Chi Ming, Liu Fei, Chi Haifeng, Liu Ping, Xu Bo, Zhang Dawei
Department of Pediatrics, The 960th Hospital of the Joint Logistics Support Force of the People's Liberation Army of China, Jinan, China.
Department of Urology, Affiliated Hospital of Sergeant School of Army Medical University, Shijiazhuang, China.
Transl Pediatr. 2025 Jan 24;14(1):25-41. doi: 10.21037/tp-24-386. Epub 2025 Jan 21.
Screening for risk factors for the occurrence of multiple organ dysfunction syndrome (MODS) caused by pediatric influenza is an essential approach to improving treatment interventions and stratifying prognosis. This study aimed to select characteristic genes in MODS samples, demonstrate the correlation between characteristic genes and clinical variables, show the changes in expression levels of characteristic genes in the progression of MODS, and establish a predictive prolonged MODS (PM) line chart model.
We downloaded the pediatric influenza blood messenger ribonucleic acid (mRNA) dataset (GSE236877) from the Gene Expression Omnibus (GEO) database. Multiple logistic regression analyses were employed to screen for risk factors and independent risk factors, and to establish nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of variables on disease occurrence, where a larger area under the curve (AUC) indicates better predictive performance. Calibration curves and the Hosmer-Lemeshow goodness-of-fit test were utilized to describe whether the curves exhibited deviation. Decision curve analysis (DCA) was employed to assess the predictive efficacy of the model.
was an independent risk factor that increased the risk of PM (OR =0.356, P<0.001). (OR =4.598, P<0.001) and (OR =2.158, P=0.002) were protective factors that reduced the risk of PM occurrence. These three genes were combined with clinical variables, including age, influenza virus type, and bacterial co-infection, to construct a nomogram model for predicting the risk of MODS in children with influenza. The AUC of the nomogram score was 0.946, which was larger than the AUC of individual genes and clinical variables. Nomogram model can increase the net benefit of patients compared with clinical variables.
were characteristic genes that distinguished between never MODS (NM) and PM samples. , , and can serve as independent predictive factors for MODS. A nomogram model based on , and clinical variables (age, influenza virus type, and bacterial co-infection status) demonstrated better predictive performance for the risk of MODS in children with influenza compared to clinical variables and single genes.
筛查小儿流感所致多器官功能障碍综合征（MODS）发生的危险因素是改善治疗干预措施和判断预后分层的重要方法。本研究旨在筛选MODS样本中的特征基因，论证特征基因与临床变量之间的相关性，展示特征基因在MODS进展过程中表达水平的变化，并建立预测持续性MODS（PM）的列线图模型。
我们从基因表达综合数据库（GEO）下载了小儿流感血液信使核糖核酸（mRNA）数据集（GSE236877）。采用多因素逻辑回归分析来筛选危险因素和独立危险因素，并建立列线图模型。采用受试者工作特征（ROC）曲线评估变量对疾病发生的预测效能，曲线下面积（AUC）越大表明预测性能越好。利用校准曲线和Hosmer-Lemeshow拟合优度检验来描述曲线是否存在偏差。采用决策曲线分析（DCA）评估模型的预测效能。
是增加PM风险的独立危险因素（OR =0.356，P<0.001）。（OR =4.598，P<0.001）和（OR =2.158，P=0.002）是降低PM发生风险的保护因素。将这三个基因与年龄、流感病毒类型和细菌合并感染等临床变量相结合，构建了预测流感患儿MODS风险的列线图模型。列线图评分的AUC为0.946，大于单个基因和临床变量的AUC。与临床变量相比，列线图模型可增加患者的净效益。
是区分非MODS（NM）和PM样本的特征基因。、和可作为MODS的独立预测因素。与临床变量和单个基因相比，基于、和临床变量（年龄、流感病毒类型和细菌合并感染状态）的列线图模型对流感患儿MODS风险的预测性能更好。
