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Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier
Background: Low-grade inflammation is a significant component of chronic kidney disease (CKD). Systemic immune inflammation index (SII), a newly defined ratio combining neutrophil, lymphocyte, and platelet counts, has not yet been evaluated in the pediatric CKD population nor in the context of CKD p...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647735/ https://www.ncbi.nlm.nih.gov/pubmed/37959376 http://dx.doi.org/10.3390/jcm12216911 |
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author | Kawalec, Anna Stojanowski, Jakub Mazurkiewicz, Paulina Choma, Anna Gaik, Magdalena Pluta, Mateusz Szymański, Michał Bruciak, Aleksandra Gołębiowski, Tomasz Musiał, Kinga |
author_facet | Kawalec, Anna Stojanowski, Jakub Mazurkiewicz, Paulina Choma, Anna Gaik, Magdalena Pluta, Mateusz Szymański, Michał Bruciak, Aleksandra Gołębiowski, Tomasz Musiał, Kinga |
author_sort | Kawalec, Anna |
collection | PubMed |
description | Background: Low-grade inflammation is a significant component of chronic kidney disease (CKD). Systemic immune inflammation index (SII), a newly defined ratio combining neutrophil, lymphocyte, and platelet counts, has not yet been evaluated in the pediatric CKD population nor in the context of CKD progression or dialysis. Thus, this study aimed to analyze the complete blood cell count (CBC)-driven parameters, including SII, in children with CKD and to assess their potential usefulness in the prediction of the need for chronic dialysis. Methods: A single-center, retrospective study was conducted on 27 predialysis children with CKD stages 4–5 and 39 children on chronic dialysis. The data were analyzed with the artificial intelligence tools. Results: The Random Forest Classifier (RFC) model with the input variables of neutrophil count, mean platelet volume (MPV), and SII turned out to be the best predictor of the progression of pediatric CKD into end-stage kidney disease (ESKD) requiring dialysis. Out of these variables, SII showed the largest share in the prediction of the need for renal replacement therapy. Conclusions: Chronic inflammation plays a pivotal role in the progression of CKD into ESKD. Among CBC-driven ratios, SII seems to be the most useful predictor of the need for chronic dialysis in CKD children. |
format | Online Article Text |
id | pubmed-10647735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106477352023-11-03 Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier Kawalec, Anna Stojanowski, Jakub Mazurkiewicz, Paulina Choma, Anna Gaik, Magdalena Pluta, Mateusz Szymański, Michał Bruciak, Aleksandra Gołębiowski, Tomasz Musiał, Kinga J Clin Med Article Background: Low-grade inflammation is a significant component of chronic kidney disease (CKD). Systemic immune inflammation index (SII), a newly defined ratio combining neutrophil, lymphocyte, and platelet counts, has not yet been evaluated in the pediatric CKD population nor in the context of CKD progression or dialysis. Thus, this study aimed to analyze the complete blood cell count (CBC)-driven parameters, including SII, in children with CKD and to assess their potential usefulness in the prediction of the need for chronic dialysis. Methods: A single-center, retrospective study was conducted on 27 predialysis children with CKD stages 4–5 and 39 children on chronic dialysis. The data were analyzed with the artificial intelligence tools. Results: The Random Forest Classifier (RFC) model with the input variables of neutrophil count, mean platelet volume (MPV), and SII turned out to be the best predictor of the progression of pediatric CKD into end-stage kidney disease (ESKD) requiring dialysis. Out of these variables, SII showed the largest share in the prediction of the need for renal replacement therapy. Conclusions: Chronic inflammation plays a pivotal role in the progression of CKD into ESKD. Among CBC-driven ratios, SII seems to be the most useful predictor of the need for chronic dialysis in CKD children. MDPI 2023-11-03 /pmc/articles/PMC10647735/ /pubmed/37959376 http://dx.doi.org/10.3390/jcm12216911 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kawalec, Anna Stojanowski, Jakub Mazurkiewicz, Paulina Choma, Anna Gaik, Magdalena Pluta, Mateusz Szymański, Michał Bruciak, Aleksandra Gołębiowski, Tomasz Musiał, Kinga Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier |
title | Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier |
title_full | Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier |
title_fullStr | Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier |
title_full_unstemmed | Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier |
title_short | Systemic Immune Inflammation Index as a Key Predictor of Dialysis in Pediatric Chronic Kidney Disease with the Use of Random Forest Classifier |
title_sort | systemic immune inflammation index as a key predictor of dialysis in pediatric chronic kidney disease with the use of random forest classifier |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10647735/ https://www.ncbi.nlm.nih.gov/pubmed/37959376 http://dx.doi.org/10.3390/jcm12216911 |
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