Alternative therapies in health and medicine · 2024
Influence Factors of the Therapeutic Effect of Budesonide Combined with N-Acetylcysteine in Children with Mycoplasma Pneumoniae Infection Analyzed by Lasso-Logistic and Construction of a Nomogram Prediction Model.
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Chen Jing, Zhu Ying, Zheng Chunfeng, Zhao Wei, Liu Qi
Аннотация
OBJECTIVE: This study aims to analyze the factors influencing the efficacy of budesonide (BUD) combined with N-acetylcysteine (NAC) treatment in children with Mycoplasma pneumoniae (MP) infection through Lasso-Logistic analysis, construct a Nomogram predictive model, and provide personalized treatment plans for clinicians. Additionally, it aims to fill the knowledge gap regarding the treatment of MP-infected children with BUD combined with NAC.
METHODS: We retrospectively analyzed clinical data from 96 children treated with BUD and NAC for MP infection at our hospital from January 2022 to May 2023. Treatment outcomes were categorized as good or poor. Logistic regression and Lasso-Logistic analysis were used to identify independent factors influencing outcomes and construct a predictive Nomogram model, which was validated through ROC curve analysis.
RESULTS: Logistic regression identified prolonged fever (≥7 days), high fever, and elevated levels of TNF-α, IL-6, and CRP as independent risk factors for poor outcomes. The Nomogram model, based on these factors, demonstrated excellent predictive accuracy with a C-index of 0.992 and AUC values of 0.987 and 0.948 in the modeling and validation cohorts, respectively.
CONCLUSION: The developed Nomogram model provides clinicians with a reliable tool to predict poor treatment outcomes in children with MP infection treated with BUD and NAC, supporting more personalized and effective treatment plans.
METHODS: We retrospectively analyzed clinical data from 96 children treated with BUD and NAC for MP infection at our hospital from January 2022 to May 2023. Treatment outcomes were categorized as good or poor. Logistic regression and Lasso-Logistic analysis were used to identify independent factors influencing outcomes and construct a predictive Nomogram model, which was validated through ROC curve analysis.
RESULTS: Logistic regression identified prolonged fever (≥7 days), high fever, and elevated levels of TNF-α, IL-6, and CRP as independent risk factors for poor outcomes. The Nomogram model, based on these factors, demonstrated excellent predictive accuracy with a C-index of 0.992 and AUC values of 0.987 and 0.948 in the modeling and validation cohorts, respectively.
CONCLUSION: The developed Nomogram model provides clinicians with a reliable tool to predict poor treatment outcomes in children with MP infection treated with BUD and NAC, supporting more personalized and effective treatment plans.