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一种用于预测儿童重症腺病毒肺炎的列线图。

A nomogram for predicting severe adenovirus pneumonia in children.

作者信息

Zhang Jiamin, Xu Changdi, Yan Shasha, Zhang Xuefang, Zhao Deyu, Liu Feng

机构信息

Department of Respiratory Medicine, Children's Hospital of Nanjing Medical University, Nanjing, China.

出版信息

Front Pediatr. 2023 Mar 1;11:1122589. doi: 10.3389/fped.2023.1122589. eCollection 2023.

Abstract

Adenoviral pneumonia in children was an epidemic that greatly impacted children's health in China in 2019. Currently, no simple or systematic scale has been introduced for the early identification and diagnosis of adenoviral pneumonia. The early recognition scale of pediatric severe adenovirus pneumonia was established based on an analysis of the children's community-acquired pneumonia clinical cohort. This study analyzed the clinical data of 132 children with adenoviral pneumonia who were admitted to the Children's Hospital of Nanjing Medical University. The clinical parameters and imaging features were analyzed using univariate and multivariate logistic regression analyses. A nomogram was constructed to predict the risk of developing severe adenovirus pneumonia in children. There were statistically significant differences in age, respiratory rate, fever duration before admission, percentage of neutrophils and lymphocytes, CRP, ALT, and LDH between the two groups. Logistic regression analysis was conducted using the R language, and respiratory rate, percentage of neutrophils, percentage of lymphocytes, and LDH were used as scale indicators. Using the ROC curve, the sensitivity and specificity of the scale were 93.3% and 92.1%. This scale has good sensitivity and specificity through internal verification, which proves that screening for early recognition of severe adenovirus pneumonia can be realized by scales. This predictive scale helps determine whether a child will develop severe adenovirus pneumonia early in the disease course.

摘要

儿童腺病毒肺炎是2019年在中国对儿童健康产生重大影响的一种流行病。目前,尚未引入用于早期识别和诊断腺病毒肺炎的简单或系统的量表。小儿重症腺病毒肺炎早期识别量表是基于对儿童社区获得性肺炎临床队列的分析而建立的。本研究分析了南京医科大学附属儿童医院收治的132例腺病毒肺炎患儿的临床资料。采用单因素和多因素logistic回归分析临床参数和影像学特征。构建列线图以预测儿童发生重症腺病毒肺炎的风险。两组在年龄、呼吸频率、入院前发热持续时间、中性粒细胞和淋巴细胞百分比、CRP、ALT和LDH方面存在统计学显著差异。使用R语言进行logistic回归分析,并将呼吸频率、中性粒细胞百分比、淋巴细胞百分比和LDH用作量表指标。利用ROC曲线,该量表的敏感性和特异性分别为93.3%和92.1%。通过内部验证,该量表具有良好的敏感性和特异性,证明可通过量表实现对重症腺病毒肺炎早期识别的筛查。这种预测量表有助于在病程早期确定儿童是否会发生重症腺病毒肺炎。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6e9/10014818/920fe041b33b/fped-11-1122589-g001.jpg

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