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

Prognostic Impact of Renal Microcirculatory Dysfunction in Heart Failure Assessed by the Advanced Doppler technique, Superb Microvascular Imaging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prognostic Impact of Renal Microcirculatory Dysfunction in Heart Failure Assessed by the Advanced Doppler technique, Superb Microvascular Imaging Kiyomi Kayama, Shohei Kikuchi, Tadafumi Sugimoto, Yoshihiro Seo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4806169/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract 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 0.70, 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction 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. Methods 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. Results 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) 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) = 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 0.70, 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 ). Discussion 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. Conclusion 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. Abbreviations 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 Declarations 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 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. 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 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. 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Focus on renal congestion in heart failure. Clin Kidney J 9, 39–47 (2016). Deferrari, G., Cipriani, A. & La Porta, E. Renal dysfunction in cardiovascular diseases and its consequences. J Nephrol 34, 137–153 (2021). Boorsma, E. M., Ter Maaten, J. M., Voors, A. A. & van Veldhuisen, D. J. Renal Compression in Heart Failure: The Renal Tamponade Hypothesis. JACC Heart Fail 10, 175–183 (2022). Tokas, T. et al. Pressure matters: intrarenal pressures during normal and pathological conditions, and impact of increased values to renal physiology. World Journal of Urology 37, 125–131 (2019). Nijst, P., Martens, P., Dupont, M., Tang, W. H. W. & Mullens, W. Intrarenal Flow Alterations During Transition From Euvolemia to Intravascular Volume Expansion in Heart Failure Patients. JACC Heart Fail 5, 672–681 (2017). Seo, Y. et al. Doppler-Derived Intrarenal Venous Flow Mirrors Right-Sided Heart Hemodynamics in Patients With Cardiovascular Disease. Circulation Journal 84, 1552–1559 (2020). Ter Maaten, J. M. et al. The Effect of Decongestion on Intrarenal Venous Flow Patterns in Patients With Acute Heart Failure. Journal of Cardiac Failure 27, 29–34 (2021). Fujii, K. et al. Association between intrarenal venous flow from Doppler ultrasonography and acute kidney injury in patients with sepsis in critical care: a prospective, exploratory observational study. Crit Care 27, 278 (2023). Abuelo, J. G. Normotensive ischemic acute renal failure. N Engl J Med 357, 797–805 (2007). Additional Declarations 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. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Feb, 2025 Reviews received at journal 25 Feb, 2025 Reviewers agreed at journal 12 Feb, 2025 Reviews received at journal 26 Nov, 2024 Reviewers agreed at journal 19 Nov, 2024 Reviews received at journal 30 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers invited by journal 17 Aug, 2024 Editor assigned by journal 12 Aug, 2024 Editor invited by journal 06 Aug, 2024 Submission checks completed at journal 05 Aug, 2024 First submitted to journal 26 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4806169","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":346853330,"identity":"ef2c5318-39ef-4cff-a2d5-d28f27662891","order_by":0,"name":"Kiyomi Kayama","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACCWYQaZDAwMbM2HDgYwOIx9h4gCgtfOzMBw/ObGCQAGppwK8FQiUwyPGzJR/mbYAI4NUi2c5j+OhGQRrQYTwGh2132NTpth8G2lJjE41LizQzj7FxjkEOREvumTQJszOJQC3H0nIbcGiRY+Yxk84xqIBqaTssYXYAqIWx4TCRWixBWs4/xK9FGqIF5DC2hMOMIC03CNgi2cxWDPRLGg8bM/OBg71taZLbbgBtScDjF4nzhzc+zvmTLCfff7D5w882G36z8+kPH3yoscGphYGBwwBE8qAKJuBUDgLsD/BKj4JRMApGwShgAADY51gGwZ846QAAAABJRU5ErkJggg==","orcid":"","institution":"Nagoya City University","correspondingAuthor":true,"prefix":"","firstName":"Kiyomi","middleName":"","lastName":"Kayama","suffix":""},{"id":346853333,"identity":"a823d119-04ec-4ce5-a2b5-daaffdf70aad","order_by":1,"name":"Shohei Kikuchi","email":"","orcid":"","institution":"Nagoya City University","correspondingAuthor":false,"prefix":"","firstName":"Shohei","middleName":"","lastName":"Kikuchi","suffix":""},{"id":346853335,"identity":"f0274850-4ab2-4af3-b868-ac6dd3d52434","order_by":2,"name":"Tadafumi Sugimoto","email":"","orcid":"","institution":"Nagoya City University Mirai Kousei Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tadafumi","middleName":"","lastName":"Sugimoto","suffix":""},{"id":346853337,"identity":"16efa1e9-301f-4e74-9306-705483dc7987","order_by":3,"name":"Yoshihiro Seo","email":"","orcid":"","institution":"Nagoya City University","correspondingAuthor":false,"prefix":"","firstName":"Yoshihiro","middleName":"","lastName":"Seo","suffix":""}],"badges":[],"createdAt":"2024-07-26 07:32:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4806169/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4806169/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-43872-3","type":"published","date":"2026-03-18T15:58:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64191614,"identity":"8ed40de6-6397-4cd2-93bb-e0706252a5c9","added_by":"auto","created_at":"2024-09-09 18:45:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283691,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow of the current study.\u003c/p\u003e","description":"","filename":"figure1studyflow.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4806169/v1/a38c427df8a3ea4399fb2a2d.jpg"},{"id":64191616,"identity":"42acbd38-c939-4099-989c-b5369812b0bd","added_by":"auto","created_at":"2024-09-09 18:45:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1199912,"visible":true,"origin":"","legend":"\u003cp\u003eAn anatomical illustration of vascular architecture in the renal cortex observed in this study and representative cases of SMI image analysis. Case a shows a patient with low IRPI, and Case b shows a patient with high IRPI. The left panels display renal cortical images at the point of Max.VI, the middle panels display renal cortical images at the point of Min.VI, and the right panels display graphs showing the changes in VI throughout the cardiac cycle.\u003c/p\u003e\n\u003cp\u003eAbbreviations 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.\u003c/p\u003e","description":"","filename":"figure2anatomicalillustrationandrepresentativecases.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4806169/v1/e207544dd223f92b2c73a6f2.jpg"},{"id":64191613,"identity":"8ef4203c-0f22-4e48-bb30-ca2ccb5ab508","added_by":"auto","created_at":"2024-09-09 18:45:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":354116,"visible":true,"origin":"","legend":"\u003cp\u003eHistograms of SMI parameters. Panel a shows Max.VI, Panel b shows Min.VI, and Panel c shows IRPI. The green bars represent the distribution of the entire patient cohort, while the orange bars represent the distribution of patients who experienced composite events. Abbreviations are shown in Figure 2.\u003c/p\u003e","description":"","filename":"figure3histogram.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4806169/v1/23ad1240e64ee412e0058c6c.jpg"},{"id":64191617,"identity":"2114edd1-210e-4fe0-a2d6-163ff165a9b4","added_by":"auto","created_at":"2024-09-09 18:45:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":651573,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curves comparing SMI parameters and creatinine levels.\u003c/p\u003e\n\u003cp\u003eAbbreviations are listed as follow: AUC; Area Under the Curve, Cr; creatinine. Other abbreviations are shown in Figure 2.\u003c/p\u003e","description":"","filename":"figure4comparisonROCanalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4806169/v1/4be2f0ef6753826f29c9cda7.jpg"},{"id":64191615,"identity":"21264a58-3f99-42d3-9cdd-d56de780c451","added_by":"auto","created_at":"2024-09-09 18:45:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":534893,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for composite events based on SMI parameters. Panel a shows Max.VI, Panel b shows Min.VI, and Panel c shows IRPI. Abbreviations are shown in Figure 2.\u003c/p\u003e","description":"","filename":"figure5KMcurve.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4806169/v1/b639070ae7658da7e5cdd21e.jpg"},{"id":105223419,"identity":"d22b7622-cfe2-4102-8239-32bcfcb5e2d7","added_by":"auto","created_at":"2026-03-23 16:06:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3951482,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4806169/v1/2ee89cff-2705-4711-a23c-a5d6e139780b.pdf"}],"financialInterests":"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.","formattedTitle":"Prognostic Impact of Renal Microcirculatory Dysfunction in Heart Failure Assessed by the Advanced Doppler technique, Superb Microvascular Imaging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the rapid increase in heart failure (HF) patients and their poor prognosis have emerged as significant social issues.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] 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.\u003c/p\u003e \u003cp\u003eAppropriate 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.[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] 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.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Nevertheless, this indicator reflects congestion without considering the perfusion aspect, which remains a challenge.\u003c/p\u003e \u003cp\u003eTo 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.[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] 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.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] This study aims to investigate the impact of indicators obtained from SMI images on the prognosis of heart failure patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects and Data collection\u003c/h2\u003e \u003cp\u003eIn 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\u0026thinsp;=\u0026thinsp;27). We also excluded patients who were lost to follow-up(n\u0026thinsp;=\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData were collected retrospectively from electronic health records. We extracted relevant clinical information, including diagnostic codes, treatment details, and follow-up records.\u003c/p\u003e \u003cp\u003e 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.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRenal ultrasound study\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e1)SMI study\u003c/h2\u003e \u003cp\u003eRenal 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.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWe present representative images in which, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B, the blood flow is intermittent, and there are variations in blood flow with the cardiac cycle, demonstrating a high IRPI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2) Intrarenal Doppler ultrasonography\u003c/h2\u003e \u003cp\u003eIntrarenal Doppler ultrasonography (IRD) measurements were conducted as previously described. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] 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. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] All measurements were averaged over three cardiac cycles during sinus rhythm. For patients with atrial fibrillation, an index beat\u0026mdash;defined as the beat following two consecutive cardiac cycles of equal duration\u0026mdash;was utilized for each measurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eEchocardiography and Laboratory data\u003c/h2\u003e \u003cp\u003eComprehensive 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\u0026prime;), assessed in the apical four-chamber view. The E/e\u0026prime; ratio was calculated by dividing the E velocity by e\u0026prime;. 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\u0026mdash;3, 8, and 15 mmHg\u0026mdash;as per standard guidelines.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eBlood 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.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAll echocardiographic examinations were recorded concurrently with the renal ultrasound. Blood tests utilized sample results obtained within a few days of the renal ultrasound recording.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical outcome\u003c/h2\u003e \u003cp\u003e 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll continuous variables were expressed as the mean (\u0026plusmn;\u0026thinsp;standard deviation, SD) or median (25\u0026ndash;75 percentiles) as appropriate, and categorical variables were expressed as percentages. Student\u0026rsquo;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\u0026rsquo;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\u0026ndash;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).\u003c/p\u003e \u003cp\u003eA P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. MedCalc version 17.11.5\u0026ndash;64 bit (MedCalc Software bvba) and EZR version 1.67 (Saitama Medical Center, Jichi Medical University, Saitama, Japan) were used for the statistical analysis.\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of baseline characteristics in patients with and without composite events\u003c/h2\u003e \u003cp\u003eDuring a mean follow-up period of 1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. 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.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study patients with and without composite endpoints.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eoverall cohort\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003ecomposite endpoints\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith (N\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWithout (N\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Men, % )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114\u0026thinsp;\u0026plusmn;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Failure (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE inhibitors/ARB (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSacbitril/Varsaltan (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-blockers (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoop diuretics (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT2 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 [0.8, 1.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 [0.9, 1.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 [0.8, 1.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 [14, 33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eestimated GFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 [42, 67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 [30, 64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 [43, 66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum sodium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 [70, 552]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e612 [324, 694]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177 [49, 416]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEchocardiography\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 [57, 97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 [65, 107]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 [54, 92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ee'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/e'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eestimated CVP,\u003c/p\u003e \u003cp\u003e3/8/15 mmHg (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77/20/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84/10/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46/54/0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal echocardiography\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRVF pattern,\u003c/p\u003e \u003cp\u003e(C/B/M: %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47/44/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17/50/33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52/43/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emaximum VI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eminimum VI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18 [0.08, 0.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 [0.03, 0.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 [0.11, 0.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003evalues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or %, ACEI\u0026thinsp;=\u0026thinsp;angiotensin converting enzyme inhibitor; ARB\u0026thinsp;=\u0026thinsp;angiotensin receptor blocker; ARNI\u0026thinsp;=\u0026thinsp;angiotensin receptor neprilysin inhibitor; BNP\u0026thinsp;=\u0026thinsp;brain natriuretic peptide; BP\u0026thinsp;=\u0026thinsp;blood pressure; BUN\u0026thinsp;=\u0026thinsp;blood urea nitrogen; CVP\u0026thinsp;=\u0026thinsp;central venous pressure, C/B/M\u0026thinsp;=\u0026thinsp;continuous / biphasic / monophasic; E/e' = ratio of early diastolic peak velocity of doppler transmitral flow to early diastolic mitral annular velocity; GFR\u0026thinsp;=\u0026thinsp;glomerular filtration rate; IRPI\u0026thinsp;=\u0026thinsp;intra-renal perfusion index; IRVF\u0026thinsp;=\u0026thinsp;intrarenal venous flow; LVEF\u0026thinsp;=\u0026thinsp;left ventricular ejection fraction; MRAs\u0026thinsp;=\u0026thinsp;mineralocorticoid receptor antagonists; SGLT2i\u0026thinsp;=\u0026thinsp;sodium-glucose co-transporter-2 inhibitor; TRPG\u0026thinsp;=\u0026thinsp;tricuspid regurgitant pressure gradient; VI\u0026thinsp;=\u0026thinsp;vascular index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic analysis by Renal ultrasonography\u003c/h2\u003e \u003cp\u003eThe distribution of Max.VI, Min.VI and IRPI is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. 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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn univariable Cox regression analyses, Max.VI, Min.VI and IRPI, as continuous variables, were significantly associated with composite events (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). 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\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable Cox proportional hazards analysis to identify patients at risk of composite events\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eUnivariate\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.00-1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog BNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.80 (1.39\u0026ndash;16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecreatinine (pg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.30 (1.45\u0026ndash;3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.03\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/m\u0026sup2;/1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98(0.96\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE (cm/sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02(1.00-1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/e'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.05\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR-PG (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.01\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eestimated CVP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.55 (1.53\u0026ndash;13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRVF (not continuous pattern)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.71 (1.02\u0026ndash;13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emaximum VI (\u0026times;1/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.91\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eminimum VI (\u0026times;1/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.85\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRPI (\u0026times;1/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.01\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHR\u0026thinsp;=\u0026thinsp;hazard ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval; BNP\u0026thinsp;=\u0026thinsp;brain natriuretic peptide; BP\u0026thinsp;=\u0026thinsp;blood pressure; BUN\u0026thinsp;=\u0026thinsp;blood urea nitrogen; CVP\u0026thinsp;=\u0026thinsp;central venous pressure; E/e' = ratio of early diastolic peak velocity of doppler transmitral flow to early diastolic mitral annular velocity; eGFR\u0026thinsp;=\u0026thinsp;estimated glomerular filtration rate; IRPI\u0026thinsp;=\u0026thinsp;intra-renal perfusion index; IRVF\u0026thinsp;=\u0026thinsp;intrarenal venous flow; LVEF\u0026thinsp;=\u0026thinsp;left ventricular ejection fraction; TRPG\u0026thinsp;=\u0026thinsp;tricuspid regurgitant pressure gradient; VI\u0026thinsp;=\u0026thinsp;vascular index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Cox proportional hazards analysis to identify patients at risk of composite events\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eadjusted for creatinine\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eadjusted for CVP\u0026thinsp;\u0026ge;\u0026thinsp;8\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eadjusted for IRVF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum VI (1/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.92\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 (0.92\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95 (0.91\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum VI (1/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.86\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93 (0.87\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92 (0.85\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRPI (1/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.99\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (0.99\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.03 (0.99\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eHR\u0026thinsp;=\u0026thinsp;hazard ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval; CVP\u0026thinsp;=\u0026thinsp;central venous pressure; IRPI\u0026thinsp;=\u0026thinsp;intra-renal perfusion index; IRVF\u0026thinsp;=\u0026thinsp;intrarenal venous flow; VI\u0026thinsp;=\u0026thinsp;vascular index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKaplan-Meier analysis revealed that lower Max.VI (\u0026lt;\u0026thinsp;0.31, as determined by ROC analysis; 43% vs. 7%, log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower Min.VI (\u0026lt;\u0026thinsp;0.08, as determined by ROC analysis; 42% vs. 8%, log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher IRPI (\u0026gt;\u0026thinsp;0.70, as determined by ROC analysis; 39% vs. 10%, log-rank p\u0026thinsp;=\u0026thinsp;0.002) were significantly associated with a greater risk of composite events (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis 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.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe significance of SMI indicators in reflecting renal microcirculation\u003c/h2\u003e \u003cp\u003eIn 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.\u003c/p\u003e \u003cp\u003eOne of the factors contributing to decreased GFR in HF patients is renal hypoperfusion due to reduced cardiac output.[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] 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.\u003c/p\u003e \u003cp\u003eAnother critical factor in the reduced renal blood flow in heart failure patients is renal congestion.[\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] 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.[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe limitations of using CVP to estimate optimal fluid balance\u003c/h2\u003e \u003cp\u003eAppropriate 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.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] 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.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] 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.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] 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.\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations:\u003c/h2\u003e \u003cp\u003eOne 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBUN: 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\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cb\u003eFunding Support and Author Disclosures\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThis 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.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThis 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.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor contributions statement:\u003c/h2\u003e \u003cp\u003eYoshihiro 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.\u003c/p\u003e \u003cp\u003eAbbreviations 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.\u003c/p\u003e \u003cp\u003eAbbreviations are listed as follow: AUC; Area Under the Curve, Cr; creatinine. Other abbreviations are shown in Fig.\u0026nbsp;2.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eYoshihiro Seo received a research grant from Canon Medical Systems Corporation.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYoshihiro 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.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSavarese, G. \u0026amp; Lund, L. H. Global Public Health Burden of Heart Failure. Card Fail Rev 3, 7\u0026ndash;11 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchrier, R. W. \u0026amp; Abraham, W. T. Hormones and hemodynamics in heart failure. N Engl J Med 341, 577\u0026ndash;585 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMentz, R. J. \u003cem\u003eet al.\u003c/em\u003e Decongestion in acute heart failure. Eur J Heart Fail 16, 471\u0026ndash;482 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDamman, K. \u0026amp; Testani, J. M. The kidney in heart failure: an update. Eur Heart J 36, 1437\u0026ndash;1444 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIida, N. \u003cem\u003eet al.\u003c/em\u003e Clinical Implications of Intrarenal Hemodynamic Evaluation by Doppler Ultrasonography in Heart Failure. JACC Heart Fail 4, 674\u0026ndash;682 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu, Z. \u003cem\u003eet al.\u003c/em\u003e Clinical Applications of Superb Microvascular Imaging in the Superficial Tissues and Organs: A Systematic Review. Academic Radiology 28, 694\u0026ndash;703 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao, J., Thai, A. \u0026amp; Erpelding, T. Comparison of superb microvascular imaging to conventional color Doppler ultrasonography in depicting renal cortical microvasculature. Clin Imaging 58, 90\u0026ndash;95 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, D. B., Zhou, J., Feng, L., Xu, R. \u0026amp; Wang, Y. C. Value of superb micro-vascular imaging in predicting ischemic stroke in patients with carotid atherosclerotic plaques. World J Clin Cases 7, 839\u0026ndash;848 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKikuchi, S., Kayama, K., Kawada, Y., Kitada, S. \u0026amp; Seo, Y. Evaluation of renal circulation in heart failure using superb microvascular imaging, a microvascular flow imaging system. \u003cem\u003eJournal of Medical Ultrasonics\u003c/em\u003e2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLang, R. M. \u003cem\u003eet al.\u003c/em\u003e Recommendations for Cardiac Chamber Quantification by Echocardiography in Adults: An Update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. European Heart Journal - Cardiovascular Imaging 16, 233\u0026ndash;271 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuo, S. \u003cem\u003eet al.\u003c/em\u003e Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 53, 982\u0026ndash;992 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRonco, C., Cicoira, M. \u0026amp; McCullough, P. A. Cardiorenal syndrome type 1: pathophysiological crosstalk leading to combined heart and kidney dysfunction in the setting of acutely decompensated heart failure. J Am Coll Cardiol 60, 1031\u0026ndash;1042 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShamseddin, M. K. \u0026amp; Parfrey, P. S. Mechanisms of the cardiorenal syndromes. Nat Rev Nephrol 5, 641\u0026ndash;649 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanoli, L. \u003cem\u003eet al.\u003c/em\u003e Arterial Stiffness in the Heart Disease of CKD. J Am Soc Nephrol 30, 918\u0026ndash;928 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel, K. P., Katsurada, K. \u0026amp; Zheng, H. Cardiorenal Syndrome: The Role of Neural Connections Between the Heart and the Kidneys. Circ Res 130, 1601\u0026ndash;1617 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMullens, W. \u003cem\u003eet al.\u003c/em\u003e Importance of venous congestion for worsening of renal function in advanced decompensated heart failure. J Am Coll Cardiol 53, 589\u0026ndash;596 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ, F. G., von Haehling, S., Anker, S. D., Raj, D. S. \u0026amp; Radhakrishnan, J. The relevance of congestion in the cardio-renal syndrome. Kidney Int 83, 384\u0026ndash;391 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfsar, B. \u003cem\u003eet al.\u003c/em\u003e Focus on renal congestion in heart failure. Clin Kidney J 9, 39\u0026ndash;47 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeferrari, G., Cipriani, A. \u0026amp; La Porta, E. Renal dysfunction in cardiovascular diseases and its consequences. J Nephrol 34, 137\u0026ndash;153 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoorsma, E. M., Ter Maaten, J. M., Voors, A. A. \u0026amp; van Veldhuisen, D. J. Renal Compression in Heart Failure: The Renal Tamponade Hypothesis. JACC Heart Fail 10, 175\u0026ndash;183 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTokas, T. \u003cem\u003eet al.\u003c/em\u003e Pressure matters: intrarenal pressures during normal and pathological conditions, and impact of increased values to renal physiology. World Journal of Urology 37, 125\u0026ndash;131 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNijst, P., Martens, P., Dupont, M., Tang, W. H. W. \u0026amp; Mullens, W. Intrarenal Flow Alterations During Transition From Euvolemia to Intravascular Volume Expansion in Heart Failure Patients. JACC Heart Fail 5, 672\u0026ndash;681 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo, Y. \u003cem\u003eet al.\u003c/em\u003e Doppler-Derived Intrarenal Venous Flow Mirrors Right-Sided Heart Hemodynamics in Patients With Cardiovascular Disease. Circulation Journal 84, 1552\u0026ndash;1559 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTer Maaten, J. M. \u003cem\u003eet al.\u003c/em\u003e The Effect of Decongestion on Intrarenal Venous Flow Patterns in Patients With Acute Heart Failure. Journal of Cardiac Failure 27, 29\u0026ndash;34 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujii, K. \u003cem\u003eet al.\u003c/em\u003e Association between intrarenal venous flow from Doppler ultrasonography and acute kidney injury in patients with sepsis in critical care: a prospective, exploratory observational study. Crit Care 27, 278 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbuelo, J. G. Normotensive ischemic acute renal failure. N Engl J Med 357, 797\u0026ndash;805 (2007).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"renal microcirculation, cardiorenal interaction, heart failure, prognostic marker","lastPublishedDoi":"10.21203/rs.3.rs-4806169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4806169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe 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\u0026thinsp;\u0026plusmn;\u0026thinsp;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 (\u0026lt;\u0026thinsp;0.31, as determined by ROC analysis; 43% vs. 7%, log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Min.VI (\u0026lt;\u0026thinsp;0.08, 42% vs. 8%, log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and IRPI (\u0026gt;\u0026thinsp;0.70, 39% vs. 10%, log-rank p\u0026thinsp;=\u0026thinsp;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.\u003c/p\u003e","manuscriptTitle":"Prognostic Impact of Renal Microcirculatory Dysfunction in Heart Failure Assessed by the Advanced Doppler technique, Superb Microvascular Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-09 18:44:55","doi":"10.21203/rs.3.rs-4806169/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-26T04:05:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-25T12:18:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112929904587769225803747065573248741838","date":"2025-02-12T09:11:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-26T21:00:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208695229697371209809842986883724984145","date":"2024-11-19T12:44:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-31T00:20:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225709101619549349195103413703652402557","date":"2024-08-19T13:00:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-17T12:11:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-12T12:00:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-06T15:48:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-05T06:22:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-26T07:31:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f11d745-5ba9-4292-ad1d-ca43256c0cee","owner":[],"postedDate":"September 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36787054,"name":"Health sciences/Nephrology/Kidney"},{"id":36787055,"name":"Health sciences/Cardiology/Cardiovascular biology"},{"id":36787056,"name":"Health sciences/Medical research/Outcomes research"}],"tags":[],"updatedAt":"2026-03-23T16:02:59+00:00","versionOfRecord":{"articleIdentity":"rs-4806169","link":"https://doi.org/10.1038/s41598-026-43872-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-18 15:58:14","publishedOnDateReadable":"March 18th, 2026"},"versionCreatedAt":"2024-09-09 18:44:55","video":"","vorDoi":"10.1038/s41598-026-43872-3","vorDoiUrl":"https://doi.org/10.1038/s41598-026-43872-3","workflowStages":[]},"version":"v1","identity":"rs-4806169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4806169","identity":"rs-4806169","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}