Abstract
Aims
In acute heart failure (AHF), kidney congestion is basic to treatment and prognosis. Its aetiology is manifold and quite unexplored in details mainly regarding the right heart to pulmonary circulation (Pc) coupling. We investigated the right heart to kidney interrelationship by Doppler renal flow pattern, right atrial dynamics, and right ventricular (RV) function to Pc coupling in AHF.
Methods and results
In 119 AHF patients, echocardiographic and renal Doppler data were analysed. Univariate and multivariate regression models were performed to define the determinants of a quantitative parameter of renal congestion, the renal venous stasis index (RVSI). When grouped according to different intra‐renal venous flow patterns, no differences were observed in haemodynamics and baseline renal function. Nonetheless, patients with renal Doppler evidence of congestion showed a reduced RV function [tricuspid annular plane systolic excursion (TAPSE), S′‐wave velocity, and fractional area change], impaired RV to Pc coupling [TAPSE/pulmonary artery systolic pressure (PASP) ratio], and right atrial peak longitudinal strain (RAPLS), along with signs of volume overload [increased inferior vena cava (IVC) diameters and estimated right atrial pressure]. Univariate and multivariate regression analyses confirmed TAPSE/PASP, RAPLS, and IVC diameter as independent determinants of the RVSI. RVSI was the only variable predicting the composite outcome (cardiac death, heart failure hospitalization, and haemodialysis). An easy‐to‐use echo‐derived right heart score of four variables provided good accuracy in identifying kidney congestion.
Conclusions
In AHF, the renal venous flow pattern combined with a right heart study phenotypes congestion and clinical evolution. Keys to renal flow disruption are an impaired right atrial dynamics and RV–Pc uncoupling. Integration of four right heart echocardiographic variables may be an effective tool for scoring the renal congestive phenotype in AHF.
Keywords: Congestion, Acute heart failure, Right heart, Renal Doppler
Introduction
In heart failure (HF), a large amount of interest has long been placed on the heart–kidney interaction in view of its strong pathophysiological and clinical relevance, 1 and this is true especially for acutely decompensated HF in which kidney and susceptibility to fluid overload and retention are central to the treatment.
There is a new‐found focus on renal venous congestion and its link with right HF. 2 Despite the impressive amount of information on this topic, the complex pathophysiology of the congested kidney and its clinical significance are matters of continuous evolution. A reference non‐invasive, easy‐to‐use, and daily practice feasible method to evaluate HF‐induced renal congestion has not been found yet, but some promising perspectives come from the Doppler renal flow quantification and an analysis of the arterial and venous patterns of organ flow dynamics.
The Doppler evaluation of intra‐renal venous flow (IRVF) has been applied in the evaluation of renal haemodynamics in pregnancy, diabetes, and obstructive kidney disease 3 and, most recently, in HF, 4 , 5 pulmonary hypertension (PH), 6 and post‐cardiac surgical setting. 7
The information available in the HF scenario primarily concerns the chronic and stable clinical condition, and just two papers on acute HF (AHF) patients have been published. 8 , 9
We therefore aimed at implementing this area of evidence linking the Doppler renal flow with right atrial (RA) dynamics and right ventricular (RV) to pulmonary circulation (Pc) coupling in AHF. In this context, we also aimed at developing a simple ultrasound‐derived score, based on readily available cardiac parameters, to better phenotype and predict the extent of renal congestion in this population.
Materials and methods
Study design and population
One hundred nineteen AHF patients admitted to the HF unit of a tertiary centre community hospital were enrolled from October 2017 to December 2019. Renal Doppler data were acquired by a single operator; as such, only patients being admitted during those physician working days have been included. AHF was diagnosed based on the current guidelines criteria, 10 and all patients required intravenous (i.v.) diuretic therapy at presentation. Pregnant women and patients with end‐stage chronic kidney disease (CKD Stage 5 or haemodialysis) or known obstructive kidney disease were excluded. Due to inadequate renal Doppler image quality, nine patients have been omitted from the final analysis (Figure 1 ). Clinical, ultrasound, and laboratory data were obtained on the same day of admission. Echocardiographic and renal Doppler data were acquired consecutively by the same examiner at the time of admission in our unit. Although patients received i.v. diuretics in the Emergency Department, they were all deemed still clinically decompensated at the time of enrolment and data collection. The investigation conformed to the principles outlined in the Declaration of Helsinki.
Figure 1.
Study flow chart. AHF, acute heart failure; CKD, chronic kidney disease.
Cardiac echocardiographic evaluation
Comprehensive transthoracic echocardiography was performed according to the current guidelines. 11 RV function was assessed by tricuspid annular plane systolic excursion (TAPSE), systolic free wall myocardial velocity (S′‐wave velocity), fractional area change (FAC), and TAPSE/pulmonary artery systolic pressure (PASP) ratio, a recognized non‐invasive marker of RV to Pc coupling, highly prognostic in HF and PH. 12 , 13 RA pressure (RAP) was estimated from inferior vena cava (IVC) diameters and collapsibility 11 using a three‐grade classification (5, 10, and 15 mmHg). RA size and function were assessed from standard two‐dimensional (2D) and 2D speckle tracking echocardiography (STE) apical four‐chamber images and cine loops, respectively, as recommended. 14 RA volume index (RAVi) was estimated from RA end‐systolic area. Strain analysis is detailed in the Supporting Information.
The analysis has been focused on RA peak longitudinal strain (RAPLS) as the most commonly altered atrial function parameter in both left heart dysfunction and right heart dysfunction and with high prognostic value for all‐cause mortality and HF‐related hospitalizations. 15
Tricuspid regurgitation (TR) severity was evaluated using a four‐grade classification (mild, moderate, severe, and massive/torrential), according to the current criteria. 16 Stroke volume (SV) and cardiac output (CO) calculations are reported in the Supporting Information.
Renal Doppler evaluation
IRVF colour and spectral Doppler evaluation were assessed with the same cardiac probe with a sector transducer frequency range of 1–5 MHz while the patient was lying in the left lateral decubitus. The colour Doppler velocity range was set to ~16 cm/s. After proper alignment, the right kidney interlobar vessels were identified recording pulsed Doppler flows. In accordance with previous reports, IRVF was evaluated in the right kidney only, due to left kidney venous flow phasicity attenuation by perpendicular renal vein confluence with the IVC and possible entrapment between the abdominal aorta and the superior mesenteric artery. 5 , 17 IRVFs were obtained by the mean of three measurements in sinus rhythm or three index beats (beat following two preceding cardiac cycles of equal duration) in atrial fibrillation (AF). 4 The renal resistive index (RRI) 18 was calculated as the maximum arterial flow velocity minus the diastolic flow velocity, divided by the maximum flow velocity. The venous flow pattern was categorized as continuous or discontinuous based on its morphology. Continuous pattern was defined as venous flow nadir >0 throughout the cardiac cycle. Discontinuous flows, in which the nadir reached zero, were further subdivided into pulsatile, biphasic, and monophasic, based on whether flow interruption was present just during end‐diastole, end‐diastole and late‐systole, or when extended through most of the cardiac cycle, permitting only an early diastolic flow, respectively (Figure 2 A–D ). The venous impedance index (VII) was measured as the peak maximum flow velocity minus minimal flow velocity, divided by maximum flow velocity; therefore, VII was calculated as 1 in discontinuous patterns (Figure 2 E ). To quantitatively assess discontinuous patterns, the recently proposed index of renal congestion was calculated, that is, the renal venous stasis index (RVSI) 6 reflecting the ratio between the renal venous no‐flow time and cardiac cycle duration (Figure 2 F ).
Figure 2.
Venous flow patterns: (A) continuous, (B) discontinuous pulsatile, (C) discontinuous biphasic, (D) discontinuous monophasic, (E) venous impedance index, and (F) renal venous stasis index (RVSI). RVSI represents the percentage of cardiac cycle free of renal venous outflow resulting in 0 in the continuous pattern.
Laboratory data are reported in the Supporting Information.
Statistical analysis
Continuous variables are expressed as mean ± standard deviation (SD), when normally distributed, otherwise as median and inter‐quartile range (IQR). Categorical data were expressed as frequency and percentage. Data normality was assessed by the Shapiro–Wilk test. Comparisons between two groups were performed by Student's t‐test or Mann–Whitney U test for continuous variables and χ 2 test for categorical variables. One‐way analysis of variance (ANOVA) was used to compare multiple variables with IRVF patterns. To assess differences between non‐normally distributed variables among IRVF groups, the Kruskal–Wallis H test was used. A P value <0.05 was considered statistically significant. Spearman and Pearson correlation analysis was performed between various continuous variables and IRVF patterns, with a >0.3 relevance cut‐off for coefficient values. To expose the independent determinants of RVSI, a multivariate logistic regression analysis model was built using significant univariate factors. Intra‐ and inter‐observer reliability of IRVF pattern and RVSI assessment was determined by calculation of the weighted Cohen's kappa coefficient and intra‐class correlation coefficient (ICC), respectively, on a random sample of 20 patients. A receiver operating characteristic (ROC) curve was elaborated to evaluate the performance of an echocardiographic score in predicting marked renal venous congestion (i.e. monophasic IRVF pattern or RVSI ≥ 0.75). Univariate and multivariate Cox regression analysis was performed to identify the predictors of events, assessed as composite endpoint (cardiac death, HF hospitalization, and haemodialysis initiation). All calculations were performed with SPSS Version 23.0 software (IBM Corp.).
Results
Demographic, clinical characteristics, echocardiographic, and laboratory data
Of the 119 patients, 9 had an inadequate renal venous flow image quality. As reported in Table 1, the mean age was 73 ± 12 years, with 63% males presenting in New York Heart Association (NYHA) Class III or IV (64%). The most prevalent aetiology of HF was ischaemic, followed by valvular and ‘idiopathic’ forms.
Table 1.
Demographic, clinical characteristics, echocardiographic, and laboratory data
| All (119) | Continuous (29) | Pulsatile (25) | Biphasic (17) | Monophasic (39) | P value | |
|---|---|---|---|---|---|---|
| Demographics | ||||||
| Age (years) | 73 ± 12 | 72 ± 13 | 77 ± 7 | 70 ± 11 | 73 ± 12 | 0.2814 |
| Males, n (%) | 69 (63) | 14 (45) | 13 (59) | 13 (76) | 29 (73) | 0.0651 |
| BSA (m2) | 1.81 ± 0.21 | 1.77 ± 0.18 | 1.82 ± 0.20 | 1.85 ± 0.28 | 1.8 ± 0.17 | 0.6441 |
| Clinical data | ||||||
| NYHA class, n (%) | 0.0628 | |||||
| I | 8 (8) | 6 (21) | 0 (0) | 1 (6) | 1 (3) | |
| II | 30 (28) | 10 (34) | 6 (27) | 1 (6) | 13 (34) | |
| III | 59 (56) | 12 (41) | 15 (68) | 13 (76) | 19 (50) | |
| IV | 8 (8) | 1 (3) | 1 (5) | 2 (12) | 4 (11) | |
| Hypertension, n (%) | 82 (77) | 25 (86) | 18 (82) | 11 (65) | 28 (74) | 0.3349 |
| Diabetes mellitus, n (%) | 41 (39) | 8 (28) | 12 (55) | 6 (35) | 15 (39) | 0.2689 |
| Smoking, n (%) | 44 (42) | 9 (31) | 11 (50) | 10 (59) | 14 (37) | 0.221 |
| Dyslipidaemia, n (%) | 61 (58) | 15 (52) | 14 (64) | 11 (65) | 21 (55) | 0.7592 |
| Family history, n (%) | 24 (23) | 7 (24) | 5 (23) | 3 (18) | 9 (24) | 0.9595 |
| AF history, n (%) | 51 (48) | 13 (45) | 11 (50) | 7 (41) | 20 (53) | 0.8332 |
| AF during echo, n (%) | 49 (41) | 7 (24) | 10 (40) | 3 (18) | 24 (62) | 0.003 |
| Previous cardiac surgery, n (%) | 23 (22) | 1 (3) | 7 (32) | 4 (25) | 11 (29) | 0.0415 |
| COPD, n (%) | 25 (24) | 2 (7) | 8 (36) | 7 (41) | 8 (21) | 0.023 |
| CKD stage, n (%) | 0.6959 | |||||
| 1 | 5 (5) | 0 (0) | 2 (9) | 0 (0) | 3 (8) | |
| 2 | 6 (6) | 1 (3) | 1 (5) | 2 (12) | 2 (5) | |
| 3 | 18 (17) | 4 (14) | 4 (18) | 3 (18) | 7 (18) | |
| 4 | 5 (5) | 2 (7) | 0 (0) | 0 (0) | 3 (8) | |
| Known heart disease, n (%) | 0.0992 | |||||
| Ischaemic | 44 (42) | 7 (24) | 11 (50) | 12 (71) | 14 (37) | |
| Valvular | 19 (18) | 6 (21) | 7 (32) | 0 (0) | 6 (16) | |
| Idiopathic | 18 (17) | 6 (21) | 1 (5) | 4 (24) | 7 (18) | |
| Tachycardiomyopathy | 6 (6) | 2 (7) | 1 (5) | 0 (0) | 3 (8) | |
| Infiltrative | 3 (3) | 0 (0) | 1 (5) | 0 (0) | 2 (5) | |
| Hypertrophic | 1 (1) | 0 (0) | 0 (0) | 0 (0) | 1 (3) | |
| Other/NA | 15 (14) | 8 (28) | 1 (5) | 1 (6) | 5 (13) | |
| Therapy, n (%) | ||||||
| Beta‐blockers | 78 (75) | 19 (66) | 17 (85) | 11 (65) | 31 (82) | 0.2313 |
| ACEis/ARBs | 53 (51) | 17 (59) | 9 (45) | 8 (47) | 19 (50) | 0.7812 |
| CCBs | 21 (20) | 10 (34) | 6 (30) | 1 (6) | 4 (11) | 0.0264 |
| MRAs | 47 (45) | 12 (41) | 13 (65) | 7 (41) | 15 (39) | 0.2688 |
| ARNIs | 6 (6) | 1 (3) | 0 (0) | 1 (6) | 4 (11) | 0.3773 |
| PH‐specific therapy | 2 (2) | 1 (3) | 1 (5) | 0 (0) | 0 (0) | 0.4689 |
| Echocardiography | ||||||
| EDDi (cm/m2) | 3.0 ± 0.6 | 2.9 ± 0.6 | 3.0 ± 0.7 | 3.2 ± 0.5 | 3.1 ± 0.6 | 0.2171 |
| EDVi (mL/m2) | 73 ± 34 | 64 ± 27 | 64 ± 29 | 87 ± 30 | 79 ± 37 | 0.0374 |
| ESVi (mL/m2) | 46 ± 31 | 35 ± 23 | 38 ± 26 | 61 ± 28 | 54 ± 33 | 0.0045 |
| EF (%) | 41.4 ± 15.8 | 49.4 ± 14.6 | 45.9 ± 16.5 | 33.1 ± 12.9 | 35.0 ± 13.8 | <0.0001 |
| SV (mL) | 45 ± 15 | 49 ± 17 | 47 ± 15 | 47 ± 13 | 40 ± 13 | 0.0780 |
| CO (L/min) | 3.51 ± 1.07 | 3.61 ± 1.31 | 3.73 ± 1.08 | 3.59 ± 1.29 | 3.27 ± 0.8 | 0.4723 |
| CI (L/min/m2) | 1.94 ± 0.64 | 2.03 ± 0.75 | 2.00 ± 0.68 | 1.98 ± 0.66 | 1.77 ± 0.55 | 0.426 |
| LA area (cm2) | 26.6 ± 7.9 | 23.9 ± 6.8 | 28.1 ± 7.0 | 25.8 ± 6.3 | 28.1 ± 9.3 | 0.1471 |
| LAVi (mL/m2) | 51 ± 24 | 46 ± 19 | 54 ± 22 | 48 ± 13 | 54 ± 30 | 0.4474 |
| RAVi (mL/m2) | 38 ± 23 | 23 ± 10 | 34 ± 17 | 31 ± 14 | 56 ± 27 | <0.0001 |
| RAPLS (%) | 20.9 ± 13.0 | 33.1 ± 13.3 | 22.5 ± 10.2 | 20.4 ± 6.9 | 10.3 ± 4.2 | <0.0001 |
| TAPSE (mm) | 15.7 ± 4.7 | 18.3 ± 4.6 | 17.3 ± 4.5 | 15.1 ± 3.4 | 12.7 ± 3.9 | <0.0001 |
| FAC (%) | 35.7 ± 13.4 | 39.8 ± 13.1 | 41.2 ± 12.9 | 34.8 ± 16.1 | 29.3 ± 11.9 | 0.0166 |
| RV TDI S′ velocity (cm/s) | 9.4 ± 3.1 | 11.3 ± 3.6 | 9.6 ± 2.0 | 9.1 ± 2.9 | 7.7 ± 2.3 | 0.0002 |
| E/E′ | 16.21 ± 8.29 | 12.68 ± 6.47 | 21.32 ± 11.29 | 15.47 ± 4.94 | 16.71 ± 7.75 | 0.0060 |
| TR severity, n (%) | <0.0001 | |||||
| Mild | 57 (48) | 22 (76) | 16 (64) | 9 (53) | 4 (10) | |
| Moderate | 36 (30) | 5 (17) | 8 (32) | 5 (29) | 16 (41) | |
| Severe | 16 (13) | 2 (7) | 0 (0) | 3 (18) | 10 (26) | |
| Massive or torrential | 10 (8) | 0 (0) | 1 (4) | 0 (0) | 9 (23) | |
| TR velocity (cm/s) | 2.87 ± 0.58 | 2.68 ± 0.5 | 2.97 ± 0.71 | 2.81 ± 0.43 | 3 ± 0.63 | 0.2511 |
| RV–RA grad (mmHg) | 33.1 ± 14.6 | 28.8 ± 12.5 | 35.9 ± 18.8 | 33.7 ± 10.1 | 35.2 ± 15.8 | 0.3310 |
| PASP (mmHg) | 44.1 ± 17.3 | 34.0 ± 13.4 | 44.0 ± 19.9 | 47.4 ± 9.4 | 50.6 ± 17.3 | 0.0004 |
| TAPSE/PASP (mm/mmHg) | 0.41 ± 0.22 | 0.6 ± 0.23 | 0.44 ± 0.15 | 0.34 ± 0.14 | 0.27 ± 0.10 | <0.0001 |
| IVC expiratory diameter (cm) | 1.93 ± 0.60 | 1.57 ± 0.42 | 1.62 ± 0.42 | 2.11 ± 0.49 | 2.28 ± 0.63 | <0.0001 |
| IVC inspiratory diameter (cm) | 1.22 ± 0.69 | 0.71 ± 0.39 | 0.84 ± 0.39 | 1.66 ± 0.67 | 1.71 ± 0.62 | <0.0001 |
| CI (%) | 41 ± 22 | 57 ± 17 | 51 ± 16 | 27 ± 19 | 26 ± 16 | <0.0001 |
| eRAP (mmHg) | 10 ± 4.1 | 7.2 ± 3.2 | 7.8 ± 3.5 | 12.5 ± 3.4 | 12.5 ± 3.3 | <0.0001 |
| Renal Doppler | ||||||
| RRI | 0.68 ± 0.11 | 0.68 ± 0.09 | 0.66 ± 0.09 | 0.7 ± 0.08 | 0.68 ± 0.14 | 0.7681 |
| VII | 0.15 (0.08–0.32) | |||||
| RVSI | 0.56 (0.35–0.75) | 0.22 (0.15–0.36) | 0.54 (0.42–0.60) | 0.75 (0.64–0.78) | <0.0001 | |
| Laboratory data | ||||||
| Creatinine (mg/dL) | 1.27 ± 0.49 | 1.22 ± 0.57 | 1.15 ± 0.43 | 1.39 ± 0.56 | 1.34 ± 0.48 | 0.3568 |
| eGFR (mL/min) | 57 ± 20 | 59 ± 23 | 60 ± 20 | 55 ± 23 | 55 ± 18 | 0.6923 |
| Na (mEq/L) | 140 ± 3 | 141 ± 2 | 140 ± 4 | 140 ± 2 | 140 ± 4 | 0.1669 |
| Hb (g/dL) | 12.4 ± 2.0 | 12.8 ± 2.1 | 12.3 ± 2.4 | 12.2 ± 1.7 | 12.4 ± 1.8 | 0.7525 |
| NT‐proBNP (ng/L) | 3229 (1498–7287) | 2135 (617–3618) | 2367 (1512–6381) | 6457 (2346–21 839) | 4044 (2405–9073) | <0.0001 |
ACEis, angiotensin‐converting enzyme inhibitors; AF, atrial fibrillation; ARBs, angiotensin receptor blockers; ARNIs, angiotensin receptor neprilysin inhibitors; BSA, body surface area; CCBs, calcium channel blockers; CI, cardiac index; CKD, chronic kidney disease; CO, cardiac output; COPD, chronic obstructive pulmonary disease; EDDi, end‐diastolic diameter index; EDVi, end‐diastolic volume index; EF, ejection fraction; eGFR, estimated glomerular filtration rate; eRAP, estimated right atrial pressure; ESVi, end‐systolic volume index; FAC, fractional area change; IVC, inferior vena cava; LA, left atrial; LAVi, left atrial volume index; MRAs, mineralocorticoid receptor antagonists; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; NYHA, New York Heart Association; PASP, pulmonary artery systolic pressure; PH, pulmonary hypertension; RA, right atrial; RAPLS, right atrial peak longitudinal strain; RAVi, right atrial volume index; RRI, renal resistive index; RV, right ventricular; RVSI, renal venous stasis index; SV, stroke volume; TAPSE, tricuspid annular plane systolic excursion; TDI, tissue Doppler imaging; TR, tricuspid regurgitation.
Systemic arterial hypertension, dyslipidaemia, smoking habit, and diabetes mellitus were highly prevalent (77%, 58%, 42%, and 39%, respectively), and the same was true for AF (previous history 48% and present during the echo examination 41%). Chronic obstructive pulmonary disease (COPD) rate was 24%. At hospital admission, diuretics, beta‐blockers, and angiotensin‐converting enzyme (ACE) inhibitors/angiotensin receptor blockers were part of the home therapy in 75%, 70%, and 51% of cases. Angiotensin receptor neprilysin inhibitors (ARNIs) were taken by only 10% of patients.
The average ejection fraction (EF) was moderately reduced (41%), with HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and HF with mildly reduced ejection fraction (HFmrEF) accounting for 49%, 34%, and 17%. SV, CO, and cardiac index (CI) were slightly reduced.
RV dysfunction was highly prevalent, as shown by the average TAPSE (16 mm), S′‐wave velocity (9 cm/s), FAC (36%), and TAPSE/PASP ratio (0.44).
There was evidence of biatrial enlargement, with clear depression of RA function, as expressed by a mean RAPLS of 21%. 19
Systemic congestion was common, as suggested by increased IVC diameter with limited collapsibility and an estimated RAP (eRAP) >10 mmHg. Average left ventricular (LV) filling pressure was abnormally elevated (mean E/e′ of 16).
The median creatinine was slightly elevated, with an estimated glomerular filtration rate (eGFR) of 56 mL/min. Average N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) values were markedly abnormal (3229 ng/L).
Renal haemodynamic data
The percentage of non‐interpretable studies was low (7.6%), even if slightly higher compared with previous reports (3%). This small difference may be expected considering that no dedicated curvilinear probe was available in our unit at the time of the study. Kidney arterial haemodynamics were unremarkable, with a mean RRI of 0.68 and only 25% of patients showing a continuous IRVF pattern. A pulsatile, biphasic, and monophasic pattern was observed in 21%, 14%, and 33% of cases, respectively. Median RVSI was 0.56 in patients with a discontinuous renal venous flow, and median VII was 0.15 among continuous‐flow phenotypes. No differences of IRVF pattern were observed according to age, sex, NYHA class, aetiology of HF, cardiovascular risk factors prevalence, and baseline renal function (evaluated by CKD stage, serum creatinine, and eGFR). As for the latter, because no paired data on renal function–IRVF pattern at follow‐up are available, the relation cannot be adequately evaluated.
Patients with COPD or previous cardiac surgery showed a significantly higher discontinuous IRVF patterns. As anticipated, COPD patients had significantly higher PASP and lower TAPSE/PASP ratio values. Instead, post‐surgery subjects exhibited a lower TAPSE, S′‐wave velocity, and RAPLS, with non‐significant differences in TAPSE/PASP ratio (P = 0.058), PASP (P = 0.902), and FAC (P = 0.374).
No differences were observed in home therapeutic regimens, except for calcium channel blocker (CCB) intake, which was linked to a better renal venous flow pattern. As expected, patients receiving CCB medications had significantly higher EF compared with others (49 ± 16% vs. 39 ± 15%, P = 0.006).
AF history did not differ across the IRVF spectrum, whereas, during IRVF recording, AF was more prevalent in the monophasic group (P = 0.003). Normal continuous IRVF pattern exhibited smaller LV volume and higher EF, although no differences in SV, CO, and CI were noted. Left atrial volume index (LAVi) did not differ between IRVF groups, while higher RAVi and lower strain values (RAPLS) were associated with discontinuous renal flow patterns.
RV dimensions were smaller, and function parameters were better (TAPSE, S′‐wave velocity, and FAC) in patients with continuous vs. discontinuous IRVF pattern.
Interestingly, a lower TAPSE/PASP ratio and a higher PASP coupled with discontinuous patterns. Similarly, higher IVC diameter, lower IVC collapsibility ratio, higher eRAP, and more severe degrees of TR were more common in the discontinuous group. RVSI progressively increased from pulsatile to monophasic group, reflecting worsening degrees of renal congestion, whereas no differences in RRI were observed. NT‐proBNP levels were higher in patients with discontinuous IRVF patterns. RVSI correlation analysis is reported in Supporting Information, Results and Table S1 .
TAPSE/PASP ratio distribution is reported in Supporting Information, Results and Table S2 .
Univariate and multivariate regression analyses
Univariate regression analysis of RVSI was performed, and RV function parameters like TAPSE, S′‐wave velocity, FAC, and TAPSE/PASP ratio resulted as significantly associated factors. The same was true for RA parameters (RAPLS and RAVi), IVC diameters and eRAP, severity of TR, PASP, and LV EF. No significant association was found regarding LV dimensions and volumes, haemodynamic indexes, and baseline renal function (Table 2 ).
Table 2.
RVSI univariate linear regression analysis
| β (SE) | t | P value | |
|---|---|---|---|
| TAPSE/PASP | −1.08 (0.13) | −8.08 | <0.0001 |
| EF | −0.00 (0.00) | −3.29 | 0.0015 |
| SVi | 0.00 (0.00) | −1.95 | 0.0550 |
| CO | 0.03(0.03) | −0.92 | 0.3632 |
| LAVi | 0.00 (0.00) | 0.88 | 0.3795 |
| TAPSE | 0.03 (0.00) | −5.99 | <0.0001 |
| FAC | −0.01 (0.00) | −4.44 | <0.0001 |
| RV TDI S′ velocity | −0.05 (0.01) | −3.96 | 0.0002 |
| TR velocity | 0.04 (0.05) | 0.75 | 0.4558 |
| TR severity | 0.17 (0.03) | 6.43 | <0.0001 |
| E/E′ | −0.00(0.00) | −0.59 | 0.5571 |
| PASP | 0.00 (0.00) | 2.13 | 0.0361 |
| IVC expiratory diameter | 0.16 (0.04) | 4.23 | <0.0001 |
| CI | −0.00 (0.00) | −4.94 | <0.0001 |
| Creatinine | 0.08 (0.05) | 1.60 | 0.1134 |
| eGFR | −0.00 (0.00) | −1.00 | 0.3217 |
| NT‐proBNP | 0.00 (0.00) | 1.45 | 0.1501 |
| Na+ | −0.00(0.00) | −1.54 | 0.8413 |
| RRI | 0.16 (0.24) | 0.67 | 0.5030 |
| RAPLS | −0.02 (0.00) | −6.86 | <0.0001 |
CI, cardiac index; CO, cardiac output; EF, ejection fraction; eGFR, estimated glomerular filtration rate; FAC, fractional area change; IVC, inferior vena cava; LAVi, left atrial volume index; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; PASP, pulmonary artery systolic pressure; RAPLS, right atrial peak longitudinal strain; RRI, renal resistive index; RV, right ventricular; RVSI, renal venous stasis index; SVi, stroke volume index; TAPSE, tricuspid annular plane systolic excursion; TDI, tissue Doppler imaging; TR, tricuspid regurgitation.
To further explore the independent determinants of RVSI, a multivariate linear regression analysis model of significant univariate factors was performed. Interestingly, TAPSE/PASP ratio, RAPLS, and IVC expiratory diameter emerged as significant independent RVSI determinants (R 2 = 0.617) (Table 3 ).
Table 3.
Multivariate linear regression analysis of RVSI determinants
| R 2 | Root MSE | RVSI mean | ||
|---|---|---|---|---|
| 0.617116 | 0.195542 | 0.403763 |
| Parameter | Estimate | SE | t value | Pr > |t| |
|---|---|---|---|---|
| Intercept | 0.592008 | 0.108250 | 5.468917 | <0.0001 |
| TAPSE/PASP | −0.606472 | 0.139613 | −4.343959 | <0.0001 |
| IVC expiratory diameter | 0.106349 | 0.038810 | 2.740233 | 0.007 |
| RAPLS | −0.007644 | 0.002231 | −3.426625 | <0.0001 |
IVC, inferior vena cava; MSE, mean standard error; PASP, pulmonary artery systolic pressure; RAPLS, right atrial peak longitudinal strain; RVSI, renal venous stasis index; TAPSE, tricuspid annular plane systolic excursion.
Interestingly, the degree of TR (severe vs. non‐severe) did not result as an independent RVSI determinant. Similar results were obtained when stratifying patients by NT‐proBNP tertiles and with IVC expiratory diameter ≥21 mm. However, in patients with non‐dilated IVC (i.e. <21 mm), more than moderate TR resulted as a significant predictor of renal congestion (P = 0.026) (Table 4 ).
Table 4.
Multivariate linear regression analysis of RVSI determinants when IVC < 21 mm
| R 2 | Root MSE | RVSI mean | ||
|---|---|---|---|---|
| 0.560173 | 0.196343 | 0.278000 |
| Parameter | Estimate | SE | t value | Pr > |t| |
|---|---|---|---|---|
| Intercept | 0.681168 | 0.067175 | 10.140132 | <0.0001 |
| TAPSE/PASP | −0.639034 | 0.158625 | −4.028574 | <0.0001 |
| TR > moderate | 0.206991 | 0.088407 | 2.341345 | 0.228 |
| RAPLS | −0.005212 | 0.002406 | −2.165905 | 0.0346 |
IVC, inferior vena cava; MSE, mean standard error; PASP, pulmonary artery systolic pressure; RAPLS, right atrial peak longitudinal strain; RVSI, renal venous stasis index; TAPSE, tricuspid annular plane systolic excursion; TR, tricuspid regurgitation.
Composite endpoint
Kaplan–Meier cumulative survival curves are presented for TAPSE/PASP, RVSI, and NT‐proBNP tertiles and compared using the log‐rank test (Figure 3 ). At multivariate analysis, variables significantly associated with the primary composite endpoint were RVSI and NT‐proBNP (Table 5 ). Of note, renal Doppler was the only echo‐derived haemodynamic parameter with prognostic significance.
Figure 3.
Kaplan–Meier curves of (A) renal venous stasis index, (B) tricuspid annular plane systolic excursion/pulmonary artery systolic pressure, and (C) N‐terminal pro‐brain natriuretic peptide.
Table 5.
Univariate and multivariate analyses of variables associated with primary endpoint
| Univariate | Multivariable | |||||
|---|---|---|---|---|---|---|
| HR | 95% CI | P value | HR | 95% CI | P value | |
| TAPSE/PASP | 0.085 | 0.014–0.525 | 0.0080 | 1.944 | 0.118–32.026 | 0.6419 |
| RVSI | 8.359 | 2.656–26.308 | 0.0003 | 6.614 | 0.984–44.474 | 0.0520 |
| RAPLS | 0.974 | 0.947–1.002 | 0.0640 | — | — | — |
| IVC expiratory diameter | 1.441 | 0.877–2.369 | 0.1498 | — | — | — |
| TR severity | 1.294 | 0.953–1.759 | 0.0990 | — | — | — |
| NT‐proBNP | 1.005 | 1.002–1.008 | 0.0006 | 1.009 | 1.004–1.013 | 0.0002 |
| Creatinine | 1.786 | 1.062–3.003 | 0.0286 | 1.623 | 0.846–3.113 | 0.1455 |
| EDVi | 1.006 | 0.997–1.014 | 0.1979 | — | — | — |
| EF | 0.985 | 0.965–1.006 | 0.0135 | 1.019 | 0.987–1.051 | 0.2454 |
| CO | 0.876 | 0.615–1.248 | 0.4633 | — | — | — |
| E/e′ | 0.995 | 0.958–0.995 | 0.7742 | — | — | — |
CI, confidence interval; CO, cardiac output; EDVi, end‐diastolic volume index; EF, ejection fraction; HR, hazard ratio; IVC, inferior vena cava; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; PASP, pulmonary artery systolic pressure; RAPLS, right atrial peak longitudinal strain; RVSI, renal venous stasis index; TAPSE, tricuspid annular plane systolic excursion; TR, tricuspid regurgitation.
Outcome analysis and follow‐up
Thirty‐three patients (27%) were lost at follow‐up. During the observation period (median 24 months, IQR 17–39 months; range 1–47 months), 43 patients (50%) met the primary endpoint. Twenty‐six patients died from cardiovascular disease (20 pump failure and 6 arrhythmic event), 14 patients experienced at least one unplanned hospitalization from HF, and 3 patients underwent haemodialysis due to severe renal function worsening.
Receiver operating characteristic curve analysis and score derivation
Based on the multivariate analysis, we modelled a score to predict severe renal congestion by combining RAPLS, TAPSE/PASP ratio, IVC expiratory diameter, and degree of TR as main determinants of renal congestion. One point was attributed for a marked abnormality of the following parameters: TAPSE/PASP ratio ≤0.30, RAPLS ≤ 20%, IVC expiratory diameter ≥21 mm, and more than mild TR (Figure 4). A RAPLS ≤ 20% was taken as an indicator of RA dysfunction based on reported normal values. 19 The TAPSE/PASP ratio cut‐off of 0.30 was selected on published data 12 and corresponded also to the lower tertile of our sample. An IVC expiratory diameter ≥21 mm is the pathological cut‐off by the current guidelines. 10 Furthermore, inclusion of TR was based on previous reports. 4 , 6 , 20 To test the score diagnostic performance (i.e. identification of severe vs. non‐severe renal congestion), results were compared with the actual renal Doppler data and ROC curves were calculated. A score of 3 provided the best diagnostic accuracy for monophasic IRVF pattern [sensitivity of 80%, specificity of 87%, diagnostic accuracy of 84%, area under the ROC curve (AUC) of 0.898, P < 0.0001] or an RVSI ≥ 0.75 (sensitivity of 91%, specificity of 89%, diagnostic accuracy of 89%, AUC of 0.904, P < 0.0001; Figure 5) identification. The intra‐observer and inter‐observer reproducibility is reported in the Supporting Information.
Figure 4.
Four‐elements echo score to predict renal congestion. One point is assigned to tricuspid annular plane systolic excursion (TAPSE)/pulmonary artery systolic pressure (PASP) ratio ≤0.30, right atrial peak longitudinal strain (RAPLS) ≤20%, inferior vena cava (IVC) expiratory diameter ≥21 mm, or more than mild tricuspid regurgitation. Marked renal congestion (i.e. monophasic intra‐renal venous flow pattern or renal venous stasis index ≥0.75) is likely reflected by a score ≥3.
Figure 5.
Receiver operating characteristic curves of echo renal congestion score performance. A score of 3 identified in patients with a monophasic intra‐renal venous flow (IRVF) pattern (top) shows a sensitivity and specificity of 80% and 87%, respectively, with a diagnostic accuracy of 84% [area under the ROC curve (AUC) of 0.898, P < 0.0001]. Similarly, a score of 3 was associated with renal venous stasis index (RVSI) ≥0.75 (bottom) (sensitivity of 91%, specificity of 89%, diagnostic accuracy of 89%, AUC of 0.904, P < 0.0001).
Discussion
The evaluation of intra‐renal haemodynamics by means of echo‐Doppler imaging is steadily emerging and receiving progressive attention as a relevant clinical tool in the diagnostic armamentarium and clinical management of congestion in HF. 20 , 21 Our data point on a combined source of information through the definition of the renal Doppler ‘congestive phenotype’ and the right heart dynamics reporting the first analysis of renal blood flow characteristics in AHF. Notably, RVSI emerged as the only haemodynamic variable predictive of clinical events. We also report a pragmatic echocardiographic score correlating with renal congestion at presentation.
Implications of renal flow analysis in acute heart failure
Renal function deterioration in HF is the result of the complex interaction and balance of various extravascular and intravascular determinants that yield a reduced net glomerular capillary blood flow and filtration pressure. Those factors include, for example, renal hypoperfusion beyond autoregulatory range (perfusion pressure ≤80 mmHg), HF‐induced up‐regulation of sympathetic nervous and renin–angiotensin–aldosterone (RAS) systems, intra‐abdominal hypertension, increased interstitial renal pressure, and dysregulation of gut flora and endothelial function. 22 Initial triggers of these abnormalities are a RAP elevation and renal stasis, promoting splanchnic and gut venous congestion as classically described by Guyton's model. 23 , 24 The identification of renal congestion by IRVF pattern is simple and feasible in most patients and carries important prognostic implications. Previous works have shown that mortality and renal function deterioration markedly increase in HF patients with renal congestion during hospital admission or at follow‐up, 4 , 5 , 6 especially when RV dysfunction is present. 9 It was also demonstrated that IRVF may change acutely according to volume status and decongestive therapies. 8 , 17 In this scenario, data on AHF patients are lacking and thorough determinants of the renal congestion besides RAP elevation are still elusive.
The haemodynamic profile of our patients closely matches the one of previous data, with the only exception of a lower EF among the discontinuous IRVF patterns, which is likely explained by the higher prevalence of reduced EF patients in our AHF sample, compared with the relatively preserved and homogenous EF reported in previous works involving stable/chronic HF patients. 2 , 4 , 20 When compared with the other single work published on AHF patients, 9 our population showed slightly worse RV function (mean TAPSE 16 vs. 18 mm) and CO (3.5 vs. 4.7 L/min), while PASP was marginally lower (44 vs. 54 mmHg). Other significant haemodynamic, renal Doppler, laboratory, and demographic parameters were almost identical (EF, eRAP, discontinuous IRVF pattern prevalence, serum creatinine, eGFR, NT‐proBNP, age, and NYHA class).
Also, previous works 4 , 6 report a slight decline in renal function over the IRVF congestive spectrum. No differences were observed in our populations, as it may be expected in AHF. 6
Because renal congestion evaluated by RVSI proved to be a highly prognostic parameter in this population of HF patients, we suggest incorporating its routine measurement in clinical practice.
Interaction between renal flow, right atrial dynamics, and right ventricular to pulmonary circulation coupling
The relation between RAP and venous return, a matter debated for decades by physiologists, is not univocal with both variables each other dependent on a dynamic equilibrium of pulmonary engorgement, cardiac, and systemic venous return function. 9 Besides the single numerical value of RAP, conventionally measured at the end‐expiratory pre‐C point, a role also for pressure waveform morphology as contributor to determine the effective renal venous outflow has been anticipated. Our findings extend on the role of an elevated RAP together with impaired right atrioventricular dynamics as mechanical mediators of the backward pulsatile load impeding the normal venous outflow, configuring the pathophysiological hallmark of renal stasis. Specifically, the combined contribution of both atrial and ventricular functions, along with the presence and degree of tricuspid valve pathology, is key elements. 20 Remarkably, identical RAPs may match with different combinations of the aforementioned factors yielding to specific degrees of renal venous congestion and venous flow disruption, as shown in Figure 6 .
Figure 6.
(A) Positive pressure gradient (grey) between the mean systemic filling pressure (dotted line) and systemic veins, in the presence of normal right atrial pressure (RAP) and regular jugular venous pulse morphology [no significant right ventricular (RV) or right atrial (RA) dysfunction or tricuspid valve disease]. (B) Pressure gradient is reduced (grey) when RAP is elevated, leading to discontinuous venous outflow. (C) Marked reduction in venous flow (grey) due to jugular waveform disruption (as with RV or RA pathology or tricuspid valve disease), independently from RAP values.
For example, advanced RV dysfunction or TR combines with marked elevation of the jugular pulse x‐descent and v‐wave; similarly, when the compensatory increase in RA function to maintain RV filling (the so‐called preload reserve) is exhausted, a‐wave elevation ensues, possibly leading to abnormal renal venous flow despite normal RAP values. 20 , 25
Statistical analysis of univariate and multivariate determinants of RVSI highlighted TAPSE/PASP ratio, RAPLS, and IVC expiratory diameter as significant and independent parameters, thus suggesting a crucial role of the right heart in determining renal congestion in AHF, regardless of left heart function. This finding, although previously anticipated, has never been the subject of a dedicated investigation to validate it.
In addition, while a role for TR in determining splanchnic stasis may be expected and has been extensively described, in our AHF population, it became independently relevant only among patients with a near‐normal intravascular volume (i.e. IVC < 21 mm).
Although we do not have a definite explanation, we think these findings may be explained by the dynamic nature and exquisite volume dependency of TR.
In patients with marked fluid overload, a certain degree of functional TR is expected, which may not be independently responsible for venous stasis but rather act as ‘proportionate’ TR. However, when the severity of regurgitation exceeds the degree of intravascular congestion, it may disrupt splanchnic venous outflow. Again, to fully support this hypothesis, a longitudinal follow‐up during decongestion would be needed.
Renal phenotyping score
We here propose a simple echocardiographic score integrating relevant parameters to identify severe degrees of renal congestion as a surrogate for renal Doppler evaluation, which, albeit simple and reproducible, may not yet be routinely endorsed by cardiologists in clinical practice. We anticipate that a simple tool like a score may encourage and simplify patient assessment and help to identify those with an advanced congestive phenotype, warranting more aggressive volume depleting therapy. However, because paired data on score values and renal Doppler profile were collected only at admission, the findings cannot be extended to predict renal congestion at discharge and follow‐up.
Study limitations
This is a relatively small sample size study requiring observational longitudinal studies. Moreover, due to its single‐centre and single‐unit design, patient selection may suffer of some biases, especially excluding AHF patients acutely admitted to intensive care unit (ICU). No invasive data of atrial, pulmonary, and RV pressures were available. The lack of an RV strain analysis with its ratio derivation with PASP may generate some lack in precision. Nonetheless, the TAPSE/PASP analysis is the only non‐invasive echo‐derived method validated against RV pressure volume curves. As to the proposed echocardiographic score, a validation process across centres is required. Moreover, a serial reassessment during decongestive therapy is warranted, and most importantly, it should be correlated with changes in renal pattern to verify whether it can actually describe and predict its fluctuations.
Conclusions
Phenotyping renal congestion in AHF through renal Doppler analysis combined with a right heart haemodynamics appears feasible and informative. The integration of ultrasound‐derived right heart and kidney flow variables into a four‐element echocardiographic score seems a simple and effective tool to identify the renal congestive phenotype of AHF when renal flow analysis is not available. Present findings extend the evidence on the cardio‐renal syndrome approach, paving the way to intervention strategies with proven effectiveness on the right heart that may benefit the kidney.
Conflict of interest
None declared.
Funding
The present investigation was supported by a grant of the Fondazione Italo Monzino, Milan, Italy.
Supporting information
Table S1. RVSI Correlation Analysis.
Table S2. Demographic, Clinical Characteristics, Echocardiographic, and Laboratory Data according to TAPSE/PASP Ratio.
Figure S1. Example of a patient's right atrial strain curve at admission: RAPLS 14%. The patient had a monophasic venous flow pattern and a TAPSE/PASP ratio of 0.22, A. The same patient after decongestion was achieved: RAPLS improved to 28.9%, IRVF pattern became continuous and TAPSE/PASP ratio increased to 0.49, B (readapted to match scale with A). ɛs: reservoir function. ɛe conduit function. ɛa: booster function.
Vella, A. , Labate, V. , Carenini, G. , Alfonzetti, E. , Milani, V. , Bandera, F. , Oliva, O. , and Guazzi, M. (2023) Phenotyping congestion in acute heart failure by renal flow and right heart to pulmonary circulation coupling. ESC Heart Failure, 10: 3546–3558. 10.1002/ehf2.14522.
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Associated Data
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Supplementary Materials
Table S1. RVSI Correlation Analysis.
Table S2. Demographic, Clinical Characteristics, Echocardiographic, and Laboratory Data according to TAPSE/PASP Ratio.
Figure S1. Example of a patient's right atrial strain curve at admission: RAPLS 14%. The patient had a monophasic venous flow pattern and a TAPSE/PASP ratio of 0.22, A. The same patient after decongestion was achieved: RAPLS improved to 28.9%, IRVF pattern became continuous and TAPSE/PASP ratio increased to 0.49, B (readapted to match scale with A). ɛs: reservoir function. ɛe conduit function. ɛa: booster function.