Aims The presence of pulmonary hypertension (PH) severely aggravates the clinical course of heart failure with preserved ejection fraction (HFpEF). To date, neither established heart failure therapies nor pulmonary vasodilators proved beneficial. This study investigated the efficacy of chronic treatment with the oral soluble guanylate cyclase stimulator riociguat in patients with PH-HFpEF. Methods and Results The phase IIb, randomized, double-blind, placebo-controlled, parallel-group, multicentre DYNAMIC trial assessed riociguat in PH-HFpEF. Patients were recruited at five hospitals across Austria and Germany. Key eligibility criteria were mean pulmonary artery pressure ≥25 mmHg, pulmonary arterial wedge pressure >15 mmHg, and left ventricular ejection fraction ≥50%. Patients were randomized to oral treatment with riociguat or placebo (1:1). Patients started at 0.5 mg three times daily (TID) and were up-titrated to 1.5 mg TID. The primary efficacy endpoint was change from baseline to week 26 in cardiac output (CO) at rest, measured by right heart catheterization. Primary efficacy analyses were performed on the full analysis set. Fifty-eight patients received riociguat and 56 patients placebo. After 26 weeks, CO increased by 0.37 ± 1.263 L/min in the riociguat group and decreased by −0.11 ± 0.921 L/min in the placebo group (least-squares mean difference: 0.54 L/min, 95% confidence interval 0.112, 0.971; P = 0.0142). Five patients dropped out due to riociguat-related adverse events but no riociguat-related serious adverse event or death occurred. Conclusion The vasodilator riociguat improved haemodynamics in PH-HFpEF. Riociguat was safe in most patients but led to more dropouts as compared to placebo and did not change clinical symptoms within the study period.

(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.

SummaryHundreds of millions got infected, and millions have died worldwide and still the number of cases is rising.Chest radiographs and computed tomography (CT) are useful for imaging the lung but their use in infectious diseases is limited due to hygiene and availability.Lung ultrasound has been shown to be useful in the context of the pandemic, providing clinicians with valuable insights and helping identify complications such as pleural effusion in heart failure or bacterial superinfections. Moreover, lung ultrasound is useful for identifying possible complications of procedures, in particular, pneumothorax.Associations between coronavirus disease 2019 (COVID-19) and cardiac complications, such as acute myocardial infarction and myocarditis, have been reported. As such, point of care echocardiography as well as a comprehensive approach in later stages of the disease provide important information for optimally diagnosing and treating complications of COVID-19.In our experience, lung ultrasound in combination with echocardiography, has a great impact on treatment decisions. In the acute state as well as in the follow-up setting after a severe or critical state of COVID-19, ultrasound can be of great impact to monitor the progression and regression of disease.

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.

This study sought to characterize cardiac amyloidosis (CA) patients with respect to hemodynamic parameters and asses their prognostic impact in different CA cohorts. Intracardiac and pulmonary arterial pressures (PAPs) are among the strongest predictors of outcomes in patients with heart failure (HF). Despite that, the hemodynamic profiles of patients with CA and their relation to prognosis have rarely been investigated. Invasive hemodynamic, clinical, and laboratory assessment, as well as cardiac magnetic resonance imaging were performed in our CA cohort. A total of 61 patients, 35 (57.4%) with wild-type transthyretin amyloidosis (ATTRwt) and 26 (42.6%) with light-chain amyloidosis (AL) were enrolled. ATTRwt patients had lower N-terminal prohormone of brain natriuretic peptide values and were less frequently in New York Heart Association class ≥ III. Intracardiac and PAPs were elevated, but hemodynamic parameters did not differ between CA groups. Whereas in ATTRwt, the median mean PAP (hazard ratio (HR): 1.130, p = 0.040) and pulmonary vascular resistance (HR: 1.010, p = 0.046) were independent predictors of outcome, no hemodynamic parameter was associated with outcome in the AL group. Cardiac ATTRwt and AL patients feature elevated intracardiac and PAPs and show similar hemodynamic profiles. However, hemodynamic parameters are of greater prognostic relevance in ATTRwt, potentially providing a new therapeutic target.