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Abstract Despite significant advances in drug‐based and device‐based therapies, heart failure remains a major and growing public health problem associated with substantial disability, frequent hospitalizations, and high economic costs. Keeping patients well and out of the hospital has become a major focus of heart failure disease management. Achieving and maintaining such stability in heart failure patients requires a holistic approach, which includes at least the management of the underlying heart disease, the management of comorbidities and the social and psychological aspects of the disease, and the management of haemodynamic/fluid status. In this regard, accurate assessment of elevated ventricular filling pressures or volume overload, that is, haemodynamic or pulmonary congestion, respectively, before the onset of worsening heart failure symptoms represents an important management strategy. Unfortunately, conventional methods for assessing congestion, such as physical examination and monitoring of symptoms and daily weights, are insensitive markers of worsening heart failure. Assessment tools that directly measure congestion, accurately and in absolute terms, provide more actionable information that enables the application of treatment algorithms designed to restore patient stability, in a variety of clinical settings. Two such assessment tools, implantable haemodynamic monitors and remote dielectric sensing (ReDS), meet the prerequisites for useful heart failure management tools, by providing accurate, absolute, and actionable measures of congestion, to guide patient management. This review focuses on the use of such technologies, across the spectrum of heart failure treatment settings. Clinical data are presented that support the broad use of pulmonary artery pressure‐guided and/or ReDS‐guided heart failure management in heart failure patients with reduced and preserved left ventricular ejection fraction.

Abstract Aims This study aimed to assess the ability of a voice analysis application to discriminate between wet and dry states in chronic heart failure (CHF) patients undergoing regular scheduled haemodialysis treatment due to volume overload as a result of their chronic renal failure. Methods and results In this single‐centre, observational study, five patients with CHF, peripheral oedema of ≥2, and pulmonary congestion‐related dyspnoea, undergoing haemodialysis three times per week, recorded five sentences into a standard smartphone/tablet before and after haemodialysis. Recordings were provided that same noon/early evening and the next morning and evening. Patient weight was measured at the hospital before and after each haemodialysis session. Recordings were analysed by a smartphone application (app) algorithm, to compare speech measures (SMs) of utterances collected over time. On average, patients provided recordings throughout 25.8 ± 3.9 dialysis treatment cycles, resulting in a total of 472 recordings. Weight changes of 1.95 ± 0.64 kg were documented during cycles. Median baseline SM prior to dialysis was 0.87 ± 0.17, and rose to 1.07 ± 0.15 following the end of the dialysis session, at noon (P = 0.0355), and remained at a similar level until the following morning (P = 0.007). By the evening of the day following dialysis, SMs returned to baseline levels (0.88 ± 0.19). Changes in patient weight immediately after dialysis positively correlated with SM changes, with the strongest correlation measured the evening of the dialysis day [slope: −0.40 ± 0.15 (95% confidence interval: −0.71 to −0.10), P = 0.0096]. Conclusions The fluid‐controlled haemodialysis model demonstrated the ability of the app algorithm to identify cyclic changes in SMs, which reflected bodily fluid levels. The voice analysis platform bears considerable potential as a harbinger of impending fluid overload in a range of clinical scenarios, which will enhance monitoring and triage efforts, ultimately optimizing remote CHF management.

Abstract Aims Aortic pulsatility index (API), calculated as (systolic–diastolic blood pressure)/pulmonary capillary wedge pressure (PCWP), is a novel haemodynamic measurement representing both cardiac filling pressures and contractility. We hypothesized that API would better predict clinical outcomes than traditional haemodynamic metrics of cardiac function. Methods and results The Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness (ESCAPE) trial individual‐level data were used. Routine haemodynamic measurements, including Fick cardiac index (CI), and the advanced haemodynamic metrics of API, cardiac power output (CPO), and pulmonary artery pulsatility index (PAPI) were calculated after final haemodynamic‐monitored optimization. The primary outcome was a composite endpoint of death or need for orthotopic heart transplant (OHT) or left ventricular assist device (LVAD) at 6 months. A total of 433 participants were enrolled in the ESCAPE trial of which 145 had final haemodynamic data. Final API measurements predicted the primary outcome, OR 0.47 (95% CI 0.32–0.70, P

Abstract Aims Readmission after hospitalization for acute decompensated heart failure (HF) remains a major public health problem. Use of remote dielectric sensing (ReDS) to measure lung water volume allows for an objective assessment of volume status and may guide medical optimization for HF. We hypothesized that the use of ReDS would lower 30 day readmission in patients referred to rapid follow‐up (RFU) clinic after HF discharge. Methods and results We conducted a retrospective analysis of the use of ReDS for patients scheduled for RFU within 10 days post‐discharge for HF at Mount Sinai Hospital between 1 July 2017 and 31 July 2018. Diuretics were adjusted using a pre‐specified algorithm. The association between use of ReDS and 30 day readmission was evaluated. A total of 220 patients were included. Mean age was 62.9 ± 14.7 years, and 36.4% were female. ReDS was performed in 80 (36.4%) and led to medication adjustment in 52 (65%). Use of ReDS was associated with a lower rate of 30 day cardiovascular readmission [2.6% vs. 11.8%, hazard ratio (HR): 0.21; 95% confidence interval (CI): 0.05–0.89; P = 0.04] and a trend towards lower all‐cause readmission (6.5% vs. 14.1%, HR: 0.43; 95% CI: 0.16–1.15; P = 0.09) as compared with patients without a ReDS assessment. Conclusions ReDS‐guided HF therapy during RFU after HF hospitalization may be associated with lower risk of 30 day readmission.

There is a desperate search to discover effective therapies against coronavirus disease-2019 (COVID-19). Patients with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) comprise a unique population whose clinical course may provide insights into the effects of antiretroviral therapy on COVID-19. We describe the case of a patient with HIV/AIDS on left ventricular assist device support who was hospitalized and recovered from COVID-19. (Level of Difficulty: Intermediate.)

As health systems worldwide grapple with the coronavirus disease-2019 (COVID-19) pandemic, patients with durable LVAD support represent a unique population at risk for the disease. This paper outlines the case of such a patient who developed COVID-19 complicated by a “cytokine storm” with severe acute respiratory distress syndrome and myocardial injury and describes the challenges that arose during management.

BackgroundScore-based survival prediction in patients with advanced heart failure (HF) is complicated. Easy-to-use prognostication tools could inform clinical decision-making and palliative care delivery.ObjectiveTo compare the prognostic utility of the Seattle HF model (SHFM), the surprise question (SQ), and the number of HF hospitalizations (NoH) within the last 12 months for predicting 1-year survival in patients with advanced HF.MethodsWe retrospectively analyzed data from a cluster-randomized controlled trial of advanced HF patients, predominantly with reduced ejection fraction. Primary outcome was the prognostic discrimination of SHFM, SQ (“Would you be surprised if this patient were to die within 1 year?”) answered by HF cardiologists, and NoH, assessed by receiver operating characteristic (ROC) curve analysis. Optimal cut-offs were calculated using Youden’s index (SHFM: