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Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia

OBJECTIVE: To analyze the predictive factors for necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP) and construct a prediction model. METHODS: The clinical data with MPP at the Children’s Hospital of Kunming Medical University from January 2014 to November 2022 were re...

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Autores principales: Luo, Yonghan, Wang, Yanchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198274/
https://www.ncbi.nlm.nih.gov/pubmed/37215376
http://dx.doi.org/10.2147/JIR.S413161
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author Luo, Yonghan
Wang, Yanchun
author_facet Luo, Yonghan
Wang, Yanchun
author_sort Luo, Yonghan
collection PubMed
description OBJECTIVE: To analyze the predictive factors for necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP) and construct a prediction model. METHODS: The clinical data with MPP at the Children’s Hospital of Kunming Medical University from January 2014 to November 2022 were retrospectively analyzed. Eighty-four children with MPP who developed NP were divided into the necrotizing group, and 168 children who did not develop NP were divided into the non-necrotizing group by propensity-score matching. LASSO regression was used to select the optimal factors, and multivariate logistic regression analysis was used to establish a clinical prediction model. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the nomogram. Clinical decision curve analysis was used to evaluate the clinical predictive value. RESULTS: LASSO regression analysis showed that bacterial co-infection, chest pain, LDH, CRP, duration of fever, and D-dimer were the influencing factors for NP in children with MPP (P < 0.05). The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting necrotizing MPP was 0.870 (95% CI: 0.813–0.927, P < 0.001) in the training set and 0.843 (95% CI: 0.757–0.930, P < 0.001) in the validation set. The Bootstrap repeated sampling for 1000 times was used for internal validation, and the calibration curve showed that the model had good consistency. The Hosmer-Lemeshow test showed that the predicted probability of the model had a good fit with the actual probability in the training set and the validation set (P values of 0.366 and 0.667, respectively). The clinical decision curve showed that the model had good clinical application value. CONCLUSION: The prediction model based on bacterial co-infection, chest pain, LDH, CRP, fever duration, and D-dimer has a good predictive value for necrotizing MPP.
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spelling pubmed-101982742023-05-20 Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia Luo, Yonghan Wang, Yanchun J Inflamm Res Original Research OBJECTIVE: To analyze the predictive factors for necrotizing pneumonia (NP) in children with Mycoplasma pneumoniae pneumonia (MPP) and construct a prediction model. METHODS: The clinical data with MPP at the Children’s Hospital of Kunming Medical University from January 2014 to November 2022 were retrospectively analyzed. Eighty-four children with MPP who developed NP were divided into the necrotizing group, and 168 children who did not develop NP were divided into the non-necrotizing group by propensity-score matching. LASSO regression was used to select the optimal factors, and multivariate logistic regression analysis was used to establish a clinical prediction model. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the nomogram. Clinical decision curve analysis was used to evaluate the clinical predictive value. RESULTS: LASSO regression analysis showed that bacterial co-infection, chest pain, LDH, CRP, duration of fever, and D-dimer were the influencing factors for NP in children with MPP (P < 0.05). The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting necrotizing MPP was 0.870 (95% CI: 0.813–0.927, P < 0.001) in the training set and 0.843 (95% CI: 0.757–0.930, P < 0.001) in the validation set. The Bootstrap repeated sampling for 1000 times was used for internal validation, and the calibration curve showed that the model had good consistency. The Hosmer-Lemeshow test showed that the predicted probability of the model had a good fit with the actual probability in the training set and the validation set (P values of 0.366 and 0.667, respectively). The clinical decision curve showed that the model had good clinical application value. CONCLUSION: The prediction model based on bacterial co-infection, chest pain, LDH, CRP, fever duration, and D-dimer has a good predictive value for necrotizing MPP. Dove 2023-05-15 /pmc/articles/PMC10198274/ /pubmed/37215376 http://dx.doi.org/10.2147/JIR.S413161 Text en © 2023 Luo and Wang. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Luo, Yonghan
Wang, Yanchun
Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia
title Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia
title_full Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia
title_fullStr Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia
title_full_unstemmed Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia
title_short Risk Prediction Model for Necrotizing Pneumonia in Children with Mycoplasma pneumoniae Pneumonia
title_sort risk prediction model for necrotizing pneumonia in children with mycoplasma pneumoniae pneumonia
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198274/
https://www.ncbi.nlm.nih.gov/pubmed/37215376
http://dx.doi.org/10.2147/JIR.S413161
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