The Prognostic Impact of Estimated Creatinine Clearance by Bioelectrical Impedance Analysis in Heart Failure: Comparison of Different eGFR Formulas.

The estimation of glomerular filtration rate (eGFR) provides prognostic information in patients with heart failure (HF). Bioelectrical impedance analysis may calculate eGFR (Donadio formula). The aim of this study was to evaluate the impact of the Donadio formula in predicting all-cause mortality in patients with HF as compared to Cockroft-Gault, MDRD-4 (Modification of Diet in renal Disease Study), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas. Four-hundred thirty-six subjects with HF (52% men; mean age 75 ± 11 years; 42% acute HF) were enrolled. Ninety-two patients (21%) died during the follow-up (median 463 days, IQR 287-669). The area under the receiver operator characteristic curve for eGFR, as estimated by Cockroft-Gault formula (AUC = 0.75), was significantly higher than those derived from Donadio (AUC = 0.72), MDRD-4 (AUC = 0.68), and CKD-EPI (AUC = 0.71) formulas. At multivariate analysis, all eGFR formulas were independent predictors of death; 1 mL/min/1.73 m2 increase in eGFR-as measured by Cockroft-Gault, Donadio, MDRD-4, and CKD-EPI formulas-provided a 2.6%, 1.5%, 1.2%, and 1.6% increase, respectively, in mortality rate. Conclusions. eGFR, as calculated with the Donadio formula, was an independent predictor of mortality in patients with HF as well as the measurements derived from MDRD4 and CKD-EPI formulas, but less accurate than Cockroft-Gault.

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.

Multiparametric approach to congestion for predicting long-term survival in heart failure.

BACKGROUND: Congestion is a marker of adverse prognosis in patients with heart failure (HF). In addition to brain natriuretic peptide (BNP), estimated plasma volume status (ePVS), bioimpedance vector analysis (BIVA), and blood urea nitrogen/creatinine ratio (BUN/Cr) are emerging as new markers for congestion. The aim of this study was to evaluate the prognostic value of BNP, ePVS, BIVA, and BUN/Cr in HF. METHODS: We analyzed the data from 436 patients with acute or chronic heart failure (AHF, n=184, and CHF, n=252, respectively). BNP, ePVS, hydration index (HI%), and BUN/Cr were collected from all patients at admission. The endpoint was all-cause mortality. RESULTS: Ninety-two patients died after a median follow-up of 463 days (IQR: 287-669). The cumulative mortality of all of the patients was 21% (31% and 13% in AHF and CHF, respectively, p<0.0001). The optimal cut-offs for death occurrence were BNP: >441pg/mL, ePVS: >5.3dL/gr, HI: >73.8%, BUN/Cr: >25. Multivariate Cox regression analysis maintained an independent predictive value for mortality (HR 2. 1, HR 2.2, HR 2.1, and HR 1.7; C-index 0.756). AHF status was no longer associated with death. Together, these variables explained 40% of the risk of death (R2 adjusted=0.40). Patients with all four parameters below or above their optimal cut-off had mortality rates of 4% and 59%, respectively. CONCLUSIONS: BNP, ePVS, BIVA, and BUN/Cr at admission provide independent and complementary prognostic information in patients with HF and, when combined, explain the 40% risk of death in these patients independent from the acute or chronic HF condition.

Serum biochemical determinants of peripheral congestion assessed by bioimpedance vector analysis in acute heart failure.

BACKGROUND: The pathophysiology of peripheral congestion is poorly investigated in patients with acute heart failure (AHF). OBJECTIVES: to evaluate the relative contribution of serum colloid osmotic pressure (COP), relative plasma volume status (PVS), biomarkers of renal function, electrolytes, haemoglobin, and brain natriuretic peptide (BNP) in peripheral fluid overload using bioimpedance vector analysis (BIVA). METHODS: We retrospectively analysed data from 485 patients with AHF. Hydration status was evaluated by semiquantitative and quantitative approach using BIVA (R/Xc graph) and Hydration Index (HI), respectively. COP was calculated from albumin and total protein concentration, while relative PVS was calculated from validated equations. RESULTS: Congestion assessed by BIVA was observed in 304 (63%) patients and classified as mild (30%), moderate (42%), and severe (28%). On univariate analysis, HI was inversely correlated with COP (P < 0.01), glomerular filtration rate (P < 0.01), and haemoglobin (P < 0.01), while positive correlations were found for relative PVS (P < 0.05), BNP (P < 0.01), and blood urea nitrogen (BUN; P < 0.01). On stepwise multivariate analysis, COP explained 12% of the total variability, while BUN, PVS, haemoglobin, and BNP added a further 6%, 4%, 2%, and 1%, respectively, to the final explanatory model. CONCLUSIONS: COP was the major determinant of the presence and entity of peripheral congestion assessed by BIVA. BUN, PVS, haemoglobin, and BNP revealed reduced influence on congestion as compared with COP. Routine laboratory testing could be useful in peripheral fluid accumulation. Future studies should evaluate the relationship between COP and pharmacological target therapies for the fluid management of AHF patients.