Literature DB >> 27796695

Role of ultrasonographic chronic kidney disease score in the assessment of chronic kidney disease.

Mustafa Yaprak1, Özgür Çakır2, Mehmet Nuri Turan3, Ramazan Dayanan4, Selçuk Akın5, Elif Değirmen5, Mustafa Yıldırım6, Faruk Turgut7.   

Abstract

PURPOSE: Ultrasonography (US) is an inexpensive, noninvasive and easy imaging procedure to comment on the kidney disease. Data are limited about the relation between estimated glomerular filtration rate (e-GFR) and all 3 renal US parameters, including kidney length, parenchymal thickness and parenchymal echogenicity, in chronic kidney disease (CKD). In this study, we aimed to investigate the association between e-GFR and ultrasonographic CKD score calculated via these ultrasonographic parameters.
METHODS: One hundred and twenty patients with stage 1-5 CKD were enrolled in this study. The glomerular filtration rate was estimated by the Chronic Kidney Disease Epidemiology Collaboration equation. US was performed by the same radiologist who was blinded to patients' histories and laboratory results. US parameters including kidney length, parenchymal thickness and parenchymal echogenicity were obtained from both kidneys. All 3 parameters were scored for each kidney, separately. The sum of the average scores of these parameters was used to calculate ultrasonographic CKD score.
RESULTS: The mean age of patients was 63.34 ± 14.19 years. Mean kidney length, parenchymal thickness, ultrasonographic CKD score and median parenchymal echogenicity were found as 96.2 ± 12.3, 10.97 ± 2.59 mm, 6.28 ± 2.52 and 1.0 (0-3.5), respectively. e-GFR was positively correlated with kidney length (r = 0.343, p < 0.001), parenchymal thickness (r = 0.37, p < 0.001) and negatively correlated with CKD score (r = -0.587, p < 0.001) and parenchymal echogenicity (r = -0.683, p < 0.001). Receiver operating characteristic curve analysis for distinction of e-GFR lower than 60 mL/min showed that the ultrasonographic CKD score higher than 4.75 was the best parameter with the sensitivity of 81% and positive predictivity of 92% (AUC, 0.829; 95% CI, 0.74-0.92; p < 0.001).
CONCLUSION: We found correlation between e-GFR and ultrasonographic CKD score via using all ultrasonographic parameters. Also, our study showed that ultrasonographic CKD score can be useful for distinction of CKD stage 3-5 from stage 1 and 2. We suggested that the ultrasonographic CKD score provided more objective data in the assessment of CKD.

Entities:  

Keywords:  Chronic kidney disease; Kidney length; Parenchymal echogenicity; Parenchymal thickness; Ultrasonographic chronic kidney disease score; e-Glomerular filtration rate

Mesh:

Year:  2016        PMID: 27796695     DOI: 10.1007/s11255-016-1443-4

Source DB:  PubMed          Journal:  Int Urol Nephrol        ISSN: 0301-1623            Impact factor:   2.370


  22 in total

1.  Renal volume measurements: accuracy and repeatability of US compared with that of MR imaging.

Authors:  J Bakker; M Olree; R Kaatee; E E de Lange; K G Moons; J J Beutler; F J Beek
Journal:  Radiology       Date:  1999-06       Impact factor: 11.105

2.  Do kidney sizes in ultrasonography correlate to glomerular filtration rate in healthy children?

Authors:  A Adibi; I Adibi; P Khosravi
Journal:  Australas Radiol       Date:  2007-12

3.  Kidney dimensions at sonography: correlation with age, sex, and habitus in 665 adult volunteers.

Authors:  S A Emamian; M B Nielsen; J F Pedersen; L Ytte
Journal:  AJR Am J Roentgenol       Date:  1993-01       Impact factor: 3.959

4.  What is the value of measuring renal parenchymal thickness before renal biopsy?

Authors:  S D Roger; A M Beale; W R Cattell; J A Webb
Journal:  Clin Radiol       Date:  1994-01       Impact factor: 2.350

5.  Ultrasonographic determination of renal mass and renal volume.

Authors:  T B Jones; L R Riddick; M D Harpen; R L Dubuisson; D Samuels
Journal:  J Ultrasound Med       Date:  1983-04       Impact factor: 2.153

6.  Renal cortical thickness measured at ultrasound: is it better than renal length as an indicator of renal function in chronic kidney disease?

Authors:  Michael D Beland; Nicholas L Walle; Jason T Machan; John J Cronan
Journal:  AJR Am J Roentgenol       Date:  2010-08       Impact factor: 3.959

7.  Can renal sonography be a reliable diagnostic tool in the assessment of chronic kidney disease?

Authors:  Gaetano Lucisano; Nicolino Comi; Elena Pelagi; Paola Cianfrone; Laura Fuiano; Giorgio Fuiano
Journal:  J Ultrasound Med       Date:  2015-02       Impact factor: 2.153

8.  Diabetes mellitus: the predominant cause of bilateral renal enlargement.

Authors:  M C Segel; J W Lecky; B S Slasky
Journal:  Radiology       Date:  1984-11       Impact factor: 11.105

9.  A new equation to estimate glomerular filtration rate.

Authors:  Andrew S Levey; Lesley A Stevens; Christopher H Schmid; Yaping Lucy Zhang; Alejandro F Castro; Harold I Feldman; John W Kusek; Paul Eggers; Frederick Van Lente; Tom Greene; Josef Coresh
Journal:  Ann Intern Med       Date:  2009-05-05       Impact factor: 25.391

10.  Correlation of ultrasonographic parameters with serum creatinine in chronic kidney disease.

Authors:  Jagdeesh K Siddappa; Saurabh Singla; Mohammed Al Ameen; S C Rakshith; Naveen Kumar
Journal:  J Clin Imaging Sci       Date:  2013-06-30
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Authors:  Mitsuhiro Matsuo; Fuminori Yamagishi; Akiko Higuchi
Journal:  JMA J       Date:  2019-02-20

Review 2.  The old becomes new: advances in imaging techniques to assess nephron mass in children.

Authors:  Marissa J DeFreitas; Chryso P Katsoufis; Juan C Infante; Michael L Granda; Carolyn L Abitbol; Alessia Fornoni
Journal:  Pediatr Nephrol       Date:  2020-01-17       Impact factor: 3.714

3.  Renal resistive index in chronic kidney disease patients: Possible determinants and risk profile.

Authors:  Michele Provenzano; Laura Rivoli; Carlo Garofalo; Teresa Faga; Elena Pelagi; Maria Perticone; Raffaele Serra; Ashour Michael; Nicolino Comi; Michele Andreucci
Journal:  PLoS One       Date:  2020-04-01       Impact factor: 3.240

4.  Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency.

Authors:  Jonghyon Yi; Ho Kyung Kang; Jae-Hyun Kwon; Kang-Sik Kim; Moon Ho Park; Yeong Kyeong Seong; Dong Woo Kim; Byungeun Ahn; Kilsu Ha; Jinyong Lee; Zaegyoo Hah; Won-Chul Bang
Journal:  Ultrasonography       Date:  2020-09-14

5.  A Comparability of Renal Length and Volume Measurements in MRI and Ultrasound in Children.

Authors:  Dominik Świętoń; Weronika Bernard; Małgorzata Grzywińska; Piotr Czarniak; Agata Durawa; Mariusz Kaszubowski; Maciej Piskunowicz; Edyta Szurowska
Journal:  Front Pediatr       Date:  2021-12-08       Impact factor: 3.418

  5 in total

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