Exploration of the Therapeutic Efficacy of Azithromycin Sequential Therapy in Children With Mycoplasma Pneumonia.

IF 1 4区 医学 Q3 MEDICINE, GENERAL & INTERNAL
British journal of hospital medicine Pub Date : 2025-06-25 Epub Date: 2025-06-13 DOI:10.12968/hmed.2025.0005
Heng Huang, Fanglu Ji
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引用次数: 0

Abstract

Aims/Background Mycoplasma pneumonia (MP) is a relatively common infection in children. While sequential treatment with azithromycin is a commonly used regimen, therapeutic response varies substantially among children. This study aims to establish a column chart prediction model based on the clinical characteristics and pathogenic outcomes of Mycoplasma pneumonia in children, enabling accurate decision-making for clinical interventions. Methods This retrospective study analysed the clinical data of 234 children with Mycoplasma pneumonia admitted to Cangnan Hospital of Wenzhou Medical University between March 2021 and October 2023. The data included general information, clinical symptoms, laboratory examination, and pathogenic profiles. The children were randomly divided into a training set (n = 164) and a validation set (n = 70) in a 7:3 ratio. Based on the efficacy of azithromycin sequential therapy, children in the training set were further divided into a poor efficacy group (n = 36) and a good efficacy group (n = 128). Independent risk factors for Mycoplasma pneumonia in the training set were identified using multiple logistic regression analysis. Furthermore, a column chart prediction model was constructed, and the model's performance was evaluated using receiver operating characteristic (ROC) curve analysis, followed by calibration curves. The predictive model was validated using an independent validation set, and decision curve analysis (DCA) assessed the model's clinical utility. Results In the training set, 36 cases (21.95%) showed poor therapeutic effects, while 24 cases (34.29%) in the validation set exhibited poor treatment response. There was no significant difference in clinical data between the two groups (p > 0.05). Univariate analysis showed significant differences (p < 0.05) across several factors, such as fever duration, cough severity, presence of pulmonary wet rales, white blood cell count, C-reactive protein (CRP) levels, Mycoplasma antibody titers, and Mycoplasma nucleic acid test findings among different treatment groups. Logistic regression analysis revealed prolonged fever duration, severe cough, presence of wet rales in the lungs, high white blood cell count, high CRP levels, high Mycoplasma antibody titers, and positive Mycoplasma nucleic acid test as independent risk factors of poor efficacy for azithromycin sequential treatment (p < 0.05). The Concordance index (C-index) of the column chart model was 0.804 in the training set and 0.861 in the validation set. The average absolute errors of the predicted and actual values were 0.129 and 0.081, respectively. The Hosmer-Lemeshow test results were χ2 = 10.288, p = 0.245 for the training set and χ2 = 7.922, p = 0.441 for the validation set, suggesting good model calibration. The ROC curve analysis revealed that the area under the ROC curve (AUC) for predicting the poor efficacy of azithromycin sequential therapy was 0.802 (95% confidence interval [CI]: 0.698-0.907) and 0.861 (95% CI: 0.704-1.000) for training and validation sets, respectively. Sensitivity and specificity were 0.655 and 0.907 in the training set and 0.898 and 0.952 in the validation set. Sensitivity analysis revealed that the model performed well across the decision subgroups, and the decision curve analysis indicated that the model demonstrated significant advantages when the threshold probability ranged between 0.1 and 0.98. Conclusion This study is the first to construct a column chart prediction model using the clinical characteristics of Mycoplasma pneumonia in children, addressing the lack of prediction tools in this area. This model can offer a valuable reference for assessing the prognosis of azithromycin sequential treatment, helping clinicians develop more targeted and individualised treatment strategies.

阿奇霉素序贯治疗儿童肺炎支原体的疗效探讨。
目的/背景肺炎支原体(Mycoplasma pneumonia, MP)是一种比较常见的儿童感染。虽然阿奇霉素序贯治疗是一种常用的治疗方案,但儿童的治疗反应差异很大。本研究旨在根据儿童肺炎支原体的临床特点和致病结局,建立柱状图预测模型,为临床干预提供准确决策依据。方法回顾性分析2021年3月至2023年10月温州医科大学苍南医院收治的234例肺炎支原体患儿的临床资料。数据包括一般信息、临床症状、实验室检查和病原谱。将儿童按7:3的比例随机分为训练组(n = 164)和验证组(n = 70)。根据阿奇霉素序贯治疗的疗效,将训练集中的患儿进一步分为疗效差组(n = 36)和疗效好组(n = 128)。使用多元logistic回归分析确定训练集中肺炎支原体的独立危险因素。构建柱状图预测模型,利用受试者工作特征(ROC)曲线分析对模型的性能进行评价,并绘制校正曲线。使用独立验证集对预测模型进行验证,并通过决策曲线分析(DCA)评估模型的临床效用。结果训练集中36例(21.95%)疗效较差,验证集中24例(34.29%)疗效较差。两组临床资料比较差异无统计学意义(p < 0.05)。单因素分析显示,不同治疗组的发热持续时间、咳嗽严重程度、肺湿疹、白细胞计数、c反应蛋白(CRP)水平、支原体抗体滴度和支原体核酸检测结果等多个因素存在显著差异(p < 0.05)。Logistic回归分析显示,阿奇霉素序贯治疗疗效不佳的独立危险因素为发热时间延长、咳嗽严重、肺部出现湿音、白细胞计数高、CRP水平高、支原体抗体滴度高、支原体核酸检测阳性等(p < 0.05)。柱图模型的一致性指数(C-index)在训练集为0.804,在验证集为0.861。预测值和实测值的平均绝对误差分别为0.129和0.081。Hosmer-Lemeshow检验结果:训练集χ2 = 10.288, p = 0.245;验证集χ2 = 7.922, p = 0.441,模型校正良好。ROC曲线分析显示,训练集和验证集预测阿奇霉素序贯治疗不良疗效的ROC曲线下面积(AUC)分别为0.802(95%可信区间[CI]: 0.698-0.907)和0.861 (95% CI: 0.704-1.000)。训练集的敏感性和特异性分别为0.655和0.907,验证集的敏感性和特异性分别为0.898和0.952。灵敏度分析表明,该模型在各决策子组中均表现良好,决策曲线分析表明,当阈值概率在0.1 ~ 0.98之间时,该模型具有显著的优势。结论本研究首次利用儿童肺炎支原体的临床特征构建柱状图预测模型,解决了该领域预测工具的不足。该模型可为评估阿奇霉素序贯治疗的预后提供有价值的参考,帮助临床医生制定更有针对性和个性化的治疗策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
British journal of hospital medicine
British journal of hospital medicine 医学-医学:内科
CiteScore
1.50
自引率
0.00%
发文量
176
审稿时长
4-8 weeks
期刊介绍: British Journal of Hospital Medicine was established in 1966, and is still true to its origins: a monthly, peer-reviewed, multidisciplinary review journal for hospital doctors and doctors in training. The journal publishes an authoritative mix of clinical reviews, education and training updates, quality improvement projects and case reports, and book reviews from recognized leaders in the profession. The Core Training for Doctors section provides clinical information in an easily accessible format for doctors in training. British Journal of Hospital Medicine is an invaluable resource for hospital doctors at all stages of their career. The journal is indexed on Medline, CINAHL, the Sociedad Iberoamericana de Información Científica and Scopus.
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