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Development and validation of nomogram prediction model for severe kidney disease in children with Henoch–Schönlein purpura: A prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized Henoch–Schönlein purpura children

This study aimed to develop and validate a nomogram to forecast severe kidney disease (SKD) outcomes for hospitalized Henoch–Schönlein purpura (HSP) children. The predictive model was built based on a primary cohort that included 2,019 patients with HSP who were diagnosed between January 2009 and De...

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Autores principales: Wang, Ke, Sun, Xiaomei, Jing, Shuolan, Lin, Li, Cao, Yao, Peng, Xin, Qiao, Lina, Dong, Liqun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597459/
https://www.ncbi.nlm.nih.gov/pubmed/36311786
http://dx.doi.org/10.3389/fimmu.2022.999491
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author Wang, Ke
Sun, Xiaomei
Jing, Shuolan
Lin, Li
Cao, Yao
Peng, Xin
Qiao, Lina
Dong, Liqun
author_facet Wang, Ke
Sun, Xiaomei
Jing, Shuolan
Lin, Li
Cao, Yao
Peng, Xin
Qiao, Lina
Dong, Liqun
author_sort Wang, Ke
collection PubMed
description This study aimed to develop and validate a nomogram to forecast severe kidney disease (SKD) outcomes for hospitalized Henoch–Schönlein purpura (HSP) children. The predictive model was built based on a primary cohort that included 2,019 patients with HSP who were diagnosed between January 2009 and December 2013. Another cohort consisting of 461 patients between January 2014 and December 2016 was recruited for independent validation. Patients were followed up for 24 months in development/training and validation cohorts. The data were gathered at multiple time points after HSP (at 3, 6, 12, and 24 months) covering severe kidney disease as the severe outcome after HSP. The least absolute shrinkage and selection operator (LASSO) regression model was utilized to decrease data dimension and choose potentially relevant features, which included socioeconomic factors, clinical features, and treatments. Multivariate Cox proportional hazards analysis was employed to establish a novel nomogram. The performance of the nomogram was assessed on the aspects of its calibration, discrimination, and clinical usefulness. The nomogram comprised serious skin rash or digestive tract purpura, severe gastrointestinal (GI) manifestations, recurrent symptoms, and renal involvement as predictors of SKD, providing favorable calibration and discrimination in the training dataset with a C-index of 0.751 (95% CI, 0.734–0.769). Furthermore, it demonstrated receivable discrimination in the validation cohort, with a C-index of 0.714 (95% CI, 0.678–0.750). With the use of decision curve analysis, the nomogram was proven to be clinically useful. The nomogram independently predicted SKD in HSP and displayed favorable discrimination and calibration values. It could be convenient to promote the individualized prediction of SKD in patients with HSP.
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spelling pubmed-95974592022-10-27 Development and validation of nomogram prediction model for severe kidney disease in children with Henoch–Schönlein purpura: A prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized Henoch–Schönlein purpura children Wang, Ke Sun, Xiaomei Jing, Shuolan Lin, Li Cao, Yao Peng, Xin Qiao, Lina Dong, Liqun Front Immunol Immunology This study aimed to develop and validate a nomogram to forecast severe kidney disease (SKD) outcomes for hospitalized Henoch–Schönlein purpura (HSP) children. The predictive model was built based on a primary cohort that included 2,019 patients with HSP who were diagnosed between January 2009 and December 2013. Another cohort consisting of 461 patients between January 2014 and December 2016 was recruited for independent validation. Patients were followed up for 24 months in development/training and validation cohorts. The data were gathered at multiple time points after HSP (at 3, 6, 12, and 24 months) covering severe kidney disease as the severe outcome after HSP. The least absolute shrinkage and selection operator (LASSO) regression model was utilized to decrease data dimension and choose potentially relevant features, which included socioeconomic factors, clinical features, and treatments. Multivariate Cox proportional hazards analysis was employed to establish a novel nomogram. The performance of the nomogram was assessed on the aspects of its calibration, discrimination, and clinical usefulness. The nomogram comprised serious skin rash or digestive tract purpura, severe gastrointestinal (GI) manifestations, recurrent symptoms, and renal involvement as predictors of SKD, providing favorable calibration and discrimination in the training dataset with a C-index of 0.751 (95% CI, 0.734–0.769). Furthermore, it demonstrated receivable discrimination in the validation cohort, with a C-index of 0.714 (95% CI, 0.678–0.750). With the use of decision curve analysis, the nomogram was proven to be clinically useful. The nomogram independently predicted SKD in HSP and displayed favorable discrimination and calibration values. It could be convenient to promote the individualized prediction of SKD in patients with HSP. Frontiers Media S.A. 2022-10-12 /pmc/articles/PMC9597459/ /pubmed/36311786 http://dx.doi.org/10.3389/fimmu.2022.999491 Text en Copyright © 2022 Wang, Sun, Jing, Lin, Cao, Peng, Qiao and Dong https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Wang, Ke
Sun, Xiaomei
Jing, Shuolan
Lin, Li
Cao, Yao
Peng, Xin
Qiao, Lina
Dong, Liqun
Development and validation of nomogram prediction model for severe kidney disease in children with Henoch–Schönlein purpura: A prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized Henoch–Schönlein purpura children
title Development and validation of nomogram prediction model for severe kidney disease in children with Henoch–Schönlein purpura: A prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized Henoch–Schönlein purpura children
title_full Development and validation of nomogram prediction model for severe kidney disease in children with Henoch–Schönlein purpura: A prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized Henoch–Schönlein purpura children
title_fullStr Development and validation of nomogram prediction model for severe kidney disease in children with Henoch–Schönlein purpura: A prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized Henoch–Schönlein purpura children
title_full_unstemmed Development and validation of nomogram prediction model for severe kidney disease in children with Henoch–Schönlein purpura: A prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized Henoch–Schönlein purpura children
title_short Development and validation of nomogram prediction model for severe kidney disease in children with Henoch–Schönlein purpura: A prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized Henoch–Schönlein purpura children
title_sort development and validation of nomogram prediction model for severe kidney disease in children with henoch–schönlein purpura: a prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized henoch–schönlein purpura children
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597459/
https://www.ncbi.nlm.nih.gov/pubmed/36311786
http://dx.doi.org/10.3389/fimmu.2022.999491
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