Prognostic value of integral assessment of congestion in patients hospitalized with acute decompensated chronic heart failure: A single center study

Zhanna Davidovna Kobalava; Veronica Vladimirovna Tolkacheva; Flora Elisa Cabello Montoya; Bekbolot Kanatbekovich Sarlykov; Mohammed Al-Jarallah; Peter A Brady; Rajesh Rajan

doi:10.4103/ACCJ.ACCJ_15_22

ORIGINAL ARTICLE

Year : 2022 | Volume : 4 | Issue : 2 | Page : 77-84

Prognostic value of integral assessment of congestion in patients hospitalized with acute decompensated chronic heart failure: A single center study

Zhanna Davidovna Kobalava¹, Veronica Vladimirovna Tolkacheva¹, Flora Elisa Cabello Montoya¹, Bekbolot Kanatbekovich Sarlykov¹, Mohammed Al-Jarallah², Peter A Brady³, Rajesh Rajan⁴
¹ Department of Internal Diseases with Courses of Cardiology and Functional Diagnostics, Peoples' Friendship University of Russia (RUDN University), Moscow, Russia
² Department of Cardiology, Sabah Al Ahmed Cardiac Centre, Kuwait City, Kuwait
³ Department of Cardiology, Illinois Masonic Medical Center, Chicago, Illinois, USA
⁴ Department of Internal Diseases with Courses of Cardiology and Functional Diagnostics, Peoples' Friendship University of Russia (RUDN University), Moscow, Russia; Department of Cardiology, Sabah Al Ahmed Cardiac Centre, Kuwait City, Kuwait

Date of Submission	11-Aug-2022
Date of Decision	15-Sep-2022
Date of Acceptance	17-Sep-2022
Date of Web Publication	17-Dec-2022

Correspondence Address:
Dr. Rajesh Rajan
Department of Cardiology, Sabah Al Ahmed Cardiac Centre, Kuwait City – 13001

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ACCJ.ACCJ_15_22

Abstract

Background: Systemic congestion is the leading pathophysiological mechanism of decompensated heart failure (HF), and hospitalization and poor prognosis. Unfortunately, patients are discharged with residual congestion, possibly due to the lack of a clear strategy for its assessment. The existing criteria for discharge of patients from the hospital are more often based on a subjective assessment and poorly correlate with the state of hemodynamic stabilization, and the search for methods for detecting congestion remains relevant. Objective: The objective is to determine the prognostic value of an integrated assessment of congestion based on novel diagnostic methods in patients hospitalized with acute decompensated chronic HF (ADCHF). Methods: Single-center prospective study in 171 patients hospitalized with ADCHF. All patients underwent physical examination, paraclinical (laboratory and instrumental) investigations-N-terminal pro-brain natriuretic peptide (NT-proBNP) level, lung ultrasound, transient elastography (TE), bioimpedance vector analysis (BIVA) on admission and discharge. Clinical congestion was assessed in accordance with the HF Association consensus document. Clinical outcomes were assessed by structured telephone survey 1, 3, 6, 12 months after discharge. Combined rates of all-cause mortality and re-admissions were used as the study endpoint. Results: Patients hospitalized with ADCHF had the following congestion status at discharge as assessed by individual methods (TE, lung ultrasound, BIVA and NT-proBNP): The incidence of clinical residual Congestion I ranged 33%–39%, the incidence of subclinical congestion was 12%–24%, and patients with euvolemia accounted for 19%–32%. According to the integral assessment of hydration status, the incidences of clinical residual Congestion I, subclinical congestion, and euvolemia were 57%, 31% and 12%, respectively. The study has demonstrated a significant worsening of all congestion parameters with increasing number of methods (1–4) that had detected congestion. Patients with congestion detected at discharge by 2, 3, or 4 methods were at a significantly higher risk of all-cause mortality or readmission. TE + NT-proBNP had a higher prognostic value in regard to the risk of endpoint event, while the combination of all four methods was the most predictive. Conclusions: Patients hospitalized with ADCHF should undergo an integral assessment of residual and subclinical congestion at discharge. The introduction of integral congestion assessment into the routine practice will help identify patients with less favorable prognosis in terms of the risk of death and re-admission, as well as to enhance pharmacologic therapy and follow-up.

Keywords: Acute decompensated chronic heart failure, integral assessment of congestion, residual congestion, subclinical congestion

How to cite this article:
Kobalava ZD, Tolkacheva VV, Cabello Montoya FE, Sarlykov BK, Al-Jarallah M, Brady PA, Rajan R. Prognostic value of integral assessment of congestion in patients hospitalized with acute decompensated chronic heart failure: A single center study. Ann Clin Cardiol 2022;4:77-84

How to cite this URL:
Kobalava ZD, Tolkacheva VV, Cabello Montoya FE, Sarlykov BK, Al-Jarallah M, Brady PA, Rajan R. Prognostic value of integral assessment of congestion in patients hospitalized with acute decompensated chronic heart failure: A single center study. Ann Clin Cardiol [serial online] 2022 [cited 2023 May 29];4:77-84. Available from: http://www.onlineacc.org/text.asp?2022/4/2/77/364173

Introduction

Systemic congestion is the leading pathophysiological mechanism of decompensated heart failure (HF), and hospitalization,^[1] and poor prognosis.^[2] Congestion is the key clinical sign of acute decompensated chronic HF (ADCHF) and the main objective of inpatient treatment.^[3] However, patients are often discharged despite residual congestion, perhaps due to the lack of a clear strategy for its assessment/detection since current practice relies on more subjective assessments that often do not correlate well with hemodynamic stabilization.^[4] According to some studies, 48% of discharged patients have peripheral congestion.^[5] According to the retrospective analyses the Diuretic Optimization Strategy Evaluation in Acute Decompensated HF and Cardiorenal Rescue Study-HF,^[6],[7] only half of the patients had no signs of congestion at discharge. Residual congestion is one of the causes for re-admission of patients with acute decompensated HF, the rate of which reaches 18% within the 1^st 30 days of discharge. Residual congestion was also associated with increased mortality and re-admissions within 60 days,^[5] while congestion on the 7^th day of hospitalization was associated with a higher risk of re-admission for HF within 180 days of discharge, compared with patients without congestion.^[8] Thus, improved clinical detection of congestion is crucial. Paraclinical parameters that assess congestion include N-terminal pro-brain natriuretic peptide levels (NT-proBNP), lung ultrasound B-lines, liver stiffness by transient elastography (TE), and hydration by bioimpedance vector analysis (BIVA). Previous studies have demonstrated high detection rates of residual congestion using individual paraclinical methods,^{[9],[10],[11]} however, few studies have evaluated integral assessment of residual and subclinical congestion by several paraclinical methods and their prognostic inputs.^[2],[12] The aim of the study was to analyze the clinical and prognostic value of integral assessment of hydration status and its dynamics in patients with ADCHF.

Methods

The study cohort included patients admitted with ADCHF to the Center for HF at the Vinogradov City Clinical Hospital in Moscow, Russia. ADCHF was diagnosed based on standard criteria: the appearance or rapid aggravation of symptoms and signs of HF, which requires emergency hospitalization of the patient and intensive therapy in combination with objective signs of heart damage (systolic and/or diastolic dysfunction, left ventricular hypertrophy, left atrium enlargement according to echocardiographic examination) and an increase in the level of NT-proBNP.^[13] Patients with acute coronary syndrome, severe medical conditions or malignancies, edematous syndrome of another etiology, acute hepatitis with elevated transaminases >5 upper normal limit, immobilized patients and those for whom BIVA could not be performed, were excluded. All patients provided a written informed consent before the study procedures. The study was performed in accordance with Good Clinical Practice and Declaration of Helsinki standards. The study protocol was approved by the local ethics committee (Approval Number 10/EC dated June 20, 2019). The study was conducted since July 2019 till June 2022.

All patients underwent a standard physical examination, laboratory instrumental investigations including NT-proBNP, lung ultrasound, TE, and BIVA at admission and at discharge. Clinical congestion was assessed using the HF Association consensus document.^[14] Orthopnea, pressure in the jugular vein, hepatomegaly, and peripheral edema were evaluated in points [Table 1]. Each clinical symptom and sign was evaluated on the day of admission and discharge. When summing up the scores, the presence of ≥1 point was considered clinical congestion at admission and sufficient clinical congestion at discharge.

Table 1: Scale of clinical evaluation of congestion of the heart failure association consensus document^[14]

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Serum NT-proBNP was determined by ELISA using NT-proBNP-ELISA-BEST test systems (Russia, ZAO Vector-Best). Lung ultrasound (VIVID iq, GE) with B-line count was performed in 8 areas (II and IV intercostal spaces between the parasternal and midclavicular lines and between the anterior and midaxillary lines on both sides).^[9],[15] TE was performed on a FibroScan® 502 touch device (Echosens, France) in the projection of the right lobe of the liver at the level of the 8^th or 9^th intercostal space along the anterior or median axillary line. Liver stiffness (elasticity) was determined in kilopascals (kPa) and the interquartile range (IQR) as a percentage.^[10],[16]

Hydration status was assessed with BIVA (ABC-01 Medass). The electrical impedance of biological tissues has two components: resistance (R) and reactance (Xc). The lower the resistance and reactance, the greater the degree of hydration. Impedance (point Z) includes the active component R and the reactive capacitive component Xc, and is c

.^{[11],[17],[18]} Lung ultrasound was used to assess pulmonary congestion, while TE, BIVA, and NT-proBNP were each used to assess systemic congestion. Long-term clinical outcomes were assessed by a structured telephone interview 1, 3, 6, and 12 months after discharge. The combined rate of all-cause mortality and readmissions was used as the endpoint.

Patient grading was based on the presence/absence of congestion evidenced by individual laboratory/instrumental data presented in [Table 2]. Clinical residual congestion I at discharge was evidenced by clinical and paraclinical evidence (individual laboratory/instrumental data). If clinical signs were present but not confirmed by paraclinical evidence, residual Congestion II was considered, which was due to congestion in the other circulation circuit. Subclinical congestion was evidenced by the absence of clinical but the presence of paraclinical data confirming congestion. The absence of clinical and paraclinical evidence of congestion was regarded as euvolemia.

Table 2: Patient grading based on congestion presence/absence evidenced by individual methods

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Statistical analysis

Statistical analyses were performed using MedCalc Software's VAT Version 19.0 and SPSS (version 22.0, IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp). Quantitative variables were summarized as arithmetic mean (M) and standard deviation (for normally distributed data) or as median (Me) and IQR (for skewed data). The survival cut off was determined for each method by constructing receiver operating characteristic (ROC) curves. P < 0.05 was considered statistically significant. The prognostic value of different methods estimating the risk of endpoint event was assessed using uni- and multivariate Cox regression analysis models. The choice of variables included in the models was based on their clinical relevance. Survival probability was estimated by constructing Kaplan–Meier survival curves, comparisons were performed using the log-rank test.

Results

Clinical and demographic characteristics of patients hospitalized with ADCHF are presented in [Table 3]. The average age of patients was 69.4 ± 12.3 years, 61% were men, more than half of the patients (57.5%) were obese, the average body mass index (BMI) was 32.2 ± 7.0 kg/m², 34% of patients smoked. The majority of patients (92%) had a history of arterial hypertension, coronary heart disease was found in 54% of cases, including 66 (39%) who had suffered from myocardial infarction in the past. Most of the patients 62% had atrial fibrillation, with 45.6% paroxysmal, and 17% permanent, stroke in the anamnesis occurred in 12% of patients. Forty (23%) patients suffered from type 2 diabetes mellitus, 25 (15%) had chronic kidney disease, 39 (23%) had anemia, 31 (18%) – chronic obstructive pulmonary disease, 11% of patients underwent percutaneous coronary intervention, and 5% of patients underwent coronary artery bypass grafting.

Table 3: Clinical and demographic characteristics of patients included in the study (n=171)

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The mean duration of hospitalization was 9.3 ± 2.5 days. Before hospitalization loop diuretics were prescribed to 72.5%, mineralocorticoid receptor antagonists (MCRA) 53.2%, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitor (ACEI/ARB/ARNI) by 77.2%, beta-blockers by 70.1%, cardiac glycosides by 17%, and oral anticoagulants by 55% of patients. During hospitalization, loop diuretics were prescribed to all patients, MCRA to 72.5%, ACEI/ARB/ARNI 92.3%, beta-blockers by 96.4%, cardiac glycosides by 17%, and oral anticoagulants by 62.5% of patients. In patients on a standard therapy, the incidence of clinical residual congestion I, based on individual assessment methods- TE, lung ultrasound, BIVA and NT-proBNP-varied from 33% to 39%, the incidence of subclinical congestion, from 12% to 24%, and the percentage of euvolemic patients was 19%–32% [Figure 1]. We identified patients with clinical residual Congestion II who had no congestion according to the paraclinical methods. To understand the reasons, we analyzed the subsets of patients with clinical residual congestion, which was not detected either by TE (n = 41) or by lung ultrasound (n = 30) [Table 4].

Figure 1: Patient grading based on congestion presence/absence evidenced by individual methods

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Table 4: Comparative characteristics of patients with clinical residual congestion II not confirmed by transient elastography (n=41) or lung ultrasound (n=30), at discharge

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Patients with clinical residual congestion undetected by TE were characterized by the absence of hepatomegaly and normal liver stiffness; however, they also had signs of pulmonary congestion (orthopnea in 59% of patients, lung ultrasound B-line count = 16). Patients with clinical residual congestion undetected by lung ultrasound were characterized by the absence of orthopnea and normal B-line count but, at the same time, showed signs of systemic congestion (hepatomegaly in 43%, bulging neck veins in 37%, and edema in 60% of patients) [Table 4]. According to the integral assessment of hydration status, the incidence of clinical residual congestion I was 57%, subclinical congestion was 31%, and euvolemia was 12% [Figure 2]. Thus, the integral assessment of congestion is superior to individual methods and can detect more patients with residual and subclinical congestion.

Figure 2: Patient grading based on congestion presence/absence evidenced by integral assessment

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Compared to patients with subclinical congestion or euvolemia, patients with residual congestion showed more pronounced congestion according to paraclinical assessment: higher liver stiffness (10 kPa vs. 7.3 kPa and 5.1 kPa; P =0.049), greater B-line count on lung ultrasound (20 vs. 6 and 3; P < 0.001), lower impedance (point Z) in BIVA (447 Ohm/m vs. 511 Ohm/m and 531 Ohm/m; P < 0.001), as well as higher NT-pro-BNP levels (2348 pg/ml vs. 2143 pg/ml and 450 pg/ml, respectively) [Table 5]. We also analyzed the severity of diagnosed congestion in ADCHF patients at discharge depending on the number of methods that had detected it [Table 6]. Patients in whom congestion was detected by all 4 methods (lung ultrasound, TE, BIVA, NT-proBNP) had worse paraclinical parameters compared to patients in whom it was detected by 3, 2, or 1 method [Table 6]. Thus, they have higher liver stiffness (17 kPa vs. 11 kPa, 7 kPa, and 5.4 kPa; P < 0.001), greater B-line count on lung ultrasound (21 vs. 16, 16 and 4; P < 0.001), lower impedance (point Z) in BIVA (383 Ohm/m vs. 479 Ohm/m, 490 Ohm/m and 521 Ohm/m; P < 0.001), as well as higher NT-pro-BNP levels (2795 pg/ml vs. 2685 pg/ml, 1649 pg/ml and 1054 pg/ml; P < 0.001, respectively).

Table 5: Comparative characteristics of hydration status phenotype based on integral assessment of congestion (n=171)

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Table 6. Congestion detection rate in patients with acute decompensated chronic heart failure at discharge depending on the number of paraclinical methods used for assessment (n=171)

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During the 12-month follow-up, there were 59 endpoint events (34.5%), including 10 deaths (5.8% of patients) and 49 re-admissions (28.7% of patients). Interestingly, there were no endpoint events in patients with euvolemia at discharge; at the same time, patients with subclinical congestion had 17 endpoint events (10% of patients), and patients with residual congestion had 42 events (in 24.5% of patients). As a result of ROC curve analysis for predicting endpoint events (death or re-admission), the following cut offs were obtained for different methods used to assess congestion at discharge: ultrasound B-line count >5 (P = 0.01), NT-proBNP >2336 pg/ml (P < 0.001), liver stiffness >9.7 kPa, (P < 0.001), and BIVA impedance (point Z) ≤479 (P = 0.01) [Table 7].

Table 7: Cut off values for outcome prediction at discharge, by parameter

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Kaplan–Meier curves of the cumulative probability of survival (all-cause mortality + re-admission) depending on the number of methods that detected congestion^{[1],[2],[3],[4]} are presented in [Figure 3]. There was a significant increase in the risk of all-cause mortality and re-admission where congestion was detected by two (Hazard Ratio (HR) 10.2 [1.3–78]; P =0.025), three (HR 21.8 [2.9–161.7], P = 0.003) or four methods (HR 32.0 [4.1–247], P = 0.001). The Kaplan–Meier curves of the cumulative probability of survival (all-cause mortality + re-admission) determined by the combination of methods that had detected congestion are presented in [Figure 4]. The risk of an endpoint event was significantly higher where congestion had been detected by the combination of two methods “lung ultrasound + TE” (HR 21 [2.2–205]; P =0.008) or by the combination of three methods “lung ultrasound + TE + NT-proBNP” (HR 28.2 [3.6–219]; P = 0.001); and the risk was the highest where congestion had been detected by all four methods (HR 33.1 [4.2–255]; P = 0.001).

Figure 3: Kaplan–Meier curves of the cumulative probability of survival (all-cause mortality + re-admission) versus the number of methods that had detected congestion

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Figure 4: Kaplan–Meier curves of the cumulative probability of survival (all-cause mortality + re-admission) depending on combination of applied assessment methods

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Thus, the use of integral assessment increased the frequency of detection of clinical residual congestion I up to 57% and that of subclinical congestion up to 31%. The two subsets of patients showed an increased frequency of endpoint events, 24.5% and 10%, respectively. All congestion parameters worsened as the number of methods that had detected congestion increased from 1 to 4. Hence, the integral assessment of congestion was superior to individual methods and was able to identify more patients with residual and subclinical congestion. NT-proBNP, lung ultrasound B-line count, liver stiffness, and BIVA impedance (point Z) assessed at discharge of patients with ADCHF had independent prognostic value with regard to the risk of endpoint events (all-cause mortality and re-admissions). ADCHF patients with congestion at discharge detected by two, three, or four methods had a significantly higher risk of endpoint event than euvolemic patients. The combination of two (lung ultrasound + TE) or three (lung ultrasound + TE + NT-proBNP) methods has shown a higher prognostic value in regard to the risk of endpoint event, while the combination of all four methods being the best one.

Discussion

This study evaluated the congestion status in patients with ADCHF based on clinical and paraclinical data. The largest number of patients with subclinical congestion at discharge were detected by NT-proBNP (24%), followed by lung ultrasound (17%), TE (15%), and finally by BIVA (12%). According to the integral assessment, the incidence of residual congestion was 57%, the incidence of subclinical congestion, 31%, and that of euvolemia, 12%. Thus, the integral assessment was superior to individual methods and detected more patients with residual and subclinical congestion. The congestion detection rate in ADCHF patients at discharge was also studied in relation to the number of methods used for the assessment. Patients, in whom congestion was detected by 4 methods (TE, lung ultrasound, BIVA, and NT-proBNP) - unlike those in whom, it was detected by 1, 2, or 3 methods - were characterized by worse paraclinical findings. We found a significant increase in the risk of all-cause mortality and re-admission in patients with congestion identified by two (HR 10.2 [1.3–78); P = 0.025), three (HR 21.8 [2.9–161.7], P = 0.003), or all four methods (HR 32.0 [4.1–247], P = 0.001). Since both circulations should be examined, any combination of methods that includes lung ultrasound and a method for assessing systemic congestion will be highly effective. The combinations of lung ultrasound + TE (two methods, HR 21 [2.2–205]; P =0.008) and lung ultrasound + TE + NT-proBNP (three methods; HR 28.2 [3.6–219]; P = 0.001) were highly predictive, while the combination of all four methods had the best prognostic value (HR 33.1 [4.2–255]; P = 0.001).

Systemic congestion in HF is an important issue and the subject of many studies. Its pathophysiology is more complicated than just accumulation of excess water in the body.^[19] The lack of effective criteria for diagnosing congestion, on the one hand, and of methods that can confirm its complete elimination or//and achievement of the so-called “euvolemia,” on the other,^[20] enhance the relevance of studies that compare the clinical and prognostic values of different approaches to diagnosing congestion. Physical examination remains an important tool for assessing congestion in clinical practice. Traditionally assessed clinical signs and symptoms of congestion reflect increase in intracardiac filling pressures and/or, as a consequence, excessive accumulation of extravascular fluid. The sensitivity and specificity of such assessment, however, is low compared with intracardiac assessment of hemodynamics.^[20]

The use of a quantitative ultrasonic method based on the calculation of B-lines may be promising. In a randomized study^[21] in 518 patients with acute onset dyspnea, it was demonstrated that an approach based on the integration of pulmonary ultrasonography into routine screening examination at the stage of diagnosis of acute HF decompensation is more significant than an approach based on traditional physical examination, chest X-ray and NT-proBNP levels assessment. The accuracy of HF diagnosis using pulmonary ultrasound was significantly higher compared to physical examination alone (area under the curve [AUC] 0.95 vs. 0.88 at, P < 0.01). The evidence indicates that the assessment of the number of B lines allows to identify a risk group for adverse long-term outcomes in both outpatients and hospitalized HF patients. In a study on outpatients, a B-line sum of ≥3 on ultrasound using a 5-zone or 8-zone scan was associated with a 4-fold risk of death or readmission with HF within 6 months.^[22] In patients with acute HF, the sum of B-lines >15 at discharge on scanning of 28 zones was associated with a more than 5-fold increase in the risk of death or readmission with HF.^{[22],[23],[24]}

The adverse effect of residual liver congestion on the prognosis of patients with HF has been shown in several studies.^[8],[25] In a study of 171 patients hospitalized with HF, an unfavorable predictive value of increased liver density at discharge was demonstrated, while patients with liver density >6.9 kPa had a higher rate of death and readmission for HF (HR = 3.57; 95% confidence interval [CI]: 1.93–6.83; P < 0.001).^[26] Thus, the authors concluded that the liver density values measured at discharge probably reflect the presence of subclinical residual liver congestion and can be used as a surrogate marker of residual congestion and adverse events even in patients with optimized therapy, without visible signs and symptoms of volume overload or increased laboratory parameters of liver function. A number of studies have shown a significant role in assessing the state of hydration by bioimpedance measurement in assessing the prognosis in patients with HF, especially in relation to overall mortality after 90 days and 1 year of observation.^[27],[28] It has been shown that the reactance index is a more significant predictor of mortality after 90 days of observation (AUC 0.712, 95% CI: 0.655–0.76; P < 0.001).^[29]

A number of studies have shown a high frequency of residual stagnation when using individual instrumental methods,^{[4],[9],[10],[11]} however, isolated studies have been devoted to the study of the integral assessment of the detection of residual and subclinical stagnation by laboratory-instrumental methods and their effect on the prognosis.^{[2],[12],[30]} It was shown that four biomarkers, BNP, estimated plasma volume status, hydration status assessed by BIVA, and the blood urea nitrogen to creatinine ratio, are able to predict the prognosis of HF patients independently from their AHF and, when combined, explain the 40% risk of death in these patients independent from the acute or chronic HF condition.^[12]

The evaluation of the prognostic value of an integrated assessment of congestion based on several paraclinical methods detecting different components of congestion in HF in regard to the combined rate of all-cause mortality and re-admissions was an important part of this study too. However, the small sample size of our study limits its validity. There is a need for further studies of the diagnostic and prognostic value of the integral congestion assessment in larger populations of HF patients, as well as in other countries.

Conclusions

This study demonstrated the prognostic value of paraclinical methods assessing different aspects of HF-associated congestion with regard to the combined endpoint “all-cause mortality plus re-admissions.” We assessed “hemodynamic” congestion based on NT-proBNP levels, “pulmonary” congestion by lung ultrasound, hepatic stagnation based on liver stiffness determined by TE, and peripheral hydration by BIVA. The results of our study open up new prospects not only for congestion assessment in HF patients but also for further development of congestion management strategies. Wider use of integral assessment of congestion in routine practice may identify patients with a less favorable prognosis. The identification of higher-risk patients before discharge may reduce re-hospitalization for decompensated HF and death.

Financial support and sponsorship

This work was prepared with the support of the “RUDN University Program 5-100.”

Conflicts of interest

There are no conflicts of interest.

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