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Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model

BACKGROUD: The current diagnostic criteria for refractory Mycoplasma pneumoniae pneumonia (RMPP) among Mycoplasma pneumoniae Pneumonia (MPP) are insufficient for early identification, and potentially delayed appropriate treatment. This study aimed to develop an effective individualized diagnostic pr...

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Autores principales: Li, Meng, Wei, Xiang, Zhang, Shan-Shan, Li, Shan, Chen, Su-Hong, Shi, Su-Jie, Zhou, Shao-Hong, Sun, Da-Quan, Zhao, Qian-Ye, Xu, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566172/
https://www.ncbi.nlm.nih.gov/pubmed/37817172
http://dx.doi.org/10.1186/s12890-023-02684-1
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author Li, Meng
Wei, Xiang
Zhang, Shan-Shan
Li, Shan
Chen, Su-Hong
Shi, Su-Jie
Zhou, Shao-Hong
Sun, Da-Quan
Zhao, Qian-Ye
Xu, Yan
author_facet Li, Meng
Wei, Xiang
Zhang, Shan-Shan
Li, Shan
Chen, Su-Hong
Shi, Su-Jie
Zhou, Shao-Hong
Sun, Da-Quan
Zhao, Qian-Ye
Xu, Yan
author_sort Li, Meng
collection PubMed
description BACKGROUD: The current diagnostic criteria for refractory Mycoplasma pneumoniae pneumonia (RMPP) among Mycoplasma pneumoniae Pneumonia (MPP) are insufficient for early identification, and potentially delayed appropriate treatment. This study aimed to develop an effective individualized diagnostic prediction nomogram for pediatric RMPP. METHODS: A total of 517 hospitalized children with MPP, including 131 with RMPP and 386 without RMPP (non-RMPP), treated at Lianyungang Maternal and Child Health Care Hospital from January 2018 to December 2021 were retrospectively enrolled as a development (modeling) cohort to construct an RMPP prediction nomogram. Additionally, 322 pediatric patients with MPP (64 with RMPP and 258 with non-RMPP, who were treated at the Affiliated Hospital of Xuzhou Medical University from June 2020 to May 2022 were retrospectively enrolled as a validation cohort to assess the prediction accuracy of model. Univariable and multivariable logistic regression analyses were used to identify RMPP risk factors among patients with MPP. Nomogram were generated based on these risk factors using the rms package of R, and the predictive performance was evaluated based on receiver operating characteristic (ROC) curves and using decision curve analysis (DCA). RESULTS: Multivariate analysis revealed five significant independent predictors of RMPP among patients with MPP: age (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.08–1.33, P = 0.038), fever duration (HR 1.34, 95%CI 1.20–1.50, P < 0.001), lymphocyte count (HR 0.45, 95%CI 0.23–0.89, P = 0.021), serum D-dimer (D-d) level (HR 1.70, 95%CI 1.16–2.49, P = 0.006), and pulmonary imaging score (HR 5.16, 95%CI 2.38–11.21, P < 0.001). The area under the ROC curve was 90.7% for the development cohort and 96.36% for the validation cohort. The internal and external verification calibration curves were almost linear with slopes of 1, and the DCA curve revealed a net benefit with the final predictive nomogram. CONCLUSION: This study proposes a predictive nomogram only based on five variables. The nomogram can be used for early identification of RMPP among pediatric patients with MPP, thereby facilitating more timely and effective intervention. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02684-1.
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spelling pubmed-105661722023-10-12 Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model Li, Meng Wei, Xiang Zhang, Shan-Shan Li, Shan Chen, Su-Hong Shi, Su-Jie Zhou, Shao-Hong Sun, Da-Quan Zhao, Qian-Ye Xu, Yan BMC Pulm Med Research BACKGROUD: The current diagnostic criteria for refractory Mycoplasma pneumoniae pneumonia (RMPP) among Mycoplasma pneumoniae Pneumonia (MPP) are insufficient for early identification, and potentially delayed appropriate treatment. This study aimed to develop an effective individualized diagnostic prediction nomogram for pediatric RMPP. METHODS: A total of 517 hospitalized children with MPP, including 131 with RMPP and 386 without RMPP (non-RMPP), treated at Lianyungang Maternal and Child Health Care Hospital from January 2018 to December 2021 were retrospectively enrolled as a development (modeling) cohort to construct an RMPP prediction nomogram. Additionally, 322 pediatric patients with MPP (64 with RMPP and 258 with non-RMPP, who were treated at the Affiliated Hospital of Xuzhou Medical University from June 2020 to May 2022 were retrospectively enrolled as a validation cohort to assess the prediction accuracy of model. Univariable and multivariable logistic regression analyses were used to identify RMPP risk factors among patients with MPP. Nomogram were generated based on these risk factors using the rms package of R, and the predictive performance was evaluated based on receiver operating characteristic (ROC) curves and using decision curve analysis (DCA). RESULTS: Multivariate analysis revealed five significant independent predictors of RMPP among patients with MPP: age (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.08–1.33, P = 0.038), fever duration (HR 1.34, 95%CI 1.20–1.50, P < 0.001), lymphocyte count (HR 0.45, 95%CI 0.23–0.89, P = 0.021), serum D-dimer (D-d) level (HR 1.70, 95%CI 1.16–2.49, P = 0.006), and pulmonary imaging score (HR 5.16, 95%CI 2.38–11.21, P < 0.001). The area under the ROC curve was 90.7% for the development cohort and 96.36% for the validation cohort. The internal and external verification calibration curves were almost linear with slopes of 1, and the DCA curve revealed a net benefit with the final predictive nomogram. CONCLUSION: This study proposes a predictive nomogram only based on five variables. The nomogram can be used for early identification of RMPP among pediatric patients with MPP, thereby facilitating more timely and effective intervention. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02684-1. BioMed Central 2023-10-10 /pmc/articles/PMC10566172/ /pubmed/37817172 http://dx.doi.org/10.1186/s12890-023-02684-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Meng
Wei, Xiang
Zhang, Shan-Shan
Li, Shan
Chen, Su-Hong
Shi, Su-Jie
Zhou, Shao-Hong
Sun, Da-Quan
Zhao, Qian-Ye
Xu, Yan
Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model
title Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model
title_full Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model
title_fullStr Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model
title_full_unstemmed Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model
title_short Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model
title_sort recognition of refractory mycoplasma pneumoniae pneumonia among myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10566172/
https://www.ncbi.nlm.nih.gov/pubmed/37817172
http://dx.doi.org/10.1186/s12890-023-02684-1
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