In-hospital bioimpedance-derived total body water predicts short-term cardiovascular mortality and re-hospitalizations in acute decompensated heart failure patients.

Alexander Herrmann (Berlin)1, A. Faragli (Berlin)1, M. Cvetkovic (Berlin)1, S. Perna (Milano)2, E. Khorsheed (Zallaq)3, F. P. Lo Muzio (Berlin)1, J. Ötvös (Berlin)1, L. Fassina (Pavia)4, E. La Porta (Genova)5, H.-D. Düngen (Berlin)1, A. Alogna (Berlin)1

1Deutsches Herzzentrum der Charite (DHZC) Klinik für Innere Medizin - Kardiologie Berlin, Deutschland; 2Università degli Studi di Milano Department of Food, Environmental and Nutritional Sciences, Division of Human Nutrition Milano, Italien; 3University of Bahrain Department of Mathematics, College of Science Zallaq, Deutschland; 4University of Pavia Department of Electrical, Computer and Biomedical Engineering Pavia, Italien; 5Giannina Gaslini Institute Department of Nephrology and Kidney Transplantation Genova, Italien


Background: Most of the hospital re-admissions in heart failure (HF) patients are caused by an acute exacerbation of their chronic congestion. Bioimpedance analysis (BIA) has emerged as a promising non-invasive method to assess the volume status of HF patients. Its correlation with clinically assessed volume status and its prognostic value in the acute intra-hospital setting are still not established.
Methods and Results: In this single-center observational study, patients (n=49) admitted to the cardiology ward for acute decompensated HF (ADHF) underwent a daily BIA-derived volume status assessment. Median hospital stay was 7 [4-10] days. Twenty patients (40%) reached the composite endpoint of cardiovascular mortality or re-hospitalization for HF over 6 months. Patients at discharge displayed improved NYHA class, lower body weight (BW), plasma and blood volume, as well as lower NT-proBNP levels compared to the admission. As compared to patients with total body water (TBW) less than or equal to that predicted by BW, those with TBW levels exceeding those predicted by BW had higher NT-proBNP as well as E/e´ (both p<0.05) at discharge. Further, patients with excess TBW at discharge showed a higher prevalence of NYHA class III compared to patients with TBW values below or equal to the expected values by BW. As compared to admission, fat mass (FM) and fat free mass (FFM) were significantly lower at discharge, while TBW was not (p=0.083). 
With the aid of machine learning tools, several Cox hazards multivariate models were derived in this study using the potential factors with measurements recorded at admission and the associated delta changes during the hospitalization period.
Based on AIC goodness-of-fit estimator, the “best” Cox Hazards prediction model included four significant variables, namely:ΔTBW, ΔFM, ΔReactance, and ΔSaturation, beside some other potential predictors. (Table 1)
In the Cox multivariate regression analysis, the BIA-derived ΔTBW between admission and discharge showed a 23% risk reduction for each unit increase (HR=0.776; CI:0.67-0.89; p=0.0006). In line with this, TBW at admission had the highest prediction importance of the combined endpoint for a subgroup of high-risk HF patients presenting a NT-proBNP ≥1000 pg/mL at admission (n=35) in a neural network analysis.
Conclusion:: In ADHF BIA derived TBW is associated with an increased risk of HF hospitalization or cardiovascular death over 6 months. Overhydration at discharge was not identified based on clinical assessment alone, emphasizing the importance of BIA. Further investigation is needed for BIA's role in prognostic stratification of HF patients.
Table 1 Survival analysis showing the prediction Cox hazards models and their predictors.
 Variables Model 1Model 2Model 3Model 4Model 5
BIA variables     
 Δ total body water -0.336*** -0.258*** -0.254***  
 Δ resistance   0.014*-0.013**
 Δ reactance-0.163***-0.1002*-0.096*-0.084*-0.076
 Δ fat mass-0.279**-0.174-0.163*-0.116-0.098
Clinical variables     
 Δ hemoglobin-1.495*-0.308-0.307-0.245-0.242
 Δ hematocrit  4.052*    
 Δ sodium -0.149 -0.019   0.039
 Δ arterial O2 saturation  -0.363* -0.378** -0.329* -0.361*
 Δ Potassium  -0.0349 -0.023 0.026 
nt-proBNP≥1000-0.34-1.149 -1.195 -0.839 -1.006
 Model statistics     
Model p-value0.0060.0040.0020.020.02
AIC 104.3 103.4 101.4 108.1 107.9
NtproBNP≥1000=1 if NtproBNP ≥1000 and 0 otherwise. Significance level: * 95%, ** 99%, *** 99.9%
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