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Nurse-led disease management programs (DMPs) decrease readmission after acute decompensated heart failure (HF). We sought whether readmissions could be further reduced by lung ultrasound (LUS)-guided decongestion before discharge and during DMP. Of 290 patients hospitalized with acute decompensated HF, 122 at high risk for readmission or mortality were randomized to receive usual care (UC) (n = 64) or UC plus intervention (DMP-Plus) (n = 58), comprising LUS-guided management before discharge and during at-home follow-up. Residual congestion was identified by ≥10 B-lines detected in 8 lung zones. The outcomes included a composite of readmission and/or mortality at 30 and 90 days, and 90-day HF readmission. Residual congestion was detected equally among the patient groups. The 30-day composite outcome occurred in 28% DMP-plus patients and 22% UC patients (odd ratio [OR], 1.36; 95% confidence interval [CI], 0.59–3.1; P =.5) and the 90-day HF readmission outcome occurred in 22% and 31%, respectively (odds ratio, 0.63; 95% CI, 0.28–1.43; P =.3). Residual congestion, identified at predischarge LUS examination in high-risk patients, was associated with early (<14-day) HF readmission (relative risk, 1.19; 95% CI, 1.06–1.32; P =.002) and multiple (≥2) readmissions over 90 days of follow-up (relative risk, 1.09; 95% CI, 1.01–1.16; P =.012), independent of demographics and comorbidities. Readmission in patients with incomplete decongestion before discharge occurs within the first 2 weeks. However, our DMP-plus strategy did not improve the primary outcome. [ABSTRACT FROM AUTHOR]

Background Recent guidelines propose N-terminal pro-B-type natriuretic peptide (NT-proBNP) for recognition of asymptomatic left ventricular (LV) dysfunction (Stage B Heart Failure, SBHF) in type 2 diabetes mellitus (T2DM). Wavelet Transform based signal-processing transforms electrocardiogram (ECG) waveforms into an energy distribution waveform (ew)ECG, providing frequency and energy features that machine learning can use as additional inputs to improve the identifcation of SBHF. Accordingly, we sought whether machine learning model based on ewECG features was superior to NT-proBNP, as well as a conventional screening tool—the Atherosclerosis Risk in Communities (ARIC) HF risk score, in SBHF screening among patients with T2DM. Methods Participants in two clinical trials of SBHF (defned as diastolic dysfunction [DD], reduced global longitudinal strain [GLS≤18%] or LV hypertrophy [LVH]) in T2DM underwent 12-lead ECG with additional ewECG feature and echocardiography. Supervised machine learning was adopted to identify the optimal combination of ewECG extracted features for SBHF screening in 178 participants in one trial and tested in 97 participants in the other trial. The accuracy of the ewECG model in SBHF screening was compared with NT-proBNP and ARIC HF. Results SBHF was identifed in 128 (72%) participants in the training dataset (median 72 years, 41% female) and 64 (66%) in the validation dataset (median 70 years, 43% female). Fifteen ewECG features showed an area under the curve (AUC) of 0.81 (95% CI 0.787–0.794) in identifying SBHF, signifcantly better than both NT-proBNP (AUC 0.56, 95% CI 0.44–0.68, p<0.001) and ARIC HF (AUC 0.67, 95%CI 0.56–0.79, p=0.002). ewECG features were also led to robust models screening for DD (AUC 0.74, 95% CI 0.73–0.74), reduced GLS (AUC 0.76, 95% CI 0.73–0.74) and LVH (AUC 0.90, 95% CI 0.88–0.89). Conclusions Machine learning based modelling using additional ewECG extracted features are superior to NTproBNP and ARIC HF in SBHF screening among patients with T2DM, providing an alternative HF screening strategy for asymptomatic patients and potentially act as a guidance tool to determine those who required echocardiogram to confrm diagnosis. [ABSTRACT FROM AUTHOR]

Cardiovascular magnetic resonance (CMR) imaging has become an essential technique for the assessment of cardiac function and morphology, and is now routinely used to monitor disease progression and intervention efficacy in the clinic. Cardiac fibrosis is a common characteristic of numerous cardiovascular diseases and often precedes cardiac dysfunction and heart failure. Hence, the detection of cardiac fibrosis is important for both early diagnosis and the provision of guidance for interventions/therapies. Experimental mouse models with genetically and/or surgically induced disease have been widely used to understand mechanisms underlying cardiac fibrosis and to assess new treatment strategies. Improving the appropriate applications of CMR to mouse studies of cardiac fibrosis has the potential to generate new knowledge, and more accurately examine the safety and efficacy of antifibrotic therapies. In this review, we provide 1) a brief overview of different types of cardiac fibrosis, 2) general background on magnetic resonance imaging (MRI), 3) a summary of different CMR techniques used in mice for the assessment of cardiac fibrosis including experimental and technical considerations (contrast agents and pulse sequences), and 4) provide an overview of mouse studies that have serially monitored cardiac fibrosis during disease progression and/or therapeutic interventions. Clinically established CMR protocols have advanced mouse CMR for the detection of cardiac fibrosis, and there is hope that discovery studies in mice will identify new antifibrotic therapies for patients, highlighting the value of both reverse translation and bench-to-bedside research. [ABSTRACT FROM AUTHOR]

Heart failure is a highly heterogeneous disease, and genetic testing may allow phenotypic distinctions that are incremental to those obtainable from imaging. Advances in genetic testing have allowed for the identification of deleterious variants in patients with specific heart failure phenotypes (dilated cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy, and hypertrophic cardiomyopathy), and many of these have specific treatment implications. The diagnostic yield of genetic testing in heart failure is modest, and many rare variants are associated with incomplete penetrance and variable expressivity. Environmental factors and co-morbidities have a large role in the heterogeneity of the heart failure phenotype. Future endeavours should concentrate on the cumulative impact of genetic polymorphisms in the development of heart failure. [ABSTRACT FROM AUTHOR]