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A grading system that predicts the risk of dialysis induction in IgA nephropathy patients based on the combination of the clinical and histological severity

Histological classification is essential in the clinical management of immunoglobulin A nephropathy (IgAN). However, there are limitations in predicting the prognosis of IgAN based on histological information alone, which suggests the need for better prognostic models. Therefore, we defined a progno...

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Autores principales: Okonogi, Hideo, Kawamura, Tetsuya, Joh, Kensuke, Koike, Kentaro, Miyazaki, Yoichi, Ogura, Makoto, Tsuboi, Nobuo, Hirano, Keita, Matsushima, Masato, Yokoo, Takashi, Horikoshi, Satoshi, Suzuki, Yusuke, Yasuda, Takashi, Shirai, Sayuri, Shibata, Takanori, Hattori, Motoshi, Akioka, Yuko, Katafuchi, Ritsuko, Hashiguchi, Akinori, Hisano, Satoshi, Shimizu, Akira, Kimura, Kenjiro, Maruyama, Shoichi, Matsuo, Seiichi, Tomino, Yasuhiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6344391/
https://www.ncbi.nlm.nih.gov/pubmed/30367317
http://dx.doi.org/10.1007/s10157-018-1657-0
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author Okonogi, Hideo
Kawamura, Tetsuya
Joh, Kensuke
Koike, Kentaro
Miyazaki, Yoichi
Ogura, Makoto
Tsuboi, Nobuo
Hirano, Keita
Matsushima, Masato
Yokoo, Takashi
Horikoshi, Satoshi
Suzuki, Yusuke
Yasuda, Takashi
Shirai, Sayuri
Shibata, Takanori
Hattori, Motoshi
Akioka, Yuko
Katafuchi, Ritsuko
Hashiguchi, Akinori
Hisano, Satoshi
Shimizu, Akira
Kimura, Kenjiro
Maruyama, Shoichi
Matsuo, Seiichi
Tomino, Yasuhiko
author_facet Okonogi, Hideo
Kawamura, Tetsuya
Joh, Kensuke
Koike, Kentaro
Miyazaki, Yoichi
Ogura, Makoto
Tsuboi, Nobuo
Hirano, Keita
Matsushima, Masato
Yokoo, Takashi
Horikoshi, Satoshi
Suzuki, Yusuke
Yasuda, Takashi
Shirai, Sayuri
Shibata, Takanori
Hattori, Motoshi
Akioka, Yuko
Katafuchi, Ritsuko
Hashiguchi, Akinori
Hisano, Satoshi
Shimizu, Akira
Kimura, Kenjiro
Maruyama, Shoichi
Matsuo, Seiichi
Tomino, Yasuhiko
author_sort Okonogi, Hideo
collection PubMed
description Histological classification is essential in the clinical management of immunoglobulin A nephropathy (IgAN). However, there are limitations in predicting the prognosis of IgAN based on histological information alone, which suggests the need for better prognostic models. Therefore, we defined a prognostic model by combining the grade of clinical severity with the histological grading system by the following processes. We included 270 patients and explored the clinical variables associated with progression to end-stage renal disease (ESRD). Then, we created a predictive clinical grading system and defined the risk grades for dialysis induction by a combination of the clinical grade (CG) and the histological grade (HG). A logistic regression analysis revealed that the 24-h urinary protein excretion (UPE) and the estimated glomerular filtration rate (eGFR) were significant independent variables. We selected UPE of 0.5 g/day and eGFR of 60 ml/min/1.73 m(2) as the threshold values for the classification of CG. The risk of progression to ESRD of patients with CG II and III was significantly higher than that of patients with CG I. The patients were then re-classified into nine compartments based on the combination of CG and HG. Furthermore, the nine compartments were grouped into four risk groups. The risk of ESRD in the moderate, high, and super-high-risk groups was significantly higher than that in the low-risk group. Herein, we are giving a detailed description of our grading system for IgA nephropathy that predicted the risk of dialysis based on the combination of CG and HG.
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spelling pubmed-63443912019-02-08 A grading system that predicts the risk of dialysis induction in IgA nephropathy patients based on the combination of the clinical and histological severity Okonogi, Hideo Kawamura, Tetsuya Joh, Kensuke Koike, Kentaro Miyazaki, Yoichi Ogura, Makoto Tsuboi, Nobuo Hirano, Keita Matsushima, Masato Yokoo, Takashi Horikoshi, Satoshi Suzuki, Yusuke Yasuda, Takashi Shirai, Sayuri Shibata, Takanori Hattori, Motoshi Akioka, Yuko Katafuchi, Ritsuko Hashiguchi, Akinori Hisano, Satoshi Shimizu, Akira Kimura, Kenjiro Maruyama, Shoichi Matsuo, Seiichi Tomino, Yasuhiko Clin Exp Nephrol Special Report Histological classification is essential in the clinical management of immunoglobulin A nephropathy (IgAN). However, there are limitations in predicting the prognosis of IgAN based on histological information alone, which suggests the need for better prognostic models. Therefore, we defined a prognostic model by combining the grade of clinical severity with the histological grading system by the following processes. We included 270 patients and explored the clinical variables associated with progression to end-stage renal disease (ESRD). Then, we created a predictive clinical grading system and defined the risk grades for dialysis induction by a combination of the clinical grade (CG) and the histological grade (HG). A logistic regression analysis revealed that the 24-h urinary protein excretion (UPE) and the estimated glomerular filtration rate (eGFR) were significant independent variables. We selected UPE of 0.5 g/day and eGFR of 60 ml/min/1.73 m(2) as the threshold values for the classification of CG. The risk of progression to ESRD of patients with CG II and III was significantly higher than that of patients with CG I. The patients were then re-classified into nine compartments based on the combination of CG and HG. Furthermore, the nine compartments were grouped into four risk groups. The risk of ESRD in the moderate, high, and super-high-risk groups was significantly higher than that in the low-risk group. Herein, we are giving a detailed description of our grading system for IgA nephropathy that predicted the risk of dialysis based on the combination of CG and HG. Springer Singapore 2018-10-26 2019 /pmc/articles/PMC6344391/ /pubmed/30367317 http://dx.doi.org/10.1007/s10157-018-1657-0 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Special Report
Okonogi, Hideo
Kawamura, Tetsuya
Joh, Kensuke
Koike, Kentaro
Miyazaki, Yoichi
Ogura, Makoto
Tsuboi, Nobuo
Hirano, Keita
Matsushima, Masato
Yokoo, Takashi
Horikoshi, Satoshi
Suzuki, Yusuke
Yasuda, Takashi
Shirai, Sayuri
Shibata, Takanori
Hattori, Motoshi
Akioka, Yuko
Katafuchi, Ritsuko
Hashiguchi, Akinori
Hisano, Satoshi
Shimizu, Akira
Kimura, Kenjiro
Maruyama, Shoichi
Matsuo, Seiichi
Tomino, Yasuhiko
A grading system that predicts the risk of dialysis induction in IgA nephropathy patients based on the combination of the clinical and histological severity
title A grading system that predicts the risk of dialysis induction in IgA nephropathy patients based on the combination of the clinical and histological severity
title_full A grading system that predicts the risk of dialysis induction in IgA nephropathy patients based on the combination of the clinical and histological severity
title_fullStr A grading system that predicts the risk of dialysis induction in IgA nephropathy patients based on the combination of the clinical and histological severity
title_full_unstemmed A grading system that predicts the risk of dialysis induction in IgA nephropathy patients based on the combination of the clinical and histological severity
title_short A grading system that predicts the risk of dialysis induction in IgA nephropathy patients based on the combination of the clinical and histological severity
title_sort grading system that predicts the risk of dialysis induction in iga nephropathy patients based on the combination of the clinical and histological severity
topic Special Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6344391/
https://www.ncbi.nlm.nih.gov/pubmed/30367317
http://dx.doi.org/10.1007/s10157-018-1657-0
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