Heart failure (HF) is a common clinical syndrome resulting from structural and/or functional cardiac abnormality. It affects 2.2% of American adults and over 12% of Americans ≥80 years of age, and its total cost in the US was estimated at $30.7 billion in 2012. As prevalence and overall cost are expected to considerably rise in the coming years, HF continues to constitute a major burden to global health.

HF is characterized with a progressive course of disease with high hospital readmission rates, which account for a major portion of the economic burden. Improving the management of congestion has been a crucial component in the efforts to reduce hospitalizations. Congestion is a cardinal manifestation of HF, presenting with dyspnea, orthopnea, and edema due to volume overload. Diuretics remain a cornerstone in the management of congestion; however, there are no clear guidelines on how to adjust their administration. Guidelines recommend monitoring of daily weight changes, but efficacy is debatable. Thus, there is a pressing need to find reliable markers to promptly recognize deteriorations and help tailor diuretic treatment to prevent them. To date, European Society of Cardiology (ESC) guidelines recommend two invasive monitoring approaches to guide management, one based on invasive wireless pulmonary artery pressure monitoring and the other on utilizing data from Implantable Cardioverter Defibrillators (ICDs). Hemodynamic monitoring using invasive right heart catheterization has not shown benefit in management of HF decompensations, and nowadays is reserved only for specific clinical scenarios. In the field of non-invasive monitoring, research efforts have focused on analyzing lung impedance, ECG , and heart sounds with some promising results; a recent study utilized machine learning to generate a personalized alert system, but with a limited number of parameters monitored. Nevertheless, their algorithm's success in predicting hospitalizations reflects the importance of recognizing the heterogeneity of HF and the advantages of an individualized approach.

In this study, we will examine the use of a non-invasive, user-friendly device (BB-613WP, Biobeat Technologies LTD, Petah Tikva, Israel), in advanced HF patients receiving IV diuresis in a hospital outpatient clinic. The device can derive measurements of several parameters, including cardiac output (CO), cardiac index (CI), blood pressure (BP) and systemic vascular resistance (SVR) using photoplethysmography (PPG) technology, and has been tested in several clinical trials. We wish to assess if CO, CI and SVR can be utilized as markers for HF clinical course, so they can ideally be used to intervene and modify treatment prior to a deterioration.

The literature describing the effects of diuresis on CO and SVR is rather limited and archaic. Most studies found a reduction in CO and a rise in SVR after diuresis, but some describe the opposite, and some describe a response changing over time. This can be explained when considering that the pathophysiology underlying the effects of volume status on cardiac performance in HF is complex. The classically described Frank-Starling principle states that contractility peaks at a muscle length that allows for optimal overlap of actin and myosin filaments and maximal calcium ions sensitivity. Up to a certain point, the longer the muscle is (represented as larger EDV, end diastolic volume), the higher the force of contraction. But beyond that length, the contractile force decreases. Since different patients are characterized with different levels of cardiac filling, volume reduction induced by diuresis may have a different impact on CO, depending on the position on the Frank Starling curve. For instance, impaired ventricular filling in HF with preserved ejection fraction can reduce systolic function, due to less effective contraction in low EDV. Similarly, SVR, which is inversely proportional to CO, is likely to be affected differently. Our primary objective is to assess whether the changes in CO, CI and SVR during IV diuretic administration correlate with short-term clinical change measured using symptoms questionnaires, change in weight and urine output. Our secondary objective is to assess the correlation between baseline values of those parameters and long-term clinical outcomes, measured by HF hospitalizations and the change in disease perception and quality of life reported in questionnaires.