Xu Huifen, Chen Wei, Lai Qinrui, Chen Yingying, Guo Yajun, Chen Jing, Li Wei
Department of Pharmacy, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310053, China.
Department of Clinical Laboratory, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310053, China.
Pathogens. 2025 Oct 31;14(11):1110. doi: 10.3390/pathogens14111110.
Identifying effective indicators and developing predictive models for the early detection of severe adenoviral pneumonia (SAP) is critical to safeguarding patients' lives. This study examined differences between 428 patients with SAP and those with non-severe adenoviral pneumonia (NSAP) from March 2022 to January 2023, focusing on variables such as age, sex, type of coinfection, and a range of clinical laboratory indicators. SAP was significantly more common in children aged 3-6 years (20/54 of all SAP cases, = 0.0258) and among those with polymicrobial coinfections ( < 1.20 × 10). Patients with SAP exhibited significantly higher prealbumin (PA) level, while C-reactive protein (CRP) level was significantly lower. Composite indicator, such as CRP -to- prealbumin ratio (CPAR), was also significantly elevated ( < 0.05). The random forest model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.699, with an accuracy of 84.5% and a precision of 91.5%. Analysis of the data revealed key predictive parameters for early-stage SAP. Indicators such as CPAR, PA, and CRP are valuable for assessing SAP risk. Moreover, commonly available clinical indicators can effectively construct a random forest-based predictive model for SAP.
识别有效的指标并开发预测模型以早期检测重症腺病毒肺炎（SAP）对于保障患者生命至关重要。本研究调查了2022年3月至2023年1月期间428例SAP患者与非重症腺病毒肺炎（NSAP）患者之间的差异，重点关注年龄、性别、合并感染类型以及一系列临床实验室指标等变量。SAP在3至6岁儿童中显著更常见（占所有SAP病例的20/54， = 0.0258），并且在多重微生物合并感染患者中（< 1.20 × 10）也更常见。SAP患者的前白蛋白（PA）水平显著更高，而C反应蛋白（CRP）水平显著更低。复合指标，如CRP与前白蛋白比值（CPAR）也显著升高（< 0.05）。随机森林模型的受试者操作特征（ROC）曲线下面积（AUC）为0.699，准确率为84.5%，精确率为91.5%。数据分析揭示了早期SAP的关键预测参数。CPAR、PA和CRP等指标对于评估SAP风险很有价值。此外，常用的临床指标可以有效地构建基于随机森林的SAP预测模型。
