A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury

Hye Eun Yoon; Da Won Kim; Dongryul Kim; Yaeni Kim; Seok Joon Shin; Yu Ri Shin

doi:10.1371/journal.pone.0235130

. 2020 Jun 24;15(6):e0235130. doi: 10.1371/journal.pone.0235130

A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury

Hye Eun Yoon ¹, Da Won Kim ¹, Dongryul Kim ¹, Yaeni Kim ², Seok Joon Shin ¹, Yu Ri Shin ^3,^*

Editor: Tatsuo Shimosawa⁴

PMCID: PMC7313752 PMID: 32579595

Abstract

Objectives

Contrast-enhanced ultrasound (CEUS) enables the assessment of real-time renal microcirculation. This study investigated CEUS-driven parameters as hemodynamic predictors for renal outcomes in patients with acute kidney injury (AKI).

Methods

Forty-eight patients who were diagnosed with AKI were prospectively enrolled and underwent CEUS at the occurrence of AKI. Parameters measured were the wash-in slope (WIS), time to peak intensity, peak intensity (PI), area under the time–intensity curve (AUC), mean transit time (MTT), time for full width at half maximum, and rise time (RT). The predictive performance of the CEUS-driven parameters for Kidney Disease Improving Global Outcomes (KDIGO) AKI stage, initiation of renal replacement therapy (RRT), AKI recovery, and chronic kidney disease (CKD) progression was assessed. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance of CEUS.

Results

Cortical RT (Odds ratio [OR] = 1.21) predicted the KDIGO stage 3 AKI. Cortical MTT (OR = 1.07) and RT (OR = 1.20) predicted the initiation of RRT. Cortical WIS (OR = 76.23) and medullary PI (OR = 1.25) predicted AKI recovery. Medullary PI (OR = 0.78) and AUC (OR = 1.00) predicted CKD progression. The areas under the ROC curves showed reasonable performance for predicting the initiation of RRT and AKI recovery. The sensitivity and specificity of the quantitative CEUS parameters were 60–83% and 62–77%, respectively, with an area under the curve of 0.69–0.75.

Conclusion

CEUS may be a supplemental tool in diagnosing the severity of AKI and predicting renal prognosis in patients with AKI.

Introduction

Acute kidney injury (AKI) is characterized by a rapid decline in kidney function within a few hours to a few days. AKI is responsible for two million deaths annually worldwide, and its incidence is increasing [1]. Although alterations in renal perfusion are thought to play a central role in its pathogenesis [2], diagnostic tools for assessing renal perfusion are lacking.

Different imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, are limited in clinical applications due to their high cost, reduced availability, long examination duration, and toxicities associated with the contrast agents used. Kidney ultrasound (US) is the most widely used imaging modality in the initial workup of AKI, because it is widely available and free of complications [3]. The rate of abnormal US findings in cases of AKI is not high, because different renal parenchymal diseases often display the same US appearance, whereas the same renal parenchymal disease may present different appearances on US according to the disease stage [4]. Doppler US provides information on renal blood flow [3]; however, it only provides indirect macrovasculature parameters. Additionally, evaluation of cortical perfusion by US is challenging, particularly when the cortical blood flow is reduced. In these situations, US contrast agents can improve the diagnostic capabilities of conventional US, and allow the development of semi-quantitative and functional assessment of renal microvascular perfusion [3]. US contrast agents are superior to those used in CT or MRI for imaging of the vasculature because they behave the same as red blood cells and do not diffuse out of the vascular space [5]. Furthermore, these agents carry no risk of nephrotoxicity due to the absence of filtration and secretion by the kidneys. Contrast-enhanced ultrasound (CEUS) has been used as an excellent technique to assess renal parenchymal perfusion in patients with chronic kidney disease (CKD) [6, 7]. However, few studies have specifically assessed the utility of noninvasive evaluation of AKI using the CEUS technique.

The purpose of this study was to investigate quantitative CEUS parameters as hemodynamic predictors for renal outcomes in patients with AKI, in terms of the severity of AKI, initiation of renal replacement therapy (RRT), AKI recovery, and CKD progression.

Materials and methods

Study design and patients

This study was a prospective cohort study conducted between November 2017 and February 2019. Patients who were admitted or referred to the nephrology department in Incheon Saint Mary’s Hospital because of a clinical diagnosis of AKI with varying degrees of renal dysfunction, and with different etiologies, were enrolled. AKI was diagnosed and staged using the serum creatinine concentration according to Kidney Disease Improving Global Outcomes (KDIGO) guidelines [8], and only those patients with known baseline serum creatinine levels within 6 months of enrollment were analyzed. Patients who were under 18 years of age, and those with contraindications to US contrast agents, such as a history of cardiac shunt, respiratory disorder, or hypersensitivity, were excluded. The evaluation proposal and draft data collection tools were reviewed and approved by the institutional review board of the Catholic University of Korea Catholic Medical Center on Oct 24, 2017. Informed written consent was obtained from all study participants (XC17BEDI0045). Fig 1 shows the flow diagram of the study population.

At the time of diagnosis of AKI, all subjects had blood samples drawn, serum creatinine and electrolyte concentrations assessed, and random urine samples collected for measurement of urine sodium, creatinine, and protein. Fractional excretion of sodium (FENa) was calculated using the following equation: [(urine sodium × serum creatinine)/(serum sodium × urine creatinine)] × 100. Baseline renal function was determined by the estimated glomerular filtration rate (eGFR), which was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation [9]. Next, the CEUS examination was performed. All CEUS examinations and quantification analyses were performed by one experienced radiologist to ensure consistency among all measurements taken. The clinical context, history taking, physical examination, and interpretation of blood and urine laboratory findings were used for the differential diagnosis of the cause of AKI. Each patient was followed up for at least three months with regular blood tests, including serum creatinine. The follow-up interval was determined according to the physician’s decision, and the frequency of follow-up differed between patients. Data consisting of patient demographics and comorbid conditions were also collected. Patients with underlying CKD were defined by evidence of impaired eGFR that had been present for > 3 months [10].

The primary outcome was the initiation of RRT. The secondary outcomes were AKI recovery and CKD progression. AKI recovery was defined as the return of serum creatinine to 25% of the baseline value. CKD progression was determined at three months after AKI and defined as new-onset proteinuria or a decline of 25% or more of eGFR compared to baseline eGFR. Baseline eGFR was defined as the most recent value before the diagnosis of AKI.

US examination

All patients underwent CEUS with an iU 22 (Philips, Bothell, WA, USA), using a 1–5-MHz convex transducer. The contrast-specific imaging mode used in this study was pulse inversion harmonic imaging. The mechanical index was set at 0.06. We selected a maximum longitudinal scanning section that included the entire kidney. After identification of adequate images of the kidney, the transducer was manually held in the same scanning plane while patients were instructed to perform only shallow breathing to minimize the variation caused by motion. Then, an intravenous infusion of 2.0 mL of SonoVue (Bracco, Milan, Italy) was administrated via an antecubital vein in a bolus injection, followed by an immediate flush of 10 mL saline solution. The contrast agent was injected before examination of each kidney. The right kidney was examined first and, after about 20 min, the same CEUS examinations (including injection of SonoVue) were performed for the left kidney. Image depth, focus, gain, and frame rate were optimized and held constant for all further measurements. Continuous imaging was captured and observed in real-time for 5 min after SonoVue was injected. All images and video clips were stored digitally on a hard disk system, and then transferred to a personal computer for further quantitative analyses using advanced US quantification software (QLAB 8.1; Philips Medical Systems). The examination protocol is illustrated in Fig 2.

Image analysis

To compensate for minor breathing artifacts, all sequences were applied with motion compensation before the start of the analysis. Three similar-sized regions of interest (ROIs) (5 × 5 mm²) were drawn at the renal cortex and medulla, which are at the same approximate location and a similar depth, while excluding interlobar and arcuate arteries. In the ROI of each renal cortex and medulla, the computer-assisted program calculated acquisition of time (s) to signal intensity (dB) curves. The results of three ROIs at the renal cortex and medulla were averaged to minimize heterogeneity of the measurements. The data were then fitted to local density random walk wash-in and wash-out curves using the raw data. Fig 3 shows an example of the time–intensity curve (TIC) of the cortex (Fig 3A) and the medulla (Fig 3B).

Fig 3 — (A) Three ROIs were drawn in the renal cortex. The left part of the image shows contrast-image mode imaging and the right shows the standard (B-mode) imaging. (B) Three ROIs were drawn in the renal medulla. The left part of the image shows contrast-image mode imaging and the right shows the standard (B-mode) imaging. (A and B) Bottom: TIC curves. The smooth curves are the fitting curves, and the non-smooth curves are the original curves.

The wash-in slope (WIS, the maximum wash-in velocity of the contrast medium; unit, dB/s), time to peak intensity (TTP, time to maximum enhancement; unit, s), peak intensity (PI, the maximum intensity of the curve; unit, dB), area under the TIC (AUC, the area under the TIC that was proportionate to the total volume of blood flow in the ROI; unit, dB), mean transit time (MTT, corresponding to the center of gravity of the perfusion model; unit, s), time for full width at half maximum (FWHM, time between the half amplitude values in each side of the maximum; unit, s), and rise time (RT, the time from injection until the peak of enhancement; unit, s) were obtained using QLAB software. For every ROI, the analysis was repeated three times, and the mean value of the perfusion parameter was obtained to minimize the transitional distance caused by respiration and to ensure the accuracy of the analyses. The final reported results of CEUS parameters represent the average value of each parameter from both kidneys of each individual.

Statistical analysis

Values are expressed as means ± standard deviation and proportion of percent, as appropriate. Continuous data were compared using the Student’s t-test or the Mann-Whitney U test, as appropriate. Pearson’s correlation analysis or Spearman correlation analyses were used to determine the correlation between CEUS parameters and FENa or lowest urine output. We conducted a univariate logistic regression analysis to assess the impact of different US parameters on the clinical outcomes, including KDIGO AKI stage 3, need of RRT, AKI recovery, and CKD progression. Since CEUS parameters are not completely independent of one another, we included one CEUS parameter for each outcome in the logistic regression analysis. To further examine the predictive performance of CEUS parameters for clinical outcomes, receiver operating characteristic (ROC) curves were constructed to determine the optimal cutoff value. The intra-class correlation coefficient (ICC) was calculated to evaluate the intra-observer agreement [11]. The ICC was interpreted as follows: < 0.6 = poor; 0.6–0.79 = moderate; > 0.8–1 = excellent agreement. The differences in the CEUS parameters between patients with underlying CKD and those without CKD were also compared using independent Student’s t-tests or Mann–Whitney U tests. All statistical analyses were conducted using SAS software (version 9.3, SAS Institute, USA). A P-value < 0.05 was considered statistically significant.

Results

Patient characteristics and CEUS-driven TIC parameters

A total of 48 consecutive patients with AKI (males, 25; females, 23; mean age, 60.65 ± 16.14 years) were enrolled. The baseline characteristics of patients are summarized in Table 1. Among them, 25 (52%) patients were diagnosed as KDIGO stage 3 AKI, 11 (23%) patients received RRT, 13 (27%) patients achieved functional recovery of AKI, and 18 (38%) patients showed CKD progression. Ten patients (20.8%) had urine output less than 500 mL per day, two patients (4.2%) developed anuria during the course of AKI, and the in-hospital mortality rate was 0%. Most of the causes of AKI were intrinsic (71%, n = 34), and prerenal causes ranked for the second common cause (25%, n = 12) (Table 1). Specific causes were as follows; drug, 42% (n = 20); infection, 17% (n = 8); glomerular disease, 13% (n = 6); gastrointestinal loss, 10% (n = 5); nephrolithiasis and benign prostate hyperplasia, 4% (n = 2); alcohol, 4% (n = 2); contrast, 2% (n = 1); hypotension, 2.1% (n = 1); others, 6% (n = 3).

Table 1. Baseline characteristics of the final trial cohort.

Characteristics	Value (n = 48)
Age (y)^*	61± 16 (25–85)
Sex
Male	25 (52%)
Female	23 (48%)
Underlying renal disease
No	21 (44%)
Diabetes mellitus	12 (26%)
Hypertension	5 (11%)
Glomerulonephritis	2 (4%)
Others	8 (17%)
KDIGO AKI stage
1	11 (23%)
2	12 (25%)
3	25 (52%)
Cause of AKI
Prerenal	12 (25%)
Intrinsic	34 (71%)
Postrenal	1 (2%)
Intrinsic and postrenal	1 (2%)
FeNa (%) ^*	2.93 ± 4.27 (0.03–22.49)
Urine protein to creatinine ratio (mg/g) ^*	3991 ± 7306 (164.3–45928)
Baseline eGFR (ml/min/1.73m²) ^*	64 ± 28 (13–127)
Serum creatinine at AKI occurrence (mg/dL)^*	4.01 ± 2.54 (1.30–14.70)
Highest serum creatinine (mg/dL)^*	4.46 ± 2.88 (1.60–14.70)
Lowest urine output (mL/day)^*	1221.20 ± 877.86 (0–3300)

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Unless otherwise indicated, data are number of patients.

*Values are mean ± SD with range in parentheses.

AKI = acute kidney injury, KDIGO = Kidney Disease: Improving Global Outcomes, FeNa = fractional excretion of sodium, eGFR = estimated glomerular filtration rate

No side-effects of the sonographic contrast agent were noted, and there was no hematuria or local pain during US examination. The duration between the time of CEUS and the time of peak serum creatinine was 3.8 ± 3.9 days (range, 0–14 days). The TIC parameters measured at the renal cortex and medulla are listed in Table 2.

Table 2. The CEUS-driven TIS parameters of the final trial cohort.

TIC parameters	Value
Cortex
WIS (dB/sec)	0.93 ±1.00 (0.41–7.56)
TTP (s)	43.13 ±11.14 (18.54–73.56)
PI (dB)	17.66 ± 3.10 (10.12–24.49)
AUC (dB)	2159 ± 497.4 (923.80–3061)
MTT (s)	71.55 ± 15.84 (26.06–94.46)
FWHM (s)	115.00 ± 25.37 (46.73–150.6)
RT (s)	16.94 ± 4.50 (3.46–27.58)
Medulla
WIS (dB/sec)	0.89 ± 0.74 (0.42–4.52)
TTP (s)	45.08 ± 11.95 (21.34–73.75)
PI (dB)	17.80 ± 3.45 (8.97–25.37)
AUC (dB)	2262 ± 542.30 (805.70–3192)
MTT (s)	75.23 ± 13.54 (33.06–95.00)
FWHM (s)	119.60 ± 25.53 (50.88–151.40)
RT (s)	18.09 ± 5.00 (5.81–36.22)

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Values are mean ± SD with range in parentheses. TIC = time-intensity curve, CKD = chronic kidney disease, WIS = wash in slope, TTP = time to peak intensity, PI = peak intensity, AUC = area under the time-intensity curve, MTT = mean transit time, FWHM = time for full width half max, RT = rise time.

The correlation coefficients between TIC parameters and FENa or lowest urine output were not statistically significant (S1 Table). The TIC parameters were compared between patients with intrinsic causes and those with prerenal or postrenal causes. None of the TIC parameters showed statistically significant difference between patients with intrinsic AKI and patients with prerenal or postrenal AKI (S2 Table).

Predictors of the severity of AKI and clinical outcomes and prognostic performance of CEUS

The univariate logistic regression analysis for the severity of AKI and clinical outcomes demonstrated that RT at the renal cortex (Odds ratio [OR], 1.21) predicted the KDIGO stage 3 AKI at the occurrence of AKI (Table 3). MTT and RT at the renal cortex (OR, 1.07 and 1.2, respectively) predicted the initiation of RRT. WIS and RT at the renal cortex and PI at the renal medulla (OR, 76.23, 0.83, and 1.25, respectively) predicted AKI recovery. In addition, PI and AUC at the renal medulla (OR, 0.78 and 1, respectively) predicted progression of CKD.

Table 3. Univariate logistic regression analysis of CEUS parameters.

TIC parameters	KDIGO AKI stage 3		Initiation of RRT		AKI recovery		CKD progression
TIC parameters	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
Cortex
WIS (dB/sec)	0.21 (0.01, 3.63)	0.28	0.34 (0.01, 9.53)	0.53	76.23 (1.47, 3955)	0.03	0.14 (0.01, 2.94)	0.21
TTP (s)	1.04 (0.98, 1.09)	0.2	1.01 (0.95, 1.07)	0.83	0.94 (0.89, 1.01)	0.08	1.05 (0.99, 1.11)	0.12
PI (dB)	0.98 (0.82, 1.18)	0.87	0.98 (0.79, 1.23)	0.89	1.21 (0.96, 1.51)	0.1	0.84 (0.69, 1.04)	0.11
AUC (dB)	1.00 (1.00, 1.00)	0.46	1.00 (1.00, 1.00)	0.5	1.00 (1.00, 1.00)	0.3	1.00 (1.00, 1.00)	0.34
MTT (s)	1.04 (1.00, 1.08)	0.07	1.07 (1.01, 1.14)	0.03	0.97 (0.92, 1.02)	0.19	1.03 (0.99, 1.07)	0.21
FWHM (s)	1.02 (0.99, 1.04)	0.14	1.03 (0.99, 1.06)	0.1	0.99 (0.96, 1.02)	0.45	1.01 (0.98, 1.03)	0.55
RT (s)	1.21 (1.03, 1.43)	0.02	1.20 (1.00, 1.42)	0.045	0.83 (0.70, 0.99)	0.04	1.17 (1.00, 1.36)	0.05
Medulla
WIS (dB/sec)	0.55 (0.18, 1.68)	0.29	0.66 (0.16, 2.78)	0.57	0.85 (0.38, 1.89)	0.68	1.08 (0.49, 2.37)	0.85
TTP (s)	1.01 (0.97, 1.07)	0.55	1.01 (0.95, 1.06)	0.85	0.99 (0.94, 1.05)	0.8	1.00 (0.96, 1.06)	0.86
PI (dB)	1.10 (0.92, 1.30)	0.29	1.14 (0.91, 1.42)	0.25	1.25 (1.02, 1.54)	0.04	0.78 (0.63, 0.96)	0.02
AUC (dB)	1.00 (1.00, 1.00)	0.28	1.00 (1.00, 1.00)	0.17	1.00 (1.00, 1.00)	0.05	1.00 (1.00, 1.00)	0.03
MTT (s)	1.02 (0.97, 1.06)	0.46	1.05 (0.99, 1.12)	0.12	0.99 (0.94, 1.04)	0.67	1.01 (0.96, 1.05)	0.8
FWHM (s)	1.01 (0.99, 1.04)	0.42	1.02 (0.98, 1.05)	0.33	1.00 (0.98, 1.03)	0.88	1.00 (0.97, 1.02)	0.87
RT (s)	1.03 (0.92, 1.16)	0.6	1.13 (0.98, 1.30)	0.1	1.03 (0.90, 1.18)	0.64	0.95 (0.83, 1.07)	0.38

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AKI = acute kidney injury, KDIGO = Kidney Disease: Improving Global Outcomes, RRT = renal replacement therapy, CKD = chronic kidney disease, TIC = time-intensity curve, OR = odds ratio, CI = confidence intervals, WIS = wash in slope, TTP = time to peak intensity, PI = peak intensity, AUC = area under the time-intensity curve, MTT = mean transit time, FWHM = time for full width half max, RT = rise time.

The ability of CEUS to predict KDIGO stage 3 AKI, initiation of RRT, AKI recovery, or CKD progression is shown in Table 4 by the AUC, sensitivity, specificity, and cut-off values. RT at the renal cortex showed reasonable prognostic performance for predicting KDIGO AKI stage 3 (AUC, 0.66, P = 0.04). MTT at the renal cortex was useful in predicting the initiation of RRT (AUC, 0.75, P = 0.006). WIS at the renal cortex and PI at the renal medulla were useful in predicting AKI recovery (AUC, 0.72 and 0.69, P = 0.01 and 0.04, respectively). PI and AUC at the renal medulla were also useful in predicting CKD progression (AUC, 0.73 and 0.7, P = 0.003 and 0.01, respectively); however, their sensitivity and specificity were low.

Table 4. Significant TIC parameters for predicting KDIGO AKI stage 3, initiation of RRT, AKI recovery, and CKD progression.

TIC parameters	AUC (95% CI)	P-value	Cut-off	Sensitivity (%) (95% CI)	Specificity (%) (95% CI)
KDIGO AKI stage 3
Cortex RT (s)	0.66 (0.51, 0.82)	0.04	17.05	60 (39, 79)	70 (47, 87)
Initiation of RRT
Cortex MTT(s)	0.75 (0.57, 0.93)	0.006	79.91	64 (31, 89)	76 (59, 88)
Cortex RT (s)	0.67 (0.48, 0.86)	0.08	23.96	27 (6, 61)	97 (86, 99)
AKI recovery
Cortex WIS (dB/sec)	0.72 (0.55, 0.9)	0.01	0.657	83 (66, 93)	62 (32, 86)
Medulla PI (dB)	0.69 (0.51, 0.87)	0.04	18.19	60 (42, 76)	77 (46, 95)
CKD progression
Medulla PI (dB)	0.73 (0.58, 0.88)	0.003	17.95	28 (10, 53)	33 (17, 53)
Medulla AUC (dB)	0.7 (0.55, 0.85)	0.01	2369.11	17 (4, 41)	47 (28, 66)

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TIC = time-intensity curve, AUC = area under receiver operating characteristic curve, CI = confidence interval, KDIGO = Kidney Disease: Improving Global Outcomes, RRT = renal replacement therapy, AKI = acute kidney injury, CKD = chronic kidney disease, WIS = wash in slope, PI = peak intensity, AUC = area under the time-intensity curve, MTT = mean transit time, RT = rise time.

Fig 4 shows actual values of selected CEUS parameters for different AKI stages (Fig 4A), initiation of RRT (Fig 4B and 4C), AKI recovery (Fig 4D and 4E) and CKD progression (Fig 4F and 4G).

Reproducibility of perfusion parameters

ICCs for quantitative TIC parameters were in the range of 0.26–0.98. The ICC for RT indicated poor agreement. The other parameters showed moderate-to-excellent agreement (Table 5).

Table 5. Reproducibility of perfusion parameters.

TIC parameters	ICC (95% CI)
TIC parameters	Cortex	Medulla
WIS (dB/sec)	0.98 (0.96, 0.99)	0.90 (0.85, 0.94)
TTP (s)	0.60 (0.45, 0.73)	0.68 (0.54, 0.79)
PI (dB)	0.69 (0.56, 0.80)	0.70 (0.57, 0.80)
AUC (dB/sec)	0.64 (0.50, 0.76)	0.76 (0.65, 0.84)
MTT (s)	0.68 (0.55, 0.79)	0.60 (0.45, 0.73)
FWHM (s)	0.68 (0.55, 0.79)	0.65 (0.51, 0.77)
RT (s)	0.26 (0.12, 0.48)	0.45 (0.29, 0.62)

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TIC = time-intensity curve, ICC = Intraclass correlation coefficient, CI = confidence interval, WIS = wash in slope, TTP = time to peak intensity, PI = peak intensity, AUC = area under the time-intensity curve, MTT = mean transit time, FWHM = time for full width half max, RT = rise time

Comparison of TIC parameters between patients with CKD and those without CKD

Comparisons of the TIC parameters according to the presence of underlying CKD are shown in S3 Table. In patients with underlying CKD, PI and AUC were significantly decreased than in those without CKD in both the cortex and medulla. Also, the MTT and FWHM were shortened in patients with CKD in both the cortex and medulla compared to those without CKD.

Discussion

In this prospective study, we identified several quantitative CEUS parameters that could be used as a predictor for renal outcomes in patients with AKI. These parameters included WIS, MTT, and RT at the renal cortex and PI and AUC at the renal medulla. Because the reproducibility of RT was poor, and the sensitivity and specificity of medullary AUC were low, based on their consistent reliability, we suggest MTT and WIS at the renal cortex and PI at the renal medulla for diagnosing the severity and predicting the renal prognosis in patients with AKI. Previous studies have shown that renal microcirculatory perfusion is impaired in animal models of AKI [12–14], and in humans with septic shock [15]. One animal study showed that perfusion impairment correlated with renal histological injury and CKD progression [14]. However, previous literature lacked any assessment of the association between CEUS-driven parameters and the clinical outcomes of human AKI, which is vital for the validity of CEUS for use in a clinical setting. To our knowledge, this study is the first to present the clinical application of CEUS to assess renal prognosis in patients with AKI.

Generally, the diagnosis of AKI is based on changes in serum creatinine concentration, but these changes poorly reflect the acute deterioration in renal function [16], and serum creatinine levels lack sensitivity and specificity, resulting in higher rates of delayed and missed diagnosis [17, 18]. Therefore, the search for new urinary and serum biomarkers, which have the potential to provide earlier diagnosis and better prognosis, is ongoing [19]. Imaging techniques usually provide information concerning the anatomy of the kidney, the possibility of obstruction, inflammation, and edema [3]. Traditionally, Doppler US has been considered as a potential imaging technique to detect renal blood perfusion abnormalities [20]. However, resistive index values only correlate with macroangiopathy and might be influenced by factors such as increased intra-abdominal pressure, pulse rate, pharmacotherapy, and the site at which it is measured [21]. Clinical use of Doppler US is limited by its lower detection limit, the inability to detect slow flow velocity, and limited accuracy in quantifying renal blood flow. CEUS is a promising tool that can be used as a noninvasive approach without the added risks of ionizing radiation and nephrotoxicity, which would impair renal perfusion and increase the risk of nephrogenic systemic fibrosis [22]. The changes in perfusion indices driven by CEUS parallel those in effective renal plasma flow [23]. In addition, in contrast to standard serum markers of renal function, it is possible to obtain a map of the kidney microvasculature with high temporal and spatial resolution [24]. Furthermore, CEUS is a relatively uncomplicated procedure that can be applied to critically ill patients [15, 25]. In a study using CEUS in patients with septic shock, the decreased cortical perfusion, which manifested as lower PI and higher MTT, was associated with severe AKI [15]. Similarly, in our study, the cortical MTT predicted the initiation of RRT. Other cortical parameters also showed meaningful results; for example, cortical RT predicted KDIGO stage 3 AKI and initiation of RRT, and cortical WIS predicted AKI recovery. Given the poor reproducibility of RT, we speculate that cortical MTT and WIS can be used to predict AKI outcomes.

Most of the CEUS measurements that were used to monitor renal microcirculatory perfusion provided information on cortical perfusion [23, 25]. Most recently, it was reported that medullary hypoxia due to intrarenal blood flow redistribution is important in the development of AKI [26–28]. Therefore, we assessed medullary perfusion and found several predictive parameters: medullary PI predicted AKI recovery and CKD progression, and medullary AUC predicted CKD progression. Because the sensitivity and specificity of medullary PI and AUC were low for predicting CKD progression, we speculate that medullary PI may be a useful parameter to predict AKI recovery. In this study, the CEUS-driven TIC parameters of the medulla were not reduced compared to those of cortex. This was unexpected, as the renal medulla receives lower blood flow than the cortex [29]. We speculate that a medullary blood congestion and consequent slowing of the blood flow occurred in our patients with AKI. This medullary congestion is a hallmark of ischemic AKI in both experimental models [30–33] and in specimens obtained from biopsy or at autopsy [34]. Currently, there is no CEUS study for medullary perfusion in humans with AKI. A recent animal study showed that medullary PI and AUC were about one-third of that of cortex in healthy dogs, on the other hand, medullary PI and AUC were almost the same as cortex values in dogs with AKI [12]. The increase in medullary PI and AUC seen in dogs with AKI, which suggests increased medullary congestion, is similar to our results. Further studies with a larger sample size are needed for validation of medullary CEUS findings in patients with AKI.

Our study found that the MTT at the renal cortex was increased in patients requiring RRT than that of patients not requiring RRT. Considering that MTT indicates the average time taken by blood to pass through the capillary network, this finding indicated that, less contrast microbubbles entered the renal cortex microvascular bed with slow perfusion in unit time in patients requiring RRT compared with those not requiring RRT. Harrois et al showed that the greater the alteration in MTT is, the higher is the risk of severe AKI, indicating that MTT seems to be mostly linked to intrarenal hemodynamics [15]. PI reflects the quantity of contrast agent microbubbles in the vascular bed of the organ, while WIS reflects the early quantity and velocity during contrast agent perfusion. These two parameters are associated with the degree of vascularization. The WIS at the renal cortex and the PI at the renal medulla was higher in the AKI recovery group than that in the non-recovery group. This finding suggests that better vascularization at the renal cortex and medulla is associated with AKI recovery. In addition, lower PI at the renal medulla was associated with CKD progression, which meant that reduced medullary perfusion is not protective against tissue recovery. This finding is consistent with a previous report which showed that PI at the renal medulla decreased as the CKD stages progressed [35]. Increased RT indicates delay in rise in echogenicity, which is related with increased resistance of glomeruli and peritubular capillaries [35]. In this study, increased RT at the cortex was associated with severe AKI stage and need of RRT. This finding appears to reflect the delayed cortical perfusion of patients with severe AKI. The AUC means the total volume of blood flow. In our study, the increase in AUC at renal medulla, which indicates medullary blood congestion, was associated with CKD progression. It is difficult to analyze the clinical implication of this finding, since the AUC is dependent on the capillary resistance, retention time and total capillary volume. We can only speculate that an alteration in the medullary capillary density may be associated with progressive renal injury. However, since the sensitivity, specificity, and reliability of medullary PI, AUC, and RT were low, the clinical significance of these parameters needs caution in interpretation.

Quantitative analysis showed that cortical and medullary PI and AUC, intensity- and blood volume-related parameters, were decreased and that cortical and medullary MTT and FWHM, timed-related parameters, were shortened, according to the presence of CKD. Therefore, in CKD patients, renal contrast enhancement was attenuated with deterioration of the renal function, which is consistent with a previous study [35]. Additionally, these results suggest that the CEUS parameters may be used to discriminate between those patients with AKI and those with AKI on CKD.

It should be noted that CEUS should not replace serum markers of renal function, but it might prove useful as a diagnostic tool in patients with inconclusive clinical, laboratory, and histological findings. The early identification of patients who will need RRT and who are at a high risk of progression to CKD would assist physicians in planning and initiating the appropriate management to improve renal outcomes, and to develop renal-preserving treatments. Furthermore, the ability to visualize and quantify changes in microperfusion could provide useful supplemental information for additional investigations of disease mechanisms or novel therapeutic strategies.

Our study had some limitations. First, it was a single-center study with a relatively small number of patients. It was difficult to enroll patients in severe illness, which explains the small number of enrollment and the reason for the low number of patients who developed anuria and the low in-hospital mortality rate. As the numbers of patients and outcome events were small, we could not perform a multivariate logistic analysis. Therefore the predictability of the CEUS perfusion parameters cannot be generalized yet. Second, the time of CEUS examination varied between patients, and the CEUS was performed during different stages of AKI evolution. However, it was designed as a pilot trial to investigate the feasibility of quantitative CUES analysis for predicting renal outcomes in AKI patients. Further studies with a larger sample size and follow-up investigations need to be carried out for further validation. Third, the causes of AKI were various, and analyses according to the cause of AKI were not carried out due to the small sample size. Different causes of AKI may alter the hemodynamics and affect the final quantitative results. The high variability of urine protein-to-creatinine ratio results from this various etiology of AKI. Fourth, heterogeneity of measurements is a common limitation in all attempts to use CEUS to quantify organ perfusion. To minimize this parameter, we aimed to locate ROIs at similar depth and distance, as recommended by Averkiou et al. [36]. In addition, three ROIs were drawn for each experimental time point, and the results were averaged to minimize heterogeneity of the measurements. Fifth, we cannot completely exclude the possibility that concomitant medication usage confounded our results. Kidney perfusion can be influenced by medications and hydration status [37].

In conclusion, we observed several alterations in the CEUS perfusion parameters that showed significance in predicting the severity of AKI and renal prognosis. By evaluating renal microvascular perfusion, CEUS may be used as a supplemental tool to estimate the severity of renal dysfunction and to predict renal outcomes after AKI.

Supporting information

S1 Table. Correlations between TIC parameters and FENa or the lowest urine output.

(DOCX)

Click here for additional data file.^{(18.9KB, docx)}

S2 Table. Comparison of TIC parameters between patients with intrinsic AKI and patients with prerenal or postrenal AKI.

(DOCX)

Click here for additional data file.^{(18.8KB, docx)}

S3 Table. Difference of TIC parameters according to the presence of underlying CKD.

(DOCX)

Click here for additional data file.^{(18.7KB, docx)}

Acknowledgments

The Statistical consultation was supported by the Department of Biostatistics of the Catholic Research Coordinating Center.

Abbreviations

AKI: acute kidney injury
AUC: area under the time-intensity curve
CEUS: contrast-enhanced ultrasound
CKD: chronic kidney disease
eGFR: estimated glomerular filtration rate
FENa: fractional excretion of sodium
FWHM: time for full width at half maximum
KDIGO: Kidney Disease Improving Global Outcomes
MTT: mean transit time
PI: peak intensity
ROI: region of interest
RRT: renal replacement therapy
RT: rise time
TIC: time–intensity curve
TTP: time to peak intensity
WIS: wash-in slope

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

Dr. Yu Ri Shin is supported by the Catholic Medical Center Research Foundation made in the program year of 2017. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0235130.r001

Decision Letter 0

Tatsuo Shimosawa

8 Apr 2020

PONE-D-20-06286

A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury

PLOS ONE

Dear Dr. Shin,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Dear Editor,

I reviewed the manuscript entitled, ‘A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury’ by Shin and collegaues.

I have the following comments and questions,

1. Since CEUS is not a widely used technology and even when it is used, the methodology varies widely, I recommend a brief description of different parameters and what each of them indicate before proceeding to further discussion of methodology. For example, what does WIS or MTT after an injection of a bolus of contrast media indicate and what do we expect to see in various forms of AKI.

2. Estimated GFR by CKD Epi formula should only be used to determine the baseline renal function and not after AKI.

3. It would be helpful to know the cause of AKI and the urine output in these patients. Data on the number of patients requiring RRT and a scale of severity of illness would also be valuable. I am surprised that mortality was zero.

4. Do CEUS variables differentiate AKI causes or correlate with FENa or FEUrea or response to volume expansion?

5. I am not sure if logistic regression is the right test, considering CEUS parameters are not completely independent of one another. If you were including one CEUS variable in the model and were adding other potential predictors of AKI outcome, that might have been useful.

6. Considering medulla receives only about 15% of renal blood flow and has very slow velocities and transition times, I am very surprised to see comparable numbers for cortex and medulla. The time to peak is almost identical for the cortex and medulla.

7. In the image provided the ROI are much smaller than what we use and other investigators have used. I am wondering why larger ROI are not used, at least for the cortex.

8. The image intensity needs to be standardized for the renal artery, aorta or left ventricle intensity. That would eliminate the issues with errors in volume of contrast, extravasation from the vein, etc.

9. None of the parameters from CEUS have good AUC or reasonable sensitivity or specificities.

10. Under discussion, I recommend more details on interpretation of results. In other words, it would be helpful to know why higher or lower values of certain parameters indicate a more severe degree of tissue injury. What is the clinical implication of these findings?

Reviewer #2: Dear Prof. Tatsuo Shimosawa,

Academic Editor,

PLOS One

The manuscript of “A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury” is interesting to develop and establish renal vessel CEUS. I expect the new method is important for evaluation of real-time renal function of AKI as well as CKD.

I have several questions about methods.

Comments;

1) There are a lot of unique abbreviations. I recommend to make abbreviation section.

2) I recommend to give a figure of the examination protocol. It is useful to understand the examination and the result data.

3) How many sonographers did exam the protocol?

4) It was written “the transducer was manually held in the same scanning plane while patients were instructed to perform only shallow breathing to minimize the variation caused by motion” in Materials and Methods. I think each holding time was very long because the protocol time should include baseline, bolus shot and follow up. And I concern the base line data should get much different when the angle of the transducer moves just a little. How they contrive ways to keep the transducer in the same place and the same angle?

5) There is not section of COI nor funding.

Reviewer #3: Summary of the research: This is a prospective study on CEUS in 48 patients with AKI. CEUS parameters measured included: wash-in slope (WIS), time to peak intensity, peak intensity (PI), area under the time-intensity curve (AUC), mean transit time (MTT), time for full width at half maximum, and rise time (RT). The outcomes assessed included KDIGO AKI stage, initiation of RRT, AKI recovery, and CKD. Significant findings included: Cortical rise time predicted KDIGO stage 3 AKI, Cortical WIS and RT predicted need for RRT. Cortical WIS and medullary PI predicted AKI recovery. Medullary PI and AUC predicted CKD progression. Because of the small patient population, subtypes of AKI were not able to be evaluated. Intraobserver evaluation with multiple ROIs was performed.

Overall Impression: The strengths of this article are many. Novelty, prospective study design, relatively equal split between patient genders, and the incorporation of intraobserver evaluation for quality control. I think that the authors did a great job of setting the groundwork for future studies with this small pilot study and were very open and realistic with study limitations (many of their limitations are understandable given the patient population and commonly seen with other renal studies such as inability to account for medications impacting renal function at time of study).

Major Issues: I do not see any major issues with the study as performed or the paper as written. I think the authors have done a wonderful and thoughtful job.

Minor Issues: None.

Miscellaneous Remarks: Valuable addition to the literature.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Linda C Kelahan

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PLoS One. 2020 Jun 24;15(6):e0235130. doi: 10.1371/journal.pone.0235130.r002

Author response to Decision Letter 0

24 Apr 2020

To the editor

We would like to thank the editor and reviewers for their insightful comments. Their comments definitely helped to improve the quality of our manuscript. We have tried our very best to answer the reviewers’ specific points. The reviewer's comments are in blue and our answers are in black. We hope our revisions improve the paper such that it is worthy of publication in PLOS ONE. Thank you.

Point by point response to reviewers’ comments

[1] Journal requirements:

When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

In response to this comment, we made sure that our manuscript was correctly formatted, according to the PLOS ONE's style requirements.

[2] Reviewer #1 comments:

We thank the reviewer for this helpful comment. However, there is limit to the number of characters in the text and how the CEUS parameters appear in various forms of AKI is unknown yet. We can only speculate what these parameters mean in clinical settings. To avoid redundancy, we additionally described the meanings and clinical implications of the quantitative CEUS parameters in the discussion section.

2. Estimated GFR by CKD Epi formula should only be used to determine the baseline renal function and not after AKI.

Thank you for the comment. We deleted the “eGFR at AKI occurrence” and the “lowest eGFR” from Table 1. Instead we included the “serum creatinine at AKI occurrence” and the “highest serum creatinine” in Table 1.

We added the causes of AKI (prerenal, intrinsic, and postrenal) in Table 1 and also described it in more detail in the result section. The lowest urine output was added in Table 1. The AKI severity was shown as the KDIGO AKI stage in Table 1 and was described in the result section as “Among them, 25 (52%) patients were diagnosed as KDIGO stage 3 AKI, 11 (23%) patients received RRT, 13 (27%) patients achieved functional recovery of AKI, and 18 (38%) patients showed CKD progression. Ten patients (20.8%) had urine output less than 500 mL per day, two patients (4.2%) developed anuria during the course of AKI, and the in-hospital mortality rate was 0%.”

As the reviewer commented, the mortality rate was zero. This was because it was difficult to enroll patients in severe illness, since patients who are hemodynamically unstable and are in a clinically downhill course cannot undergo CEUS. This was described in the limitation.

4. Do CEUS variables differentiate AKI causes or correlate with FENa or FEUrea or response to volume expansion?

As the reviewer suggested, we analyzed the correlation coefficient between the CEUS variables and FENa or urine output. None of the CEUS variables showed statistically significant correlation coefficients (supplement table 1). The data on FEUrea or response to volume expansion was not collected in this study.

The CEUS variables were compared between patients with intrinsic causes and those with prerenal or postrenal causes. None of the TIC parameters showed statistically significant difference between patients with intrinsic AKI and patients with prerenal or postrenal AKI (Supplement table 2). Therefore the CEUS variables did not differentiate AKI causes.

The description for supplement tables 1 and 2 were added in the result section.

The statistical analysis in our study was performed by statistician in our university. We consulted with the statistician about performing the multivariate analysis, and received this answer as follows.

“In statistics, the ‘one in ten rule’ is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis, while keeping the risk of overfitting low. The rule states that one predictive variable can be studied for every ten events. In this study, the outcome events were need of RRT, AKI recovery, and CKD progression. The number of the events was small, which was all less than 20. Therefore, to keep the overfitting low and make the result reliable, only one predictor can be included in the logistic regression analysis. That is why we only performed a univariate logistic regression analysis”.

We agree with the reviewer’s comment that a multivariate analysis is needed to demonstrate the predictive value of CEUS parameters. But as the number of patients and number of outcome events were small, we could not perform a multivariate analysis. We added this as a limitation in the discussion section.

“…As the numbers of patients and outcome events were small, we could not perform a multivariate logistic analysis. Therefore the predictability of the CEUS perfusion parameters cannot be generalized yet…..”

Currently, there is no CEUS study for medullary perfusion in human. A recent animal study showed that medullary PI and AUC were about one-third of that of cortex in healthy dogs, on the other hand, medullary PI and AUC were almost the same as cortex values in dogs with AKI (J Small Anim Pract. 2019 Aug;60(8):471-476). The increase in medullary PI and AUC seen in dogs with AKI, which suggests increased medullary perfusion, is similar to our results. We speculate that a medullary blood congestion and consequent slowing of the blood flow occurred in our patients with AKI. This medullary congestion was shown to be a key event in ischemic AKI murine model (Kidney Int. 1984 Sep;26(3):283-93).

7. In the image provided the ROI are much smaller than what we use and other investigators have used. I am wondering why larger ROI are not used, at least for the cortex.

According to the referenced literature (Current Drug Targets 2009;10(12):1184-9), the pattern and quantity of blood flow and tissue blood volume in different regions within the organ, i.e., cortex separately from medulla, can be determined within a few minutes with CEUS, which is particularly desirable in clinical situations in which patient transfer out of the intensive care unit is an issue. By providing information on regional changes in renal blood flow, CEUS has the potential to serve as a helpful tool in the diagnostic work up of patients with AKI.

We wanted to find out if there is a perfusion difference between cortex and medulla or to look at corticomedullary perfusion ratios of CEUS parameters. Therefore, we thought that the ROI of the cortex and medulla should be drawn in the same size and shape as possible. However, the medulla, unlike a cortex, is not easy to draw an entire ROI. So, we analyzed by drawing three ROIs of the same size in the cortex and medulla in the upper, middle, and lower regions. In order to include only the microvasculature in the outer cortex according to very strict criteria, many reported data obtained the ROI of the cortex in the same way as in our study.

We agree with to the reviewer's comment, in that a suitable arterial input function is usually not available in the bolus-administration mode. In addition, some perfusion parameters are severely influenced by ultrasound attenuation in intervening tissue. However, the heart, renal parenchyma and bowel wall represent the most established applications of CEUS in the quantification of organ perfusion in humans. Until recently, the quantitative analysis of renal tissue perfusion, measured by CEUS, has been performed without quantification standardization. In addition, although manual injection might potentially predispose to lower accuracy in contrast infusion, the majority of studies used manual injection. Therefore, we did not standardize the image intensity of the renal parenchyme to renal artery, aorta or left ventricle intensity.

9. None of the parameters from CEUS have good AUC or reasonable sensitivity or specificities.

In our study, we found that the AUC in MTT, WIS, PI values were 0.75, 0.72, and 0.69, respectively. Although diagnostic accuracy is not satisfactory, our preliminary findings are encouraging for the performance of quantitative CEUS analysis as a screening test for patients with AKI. Many factors can affect CEUS quantitative analysis, including the frequency, the mechanical index, the analytical software, the contrast dose, patient’s factor, the injection velocity of contrast agents and so on. In this regard, further standardization and investigation are needed.

We thank the reviewer for this helpful comment. In response to this comment, we added the following sentence in discussion:

“Our study found that the MTT at the renal cortex was increased in patients requiring RRT than that of patients not requiring RRT. Considering that MTT indicates the average time taken by blood to pass through the capillary network, this finding indicated that, less contrast microbubbles entered the renal cortex microvascular bed with slow perfusion in unit time in patients requiring RRT compared with those not requiring RRT. Harrois et al showed that the greater the alteration in MTT is, the higher is the risk of severe AKI, indicating that MTT seems to be mostly linked to intrarenal hemodynamics (Crit care 2018;22:161). PI reflects the quantity of contrast agent microbubbles in the vascular bed of the organ, while WIS reflects the early quantity and velocity during contrast agent perfusion. These two parameters are associated with the degree of vascularization. The WIS at the renal cortex and the PI at the renal medulla was higher in the AKI recovery group than that in the non-recovery group. This finding suggests that better vascularization at the renal cortex and medulla is associated with AKI recovery. In addition, lower PI at the renal medulla was associated with CKD progression, which meant that reduced medullary perfusion is not protective against tissue recovery. This finding is consistent with a previous report which showed that PI at the renal medulla decreased as the CKD stages progressed (Int Heart J 2010;51:176-82). Increased RT indicates delay in rise in echogenicity, which is related with increased resistance of glomeruli and peritubular capillaries (Int Heart J 2010;51:176-82). In this study, increased RT at the cortex was associated with severe AKI stage and need of RRT. This finding appears to reflect the delayed cortical perfusion of patients with severe AKI. The AUC means the total volume of blood flow. In our study, the increase in AUC at renal medulla, which indicates medullary blood congestion, was associated with CKD progression. It is difficult to analyze the clinical implication of this finding, since the AUC is dependent on the capillary resistance, retention time and total capillary volume. We can only speculate that an alteration in the medullary capillary density may be associated with progressive renal injury. However, since the sensitivity, specificity, and reliability of medullary PI, AUC, and RT were low, the clinical significance of these parameters needs caution in interpretation.”

[3] Reviewer #2 comments:

1. There are a lot of unique abbreviations. I recommend to make abbreviation section.

As the reviewer commented, we added an abbreviation section in the end of the manuscript.

2. I recommend to give a figure of the examination protocol. It is useful to understand the examination and the result data.

In response to this comment, we made a figure of the examination protocol and added the sentence in the material and methods:

“The examination protocol is illustrated in Figure 2.”

3. How many sonographers did exam the protocol?

All CEUS examinations were performed by one experienced radiologist to ensure the stability of measurements.

This sentence has already been included in the Materials and Methods.

4. It was written “the transducer was manually held in the same scanning plane while patients were instructed to perform only shallow breathing to minimize the variation caused by motion” in Materials and Methods. I think each holding time was very long because the protocol time should include baseline, bolus shot and follow up. And I concern the base line data should get much different when the angle of the transducer moves just a little. How they contrive ways to keep the transducer in the same place and the same angle?

Examinations were performed as followed: First, conventional B-mode US was performed to scan the kidney and to observe the size, position and echogenicity. During B-mode scanning, maximal longitudinal scanning was chosen as the ideal plane for CEUS. Second, the US system was then switched to contrast mode for CEUS examination. Contrast agent was injected manually as a bolus over 2 seconds followed by a flush of 10 mL saline solution. The entire CEUS process was recorded for each patient from the time of injection, until no apparent agent was observed. Our study analyzed images of kidney perfusion after the contrast agent was given, focusing only on cortex and medulla perfusion. Conventional ultrasound examination is intended to confirm the approximate target organ location for contrast-enhanced ultrasound examination to be performed later, and there is no special baseline data. In addition, the kidney is an organ located in the retroperitoneal space, and unlike other organs in the abdominal cavity, there is less movement caused by patient's breathing. In addition, we specifically instructed the patients regarding their respiration control before the CEUS examination was performed. We found that our study showed a relatively stable perfusion graph except for patients with severe respiratory disease.

5. There is not section of COI nor funding.

We entered a financial disclosure statement and COI statement during the submission process.

[4] Reviewer #3 comments:

Thank you for your informative comments.

Attachment

Submitted filename: Response_to_editor_and_reviwer_s_commonets.docx

Click here for additional data file.^{(31.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0235130.r003

Decision Letter 1

Tatsuo Shimosawa

11 May 2020

PONE-D-20-06286R1

A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury

PLOS ONE

Dear Dr. Shin,

Two experts and I have concern on statistical analysis and data collection method.

We would appreciate receiving your revised manuscript by Jun 25 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

Please include the following items when submitting your revised manuscript:

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An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Tatsuo Shimosawa, M.D., Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: Dear Editor,

I reviewed the revised manuscript and found the changes very helpful. Here are my remaining concerns and suggestions for improvement,

I am not satisfied with the response provided regarding the use of logistic regression. The independent variables (predictors) in logistic regression should have minimal or no collinearity. They need to be independent of one another and not to correlate with each other. Therefore, different CEUS parameters cannot be used in the same logistic regression analysis as they are not independent of one another.

I believe a dot plot of actual values of selected CEUS parameters (those that were significantly associated with the study outcomes) for different AKI stages or for those who required RRT versus those who didn’t would be necessary. A visual representation of the differences and overlaps between the groups would be more useful than OR.

CEUS studies were probably performed during different stages of AKI evolution. While a patient may have had their study on day one with minimal rise in serum Cr, another patient may have had it at the peak and a third one while recovering. Reporting the mean difference between the time of CEUS and time of peak serum Cr or nadir of urine output would be helpful in confirming the utility of this tool in prognostication of AKI.

The imaging study of one kidney occurred immediately after injection of the contrast bolus, while the second kidney was images later on. Certain parameters, such as time to peak cannot be obtained for the second kidney. Additionally, since contrast was given a s a bolus and not a continuous infusion, it is expected to have lower image intensity during the study of the second kidney. Please clarify if the data used is only from the first kidney or both and if there were differences between the two.

Minor changes,

Under Study Design and Patients,

Line 84, Adult patients?

Line 84, Change our hospital to the actual name of the hospital

Line 96, move number of patients to results.

Line 98, specify what was measured in urine.

Line 100. Change renal function to “baseline renal function”, since you won’t be using the equation for measuring GFR after AKI.

Line 140-105, what was the frequency of follow up?

Line 106, move AKI staging to where the definition is discussed.

Under methods please indicate how the cause of AKI was determined.

Urine output is used in KDIGO definition of AKI. If that data was recorded and used in categorizing AKI stages, please indicate it under the methods section.

Table 1. I suggest moving CEUS data to a separate table under results.

Reviewer #2: The paper is corrected better.

But I still have a question to ought to be clear, because the renal CEUS is not widely accepted yet. As all sonographic experiences were endured by one radiologist and manual probe supporting, how are they secure the standardization of the examination results?

Reviewer #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Kambiz Kalantari, MD, MS

Reviewer #2: No

Reviewer #3: Yes: Linda Kelahan

PLoS One. 2020 Jun 24;15(6):e0235130. doi: 10.1371/journal.pone.0235130.r004

Author response to Decision Letter 1

21 May 2020

To the editor

We would like to thank the editor and reviewers for their insightful comments. Their comments definitely helped to improve the quality of our manuscript. We have tried our very best to answer the reviewers’ specific points. The reviewer's comments are in back and our answers are in blue. We hope our revisions improve the paper such that it is worthy of publication in PLOS ONE. Thank you.

Point by point response to reviewers’ comments

[1] Reviewer #1 comments:

Reviewer #1: Dear Editor,

I reviewed the revised manuscript and found the changes very helpful. Here are my remaining concerns and suggestions for improvement,

1. I am not satisfied with the response provided regarding the use of logistic regression. The independent variables (predictors) in logistic regression should have minimal or no collinearity. They need to be independent of one another and not to correlate with each other. Therefore, different CEUS parameters cannot be used in the same logistic regression analysis as they are not independent of one another.

■ We agree with the reviewer’s comment that CEUS parameters are not independent of each other. The logistic regression analysis was univariate, and included “only one” CEUS parameter for each outcome. Thus, multiple CEUS parameters were not included in a same analysis.

As the reviewer pointed out in the 1st review, adding other clinical variables would be useful to clearly demonstrate the association between CEUS parameters and outcomes. But as the number of patients and number of outcome events were small, we could not perform a multivariate analysis.

As our description for the statistical analysis and the results on the logistic regression may confuse the reader, we tried to clarify this by adding the term “univariate” and a sentence “Since CEUS parameters are not completely independent of one another, we included one CEUS parameter for each outcome in the logistic regression analysis.”

2. I believe a dot plot of actual values of selected CEUS parameters (those that were significantly associated with the study outcomes) for different AKI stages or for those who required RRT versus those who didn't would be necessary. A visual representation of the differences and overlaps between the groups would be more useful than OR.

■ We thank the reviewer for this helpful comment. We made a dot plot of actual values of selected CEUS parameters as Figure 4.

3. CEUS studies were probably performed during different stages of AKI evolution. While a patient may have had their study on day one with minimal rise in serum Cr, another patient may have had it at the peak and a third one while recovering. Reporting the mean difference between the time of CEUS and time of peak serum Cr or nadir of urine output would be helpful in confirming the utility of this tool in prognostication of AKI.

■ We agree with the reviewer’s comment. We added the mean, standard deviation, and range of the time of CEUS and time of peak serum Cr in the results, and added this as a limitation of this study.

“The duration between the time of CEUS and the time of peak serum creatinine was 3.8 ± 3.9 days (range, 0 – 14 days).”

4. The imaging study of one kidney occurred immediately after injection of the contrast bolus, while the second kidney was images later on. Certain parameters, such as time to peak cannot be obtained for the second kidney. Additionally, since contrast was given as a bolus and not a continuous infusion, it is expected to have lower image intensity during the study of the second kidney. Please clarify if the data used is only from the first kidney or both and if there were differences between the two.

■ The contrast agent was injected before examination of each kidney. The right kidney was examined first and, after about 20 min, the same CEUS examinations (including injection of SonoVue) were performed for the left kidney. We added this to clarify how the CEUS was performed.

“The contrast agent was injected before examination of each kidney. The right kidney was examined first and, after about 20 min, the same CEUS examinations (including injection of SonoVue) were performed for the left kidney.”

We obtained CEUS parameters for both kidneys respectively and additionally calculated the average value of each parameter from both kidneys of each individual. Because the amount of data is too large and difficult to express, only the average value was shown in the paper. A few previous studies conducted in the same way by Wang L et al. [Biomed Res Int. 2015 and J Nephrol 2015]. Although not shown in the paper, we can see that there is no significant difference in the CEUS values of the right and left kidneys, and similar results have been confirmed in some recently published papers [Clin Hemorheol Microcirc 2016;62:229-38, Br J Radiol 2014 Oct;87(1042):20140350. doi: 10.1259/bjr.20140350]. And it has been reported that contrast agent concentrations and GFR are not significantly different between the left and the right kidney [J Vet Med Sci 2016 Feb;78:239-44].

In accordance with this comment, we have added the following sentence to clarify the meaning.

“The final reported results of CEUS parameters represent the average value of each parameter from both kidneys of each individual.”

Minor changes

Under Study Design and Patients,

1. Line 84, Adult patients?

■ Yes, only adult patients were enrolled. We added the exclusion criteria for age.

2. Line 84, Change our hospital to the actual name of the hospital

■ We added the actual name of the hospital.

3. Line 96, move number of patients to results.

■ We moved the number of patients to the result section.

4. Line 98, specify what was measured in urine.

■ Urine sodium, creatinine, and protein levels were measured. We specified this as the reviewer commented.

5. Line 100. Change renal function to "baseline renal function", since you won't be using the equation for measuring GFR after AKI.

■ We revised the term as “baseline renal function”.

6. Line 140-105, what was the frequency of follow up?

■ The frequency of follow-up differed between patients. If the patient was cared in the outpatient clinic, the follow-up interval was determined according to the physician’s decision, and ranged from 2 days to 1 week. If the patient was admitted in the hospital, the patient’s status was checked every day. If the patient’s renal function improved to his or her baseline function and the patient did not have CKD, the follow-up was stopped at 3 months post-AKI. Patients with CKD or non-recovery of AKI were followed-up more than 3 months post-AKI, and the follow-up interval was determined according to the physician’s decision.

This was briefly described in the method section.

7. Line 106, move AKI staging to where the definition is discussed.

■ We revised the sentence as the reviewer commented.

8. Under methods please indicate how the cause of AKI was determined.

■ The clinical context, history taking, physical examination, and interpretation of blood and urine laboratory findings were used for the differential diagnosis of the cause of AKI.

This was added in the method section.

9. Urine output is used in KDIGO definition of AKI. If that data was recorded and used in categorizing AKI stages, please indicate it under the methods section.

■ Not all patients were admitted to the hospital at the diagnosis of AKI. The urine output was calculated for admitted patients. Therefore the serum creatinine criteria was used for the definition and staging for AKI. We clarified such definition in the method section.

10. Table 1. I suggest moving CEUS data to a separate table under results.

■ We made a separate table for the CEUS data as the reviewer suggested.

[2] Reviewer #2 comments:

Reviewer #2: The paper is corrected better.

■ After the injection of the contrast agent, all images and video clips were stored digitally on a hard disk system, and then transferred to a personal computer for further quantitative analyses using advanced US quantification software. As the CEUS parameters are obtained from a time-intensity curve, these parameters are calculated automatically by the software program. Therefore, we can expect that simply measuring these parameters will demonstrate a good agreement among observers, because it is different from characterizing a renal mass.

In general, it can be assumed that renal mass characterization will have greater interobserver variation than simple measurements of parenchymal perfusion. Recently the biggest study cohort of patients with unclear renal mass that were evaluated using CEUS showed a great interobserver agreement between two independent readers [Eur Radiol 2018;28:4542-49]. These findings are in line with several previous studies conducted about this topic.

Many CEUS studies about renal parenchymal perfusion were performed by a single radiologist [Transplant Proc 2009;41:3024-7, Clin Hemorheol Microcirc 2016;62:229-38, Antioxid Redox Signal 2017;27:1397-1411, Abdom Radiol 2018;43:1423-1431]. As previous studies did, our study was also conducted by one researcher.

[3] Reviewer #3 comments:

■ Thank you for reviewing our manuscript.

Attachment

Submitted filename: Response-revision 2.doc

Click here for additional data file.^{(51KB, doc)}

PLoS One. doi: 10.1371/journal.pone.0235130.r005

Decision Letter 2

Tatsuo Shimosawa

3 Jun 2020

PONE-D-20-06286R2

A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury

PLOS ONE

Dear Dr. Shin,

A reviewer has concern on method. Please confirm that your method measure medullary blood flow.

Please submit your revised manuscript by Jul 18 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Tatsuo Shimosawa, M.D., Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for making all the suggested changes. The manuscript reads very well. I just wanted to mention that in my experience, the CEUS data for medulla differ significantly from that of cortex and I was surprised to see them identical in your study.

Reviewer #2: Thank you for the response. I still have a technical question. Physiologically and anatomically it is well known that the blood flow and vessels of medulla are much less than that of cortex. It feels strange the blood flow of medulla looks similar to cortex in Figure 3. But it could be limitations for the quantitative CEUS as the authors discussed.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Kambiz Kalantari, MD, MS

Reviewer #2: No

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Jun 24;15(6):e0235130. doi: 10.1371/journal.pone.0235130.r006

Author response to Decision Letter 2

5 Jun 2020

To the editor

Point by point response to reviewers’ comments

[1] Reviewer #1 comments:

Thank you for making all the suggested changes. The manuscript reads very well. I just wanted to mention that in my experience, the CEUS data for medulla differ significantly from that of cortex and I was surprised to see them identical in your study.

We thank the reviewer for this informative comment and agree with you. Unlike medulla, which normally receives less flow than cortex, we speculate that a medullary blood congestion and consequent slowing of the blood flow occurred in our patients with AKI. This medullary congestion is a hallmark of ischemic AKI in both experimental models (Kidney Int 1990; 37: 1240-1247, Kidney Int 1990; 38: 432-439, Kidney Int 1991; 40: 625-631, Lab Invest 1991; 65: 566-576) and in specimens obtained from biopsy or at autopsy (Kidney Int 1984; 26: 283-293). Currently, there is no CEUS study for medullary perfusion in humans with AKI. A recent animal study showed that medullary PI and AUC were about one-third of that of cortex in healthy dogs, on the other hand, medullary PI and AUC were almost the same as cortex values in dogs with AKI (J Small Anim Pract. 2019 Aug;60(8):471-476). The increase in medullary PI and AUC seen in dogs with AKI, which suggests increased medullary congestion, is similar to our results. However, our study had patients with mild AKI, whereas few papers on human CEUS had severe illness. Further studies with a larger sample size are needed for validation.

We added the following sentence about the medullary CEUS findings in discussion:

“In this study, the CEUS-driven TIC parameters of the medulla were not reduced compared to those of cortex. This was unexpected, as the renal medulla receives lower blood flow than the cortex (Seminars in Nephrology, Vol 39, No 6, November 2019, 520−529). We speculate that a medullary blood congestion and consequent slowing of the blood flow occurred in our patients with AKI. This medullary congestion is a hallmark of ischemic AKI in both experimental models (Kidney Int 1990; 37: 1240-1247, Kidney Int 1990; 38: 432-439, Kidney Int 1991; 40: 625-631, Lab Invest 1991; 65: 566-576) and in specimens obtained from biopsy or at autopsy (Kidney Int 1984; 26: 283-293). Currently, there is no CEUS study for medullary perfusion in humans with AKI. A recent animal study showed that medullary PI and AUC were about one-third of that of cortex in healthy dogs, on the other hand, medullary PI and AUC were almost the same as cortex values in dogs with AKI (J Small Anim Pract. 2019 Aug;60(8):471-476). The increase in medullary PI and AUC seen in dogs with AKI, which suggests increased medullary congestion, is similar to our results. Further studies with a larger sample size are needed for validation of medullary CEUS findings in patients with AKI.”

[2] Reviewer #2 comments:

Thank you for the response. I still have a technical question. Physiologically and anatomically it is well known that the blood flow and vessels of medulla are much less than that of cortex. It feels strange the blood flow of medulla looks similar to cortex in Figure 3. But it could be limitations for the quantitative CEUS as the authors discussed.

We added the discussion about the medullary CEUS findings as above.

Attachment

Submitted filename: Response_to_reviwer_s_commonets 3.docx

Click here for additional data file.^{(24.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0235130.r007

Decision Letter 3

Tatsuo Shimosawa

10 Jun 2020

A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury

PONE-D-20-06286R3

Dear Dr. Shin,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Tatsuo Shimosawa, M.D., Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: Than you for your response. I agree the comment. And I believe that this report is important for the future clinical relevant of acute renal injury.

**********

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Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0235130.r008

Acceptance letter

Tatsuo Shimosawa

15 Jun 2020

PONE-D-20-06286R3

A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury

Dear Dr. Shin:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Kind regards,

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on behalf of

Prof. Tatsuo Shimosawa

Academic Editor

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Correlations between TIC parameters and FENa or the lowest urine output.

(DOCX)

Click here for additional data file.^{(18.9KB, docx)}

S2 Table. Comparison of TIC parameters between patients with intrinsic AKI and patients with prerenal or postrenal AKI.

(DOCX)

Click here for additional data file.^{(18.8KB, docx)}

S3 Table. Difference of TIC parameters according to the presence of underlying CKD.

(DOCX)

Click here for additional data file.^{(18.7KB, docx)}

Attachment

Submitted filename: Review, Arp 6th.docx

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury

Hye Eun Yoon

Da Won Kim

Dongryul Kim

Yaeni Kim

Seok Joon Shin

Yu Ri Shin

Roles

Abstract

Objectives

Methods

Results

Conclusion

Introduction

Materials and methods

Study design and patients

Fig 1. Flow diagram of our study population.

US examination

Fig 2. Illustration of the examination protocol.

Image analysis

Fig 3. Screenshot taken from QLAB illustrating localization of ROI and determining perfusion indices.

Statistical analysis

Results

Patient characteristics and CEUS-driven TIC parameters

Table 1. Baseline characteristics of the final trial cohort.

Table 2. The CEUS-driven TIS parameters of the final trial cohort.

Predictors of the severity of AKI and clinical outcomes and prognostic performance of CEUS

Table 3. Univariate logistic regression analysis of CEUS parameters.

Table 4. Significant TIC parameters for predicting KDIGO AKI stage 3, initiation of RRT, AKI recovery, and CKD progression.

Fig 4. Actual values of selected CEUS parameters associated with clinical outcomes.

Reproducibility of perfusion parameters

Table 5. Reproducibility of perfusion parameters.

Comparison of TIC parameters between patients with CKD and those without CKD

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Tatsuo Shimosawa

Roles

Author response to Decision Letter 0

Decision Letter 1

Tatsuo Shimosawa

Roles

Author response to Decision Letter 1

Decision Letter 2

Tatsuo Shimosawa

Roles

Author response to Decision Letter 2

Decision Letter 3

Tatsuo Shimosawa

Roles

Acceptance letter

Tatsuo Shimosawa

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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