNP: P = 0.003]. Tafamidis delays myocardial amyloid progression in ATTR-CM patients, resulting in structural, functional, and clinical benefits compared to the natural course. Serial CMR including measurement of ECV may be appropriate for disease-specific therapy monitoring.

In this prospective study of 206 participants with heart failure with preserved ejection fraction, global longitudinal strain by cardiovascular MRI was associated with diffuse fibrosis and clinical... Background In heart failure with preserved ejection fraction (HFpEF), echocardiographic studies suggest that global longitudinal strain (GLS) has an impact on survival. Feature-tracking cardiovascu...

Summary: The development of new devices and treatment options has greatly increased the interest in heart valve diseases. In this context, the consideration of gender differences in diagnosis, treatment success, and prognosis is of great importance. Available data show that women and men with heart valve disease have different risk profiles, which have a significant impact on treatment outcomes and prognosis.It is the purpose of this review article to give an overview of gender-related differences in patients with valvular heart disease, regarding clinical presentation, treatment, and outcomes. In light of the emerging treatment possibilities, future research should emphasize the role of gender since both sexes benefit from tailored management.

Aims: We tested the hypothesis that artificial intelligence (AI)-powered algorithms applied to cardiac magnetic resonance (CMR) images could be able to detect the potential patterns of cardiac amyloidosis (CA). Readers in CMR centers with a low volume of referrals for the detection of myocardial storage diseases or a low volume of CMRs, in general, may overlook CA. In light of the growing prevalence of the disease and emerging therapeutic options, there is an urgent need to avoid misdiagnoses. Methods and Results: Using CMR data from 502 patients (CA: n = 82), we trained convolutional neural networks (CNNs) to automatically diagnose patients with CA. We compared the diagnostic accuracy of different state-of-the-art deep learning techniques on common CMR imaging protocols in detecting imaging patterns associated with CA. As a result of a 10-fold cross-validated evaluation, the best-performing fine-tuned CNN achieved an average ROC AUC score of 0.96, resulting in a diagnostic accuracy of 94% sensitivity and 90% specificity. Conclusions: Applying AI to CMR to diagnose CA may set a remarkable milestone in an attempt to establish a fully computational diagnostic path for the diagnosis of CA, in order to support the complex diagnostic work-up requiring a profound knowledge of experts from different disciplines.

Myocardial tissue characterization by cardiovascular magnetic resonance (CMR) T1 mapping currently receives increasing interest as a diagnostic tool in various disease settings. The T1-mapping technique allows non-invasive estimation of myocardial extracellular volume (ECV) using T1-times before and after gadolinium administration; however, for calculation of the myocardial ECV the hematocrit is needed, which limits its utility in routine application. Recently, the alternative use of the blood pool T1-time instead of the hematocrit has been described.

(1) Background: Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis. While novel therapies improve outcomes, many affected individuals remain undiagnosed due to a lack of awareness among clinicians. This study was undertaken to develop an expert-independent machine learning (ML) prediction model for CA relying on routinely determined laboratory parameters. (2) Methods: In a first step, we developed baseline linear models based on logistic regression. In a second step, we used an ML algorithm based on gradient tree boosting to improve our linear prediction model, and to perform non-linear prediction. Then, we compared the performance of all diagnostic algorithms. All prediction models were developed on a training cohort, consisting of patients with proven CA (positive cases, n = 121) and amyloidosis-unrelated heart failure (HF) patients (negative cases, n = 415). Performances of all prediction models were evaluated on a separate prognostic validation cohort with 37 CA-positive and 124 CA-negative patients. (3) Results: Our best model, based on gradient-boosted ensembles of decision trees, achieved an area under the receiver operating characteristic curve (ROC AUC) score of 0.86, with sensitivity and specificity of 89.2% and 78.2%, respectively. The best linear model had an ROC AUC score of 0.75, with sensitivity and specificity of 84.6 and 71.7, respectively. (4) Conclusions: Our work demonstrates that ML makes it possible to utilize basic laboratory parameters to generate a distinct CA-related HF profile compared with CA-unrelated HF patients. This proof-of-concept study opens a potential new avenue in the diagnostic workup of CA and may assist physicians in clinical reasoning.