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Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model
BACKGROUND: Pneumonia remains the leading cause of death among children aged 1–59 months. The early prediction of poor outcomes (PO) is of critical concern. This study aimed to explore the risk factors relating to PO in severe community-acquired pneumonia (SCAP) and build a PO-predictive nomogram mo...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552538/ https://www.ncbi.nlm.nih.gov/pubmed/37808557 http://dx.doi.org/10.3389/fped.2023.1194186 |
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author | Xu, Changjing Tao, Xuemei Zhu, Junlong Hou, Chao Liu, Yujie Fu, Liya Zhu, Wanlong Yang, Xuping Huang, Yilan |
author_facet | Xu, Changjing Tao, Xuemei Zhu, Junlong Hou, Chao Liu, Yujie Fu, Liya Zhu, Wanlong Yang, Xuping Huang, Yilan |
author_sort | Xu, Changjing |
collection | PubMed |
description | BACKGROUND: Pneumonia remains the leading cause of death among children aged 1–59 months. The early prediction of poor outcomes (PO) is of critical concern. This study aimed to explore the risk factors relating to PO in severe community-acquired pneumonia (SCAP) and build a PO-predictive nomogram model for children with SCAP. METHODS: We retrospectively identified 300 Chinese pediatric patients diagnosed with SCAP who were hospitalized in the Affiliated Hospital of Southwest Medical University from August 1, 2018, to October 31, 2021. Children were divided into the PO and the non-PO groups. The occurrence of PO was designated as the dependent variable. Univariate and multivariate logistic regression analyses were used to identify the risk factors of PO. A nomogram model was constructed from the multivariate logistic regression analysis and internally validated for model discrimination and calibration. The performance of the nomogram was estimated using the concordance index (C-index). RESULTS: According to the efficacy evaluation criteria, 56 of 300 children demonstrated PO. The multivariate logistic regression analysis resulted in the following independent risk factors for PO: co-morbidity (OR: 8.032, 95% CI: 3.556–18.140, P < 0.0001), requiring invasive mechanical ventilation (IMV) (OR: 7.081, 95% CI: 2.250–22.282, P = 0.001), and ALB < 35 g/L (OR: 3.203, 95% CI: 1.151–8.912, P = 0.026). Results of the internal validation confirmed that the model provided good discrimination (concordance index [C-index], 0.876 [95% CI: 0.828–0.925]). The calibration plots in the nomogram model were of high quality. CONCLUSION: The nomogram facilitated accurate prediction of PO in children diagnosed with SCAP and could be helpful for clinical decision-making. |
format | Online Article Text |
id | pubmed-10552538 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105525382023-10-06 Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model Xu, Changjing Tao, Xuemei Zhu, Junlong Hou, Chao Liu, Yujie Fu, Liya Zhu, Wanlong Yang, Xuping Huang, Yilan Front Pediatr Pediatrics BACKGROUND: Pneumonia remains the leading cause of death among children aged 1–59 months. The early prediction of poor outcomes (PO) is of critical concern. This study aimed to explore the risk factors relating to PO in severe community-acquired pneumonia (SCAP) and build a PO-predictive nomogram model for children with SCAP. METHODS: We retrospectively identified 300 Chinese pediatric patients diagnosed with SCAP who were hospitalized in the Affiliated Hospital of Southwest Medical University from August 1, 2018, to October 31, 2021. Children were divided into the PO and the non-PO groups. The occurrence of PO was designated as the dependent variable. Univariate and multivariate logistic regression analyses were used to identify the risk factors of PO. A nomogram model was constructed from the multivariate logistic regression analysis and internally validated for model discrimination and calibration. The performance of the nomogram was estimated using the concordance index (C-index). RESULTS: According to the efficacy evaluation criteria, 56 of 300 children demonstrated PO. The multivariate logistic regression analysis resulted in the following independent risk factors for PO: co-morbidity (OR: 8.032, 95% CI: 3.556–18.140, P < 0.0001), requiring invasive mechanical ventilation (IMV) (OR: 7.081, 95% CI: 2.250–22.282, P = 0.001), and ALB < 35 g/L (OR: 3.203, 95% CI: 1.151–8.912, P = 0.026). Results of the internal validation confirmed that the model provided good discrimination (concordance index [C-index], 0.876 [95% CI: 0.828–0.925]). The calibration plots in the nomogram model were of high quality. CONCLUSION: The nomogram facilitated accurate prediction of PO in children diagnosed with SCAP and could be helpful for clinical decision-making. Frontiers Media S.A. 2023-09-21 /pmc/articles/PMC10552538/ /pubmed/37808557 http://dx.doi.org/10.3389/fped.2023.1194186 Text en © 2023 Xu, Tao, Zhu, Hou, Liu, Fu, Zhu, Yang and Huang. 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) (https://creativecommons.org/licenses/by/4.0/) . 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 Xu, Changjing Tao, Xuemei Zhu, Junlong Hou, Chao Liu, Yujie Fu, Liya Zhu, Wanlong Yang, Xuping Huang, Yilan Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model |
title | Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model |
title_full | Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model |
title_fullStr | Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model |
title_full_unstemmed | Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model |
title_short | Clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model |
title_sort | clinical features and risk factors analysis for poor outcomes of severe community-acquired pneumonia in children: a nomogram prediction model |
topic | Pediatrics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552538/ https://www.ncbi.nlm.nih.gov/pubmed/37808557 http://dx.doi.org/10.3389/fped.2023.1194186 |
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