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Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis

OBJECTIVES: To explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction. METHODS: We retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other h...

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Autores principales: Fu, Ruqian, Yang, Manqiong, Li, Zhihui, Kang, Zhijuan, Xun, Mai, Wang, Ying, Wang, Manzhi, Wang, Xiangyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428464/
https://www.ncbi.nlm.nih.gov/pubmed/36061380
http://dx.doi.org/10.3389/fped.2022.967249
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author Fu, Ruqian
Yang, Manqiong
Li, Zhihui
Kang, Zhijuan
Xun, Mai
Wang, Ying
Wang, Manzhi
Wang, Xiangyun
author_facet Fu, Ruqian
Yang, Manqiong
Li, Zhihui
Kang, Zhijuan
Xun, Mai
Wang, Ying
Wang, Manzhi
Wang, Xiangyun
author_sort Fu, Ruqian
collection PubMed
description OBJECTIVES: To explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction. METHODS: We retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital's cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram. RESULTS: Age, persistent cutaneous purpura, erythrocyte distribution width, complement C(3), immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was ~15–82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25~84% and 14~73%, respectively. CONCLUSION: The prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability. CLINICAL TRIAL REGISTRATION: www.chictr.org.cn, identifier ChiCTR2000033435.
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spelling pubmed-94284642022-09-01 Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis Fu, Ruqian Yang, Manqiong Li, Zhihui Kang, Zhijuan Xun, Mai Wang, Ying Wang, Manzhi Wang, Xiangyun Front Pediatr Pediatrics OBJECTIVES: To explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction. METHODS: We retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital's cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram. RESULTS: Age, persistent cutaneous purpura, erythrocyte distribution width, complement C(3), immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was ~15–82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25~84% and 14~73%, respectively. CONCLUSION: The prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability. CLINICAL TRIAL REGISTRATION: www.chictr.org.cn, identifier ChiCTR2000033435. Frontiers Media S.A. 2022-08-17 /pmc/articles/PMC9428464/ /pubmed/36061380 http://dx.doi.org/10.3389/fped.2022.967249 Text en Copyright © 2022 Fu, Yang, Li, Kang, Xun, Wang, Wang and Wang. 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 Pediatrics
Fu, Ruqian
Yang, Manqiong
Li, Zhihui
Kang, Zhijuan
Xun, Mai
Wang, Ying
Wang, Manzhi
Wang, Xiangyun
Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis
title Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis
title_full Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis
title_fullStr Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis
title_full_unstemmed Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis
title_short Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis
title_sort risk assessment and prediction model of renal damage in childhood immunoglobulin a vasculitis
topic Pediatrics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428464/
https://www.ncbi.nlm.nih.gov/pubmed/36061380
http://dx.doi.org/10.3389/fped.2022.967249
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