Respiratory failure and bioelectrical phase angle are independent predictors for long-term survival in acute heart failure.

Background. The assessment of long-term mortality in acute decompensated heart failure (ADHF) is challenging. Respiratory failure and congestion play a fundamental role in risk stratification of ADHF patients. The aim of this study was to investigate the impact of arterial blood gases (ABG) and congestion on long-term mortality in patients with ADHF. Methods and results. We enrolled 252 patients with ADHF. Brain natriuretic peptide (BNP), blood urea nitrogen (BUN), phase angle as assessed by means of bioimpedance vector analysis, and ABG analysis were collected at admission. The endpoint was all-cause mortality. At a median follow-up of 447 d (interquartile range [IQR]: 248-667), 72 patients died 1-840 d (median 106, IQR: 29-233) after discharge. Respiratory failure types I and II were observed in 78 (19%) and 53 (20%) patients, respectively. The ROC analyses revealed that the cut-off points for predicting death were: BNP > 441 pg/mL, BUN > 1.67 mmol/L, partial pressure in oxygen (PaO2) ≤69.7 mmHg, and phase angle ≤4.9°. Taken together, these four variables proved to be good predictors for long-term mortality in ADHF (area under the curve [AUC] 0.78, 95% CI 0.72-0.78), thus explaining 60% of all deaths. A multiparametric score based on these variables was determined: each single-unit increase promoted a 2.2-fold augmentation of the risk for death (hazard ratio [HR] 2.2, 95% CI 1.8-2.8, p< .0001). Conclusions. A multiparametric approach based on measurements of BNP, BUN, PaO2, and phase angle is a reliable approach for long-term prediction of mortality risk in patients with ADHF.

Creatinine Clearance Measurement with Bioelectrical Impedance Analysis in Heart Failure Patients: Comparison with Estimated-Creatinine Clearance Formulas.

BACKGROUND: Kidney disease is common in patients with heart failure (HF). The Donadio equation combines plasma creatinine and bioimpedance vector analysis (BIVA) to estimate creatinine clearance. This study aimed to compare the Donadio formula to the Cockcroft-Gault (CG), Modification of Diet in Renal Disease Study (MDRD-4), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations in patients with HF. METHODS: We analysed data from 900 patients (mean age: 76 ± 10 years) with HF. All of them underwent clinical, laboratory, BIVA, and echocardiographic evaluations. RESULTS: Donadio equation overestimated eGFR as compared to CG and CKD-EPI formulas (+6.8 and +12 mL/min/1.73 m2, respectively) while computing similar results to MDRD-4 (overestimation: +0.1 mL/min/1.73 m2). According to the different formulas, the prevalence of renal insufficiency (eGFR< 30 ml/min/1.73 m2) in relation to the different formulas was as follows: 24% with Donadio, 21% with CG, 13% with MDRD-4, and 23% with CKD-EPI formulas. All the equations demonstrated a high precision rate (r>0.8 for all). There was a "good" agreement between the Donadio and CG/MDRD-4 formulas and "fair" with the CDK-EPI formula. The Donadio equation showed a high accuracy in predicting severe renal dysfunction (eGFR< 30 mL/min/1.73 m2) in patients with HF (AUC > 0.9), showing comparable performances to CG. CONCLUSION: The Donadio formula provided an estimation of GFR comparable to MDRD-4 in HF patients, independently from acute or chronic HF conditions. The use of BIVA in HF patients may be adopted both for HF management and for evaluating kidney function.

Congestion and nutrition as determinants of bioelectrical phase angle in heart failure.

BACKGROUND: The whole-body bioelectrical phase-angle (PhA) is emerging as a new tool in stratifying prognosis in patients with both acute (AHF) and chronic heart failure (CHF). OBJECTIVE: To evaluate the determinants of PhA in HF patients. METHODS: We analyzed data from 900 patients with AHF or CHF (mean age: 76±10 years, 54% AHF). Clinical, serum biochemical, echocardiographic and bioelectrical measurements were collected from all of patients. PhA was quantified in degrees. Congestion was assessed by a multiparametric approach, including the presence of peripheral edema, brain natriuretic peptides (BNP) plasma levels, blood urea nitrogen to creatinine ratio (BUN/Cr), and relative plasma volume status (PVS) calculated by Kaplan-Hakim's formula. Geriatric Nutritional Risk Index (GNRI) was adopted as indicator for nutritional status. RESULTS: At univariate analysis, PhA was significantly lower in females, in patients with peripheral edema, and AHF. PhA significantly correlates age, BNP, PVS, BUN/Cr, and GNRI. At multivariate analysis, congestion biomarkers emerged as the major determinant of PhA as they explained the 34% of data variability, while age, GNRI, and gender only explained 6%, 0.5%, and 0.5%, respectively (adjusted R2 = 0.41). In particular, PVS (regression of coefficient B=-0.17) explained the 20% of PhA variability, while peripheral congestion (B=-0.27) and BNP (B=-0.15) contributed to 10% and 2%, respectively. CONCLUSIONS: The main determinant of bioelectrical PhA in patients with HF is congestion and PVS in particular, while nutritional status has marginal impact.