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.

No abstract

SummaryBackgroundThe population of patients with established coronary artery disease (CAD) is growing because of an improvement in outcomes and survival from acute disease episodes. Nevertheless, these patients remain at high risk of cardiovascular events. Thus, CAD management is important in prevention of disease progression. The objective of this analysis was to describe disease management and clinical outcome of Austrian outpatients with stable CAD over 5 years by using data from the international CLARIFY registry.MethodsCLARIFY was an international prospective observational registry of outpatients with stable CAD, defined as prior myocardial infarction or revascularization (CABG or PCI), coronary stenosis of more than 50% by coronary angiography or chest pain with myocardial ischemia. We analyzed demographic characteristics, risk factors, treatments and clinical outcomes of 424 Austrian outpatients with established CAD who were enrolled between November 2009 and July 2010 and observed until September 2015.ResultsThe primary risk factors in Austrian outpatients with stable CAD were smoking (current smokers: 13.2%), overweight (77.1%), hypertension (78.5%), raised low-density lipoprotein (LDL) cholesterol plasma levels (81.4% ≥ 0.7 g/l or 1.8 mmol/l), elevated heart rate (≥70 bpm: 60.9% in patients with anginal symptoms) and poor physical activity (none or light activity: 63.4%). Patients received lipid-lowering drugs (predominantly statins), aspirin, beta-blockers and angiotensin-converting enzyme (ACE) inhibitors according to current recommendations. After 5 years a systolic blood pressure (SBP) < 140 mm Hg and diastolic blood pressure (DBP) < 90 mm Hg was reached in 58.5% of patients. Of the patients 70.4% had LDL cholesterol plasma levels below 1.0 g/l (2.6 mmol/l), 42.1% of smokers had stopped smoking, 42.9% of patients with anginal symptoms had a heart rate ≤60 bpm and 26.0% of diabetic patients had brought their HbA1c levels below 6.5%. Cardiovascular death, myocardial infarction or stroke occurred in 30 patients (7.1%), all-cause death in 25 cases (5.9%) and cardiovascular death in 15 cases (3.5%). Myocardial infarction was reported in 14 patients (fatal and non-fatal: 3.3%) and stroke in 8 patients (fatal and non-fatal: 1.9%), 39 patients (9.2%) underwent myocardial revascularization and 124 patients (29.2%) experienced cardiovascular hospitalization.ConclusionCharacteristics of Austrian outpatients with stable CAD corresponded to those of patients with CAD in other developed countries. Medical treatments following the recommendations of the European guidelines were prescribed in the majority of patients; however, recommended goals of life style interventions including a heart rate less than 60 bpm and general risk factor management were not achieved by a high proportion of patients. Heart rate control and life style changes remain unmet needs of cardiovascular care in Austria.

No abstract

BackgroundDiagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited.ObjectiveThe aim was to perform a thorough electrophysiological characterisation of patients with CA and derive an easy-to-use tool for diagnosis.MethodsWe applied electrocardiographic imaging (ECGI) to acquire electroanatomical maps in patients with CA and controls. A machine learning approach was then used to decipher the complex data sets obtained and generate a surface ECG-based diagnostic tool.FindingsAreas of low voltage were localised in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualised on the right ventricle. Potential maps revealed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1–V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in the inferior leads II, III and aVF. Respective surface ECG leads showed two characteristic patterns. Ten blinded cardiologists were asked to identify patients with CA by analysing 12-lead ECGs before and after training on the defined ECG patterns. Training led to significant improvements in the detection rate of CA, with an area under the curve of 0.69 before and 0.97 after training.InterpretationUsing a machine learning approach, an ECG-based tool was developed from detailed electroanatomical mapping of patients with CA. The ECG algorithm is simple and has proven helpful to suspect CA without the aid of advanced imaging modalities.