Prognostic Impact of Renal Microcirculatory Dysfunction in Heart Failure Assessed by the Advanced Doppler technique, Superb Microvascular Imaging

doi:10.21203/rs.3.rs-4806169/v1

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Prognostic Impact of Renal Microcirculatory Dysfunction in Heart Failure Assessed by the Advanced Doppler technique, Superb Microvascular Imaging

https://doi.org/10.21203/rs.3.rs-4806169/v1

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The critical role of cardio-renal interactions in heart failure (HF) prognosis has gained increasing recognition, yet standardized methods for their assessment remain elusive. This study introduces a novel approach utilizing Superb Microvascular Imaging (SMI), an advanced ultrasound technique enabling detailed microvascular flow visualization, to evaluate renal microcirculation. We conducted a retrospective analysis of 78 patients who underwent renal ultrasonography with SMI between October 2020 and May 2023. Temporal changes in the Vascular Index (VI), which quantifies the blood flow signal area within the region of interest on SMI images, were measured. Key parameters included Maximum VI (Max.VI), Minimum VI (Min.VI), and the cyclic variation of VI, calculated as the intrarenal perfusion index (IRPI) = (Max.VI - Min.VI) / Max.VI within one cardiac cycle. The primary endpoint was a composite event (CE), defined as all-cause mortality or unplanned hospitalization due to worsening HF. Over a mean follow-up period of 1.6 ± 0.8 years, 13 of 78 patients (17%) experienced CEs. Patients with CEs exhibited significantly lower Max.VI and Min.VI values, while IRPI was significantly elevated in this group compared to those without CEs. Univariable Cox regression analyses revealed significant associations between Max.VI, Min.VI, and IRPI with CEs. In multivariable Cox regression analyses, Max.VI and Min.VI maintained significant associations with CEs after adjusting for creatinine, estimated central venous pressure, and intra-renal venous flow pattern. Kaplan-Meier analysis demonstrated that Max.VI (< 0.31, as determined by ROC analysis; 43% vs. 7%, log-rank p < 0.001), Min.VI (< 0.08, 42% vs. 8%, log-rank p < 0.001), and IRPI (> 0.70, 39% vs. 10%, log-rank p = 0.002) could effectively stratify CE prognosis. This novel application of SMI for renal circulation assessment provides valuable insights into HF prognosis and enables risk stratification beyond conventional markers.

Health sciences/Nephrology/Kidney

Health sciences/Cardiology/Cardiovascular biology

Health sciences/Medical research/Outcomes research

renal microcirculation

cardiorenal interaction

heart failure

prognostic marker

In recent years, the rapid increase in heart failure (HF) patients and their poor prognosis have emerged as significant social issues.[1] Recurrent hospitalizations due to HF deteriorate patients' quality of life, restrict the social activities of their families, and significantly escalate healthcare costs, placing a substantial burden on society. Therefore, preventing hospital admissions related to HF is an urgent priority.

Appropriate fluid management is crucial for preventing HF hospitalizations, yet determining the optimal fluid balance is challenging. Proper fluid adjustment involves identifying the circulatory equilibrium point where low cardiac output and hypoperfusion are avoided, and maintaining an optimal venous return without residual organ congestion.[2–4] However, a standardized method for evaluating this optimal venous return has not been established. In clinical practice, changes in clinical findings and renal function resulting from diuretic use are often referenced. Yet, it is difficult to determine whether the deterioration in renal function during treatment is due to hypoperfusion or residual congestion based solely on conventional tests. Recently, intrarenal vein Doppler waveform analysis has been increasingly recognized as useful for evaluating residual congestion.[5] Nevertheless, this indicator reflects congestion without considering the perfusion aspect, which remains a challenge.

To address this, we focused on the significant impact of HF-induced perfusion deterioration on the blood flow velocities in the renal arteries and veins. We utilized Superb Microvascular Imaging (SMI) to visualize these changes. SMI allows for the estimation of the number of moving blood cells, including those in perfusion-related arteries, and offers greater versatility compared to pulsed Doppler methods.[6–8] In our previous studies, we reported a strong correlation between the Vascular Index (VI) obtained from SMI images and the right atrial pressure or left atrial pressure measured via right heart catheterization.[9] This study aims to investigate the impact of indicators obtained from SMI images on the prognosis of heart failure patients.

Subjects and Data collection

In the period from October 2020 to May 2023, we conducted a retrospective analysis of 109 patients who underwent renal ultrasonography using the SMI technique. Patients were excluded from this study if they underwent cardiac surgeries such as aortic valve replacement, coronary artery bypass grafting, and mitral valve repair or replacement following their renal echocardiogram(n = 27). We also excluded patients who were lost to follow-up(n = 4). After excluding these patients, we studied 78 patients. Of these, 64 (82%) patients were diagnosed with chronic heart failure, while the remaining 14 (18%) underwent both cardiac and renal blood flow ultrasonography for screening purposes (Fig. 1).

Data were collected retrospectively from electronic health records. We extracted relevant clinical information, including diagnostic codes, treatment details, and follow-up records.

The study protocol was approved by the Ethics Committee of Nagoya City University (Approval No. 60-21-0115) and was performed in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before their enrollment in the study. All data used in the analysis were anonymized."

Renal ultrasound study

1)SMI study

Renal ultrasound studies were performed using an Aplio i800 system (Canon Medical Systems, Tochigi, Japan). Using a variable-frequency 1.5 to 6.0 MHz convex transducer, the right kidney was studied. Patients were placed in the supine position for the examination. They were asked to stop breathing at the end of expiration or breathe quietly if this was impossible during image acquisition. For the settings for SMI, the color velocity scale was adjusted to 2.8 cm/s and frame rates were more than 55 Hz. Gain settings were optimized for each imaging. SMI enables the quantitative analysis of vascular parameters by utilizing the VI to quantitatively represent tissue blood flow. The VI is defined as the area percentage of blood flow within the focal lesion, calculated by the formula: VI = (area of blood flow signal / total area of the region of interest (ROI)). The ROI is delineated to encompass the renal cortex and medulla. Observations from intrarenal Doppler (IRD) studies have demonstrated that VI exhibits variability throughout the cardiac cycle. We propose that this variability, specifically the amplitude of VI fluctuation, may be accentuated in patients with HF exhibiting renal congestion. Consequently, we introduced the Intrarenal Perfusion Index (IRPI), which quantifies the cyclic variation in VI. The IRPI is computed as the difference between the maximum VI (Max.VI) and the minimum VI (Min.VI) within a single cardiac cycle relative to the Max.VI, expressed as: IRPI = (Max.VI - Min.VI) / Max.VI. Regarding data variability, previous studies have examined intra- and inter-observer variability and reported negligible discrepancies.[9]

We present representative images in which, as shown in Fig. 2-A, persistent blood flow is detected in the patient and waveforms that vary with the cardiac cycle indicate a low IRPI. Conversely, in the patient shown in Fig. 2-B, the blood flow is intermittent, and there are variations in blood flow with the cardiac cycle, demonstrating a high IRPI.

2) Intrarenal Doppler ultrasonography

Intrarenal Doppler ultrasonography (IRD) measurements were conducted as previously described. [5] Briefly, the color Doppler velocity setting was adjusted to approximately 16 cm/s. Color Doppler imaging facilitated the identification of interlobar vessels, with the sample volume determined based on color Doppler signals from the interlobar arteries. The resistance index (RI) at a lobar artery was calculated using the formula: RI = (maximum flow velocity - diastolic flow velocity) / maximum flow velocity. The Doppler waveforms of the intrarenal venous flow (IRVF) were categorized into three flow patterns: continuous, biphasic discontinuous, and monophasic discontinuous, as previously reported. [5] All measurements were averaged over three cardiac cycles during sinus rhythm. For patients with atrial fibrillation, an index beat—defined as the beat following two consecutive cardiac cycles of equal duration—was utilized for each measurement.

Echocardiography and Laboratory data

Comprehensive echocardiographic examinations were conducted using a consistent ultrasound system. Left ventricular ejection fraction (LVEF) was evaluated employing the biplane disc summation method. Measurements included peak early (E) and late (A) diastolic velocities of the left ventricular inflow, as well as the average of peak early diastolic velocity at both the septal and lateral mitral annulus corners (e′), assessed in the apical four-chamber view. The E/e′ ratio was calculated by dividing the E velocity by e′. The tricuspid regurgitation pressure gradient (TRPG) was derived from the peak velocity of the tricuspid regurgitation jet. With patients in a supine position, the diameters of the inferior vena cava (IVC) were measured in the subcostal view, 1.0 to 2.0 cm from its junction with the right atrium. Both the maximum diameter of the IVC and the percentage reduction in diameter during inspiration were recorded. Central venous pressure (CVP) was estimated based on three categorical grades—3, 8, and 15 mmHg—as per standard guidelines.[10]

Blood samples were collected for the analysis of hemoglobin, serum sodium, creatinine, blood urea nitrogen (BUN), and plasma B-type natriuretic peptide (BNP) levels. The estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease study equation, modified for isotope dilution mass spectrometry and adjusted with a Japanese coefficient.[11]

All echocardiographic examinations were recorded concurrently with the renal ultrasound. Blood tests utilized sample results obtained within a few days of the renal ultrasound recording.

Clinical outcome

All patients were followed up at our institution. Survival data were obtained by physicians making direct contact with the patients in a hospital outpatient setting. The primary endpoint of this study was a composite event, defined as all-cause death and unplanned hospitalization for worsening heart failure.

Statistical analysis

All continuous variables were expressed as the mean (± standard deviation, SD) or median (25–75 percentiles) as appropriate, and categorical variables were expressed as percentages. Student’s t-test was used to compare differences in normally distributed continuous variables, and the Mann‒Whitney rank-sum test was used to compare differences in non-normally distributed data. Fisher’s exact test and chi-square test were used to compare between-group differences in categorical variables. Receiver operating characteristic (ROC) curve analysis was performed to determine the cutoff point of Max.VI, Min.VI and IRPI for the identification of composite events. Event-free survival rates were calculated using the Kaplan–Meier method, and differences in survival rates were compared between groups using the log-rank test. Cox proportional hazards regression models were used to identify patients at risk of a composite event by calculating hazard ratios (HRs) and 95% confidence intervals (CIs).

A P value < 0.05 was considered statistically significant. MedCalc version 17.11.5–64 bit (MedCalc Software bvba) and EZR version 1.67 (Saitama Medical Center, Jichi Medical University, Saitama, Japan) were used for the statistical analysis.

In the preparation of this manuscript, a large language model was employed solely as an aid in English proofreading. Subsequently, the authors meticulously reviewed and verified the content for accuracy and appropriateness. It is important to note that no large language models were utilized in the conception of the research methodology, the analysis of data, the documentation of results, or the formulation of the manuscript's core structure. These critical aspects of the research process were conducted exclusively by the authors.

Comparison of baseline characteristics in patients with and without composite events

During a mean follow-up period of 1.6 ± 0.8 years, 13 out of 78 patients (17%) had a composite event. Baseline characteristics of patients with and without composite events are shown in Table 1. When compared to patients without cardiac events, those with cardiac events were significantly older and had a significantly higher BNP level. In the echocardiographic parameters, the E wave, E/e', TRPG, and estimated CVP were significantly higher in patients who experienced events compared to those who did not. Regarding the renal ultrasound parameters, the IRVF pattern showed significantly more biphasic and discontinuous waveform patterns in patients who experienced events. Furthermore, Max.VI and Min.VI were significantly lower, and IRPI was significantly higher in the event group. No significant difference was observed in the proportion of patients with heart failure between the two groups.

Table 1

Baseline characteristics of the study patients with and without composite endpoints.
	overall cohort	composite endpoints		p value
		With (N = 13)	Without (N = 65)	p value
Clinical data
Age (yrs)	69.7 ± 12	76 ± 9	68 ± 12	0.040
Gender (Men, % )	62	85	59	0.606
Heart rate (beats/min)	74 ± 14	78 ± 13	74 ± 15	0.351
Systolic BP (mm Hg)	117 ± 19	118 ± 18	114 ± 24	0.521
Diabetes Mellitus (%)	30	54	26	0.096
Dyslipidemia (%)	43	46	42	0.768
Hypertension (%)	75	77	54	0.218
Heart Failure (%)	84	92	82	0.324
Medication use
ACE inhibitors/ARB (%)	57	58	57	1.000
Sacbitril/Varsaltan (%)	23	23	23	1.000
MRA (%)	44	62	42	0.408
Beta-blockers (%)	58	69	55	0.540
Loop diuretics (%)	44	58	40	0.340
SGLT2 (%)	28	23	29	0.140
Laboratory data
Hemoglobin (g/dL)	12.7 ± 1.7	11.9 ± 1.5	12.9 ± 1.8	0.050
Creatinine (mg/dL)	0.9 [0.8, 1.3]	1.2 [0.9, 1.7]	0.9 [0.8, 1.2]	0.149
BUN (mg/dL)	19 [15, 25]	23 [14, 33]	19 [15, 24]	0.228
estimated GFR	54 [42, 67]	47 [30, 64]	55 [43, 66]	0.257
Serum sodium (mEq/L)	140 ± 3	138 ± 3	140 ± 3	0.116
BNP (pg/ml)	212 [70, 552]	612 [324, 694]	177 [49, 416]	0.006
Echocardiography
LVEF (%)	44 ± 18	39 ± 19	45 ± 17	0.236
E	70 [57, 97]	97 [65, 107]	68 [54, 92]	0.036
e'	6.1 ± 2.3	5.4 ± 1.6	6.2 ± 2.5	0.277
E/e'	13.1 ± 6.1	18 ± 8.4	12 ± 5.0	0.001
TRPG	20 ± 9.5	25 ± 7.7	18 ± 9.5	0.018
estimated CVP, 3/8/15 mmHg (%)	77/20/3	84/10/6	46/54/0	0.007
Renal echocardiography
IRVF pattern, (C/B/M: %)	47/44/9	17/50/33	52/43/5	0.007
maximum VI	0.43 ± 0.19	0.28 ± 0.15	0.46 ± 0.18	0.002
minimum VI	0.18 [0.08, 0.26]	0.06 [0.03, 0.13]	0.19 [0.11, 0.27]	0.001
IRPI	0.57 ± 0.18	0.68 ± 0.19	0.55 ± 0.17	0.018
values are mean ± SD or %, ACEI = angiotensin converting enzyme inhibitor; ARB = angiotensin receptor blocker; ARNI = angiotensin receptor neprilysin inhibitor; BNP = brain natriuretic peptide; BP = blood pressure; BUN = blood urea nitrogen; CVP = central venous pressure, C/B/M = continuous / biphasic / monophasic; E/e' = ratio of early diastolic peak velocity of doppler transmitral flow to early diastolic mitral annular velocity; GFR = glomerular filtration rate; IRPI = intra-renal perfusion index; IRVF = intrarenal venous flow; LVEF = left ventricular ejection fraction; MRAs = mineralocorticoid receptor antagonists; SGLT2i = sodium-glucose co-transporter-2 inhibitor; TRPG = tricuspid regurgitant pressure gradient; VI = vascular index

Prognostic analysis by Renal ultrasonography

The distribution of Max.VI, Min.VI and IRPI is shown in Fig. 3. In ROC analysis was performed, the AUC for Max.VI, Min.VI, and IRPI in predicting composite events were 0.776 (95% CI: 0.668 to 0.863), 0.789 (95% CI: 0.682 to 0.874), and 0.673 (95% CI: 0.557 to 0.775), respectively. Moreover, ROC curve analysis indicated that, although not statistically significant, the predictive power of these SMI parameters surpassed that of creatinine, a well-known conventional prognostic factor associated with renal function (Fig. 4).

In univariable Cox regression analyses, Max.VI, Min.VI and IRPI, as continuous variables, were significantly associated with composite events (Table 2). In multivariable Cox regression analyses, Max.VI and Min.VI remained significantly associated with composite events after adjusting for creatinine, estimated CVP and IRVF which were known as the conventional congestion markers (Table 3).

Table 2

Univariable Cox proportional hazards analysis to identify patients at risk of composite events
	Univariate
Variables	HR (95% CI)	P value
age (years)	1.07 (1.00-1.15)	0.040
systolic BP (mmHg)	0.99 (0.96–1.02)	0.560
log BNP	4.80 (1.39–16.6)	0.013
creatinine (pg/dl)	2.30 (1.45–3.65)	< 0.001
BUN	1.08 (1.03–1.13)	0.003
eGFR (ml/m²/1.73)	0.98(0.96–1.01)	0.316
E (cm/sec)	1.02(1.00-1.04)	0.023
E/e'	1.13 (1.05–1.20)	< 0.001
TR-PG (mmHg)	1.05 (1.01–1.10)	0.025
estimated CVP > = 8	4.55 (1.53–13.5)	0.007
IRVF (not continuous pattern)	3.71 (1.02–13.5)	0.047
maximum VI (×1/100)	0.95 (0.91–0.98)	0.004
minimum VI (×1/100)	0.92 (0.85–0.98)	0.012
IRPI (×1/100)	1.04 (1.01–1.07)	0.021
HR = hazard ratio; CI = confidence interval; BNP = brain natriuretic peptide; BP = blood pressure; BUN = blood urea nitrogen; CVP = central venous pressure; E/e' = ratio of early diastolic peak velocity of doppler transmitral flow to early diastolic mitral annular velocity; eGFR = estimated glomerular filtration rate; IRPI = intra-renal perfusion index; IRVF = intrarenal venous flow; LVEF = left ventricular ejection fraction; TRPG = tricuspid regurgitant pressure gradient; VI = vascular index

Table 3

Multivariable Cox proportional hazards analysis to identify patients at risk of composite events
	adjusted for creatinine		adjusted for CVP ≥ 8		adjusted for IRVF
	HR (95% CI)	p	HR (95% CI)	p	HR (95% CI)	p
Maximum VI (1/100)	0.95 (0.92–0.99)	0.014	0.96 (0.92–0.99)	0.021	0.95 (0.91–0.99)	0.020
Minimum VI (1/100)	0.93 (0.86–0.99)	0.033	0.93 (0.87–0.99)	0.038	0.92 (0.85–0.99)	0.047
IRPI (1/100)	1.02 (0.99–1.06)	0.108	1.03 (0.99–1.07)	0.075	1.03 (0.99–1.07)	0.204
HR = hazard ratio; CI = confidence interval; CVP = central venous pressure; IRPI = intra-renal perfusion index; IRVF = intrarenal venous flow; VI = vascular index

Kaplan-Meier analysis revealed that lower Max.VI (< 0.31, as determined by ROC analysis; 43% vs. 7%, log-rank p < 0.001), lower Min.VI (< 0.08, as determined by ROC analysis; 42% vs. 8%, log-rank p < 0.001), and higher IRPI (> 0.70, as determined by ROC analysis; 39% vs. 10%, log-rank p = 0.002) were significantly associated with a greater risk of composite events (Fig. 5).

This study is the first, to our knowledge, to demonstrate that indicators derived from SMI images are significantly associated with composite events, defined as all-cause death and unplanned hospitalization for worsening heart failure. Specifically, the maximum and minimum VI showed superior prognostic predictive ability compared to traditional indicators.

The significance of SMI indicators in reflecting renal microcirculation

In the renal cortex, the arterial system primarily branches from the arcuate arteries to the interlobular arteries, and then reaches the glomeruli through the afferent arterioles. As these vessels branch out, their diameters decrease, and blood flow becomes slower and more refined. The SMI technology allows for the detection of these fine, low-velocity blood flows without the use of contrast agents. In our study, we primarily detected blood flow in the arcuate and interlobar arteries, which implies that we could observe the actual blood flow perfusing the glomeruli.

One of the factors contributing to decreased GFR in HF patients is renal hypoperfusion due to reduced cardiac output.[12–15] Traditionally, the assessment of renal hypoperfusion has been inferred from blood tests and cardiac echocardiographic parameters. Our study demonstrates the potential to visualize and quantify renal perfusion by calculating the VI of the cortex, presenting it as a continuous variable. Specifically, the maximum VI within a cardiac cycle likely reflects arterial elements, providing us with valuable information on arterial perfusion.

Another critical factor in the reduced renal blood flow in heart failure patients is renal congestion.[16–21] Normally, renal venous blood flow is laminar regardless of the cardiac cycle. However, with increased central venous pressure, this flow becomes pulsatile and may even cease during systole. Changes in this venous blood flow, observed through renal venous Doppler waveform, have been known to correlate closely with heart failure prognosis and are becoming established indicators of renal congestion.[22–24] However, it is important to note that elevated renal venous pressure does not only affect the renal veins but also increases interstitial pressure within the kidney, compressing arterioles and further reducing blood flow, thereby exacerbating renal hypoperfusion. The SMI images used in our study detect blood flow without distinguishing between arteries and veins. Our results showed that patients who experienced composite events had significantly lower maximum and minimum VI. Furthermore, in multivariable Cox regression analysis, SMI parameters remained predictive of prognosis even after adjusting for indicators of renal function and renal congestion, such as creatinine, estimated CVP, and IRVF. This highlights its potential as a new method for the comprehensive evaluation of both renal hypoperfusion and congestion.

The limitations of using CVP to estimate optimal fluid balance

Appropriate fluid balance is crucial in the treatment of heart failure, and monitoring renal function trends throughout the treatment course is essential for estimating this balance. One of the most reliable indicators traditionally used to determine fluid balance is the directly measured CVP, obtained through invasive methods. However, it is challenging to invasively measure central venous pressure in all patients in routine clinical practice. Interestingly, Iida et al. reported that even in patients with low directly measured CVP, those exhibiting congestive patterns in their renal vein waveforms were correlated with poor outcomes.[5] Furthermore, in the field of intensive care, the study investigating the relationship between renal vein waveforms and events such as acute kidney injury or mortality in patients with severe sepsis have also found that renal vein waveforms do not necessarily correlate with CVP.[25] The kidneys maintain glomerular pressure despite low systemic blood pressure, but this autoregulation is compromised by aging, arteriosclerosis, hypertension, chronic kidney disease, and renin-angiotensin system inhibitors. When autoregulation fails, GFR declines even at high mean arterial pressures. Patients with cardiorenal syndrome often have these conditions, requiring varied mean arterial pressures to maintain adequate GFR.[26] These facts suggest that appropriate venous return and perfusion pressure vary depending on the pathological condition, and it is challenging to estimate them based solely on renal function, central venous pressure, or mean arterial pressure.

In this study, parameters obtained using the SMI method demonstrated a stronger correlation with composite events compared to renal function indicated by blood tests, estimated central venous pressure assessed by echocardiography, and renal venous Doppler waveform. This may be attributed to the capability of SMI images to visualize fine blood flow within the renal cortex without distinguishing between arteries and veins, and to present it as a continuous variable. Consequently, this allows for a more comprehensive and detailed evaluation of both renal perfusion and congestion.

Limitations:

One major limitation of this study is that it is a retrospective, single-center study. Prospective validation is required to confirm the significance of the indicators obtained through SMI. The second limitation is the small sample size, necessitating further validation in cohorts with diverse backgrounds.

The assessment of renal circulation using Superb Microvascular Imaging (SMI) offers a novel and promising approach to risk stratification in heart failure patients, surpassing the limitations of conventional markers. The ability of SMI-derived parameters, particularly Maximum VI and Minimum VI, to predict composite events more accurately than traditional indicators underscores its clinical utility. This non-invasive method reveals the complex interplay between cardiac function and renal microcirculation, potentially allowing for earlier detection of deterioration and more personalized treatment strategies.

BUN: blood urea nitrogen, BNP: plasma B-type natriuretic peptide, CE: composite event, CI: confidence intervals, CE: composite event, CVP: Central venous pressure, eGFR: estimated glomerular filtration rate, HR: hazard ratios, HF: heart failure, IRD: intrarenal Doppler ultrasonography, IRPI: Intrarenal Perfusion Index, IRVF: the intra-renal venous flow, IVC: inferior vena cava, LVEF: Left ventricular ejection fraction, Max.VI: the maximum VI, Min.VI: the minimum VI, RI: The resistance index, ROC: Receiver operating characteristic, ROI: the region of interest, SD: standard deviation, SMI: Superb Microvascular Imaging , TRPG: The tricuspid regurgitation pressure gradient, VI: Vascular Index

Funding Support and Author Disclosures:

This study was conducted as a collaborative research project with Canon Medical Systems Corporation. The authors declare Canon Medical Systems Corporation provided technical support and equipment for the study. However, Canon Medical Systems Corporation had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Yoshihiro Seo received a research grant from Canon Medical Systems Corporation. The other authors have no other relevant financial or non-financial interests to disclose.

Competing Interests

Author contributions statement:

Yoshihiro Seo conceptualized the application of SMI to renal microcirculation. Yoshihiro Seo, Shohei Kikuchi, and Kiyomi Kayama jointly developed the study design. Tadafumi Sugimoto provided guidance on data analysis. All authors reviewed the manuscript.

Abbreviations are listed as follow: IRPI; Intrarenal Perfusion Index, Max.VI; Maximum vascular index, Min.VI; Minimum vascular index, VI; Vascular index, SMI; superb microvascular imaging.

Abbreviations are listed as follow: AUC; Area Under the Curve, Cr; creatinine. Other abbreviations are shown in Fig. 2.

Funding:

Yoshihiro Seo received a research grant from Canon Medical Systems Corporation.

Author Contribution

Data Availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

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Competing interest reported. This study was conducted as a collaborative research project with Canon Medical Systems Corporation. The authors declare Canon Medical Systems Corporation provided technical support and equipment for the study. However, Canon Medical Systems Corporation had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Yoshihiro Seo received a research grant from Canon Medical Systems Corporation. The other authors have no other relevant financial or non-financial interests to disclose.

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Prognostic Impact of Renal Microcirculatory Dysfunction in Heart Failure Assessed by the Advanced Doppler technique, Superb Microvascular Imaging

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Abstract

Figures

Introduction

Methods

Subjects and Data collection

Renal ultrasound study

1)SMI study

2) Intrarenal Doppler ultrasonography

Echocardiography and Laboratory data

Clinical outcome

Statistical analysis

Results

Comparison of baseline characteristics in patients with and without composite events

Prognostic analysis by Renal ultrasonography

Discussion

The significance of SMI indicators in reflecting renal microcirculation

The limitations of using CVP to estimate optimal fluid balance

Limitations:

Conclusion

Abbreviations

Declarations

Author contributions statement:

Funding:

Author Contribution

Data Availability

References

Additional Declarations

Status:

Version 1