Prediction of risk factors of plastic bronchitis in children with severe Mycoplasma pneumoniae pneumonia.

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Shiyin Mu, Jia Zhai, Yongsheng Guo, Bing Huang, Yingxue Zou
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引用次数: 0

Abstract

Background: Plastic bronchitis (PB) is a rare but potentially life-threatening condition that requires particular attention in pediatric patients, specifically those presenting with severe Mycoplasma pneumoniae pneumonia (SMPP). This study aimed to identify risk factors associated with PB in children with SMPP and develop a comprehensive risk factor scoring system.

Methods: A retrospective analysis was conducted on SMPP patients who underwent bronchoscopy between January 2018 and October 2023. Based on bronchoscopic and pathological examination results, patients were categorized into PB (n = 142) and non-PB (n = 274) groups. Clinical manifestations, laboratory data, and imaging findings were analyzed. Risk factors for PB in SMPP children were identified through univariate and multivariate logistic regression analyses. A nomogram model incorporating independent risk factors was developed, and a PB risk factor scoring system was established. Model validation was performed through a prospective validation study.

Results: Among 416 SMPP children (197 males, 219 females), mean age at disease onset was 6.9 ± 2.9 years and 6.6 ± 2.8 years in the PB and Non-PB groups, respectively. Multivariate logistic regression analysis identified eight independent predictors of PB in SMPP children: dyspnea, decreased breath sounds, neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), mean platelet volume to platelet ratio (MPV/PLT), pleural effusion, ≥ 2/3 lobe consolidation, and atelectasis. The nomogram prediction model demonstrated excellent discriminative ability (AUC = 0.92, 95% CI 0.892-0.948, P < 0.005) and strong calibration between predicted and observed outcomes. In the prospective validation cohort (n = 565), the scoring system effectively stratified patients into risk categories: high-risk (71.62% PB incidence), intermediate-risk (59.79%), and low-risk (5.33%), with statistically significant inter-group differences (P < 0.001). The PB group exhibited significantly longer hospitalization durations, extended glucocorticoid treatment periods, higher proportions of glucocorticoid therapy utilization, and a greater frequency of bronchoscopy interventions (≥ 2 sessions) compared to the Non-PB group (all P < 0.05).

Conclusions: In this study, we developed and validated a nomogram to PB in children with SMPP. This model serves as a clinically practical tool for early PB identification, enabling physicians to initiate timely interventions and optimize disease management strategies.

重症肺炎支原体肺炎患儿塑性支气管炎危险因素的预测。
背景:塑料支气管炎(PB)是一种罕见但可能危及生命的疾病,需要特别注意儿科患者,特别是那些表现为严重肺炎支原体肺炎(SMPP)的患者。本研究旨在确定与慢性阻塞性肺损伤儿童PB相关的危险因素,并建立一个综合的危险因素评分系统。方法:回顾性分析2018年1月至2023年10月间行支气管镜检查的SMPP患者。根据支气管镜及病理检查结果将患者分为PB组(n = 142)和非PB组(n = 274)。分析临床表现、实验室资料及影像学表现。通过单因素和多因素logistic回归分析确定SMPP儿童患PB的危险因素。建立了包含独立风险因素的nomogram模型,并建立了PB风险因素评分体系。通过前瞻性验证研究进行模型验证。结果:416例SMPP患儿(男197例,女219例)中,PB组和非PB组的平均发病年龄分别为6.9±2.9岁和6.6±2.8岁。多因素logistic回归分析确定了SMPP患儿PB的8个独立预测因素:呼吸困难、呼吸音减少、中性粒细胞与淋巴细胞比值(NLR)、乳酸脱氢酶(LDH)、平均血小板体积与血小板比值(MPV/PLT)、胸腔积液、≥2/3肺叶实变和肺不张。该模型具有良好的判别能力(AUC = 0.92, 95% CI 0.892-0.948, P)。结论:本研究建立并验证了SMPP患儿PB的nomogram。该模型可作为临床早期识别PB的实用工具,使医生能够及时进行干预并优化疾病管理策略。
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
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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