Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation.

Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.

Cardiovascular outcomes trial data from EMPA-REG OUTCOME, CAROLINA and CARMELINA: Assessment of a novel staging system for type 2 diabetes.

AIMS: To apply the diabetes staging system (DSS), a novel disease staging system similar to what is used in oncology but designed to improve diabetes management, to three large type 2 diabetes (T2D) cardiovascular (CV) outcome trials to assess whether increasing DSS stage was associated with higher rates of all-cause mortality (ACM) and/or CV death. MATERIALS AND METHODS: The DSS uses discrete CV events (none to ≥3: Stage 1 to 4), end-stage kidney disease (Stage 5) and microvascular complications (none to 3: A to D) to determine disease stage in individuals with T2D. The DSS stage for patients from the CAROLINA, EMPA-REG OUTCOME and CARMELINA trials was determined. Incidence rates for ACM/CV death were calculated across DSS stages and Cox regression analyses were performed. RESULTS: The risk of ACM or CV death increased with increasing DSS (Stage 1 to 5; P for trend <0.0001) in all trials. In CAROLINA, the risk of ACM and CV death increased with increasing number of microvascular complications (A to D; both P for trend <0.0001), similar in CARMELINA (P for trend = 0.0020 and 0.0005, respectively). In EMPA-REG OUTCOME, having all three microvascular complications (Stage D), versus none, increased the risk of ACM and CV death (P = 0.0015 and 0.0010, respectively). CONCLUSIONS: Applying the DSS across T2D clinical trial populations with different CV risk revealed a significantly increased risk of ACM and CV death with higher DSS stage. The DSS may merit assessment in other T2D populations and evaluation of the impact of additional outcomes, such as heart failure, could also be worthwhile.

The PDE4 inhibitor roflumilast reduces weight gain by increasing energy expenditure and leads to improved glucose metabolism.

AIMS: To investigate the metabolic effects of the phosphodiesterase-4 (PDE4) inhibitor roflumilast, a clinically approved anti-inflammatory drug used for the treatment of chronic obstructive pulmonary disease. MATERIALS AND METHODS: The metabolic effects of roflumilast were investigated in C57BL/6J mice, fed a high-fat Western-type diet and treated with or without roflumilast for a period of 12 weeks. RESULTS: Roflumilast led to a marked reduction in body weight gain, which became apparent in the second week after treatment initiation and was attributable to a pronounced increase in energy expenditure. Furthermore, roflumilast improved glucose tolerance, reduced insulin resistance and diminished steatohepatitis in mice. Mechanistically, this was associated with hepatic protein kinase A (PKA) and cAMP response element binding protein (CREB) activation, leading to peroxisome proliferator-activated receptor gamma coactivator-1α (PCG-1α)-dependent induction of mitochondrial biogenesis. Consistently, roflumilast increased the cellular respiratory capacity of hepatocytes in a PKA-dependent manner. CONCLUSION: Roflumilast-dependent PDE4 inhibition is a new target for weight loss strategies, especially in conditions of associated comorbidities such as insulin resistance and non-alcoholic steatohepatitis.

Combined computational and experimental approach to improve the assessment of mitral regurgitation by echocardiography.

Mitral regurgitation (MR) is one of the most frequent valvular heart diseases. To assess MR severity, color Doppler imaging (CDI) is the clinical standard. However, inadequate reliability, poor reproducibility and heavy user-dependence are known limitations. A novel approach combining computational and experimental methods is currently under development aiming to improve the quantification. A flow chamber for a circulatory flow loop was developed. Three different orifices were used to mimic variations of MR. The flow field was recorded simultaneously by a 2D Doppler ultrasound transducer and Particle Image Velocimetry (PIV). Computational Fluid Dynamics (CFD) simulations were conducted using the same geometry and boundary conditions. The resulting computed velocity field was used to simulate synthetic Doppler signals. Comparison between PIV and CFD shows a high level of agreement. The simulated CDI exhibits the same characteristics as the recorded color Doppler images. The feasibility of the proposed combination of experimental and computational methods for the investigation of MR is shown and the numerical methods are successfully validated against the experiments. Furthermore, it is discussed how the approach can be used in the long run as a platform to improve the assessment of MR quantification.

Depression, psychological distress, and quality of life in patients with cardioverter defibrillator with or without cardiac resynchronization therapy.

Congestive heart failure is frequent and leads to reduced exercise capacity, reduced quality of life (QoL), and depression in many patients. Cardiac resynchronization therapy (CRT) and implantable cardioverter defibrillators (ICD) offer therapeutic options and may have an impact on QoL and depression. This study was performed to evaluate physical and mental health in patients undergoing ICD or combined CRT/ICD-implantation (CRT-D). Echocardiography, spiroergometry, and psychometric questionnaires [Beck Depression Inventory, General World Health Organization Five Well-being Index (WHO-5), Brief Symptom Inventory and 36-item Short Form (SF-36)] were obtained in 39 patients (ICD: 17, CRT-D: 22) at baseline and 6-month follow-up (FU) after device implantation. CRT-D patients had a higher NYHA class and broader left bundle branch block than ICD patients at baseline. At FU, ejection fraction (EF), peak oxygen uptake, and NYHA class improved significantly in CRT-D patients but remained unchanged in ICD patients. Patients with CRT-D implantation showed higher levels of depressive symptoms, psychological distress, and impairment in QoL at baseline and FU compared to ICD patients. These impairments remained mostly unchanged in all patients after 6 months. Overall, these findings imply that there is a need for careful assessment and treatment of psychological distress and depression in ICD and CRT-D patients in the course of device implantation as psychological burden seems to persist irrespective of physical improvement.

Layer-specific analysis of myocardial deformation for assessment of infarct transmurality: comparison of strain-encoded cardiovascular magnetic resonance with 2D speckle tracking echocardiography.

AIMS: Separate analysis of endocardial and epicardial myocardial layer deformation has become possible using strain-encoded cardiovascular magnetic resonance (SENC) and 2D-dimensional speckle tracking echocardiography (Echo). This study evaluated and compared both modalities for the assessment of infarct transmurality as defined by late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR). METHODS AND RESULTS: In 29 patients (age 62.4 ± 11.7 years, 23 male) with ischaemic cardiomyopathy, SENC using 1.5 T CMR and Echo were performed. Peak circumferential systolic strain of the endocardial and the epicardial layer of 304 myocardial segments was assessed by SENC and by Echo. The segmental transmurality of myocardial infarction was determined as relative amount of LGE (0%: no infarction; 1-50%: non-transmural infarction; 51-100%: transmural infarction). Endocardial and epicardial strain defined by SENC and by Echo differed significantly between segments of different infarct transmurality determined by CMR. Endocardial layer circumferential strain analysis by Echo and by SENC allowed distinction of segments with non-transmural infarction from non-infarcted segments with similar accuracy [area under the curve (AUC) 0.699 vs. 0.649, respectively, P = 0.239]. Epicardial layer circumferential strain analysis by Echo and by SENC allowed distinction of transmural from non-transmural myocardial infarction defined by LGE CMR with similar accuracy (AUC 0.721 vs. 0.664, respectively, P = 0.401). Endocardial strain by SENC correlated moderately with endocardial strain by Echo (r = 0.50; standard error of estimate = 5.2%). CONCLUSION: Layer-specific analysis of myocardial deformation by Echo and by SENC allows discrimination between different transmurality categories of myocardial infarction with similar accuracy. However, accuracy of both methods is non-optimal, indicating that further tools for improvement should be evaluated in the future.