The role of bioimpedance analysis in overweight and obese patients with acute heart failure: a pilot study

Abstract

Aims Residual congestion at the time of hospital discharge is an important readmission risk factor, and its detection with physical examination and usual diagnostic techniques have strong limitations in overweight and obese patients. New tools like bioelectrical impedance analysis (BIA) could help to determine when euvolaemia is reached. The aim of this study was to investigate the usefulness of BIA in management of heart failure (HF) in overweight and obese patients. Methods and results Our study is a single‐centre, single‐blind, randomized controlled trial that included 48 overweight and obese patients admitted for acute HF. The study population was randomized into two arms: BIA‐guided group and standard care. Serum electrolytes, kidney function, and natriuretic peptides were followed up during their hospital stay and at 90 days after discharge. The primary endpoint was development of severe acute kidney injury (AKI) defined as an increase in serum creatinine by >0.5 mg/dL during hospitalization, and the main secondary endpoint was the reduction of N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) levels during hospitalization and within 90 days after discharge. The BIA‐guided group showed a remarkable lower incidence of severe AKI, although no significant differences were found (41.4% vs. 16.7%; P = 0.057). The proportion of patients who achieved levels of NT‐proBNP < 1000 pg/mL at 90 days was significantly higher in the BIA‐guided group than in the standard group (58.8% vs. 25%; P = 0.049). No differences were observed in the incidence of adverse outcomes at 90 days. Conclusions Among overweight and obese patients with HF, BIA reduces NT‐proBNP levels at 90 days compared with standard care. In addition, there is a trend towards lower incidence of AKI in the BIA‐guided group. Although more studies are required, BIA could be a useful tool in decompensated HF management in overweight and obese patients.

Artificial Intelligence Applied to Electrical and Non-Invasive Hemodynamic Markers in Elderly Decompensated Chronic Heart Failure Patients

“…More specifically, categorical variables were analyzed with the The means and standard deviations of QT intervals were obtained, calculated from q to end of T wave (QTe and QTe SD ), QTpeak intervals (QTp and QTp SD ), and obtained from q to peak of T wave. Finally, we calculated the mean and standard deviation (Te SD ) of Te intervals [11,15,16,19,20,[23][24][25]34,35].…”

“…An increase in this interval and its short-period temporal dispersion was associated with mortality and decompensation in patients with acute CHF [17][18][19][20][21][22][23]. Finally, non-invasive hemodynamic parameters were studied based on bioimpedance to assess the possibility of monitoring clinical improvements in these patients [19,[24][25][26][27]. The final aim of this work is to identify the electrical, bioimpedance, and biohumoral variables which, through artificial intelligence and machine learning devices, will be able to predict the clinical exacerbation of heart failure in the preliminary phases.…”