Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5224_Библиотеки_им_академика_М_И_Перельмана.pdf
29 Prediction and Machine Learning Models for Early Prediction of AKI 347
Conclusions
AI and ML models are increasingly becoming integrated into clinical practice,
offering significant potential for early detection of AKI alongside emerging serum
and urinary biomarkers, which offer the advantage of significantly reduced consumable costs. Both standard supervised ML approaches and newer AI techniques like
NLP and sub-phenotyping hold promise for integration into clinical workflows soon.
However, to expedite their application in clinical practice, we must address the
substantial translational gap between published models and real-world applicability.
This gap can be narrowed through improvements in the regulatory framework and
the standardization of data structures in EHR systems.
References
1. Nichols JA, Herbert Chan HW, Baker MAB. Machine learning: applications of artificial
intelligence to imaging and diagnosis. Biophys Rev. 2019;11:111–8. https://doi.org/10.1007/
s12551-018-0449-9.
2. Greco M, Caruso PF, Cecconi M. Artificial intelligence in the intensive care unit. Semin Respir
Crit Care Med. 2020;42:2. https://doi.org/10.1055/s-0040-1719037.
3. Caruso PF, Greco M, Ebm C, et al. Implementing artificial intelligence: assessing the cost and
benefits of algorithmic decision-making in critical care. Crit Care Clin. 2023;39:783–93.
doi.org/10.1016/j.ccc.2023.03.007.
4. Citerio G. Big data and artificial intelligence for precision medicine in the neuro-ICU: Bla, Bla,
Bla. Neurocrit Care. 2022;37:163–5. https://doi.org/10.1007/s12028-021-01427-6.
5. Hoste EAJ, Bagshaw SM, Bellomo R, et al. Epidemiology of acute kidney injury in critically ill
patients: the multinational AKI-EPI study. Intensive Care Med. 2015;41:1411–23. https://doi.
org/10.1007/s00134-015-3934-7.
6. Ronco C, Chawla LS. Glomerular and tubular kidney stress test: new tools for a deeper
evaluation of kidney function. Nephron. 2016;134:191–4. https://doi.org/10.1159/000449235.
7. Kellum JA, Lameire N, KDIGO AKI Guideline Work Group. Diagnosis, evaluation, and
management of acute kidney injury: a KDIGO summary (part 1). Crit Care. 2013;17:204.
https://doi.org/10.1186/cc11454.
8. Swaminathan S, Rosner MH, Okusa MD. Emerging therapeutic targets of sepsis-associated
acute kidney injury. Semin Nephrol. 2015;35:38–54. https://doi.org/10.1016/j.semnephrol.
2015.01.005.
9. Gomez H, Ince C, De Backer D, et al. A unified theory of sepsis-induced acute kidney injury.
Shock. 2014;41:3–11. https://doi.org/10.1097/SHK.0000000000000052.
10. Bihorac A, Chawla LS, Shaw AD, et al. Validation of cell-cycle arrest biomarkers for acute
kidney injury using clinical adjudication. Am J Respir Crit Care Med. 2014;189:932– 9. https://
doi.org/10.1164/rccm.201401-0077OC.
11. Greco M, De Rosa S, Boehm F, et al. Kinetics of the cell cycle arrest biomarkers (TIMP2 and
IGFBP7) for the diagnosis of acute kidney injury in critically ill COVID-19 patients. Diagnostics (Basel). 2023;13:317. https://doi.org/10.3390/diagnostics13020317.
12.
Golino G,
and predictive performance of TIMP2 × IGFBP7 biomarkers for early identification of acute
kidney injury. Diagnostics (Basel). 2022;12:2481. https://doi.org/10.3390/
diagnostics12102481.
Greco M, Rigobello A, et al. Incidence of acute kidney injury in polytrauma patients
https://
348 M. Greco et al.
13. Endre ZH. Using biomarkers for acute kidney injury: barriers and solutions. Nephron Clin
Pract. 2014;127:180–4. https://doi.org/10.1159/000363555.
14. Erstad BL. Usefulness of the biomarker TIMP-2•IGFBP7 for acute kidney injury assessment in
critically ill patients: a narrative review. Ann Pharmacother. 2022;56:83–92. https://doi.org/10.
1177/10600280211005425.
15. Flechet M, Güiza F, Schetz M, et al. AKIpredictor, an online prognostic calculator for acute
16. Casaer MP, Mesotten D, Hermans G, et al. Early versus late parenteral nutrition in critically ill
17. Koyner JL, Carey KA, Edelson DP, Churpek MM. The development of a machine learning
18. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:
19. Tomašev N, Glorot X, Rae JW, et al. A clinically applicable approach to continuous prediction
20. Li Y, Yao L, Mao C, et al. Early prediction of acute kidney injury in critical care setting using
21. Rehm G, Han J, Kuhn B, et al. Creation of a robust and generalizable machine learning classifier
22. Hyland SL, Faltys M, Hüser M, et al. Early prediction of circulatory failure in the intensive care
23. Guo C, Dong G, Liang X, Dong Z. Epigenetic regulation in AKI and kidney repair: mechanisms
24. Devarajan P. Genomic and proteomic characterization of acute kidney injury. Nephron.
25. Endre ZH, Mehta RL. Identification of acute kidney injury subphenotypes. Curr Opin Crit Care.
26. Hak F, Guimarães T, Santos M. Towards effective clinical decision support systems: a
27. Medic G, Kosaner Kließ M, Atallah L, et al. Evidence-based Clinical Decision Support Systems
28.
29. Olaye IM, Seixas
30. Korot E, Pontikos N, Liu X, et al. Predicting sex from retinal fundus photographs using
31. Beaulieu-Jones BK, Yuan W, Brat GA, et al. Machine learning for patient risk stratification:
injury in adult critically ill patients: development, validation and comparison to serum
kidney
neutrophil gelatinase-associated lipocalin. Intensive Care Med. 2017;43:764–73. https://doi.
org/10.1007/s00134-017-4678-3.
adults. N Engl J Med. 2011;365:506–17. https://doi.org/10.1056/NEJMoa1102662.
inpatient acute kidney injury prediction model*. Crit Care Med. 2018;46:1070–7. https://doi.
org/10.1097/CCM.0000000000003123.
1189–232. https://doi.org/10.1214/aos/1013203451.
of future acute kidney injury. Nature. 2019;572:116–9. https://doi.org/10.1038/s41586-019-
1390-1.
clinical notes. In: 2018 IEEE international conference on Bioinformatics and Biomedicine
(BIBM). IEEE; 2018. pp 683–686.
for patient ventilator asynchrony. Methods Inf Med. 2018;57:208–19. https://doi.org/10.3414/
ME17-02-0012.
unit using machine learning. Nat Med. 2020;26:364–73.
0789-4.
and therapeutic implications. Nat Rev Nephrol. 2019;15:220–39. https://doi.org/10.1038/
s41581-018-0103-6.
2015;131:85–91. https://doi.org/10.1159/000437237.
2020;26:519–24. https://doi.org/10.1097/MCC.0000000000000772.
systematic review. PLoS One. 2022;17:e0272846. https://doi.org/10.1371/journal.pone.
0272846.
for the prediction and detection of three disease states in critical care: a systematic literature
review. F1000Res. 2019;8:1728. https://doi.org/10.12688/f1000research.20498.2.
Bakker T, Klopotowska JE, Dongelmans DA, et al. The effect of computerised decision support
alerts tailored to intensive care on the administration of high-risk drug combinations, and their
monitoring: a cluster randomised stepped-wedge trial. Lancet. 2024;403:439. https://doi.org/10.
1016/S0140-6736(23)02465-0.
AA. The gap between AI and bedside: participatory workshop on the barriers
to the integration, translation, and adoption of digital health care and AI startup technology into
clinical practice. J Med Internet Res. 2023;25:e32962. https://doi.org/10.2196/32962.
automated deep learning. Sci Rep. 2021;11:10286. https://doi.org/10.1038/s41598-021-
89743-x.
standing on, or looking over, the shoulders of clinicians? NPJ Digit Med. 2021;4:62. https://doi.
org/10.1038/s41746-021-00426-3.
https://doi.org/10.1038/s41591-020-
Chapter 30
Ultrasound in Acute Kidney Injury
Gregorio Romero-González, Nicholas S. M. Bianchi Bosisio, Sara Samoni,
and Fiorenza Ferrari
Introduction
The optimal management of AKI in the critical care setting involves multiple aspects
that may be improved by performing renal ultrasound, mainly color Doppler
(RD) and multi-organ ultrasound evaluation (MOUSE model) (Fig. 30.1).
Usually, the RD has traditionally been limited to the visualization of the renal
vasculature [1], and it is not specific enough to differentiate between causes of renal
AKI, excluding a urinary obstruction.
Supplementary Information The online version contains supplementary material available at
https://doi.org/10.1007/978-3-031-66541-7_30.
G. Romero-González
Nephrology Department,
N. S. M. Bianchi Bosisio
Department of Clinical-Surgical, Diagnostic, and Paediatric Sciences, Unit of Anaesthesia and
Intensive Care, University of Pavia, Pavia, Italy
Department of Anesthesia and Intensive Care, ASST Valtellina e Alto Lario, Sondrio, Italy
S. Samoni
Department of Nephrology, Dialysis and Kidney Transplant, Fondazione IRRCS Ca’ Granda
Ospedale Maggiore Policlinico, Milan, Italy
Department of Anesthesia, Intensive Care and Emergency, Fondazione IRRCS Ca’ Granda
Ospedale Maggiore Policlinico, Milan, Italy
F. Ferrari (
Department of Clinical-Surgical, Diagnostic, and Paediatric Sciences, Unit of Anaesthesia and
Intensive Care, University of Pavia, Pavia, Italy
Department of Anesthesia, Intensive Care and Emergency, Fondazione IRRCS Ca’ Granda
Ospedale Maggiore Policlinico, Milan, Italy
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
A. Cotoia et al. (eds.), Nutrition, Metabolism and Kidney Support,
https://doi.org/10.1007/978-3-031-66541-7_30
✉)
University Hospital Germans Trias I Pujol, Badalona, Spain
349
350 G. Romero-González et al.
Lung Ultrasound
Pneumonia
Pulmonary oedema
Pleural effusion
Venous Congestion Assessment( VExUS)
Inferior Cava Vein diameter and collasability
Congestive Hepathopathy
Congestive Nephropathy
Focused Cardiac Ultrasound
Left/Right ventricular systolic function
Diastolic disfunction
Mitral/Tricuspid regurgitation
Left/Right ventricular diameters
Pleural effusion
Kidney insonation
Renal parénquima:
Size
Echogenicity
Hydronephrosis
Arterial Dopple: Resistive Inde x
Fig. 30.1 The MOUSE model concept: multi-organ ultrasound evaluation in patients with AKI
Kidney ultrasound is useful to assess the size of the renal parenchyma (normal:
10–12 cm), cortical thickness (normal: 0.7–1 cm), the echogenicity of the parenchyma which in normal conditions is similar to that of the liver or spleen, increased
echogenicity (e.g., glomerulosclerosis), decreased echogenicity (e.g., cortical necrosis or pyelonephritis), and finally the presence of hydronephrosis or dilatation of the
urinary tract, which may be related to acute urinary retention, stones, or extrinsic
compression of the urinary tract [
1, 2].
Bedside of RD, MOUSE can contribute to the early management of AKI: it may
be used as an adjunct in the optimization of hemodynamic status to detect arterial
hypoperfusion and venous congestion. The latter is very important since a rise of
venous pressure adversely affects kidney perfusion, as demonstrated in animal
models [
3]. Futhermore, in hospitalized patients with acute congestive heart failure,
renal function deterioration has been associated with higher central venous pressure
(CVP) but not with lower cardiac index or lower mean arterial pressure [4],
suggesting that venous congestion may be a contributor in critically ill patients as
well [5].
Doppler Assesses Vascular Congestion
The presence of an inferior vena cava (IVC) greater than 2 cm that collapses less than
50% on inspiration (in nonmechanically ventilated patients) is indicative of elevated
right atrial pressure (RAP) and CVP [6]. The next step is therefore to interrogate
other vascular territories using pulsed Doppler to con firm whether the elevated RAP
is transmitted retrogradely to other venous vessels such as the hepatic veins (HVs),
30 Ultrasound in Acute Kidney Injury 351
portal vein (PV), and intrarenal vessels (IRV). The HVs drain into the IVC and have
a pulsatility that is transmitted retrogradely.
Under normal
conditions, four types of waves can be observed on the pulsed
Doppler: S wave, which occurs during right ventricular (RV) systole; V wave, which
occurs at the end of RV systole; D wave, which occurs during RV diastole; and A
wave, which occurs during right atrial (RA) contraction (end-diastole). Under
normal conditions, the S and D waves appear below the baseline, and the S wave
is deeper than the D wave (S > D)—as congestion increases, the S wave becomes
shallower than the D wave (S < D) until severe congestion occurs where the S wave
appears above the baseline (reverse S). In contrast to the HVs, PV has no or minimal
pulsatility (pulsatility index: <30%) under normal conditions. Pulsatility is observed
when pressure increases in the RA and is transmitted retrogradely (PI: 30–49%),
whereas higher pulsatility occurs in severe forms of the congestion (PI: >50%). On
the other hand, in the Doppler of intrarenal venous flow (IRVF), the increase in RAP
is transmitted retrogradely, so that a discontinuous pulse (biphasic) is observed with
the increase and a monophasic flow in severe congestion [
2]. Interestingly, the
pulsatility of IRVF in patients with heart failure is associated with increased CVP
and RAP, which ultimately leads to a decrease in renal perfusion pressure, with a
consequent increase in filtration fraction to maintain glomerular filtration rate and,
finally, increased tubular reabsorption, especially proximal, of sodium chloride and
water [7]. Gerota’s capsula does not allow the kidneys to distend when the hydrostatic pressure of the renal interstitium increases, leading to a structural damage
mediated by inflammation, oxidative stress, neurohormonal factors, and increased
activity of the renin-angiotensin-aldosterone system [8]. This process explains how
congestion is associated with worsening kidney function [4] and exploits it better
than even a decrease in cardiac output [9], at least in patients with acute heart failure,
a situation recently described as congestive nephropathy [7]. The usefulness of IRVF
in predicting the development of AKI was recently described in a meta-analysis
involving more than 600 patients, in which a lower risk of AKI was observed in
patients with continuous IRVF [RR: 0.46 CI95% 0.28–0.76)], but a large heterogeneity in the included studies was also found (I2: 68.7%; p = 0.04) [23]. This model
for assessing venous congestion was described as a system for evaluating the
presence and severity of venous congestion using the venous excess ultrasound
grading system (VeXUS score) [10].
Renal V
enou
s Stasis Index (RVSI) is an integrative Doppler measure of renal
congestion that correlates with invasive hemodynamics and provides additional
prognostic information to stratify patients with pulmonary hypertension for the
propensity to develop right heart failure [
11].
RVSI is performed from pulsed wave Doppler samples of renal congestion
patterns in the interlobar renal vessel. The upward Doppler signal usually shows
the intrarenal arterial flow, which is used to measure cardiac cycle time; the downward Doppler signal shows the venous flow, used to measure venous flow time.
Under physiological conditions, the index is zero due to the presence of a continuous
venous flow, whereas it increases with rising severity of congestion [
and
the following equation illustrate the method of measurement of RVSI [
11]. Figure
30.2
11]:
352 G. Romero-González et al.
Fig. 30.2 Renal Venous Stasis Index (RVSI): method of measurement
Renal Venous Stasis Index=
Index cardiac cycle msðÞ- venous flow msðÞ
Index cardiac cycle
msðÞ
Arterial Renal Doppler Ultrasound in AKI
Arterial renal Doppler ultrasound allows the evaluation of blood flow into the renal
arteries and intrarenal artery branches and, through the characterization of the
parenchymal blushing and the resistive index (RI) measurement, provides indirect
information on renal parenchymal blood flow.
In normal conditions, the velocity/time curve in renal arteries has a low resistance, a peak systolic velocity (PSV) of 100 ± 20 cm/s, and an end-diastolic velocity
(EDV) of 30–40 cm/s. Intraparenchymal Renal RI (RRI) is measured in the
interlobar arteries and calculated as in any distrect of the body arterial bed, according
to the following formula:
PSV - EDV
RI =
in w
hich PSV and EDV are measured in the same wave. In normal conditions, RRI
range from 0.58 to 0.70, according to gender and age [12] (Fig. 30.3). However,
several factors can influence RRI, such as the conditions of peripheral vessels, the
systemic hemodynamics, and the administration of drugs able to modify renal blood
flow [13]. All these factors should be considered, especially in critically ill patients
(Table 30.1).
Renal arteries and renal artery branches are rarely affected by acute pathologic
conditions leading to AKI. Acute renal infarction, due to the interruption of the
blood supply to part of or to the whole kidney, is uncommon. The main causes
include thromboembolism, aortic or renal artery dissection, renal trauma, and
PSV
30 Ultrasound in Acute Kidney Injury 353
Fig. 30.3 Renal Resistive Index measurement
Table 30.1 Factors modifying the Renal Resistive Index (RRI)
Extrarenal Intrarenal Mixed
Vascular stiffness (e.g., age or
atherosclerosis)
Aortic insufficiency Increased intracapsular pressure
Aortic stenosis Tubulointerstitial disease
Left ventricular dysfunction
Heart
rate Vasodilatation: " partial arterial
artery stenosis
Renal
BMI
BMI b
ody m
ass index
Renovascular disease
Atherosclerosis
Thrombotic microangiopathy
Interstitial edema
Urinary obstruction
Vasoconstriction
Hepatorenal syndrome
Acute tubular necrosis
Intravenous contrast adminis-
tration
" Partial arterial CO2 pressure
O2 pressure
Autonomic nervous
system
Vasopressors and
inotropes
iatrogenic maneuver, such as angiography. In this case, bedside color Doppler
ultrasound documents the absence of flow in a part or in the whole kidney,
depending on the artery affected. The flow may be also absent in the renal
artery [14].
354 G. Romero-González et al.
Acute renal microvascular damage is more frequent in AKI, being present in
primary vascular disease, such as vasculitis, as well as in several pathologic conditions secondarily affecting renal microvessels, such as acute tubular necrosis. Moreover, RRI may increase in several other causes of AKI, including obstruction, acute
transplant rejection, etc., that have been correlated with the nature of renal damage.
They appear (1) unmodified in conditions primarily affecting glomeruli, such as
postinfectious glomerulonephritis and rapidly progressive glomerulonephritis;
(2) increased in tubule-interstitial disorders in which the inflammatory infiltrate
squeezes the intraparenchymal vessels, as in case of renal damage secondary to
toxins; and (3) even higher in vascular diseases, such as in vasculitis, hemolyticuremic syndrome, etc. [
The assessment of renal parenchymal blood flow and the RRI measurement have
been proposed for early diagnosis of AKI. Bossard G. et Coll. have found that
increased RRI in the immediate postoperative period after cardi ac surgery with
cardiopulmonary bypass are associated with postoperative AKI [15]. The increase
of RRI has also been associated with AKI development in critically ill patients with
severe sepsis or polytrauma [16].
In addition, RRI measurement has also been suggested for the differential diagnosis of persistent vs. transient AKI, thus providing information on renal recovery.
In fact, in several studies investigating RRI in different critical settings, elevated RRI
were associated with persistent AKI and worst renal prognosis [15–18].
14].
Integration of Renal Resistive Index and Intrarenal Venous Flow
RRI is influenced by both renal and extrarenal factors as shown in Table 30.1.In
physiologic conditions, the main determinant of the RRI is the arterial pulse pressure, which depends on vascular compliance and cardiac function [
rises because of an inappropriate vasoconstriction of the intraparenchymal arteries in
the acute tubular necrosis, the thrombotic microangiopathy, and in the acute rejection of the kidney transplant. On the other hand, RRI might reflect an increase in
intracapsular pressure occurring from different causes: heart failure, pulmonary
hypertension, abdominal hypertension, fluid overload, liver failure, and positive
end expiratory ventilation.
In s
ummary, a
intraparenchymal pressure, due to the “renal edema” [7] established from different
etiologies ranging between mainly renal cause and systemic diseases.
Using an
eventually echocardiography, the mechanism of AKI might be investigated [2]:
patients with a reduced mean systemic venous pressure without an obstruction to
venous return have high IVC compliance, proven by a predominant S on the
triphasic hepatic venous flow. In case of high resistance to venous return (e.g.,
bnormal IRVF pattern, RVSI, or RRI can mirror an increased
algorithm that integrates IVC, HV, and PV ultrasound, RD, and
5]. However, RRI
30 Ultrasound in Acute Kidney Injury 355
intrathoracic obstruction to venous return or intra-abdominal hypertension), hepatic
venous flow is absent or monophasic. Finally, elevated RAP causes a distended IVC
with a predominant D on HV flow [10].
The interpretation of an
acute increase in the RRI starts comparing it to the RI in
another site (e.g., spleen, liver) and understanding the potential relationship between
renal intracapsular (PIC) pressure and the RRI. In the kidneys, which are capsulated
organs, interstitial edema resulting from renal insult translates into increased subcapsular pressure that ultimately causes a decrease in renal perfusion and a decrease
in renal vascular compliance while increasing RRI [19]. Additionally, kidneys are
also affected by increased intrabdominal pressure that could be related to abnormalities in the abdomen itself or transmission of raised airway pressure in a stiffened
lung through the diaphragm [20, 21]. In such situations, IRVF could be beneficial to
diagnose an increased pressure in a close compartment, such as Gerota’s capsule
[19]. Waveforms detected by ultrasound cannot only reflect kidney injury but also
indicate the severity of congestion [
19]. Therefore, waveforms have prognostic
significance and might be used to monitor the efficacy of a therapy.
At this point, we can differentiate between a renal or systemic cause of AKI. As
the PIC rises, diastolic velocities in the interlobar arteries decrease as the gradient
with the diastolic arterial pressure is reduced. RI evidently abnormal in the kidney
but not in other organs suggests a mainly renal damage (e.g., acute tubular necrosis);
on the other hand, a concurrent increase in the RI in all organs (splenic artery, hepatic
artery,) witness a systemic process (e.g., heart failure, septic shock) as AKI cause.
ffic
The e
acy of monitoring the changes in RD and MOUSE in response to
interventions to guide the optimization of renal perfusion is still largely unknown
on the other hand, the waveforms have prognostic significance and might be used to
monitor the efficacy of a therapy [19]. It is plausible to think that interventions
associated with reduction in RRI and an improvement of IRVF pattern and of the
VeXUS are favorable, whereas an increase in RRI, associated with a worsening of
IRVF, is detrimental. In an exploratory study, Deruddre et al. observed a reduction of
the RRI in response to an increase of mean arterial pressure from 65 mmHg to
75 mmHg achieved by titrating norepinephrine infusion in critically ill patients
[22]. Similarly, Beaubien-Souligny et al. observed a reduction of the RRI in
response to the passive leg raise maneuver in fluid-responsive cardiac surgery
patients [23].
Confirmatory studi
es are still needed to assess the feasibility of using an integrated approach including RVSI, RRI, and IRVF associated with VeXUS and AKI
biomarkers as new tool to predict and monitor AKI (Fig. 30.4).
356 G. Romero-González et al.
Fig. 30.4 Integrated
approach to diagnose and
monitor AKI
RRI
BNP/NT-
proBNP
IRVF
AKI
prediction,
Renal
Biomarkers
and Monitor
Portal vein
Doppler
diagnosis
RVSI
Hepatic
vein
Doppler
Contrast-Enhanced Ultrasound for Assessing Renal Perfusion
Contrast-enhanced ultrasound (CEUS) is a relatively new and safe imaging technique presenting several advantages: low cost, bedside and quickly performable,
does not employ ionizing radiation, and the agent used as contrast being not
nephrotoxic [24]. The contrast agents employed comprise tiny microbubbles of
gas surrounded by a stabilizing blend. The microbubbles are eliminated through
respiration in the lungs, while the stabilizing blend is metabolized by the body. This
pharmacokinetics make CEU S a particularly attractive nontoxic imaging modality
27]
25].
.
for patients suffering from renal insufficiency [
Its use in renal imaging is increasingly established and on rise. CEUS is widely
employed in the diagnostic workup of renal masses. Particularly it is used for
differentiation between solid and cystic lesions, differentiation between solid renal
masses and pseudotumors, characterization of complex cystic renal masses, characterization of renal lesions (even those with equivocal appearance at computed
tomography), and monitoring of tumor ablation [26]. Another important and
established field of application of the CEUS is the evaluation of kidney transplants:
characterization of solid and cystic transplant renal masses, assessment for pyelonephritis and identification of its complications, and evaluation of transplant complications in immediate and delayed settings [
CEUS has
been proposed as useful tool even for assessing renal perfusion [28]. It
can determine and quantify changes in renal perfusion; these changes impact on
clinical outcome as illustrated by the important findings in transplant medicine [27].