Ultrasound predictors of chronic kidney disease in children

Introduction. Prognostic diagnostics of chronic kidney disease is based on the identification of disease predictors and subsequent development of information tools that help in the work of the doctor.

Aim: To identify predictors of chronic kidney disease according to ultrasound results in children.

Materials and Methods. Data are obtained from a single-center retrospective catamnestic cohort study (2011–2022). The main group included 128 children with chronic kidney disease stages 1–4 years of age. The comparison group consisted of 30 children without diagnosed kidney pathology aged 1 to 17 years. The children of the two groups did not statistically differ significantly in gender and age. The informativeness of more than 50 features, including kidney size, structural parameters, changes in blood flow at different levels of the vascular kidney tree were evaluated. Hypotheses about statistical significance of differences in indicators were tested, correlation analysis was performed, univariate logistic regression models were built, and their ROC analysis was performed. Statistical processing was performed using Python 3.11. The software was provided by TechDepartment (Moscow).

Results. The children of the main group had increased echogenicity of kidney parenchyma, which is not characteristic of the children of the comparison group. Reliable direct associations of moderate severity with the results of the ultrasound study (kidney length, r = 0.369; kidney width, r = 0.407; parenchyma thickness, r = 0.367), with blood flow in the segmental arteries in the middle third according to the results of color Doppler mapping (r = 0.338) with kidney pathology were established. A high direct relationship between vascular resistance at different levels of blood flow (Ri in the trunk and Ri in the segmentary renal arteries r = 0.658 [0.56; 0.726], p < 0.001) was determined.

Conclusion. The identified ultrasound predictors of chronic kidney disease can be used to develop models and nomograms to help doctors identify children at high risk of developing chronic disease.

1. Dvoryakovskiy I.V., Trukhanov A.I., Dvoryakovskaya G.M., Smirnov I.E. Ultrasound diagnostics in pediatrics: history and achievements. Russian Pediatric Journal. 2022;25(1):66–72. (In Russ.). https://doi.org/10.46563/1560-9561-2022-25-1-66-72.

2. Wang S., Hossack J.A., Klibanov A.L. From anatomy to functional and molecular biomarker imaging and therapy: ultrasound is safe, ultrafast, portable, and inexpensive. Invest. Radiol. 2020;55(9):559–572. https://doi.org/10.1097/RLI.0000000000000675.

3. Zhang Y., Demosthenous A. Integrated circuits for medical ultrasound applications: imaging and beyond. IEEE Trans. Biomed. Circuits Syst. 2021;15(5):838–858. https://doi.org/10.1109/TBCAS.2021.3120886.

4. Pykov M.I., Gurevich A.I., Vatolin K.V., Bykova O.K., Ozerskaya I.A. Measurements in pediatric ultrasonography. Reference book. М: VIDAR; 2018:96. (In Russ.). ISBN 978-5-88429-240-6.

5. Papayan A.V., Savenkova N.D. Clinical nephrology of pediatric age. St. Petersburg: SOTIS; 1997:718. (In Russ.). ISBN 5-85503-115-2.

6. Mallamaci F., Tripepi G. Risk factors of chronic kidney disease progression: between old and new concepts. J. Clin. Med. 2024;13(3):678. https://doi.org/10.3390/jcm13030678.

7. Chen T.K., Knicely D.H., Grams M.E. chronic kidney disease diagnosis and management: a review. JAMA. 2019;322(13):1294–1304. https://doi.org/10.1001/jama.2019.14745.

8. Kutyrlo I.E., Savenkova N.D. CAKUT – syndrome in children. Nephrology. 2017;21(3):18–24. (In Russ.). DOI: https://doi.org/10.24884/1561-6274-2017-3-18-24.

9. Ignatova M.S., Makoveckaya G.A., Mazur L.I. Urinary tract diseases in children. Samara: Asgard; 2017:224. (In Russ.).

10. Ivanova I.I., Koval N.Yu. Chronic pyelonephritis in children with different dysplastic phenotypes. Russian Bulletin of Perinatology and Pediatrics. 2022;67(5):68–71. (In Russ.). https://doi.org/10.21508/1027-4065-2022-67-5-68-71.

11. Vafoeva N.A. Influence of kidney diseases on indicators of central hemodynamics. Scientific progress. 2021;2(2):121–127. (In Russ.). https://cyberleninka.ru/article/n/vliyanie-zabolevaniy-pochek-na-pokazateli-tsentralnoy-gemodinamiki (25.02.2025).

12. Lameire N.H., Levin A., Kellum J.A., Cheung M., Jadoul M., Winkelmayer W.C. et al. Harmonizing acute and chronic kidney disease definition and classification: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference. Kidney Int. 2021;100(3):516– 526. https://doi.org/10.1016/j.kint.2021.06.028.

13. Cherkasov N.S., Davydova O.V., Derbeneva L.I., Lutsenko Yu.A. Renocardiac syndrome in a child with non-inherited connective tissue disease. Russian Journal of Woman and Child Health. 2021;4(2):184– 188. (In Russ.). https://doi.org/10.32364/2618-8430-2021-4-2-184-188.

14. Walawender L., Becknell B., Matsell D.G. Congenital anomalies of the kidney and urinary tract: defining risk factors of disease progression and determinants of outcomes. Pediatr. Nephrol. 2023;38(12):3963–3973. https://doi.org/10.1007/s00467-023-05899-w.

15. Sedashkina O.A., Poretskova G.Yu., Makovetskaya G.A. Polymorphisms of genes of the renin-angiotensin system: significance in the progression of chronic kidney disease in children. Russian Pediatric Journal. 2023;23(2):89–94. (In Russ.). https://doi.org/10.46563/1560-9561-2023-26-2-89-94.

16. Olkhova E. B. Ekhograficheskiye aspekty nefroskleroza detey. Ultrazvukovaya diagnostika v akusherstve, ginekologii i pediatrii. 2000;2:136– 142. (In Russ.). eLIBRARY ID: 10476.

17. Kolsanov A.V., Avdeeva E.V. Brain-computer interface: fundamental and clinical aspects of development and implication of the technology. Science and Innovations in Medicine. 2017; 2(4): 6–12. (In Russ.). https://doi.org/10.35693/2500-1388-2017-0-4-6-12.

18. Zakharov A.V., Pyatin V.F., Kolsanov A.V., Poverennova I.E., Segreeva M.S., et al. Using virtual reality as a method of accelerated rehabilitation among the patients after stroke. Science and Innovations in Medicine. 2016; 1(3): 62–66. (In Russ.). https://doi.org/10.35693/2500-1388-2016-0-3-62-66.

19. Kolsanov A.V., Sedashkina O.A., Postnikov M.A., Makoveczkaya G.A., Romadanova E.M., Koposova E.V. et al. Skrining xronicheskoj bolezni pochek u detej s pomoshh`yu algoritmov mashinnogo obucheniya. Menedzher zdravooxraneniya. 2024;5:75–83. (In Russ.). DOI: http://dx.doi.org/10.21045/1811-0185-2024-5-75-84.