In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

In HFpEF patients with low aerobic capacity and non-reduced MIP, IMT was associated with marked improvement in exercise capacity and QoL.

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Congestion explains many of the signs and symptoms of acute heart failure (AHF) and disease progression. However, accurate quantification of congestion is challenging in daily practice. Antigen carbohydrate 125 (CA125) or mucin 16 (MUC16), a large glycoprotein synthesized by mesothelial cells, has emerged as a reliable proxy of congestion and inflammation in patients with heart failure (HF). In AHF syndromes, CA125 is strongly associated with right‐sided HF parameters and a higher risk of adverse clinical events beyond standard prognostic factors, including natriuretic peptides. Furthermore, CA125 has the potential for both monitoring and guide HF treatment following a decompensated HF event. The wide availability of CA125 in most clinical laboratories, together with its standardized measurement and reduced cost, makes this marker attractive for routine use in decompensated HF. Further research is required to understand better its biological role and its promising utility as a tool to guide decongestive therapy in HF.