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

The role of bioelectrical phase angle in patients with heart failure

The most challenging feature of heart failure (HF) still remains the evaluation of congestion. Residual congestion at discharge and the difficulties in perfectly dosing therapies in order to balance the hydration status of the patient are the most worrisome issues when dealing with HF.The use of bioimpedance vector analysis (BIVA) might promote a different approach in the general management of patients with HF. BIVA is a reliable, fast, bedside tool able to assess the congestion status. It proved to be helpful to physicians for diagnosing congestive status, managing therapies, and providing prognostic information in the setting of HF.Bioelectrical Phase Angle (PhA) - as derived from equations related to the parameters of BIVA - recently surged as a possible biomarker for patients with HF. Studies provided data about the application of PhA in the clinical management and in the overall risk stratification of HF patients.Basically, the use of PhA might be considered as a holistic evaluation of patients with HF which includes the need for a multiparametric approach able to effectively depict the clinical status of patients. There is no definite biomarker able to comprehensively describe and identify all the features of HF patient, but scores based on molecules/techniques able to explore the different pathogenetic mechanisms of HF are desirable.The aim of this review was to provide a comprehensive evaluation of literature related to PhA role in HF and the impact of this biomarker on clinical management and risk stratification of HF patients.

Anaemia and Congestion in Heart Failure: Correlations and Prognostic Role

The aim of this study was to evaluate the relationship between anaemia and biomarkers of central/peripheral congestion in heart failure (HF) and the impact on mortality. We retrospectively evaluated 434 acute/chronic HF (AHF/CHF) patients. Anaemia was defined as haemoglobin levels <12 g/dL (women) or <13 g/dL (men). The brain natriuretic peptide (BNP) and hydration index (HI) were measured. The endpoint of the study was all-cause mortality. Anaemia occurred in 59% of patients with AHF and in 35% with CHF (p < 0.001) and showed a significant correlation with the NYHA functional class and renal function. BNP and HI were significantly higher in patients with anaemia than in those without anaemia. Independent predictors of anaemia included BNP, estimated creatinine clearance (eCrCL), and HI. The all-cause mortality rate was 21%, which was significantly higher in patients with anaemia than in those without anaemia (30% vs. 14%, p < 0.001; hazard ratio: 2.6). At multivariate Cox regression analysis, BNP, eCrCL, and HI were independent predictors for mortality (Hazard ratios: 1.0002, 0.97, and 1.05, respectively), while anaemia was not. Anaemia correlates with HF status, functional class, renal function, BNP, and HI. Anaemia was not an independent predictor for mortality, acting as a disease severity marker in congestive patients rather than as a predictor of death.

Association Between Pre-operative BUN and Post-operative 30-Day Mortality in Patients Undergoing Craniotomy for Tumors: Data From the ACS NSQIP Database

Objective

There is limited evidence to clarify the specific relationship between pre-operative blood urea nitrogen (BUN) and post-operative 30-day mortality in patients undergoing craniotomy for tumors. Therefore, we aimed to investigate this relationship in detail.

Methods

Electronic medical records of 18,642 patients undergoing craniotomy for tumors in the ACS NSQIP from 2012 to 2015 were subjected to secondary retrospective analysis. The principal exposure was pre-operative BUN. Outcome measures were post-operative 30-day mortality. We used binary logistic regression modeling to evaluate the association between them and conducted a generalized additive model and smooth curve fitting (penalized spline method) to explore the potential relationship and its explicit curve shape. We also conducted sensitivity analyses to ensure the robustness of the results and performed subgroup analyses.

Results

A total of 16,876 patients were included in this analysis. Of these, 47.48% of patients were men. The post-operative 30-day mortality of the included cases was 2.49% (420/16,876), and the mean BUN was 16.874 ± 6.648 mg/dl. After adjusting covariates, the results showed that pre-operative BUN was positively associated with post-operative 30-day mortality (OR = 1.020, 95% CI: 1.004, 1.036). There was also a non-linear relationship between BUN and post-operative 30-day mortality, and the inflection point of the BUN was 9.804. For patients with BUN < 9.804 mg/dl, a 1 unit decrease in BUN was related to a 16.8% increase in the risk of post-operative 30-day mortality (OR = 0.832, 95% CI: 0.737, 0.941); for patients with BUN > 9.804 mg/dl, a 1 unit increase in BUN was related to a 2.8% increase in the risk of post-operative 30-day mortality (OR = 1.028, 95% CI: 1.011, 1.045). The sensitivity analysis proved that the results were robust. The subgroup analysis revealed that all listed subgroups did not affect the relationship between pre-operative BUN and post-operative 30-day mortality (P > 0.05).

Conclusion

Our study demonstrated that pre-operative BUN (mg/dl) has specific linear and non-linear relationships with post-operative 30-day mortality in patients over 18 years of age who underwent craniotomy for tumors. Proper pre-operative management of BUN and maintenance of BUN near the inflection point (9.804 mg/dl) could reduce the risk of post-operative 30-day mortality in these cases.

Predictive Value of Blood Urea Nitrogen to Albumin Ratio in Long-Term Mortality in Intensive Care Unit Patients with Acute Myocardial Infarction: A Propensity Score Matching Analysis

Background

Blood urea nitrogen to albumin ratio (BAR) has been implicated in predicting outcomes of various inflammatory-related diseases. However, the predictive value of BAR in long-term mortality in patients with acute myocardial infarction (AMI) has not yet been evaluated.

Methods

In this retrospective cohort study, the patients were recruited from the Medical Information Mart for Intensive Care III (MIMIC III) database and categorized into two groups by a cutoff value of BAR. Kaplan–Meier (K-M) analysis and Cox proportional hazard model were performed to determine the predictive value of BAR in long-term mortality following AMI. In order to adjust the baseline differences, a 1:1 propensity score matching (PSM) was carried out and the results were further validated.

Results

A total of 1827 eligible patients were enrolled. The optimal cutoff value of BAR for four-year mortality was 7.83 mg/g. Patients in the high BAR group tended to have a longer intensive care unit (ICU) stay and a higher rate of one-, two-, three- and four-year mortality (all p<0.001) compared with those in the low BAR group. K-M curves indicated a significant difference in four-year survival (p<0.001) between low and high BAR groups. The Cox proportional hazards model showed that higher BAR (>7.83) was independently associated with increased four-year mortality in the entire cohort, with a hazard ratio (HR) of 1.478 [95% CI (1.254–1.740), p<0.001]. After PSM, the baseline characteristics of 312 pairs of patients in the high and low BAR groups were well balanced, and similar results were observed in K-M curve (p=0.003).

Conclusion

A higher BAR (>7.83) was associated with four-year mortality in patients with AMI. As an easily available biomarker, BAR can predict the long-term mortality in AMI patients independently.

Sex Differences in the Evaluation of Congestion Markers in Patients with Acute Heart Failure

The impact of sex on the assessment of congestion in acute heart failure (AHF) is still a matter of debate. The objective of this analysis was to evaluate sex differences in the evaluation of congestion at admission in patients hospitalized for AHF. We consecutively enrolled 494 AHF patients (252 female). Clinical congestion assessment, B-type natriuretic peptide levels analysis, blood urea nitrogen to creatinine ratio (BUN/Cr), plasma volume status estimate (by means of Duarte or Kaplam-Hakim PVS), and hydration status evaluation through bioimpedance analysis were performed. There was no difference in medications between men and women. Women were older (79 ± 9 yrs vs. 77 ± 10 yrs, p = 0.005), and had higher left ventricular ejection fraction (45 ± 11% vs. 38 ± 11%, p < 0.001), and lower creatinine clearance (42 ± 25 mL/min vs. 47 ± 26 mL/min, p = 0.04). The prevalence of peripheral oedema, orthopnoea, and jugular venous distention were not significantly different between women and men. BUN/Cr (27 ± 9 vs. 23 ± 13, p = 0.04) and plasma volume were higher in women than men (Duarte PVS: 6.0 ± 1.5 dL/g vs. 5.1 ± 1.5 dL/g, p < 0.001; Kaplam−Hakim PVS: 7.9 ± 13% vs. −7.3 ± 12%, p < 0.001). At multivariate logistic regression analysis, female sex was independently associated with BUN/Cr and PVS. Female sex was independently associated with subclinical biomarkers of congestion such as BUN/Cr and PVS in patients with AHF. A sex-guided approach to the correct evaluation of patients with AHF might become the cornerstone for the correct management of these patients.

The impact of hydration status and fluid distribution on pulmonary function in COPD patients

Chronic Obstructive Pulmonary Disease (COPD) patients have alterations in body composition. Bioelectrical impedance analysis (BIA) evaluates body composition, hydration status, and fluid distribution. Subjects with fluid disturbances have been found to have lower FEV1, respiratory muscle strength, and poor prognosis. We aimed to evaluate the effect of hydration status and fluid distribution on pulmonary function in COPD patients. A cross-sectional study, 180 patients with a confirmed diagnosis of COPD were included. Patients with asthma, advanced renal or liver disease, acute HF, exacerbation of COPD, or pacemakers were excluded. Hydration status variables (TBW, ECW, ICW) and disturbance of fluid distribution [impedance ratio (IR) > 0.84 and phase angle (PhA)] were evaluated by BIA. Pulmonary function was assessed by spirometry. The mean population age was 71.55 ± 8.94 years; 55% were men. Subjects were divided into two groups according to the IR ≥ 0.84 or < 0.84. The group with higher IR ≥ 0.84 had lower FEV1, FVC, FEV1/FVC, DLCO and, PhA compared to those with IR < 0.84. After adjusting for confounding variables TBW, ECW, IR ≥ 0.84, PhA, and resistance/height increase were associated with decreased FEV1. In the same way, with IR ≥ 0.84, edema index ≥ 0.48, trunk and abdominal IR were negatively associated with FVC, and PhA had a positive association with FVC. Fluid distribution, especially IR and PhA, could be a useful parameter for predicting pulmonary function in COPD patients.

Bioimpedance Vector Analysis for Heart Failure: Should We Put It on the Agenda?

Heart failure is a clinical syndrome, resulting in increased intracardiac pressure and/or decreased cardiac output under rest or stress. In acute decompensated heart failure, volume assessment is essential for clinical diagnosis and management. More and more evidence shows the advantages of bioimpedance vector analysis in this issue. Here, we critically present a brief review of bioimpedance vector analysis in the prediction and management of heart failure to give a reference to clinical physicians and guideline makers.

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.

Should we overcome the resistance to bioelectrical impedance in heart failure?

INTRODUCTION

Heart failure is associated with increased neurohormonal activation that results in changes in body composition including volume overload and the loss of skeletal muscle, body fat, and bone density. Bioelectrical impedance measures body composition based on the conduction of electrical current through body fluids.

AREAS COVERED

The PubMed and Scopus databases were reviewed up to the third week of June 2020. Cross-sectional studies, retrospective observational studies, prospective observational studies, and randomized controlled trials have examined numerous bioelectrical impedance monitoring strategies to guide the diagnosis, prognosis, and treatment of heart failure. These monitoring strategies include intrathoracic impedance, lung impedance, bioelectrical impedance vector analysis, leg bioelectrical impedance, and thoracic bioreactance.

EXPERT COMMENTARY

Based on the current evidence, more studies are needed to validate bioelectrical impedance in heart failure. Lung impedance appears to be useful for guiding heart failure treatment in patients with ST-elevation myocardial infarction and improving outcomes in outpatients with heart failure. Furthermore, bioelectrical impedance has potential as a noninvasive, quantitative heart failure variable for population-based research.

The Future of Biomarkers

Numerous compounds have been tested as potential biomarkers for multiple possible applications within intensive care medicine but none is or will ever be sufficiently specific or sensitive for the heterogeneous syndromes of critical illness. New technology and access to huge patient databases are providing new biomarker options and the focus is shifting to combinations of several or multiple biomarkers rather than the single markers that research has concentrated on in the past. Biomarkers will increasingly be used as part of routine clinical practice in the future, complementing clinical examination and physician expertise to provide accurate disease diagnosis, prediction of complications, personalized treatment guidance, and prognosis.