Liu Yuanwei, Wu Qiong, Zhou Lifang, Tang Yingyuan, Li Fen, Li Shuangjie
Department of pediatric respiratory medicine, the First People's Hospital of Chenzhou, Hunan CN, China.
Shock. 2025 Apr 1;63(4):533-540. doi: 10.1097/SHK.0000000000002472. Epub 2024 Sep 11.
Background : Severe respiratory syncytial virus (RSV) pneumonia is a leading cause of hospitalization and morbidity in infants and young children. Early identification of severe RSV pneumonia is crucial for timely and effective treatment by pediatricians. Currently, no prediction model exists for identifying severe RSV pneumonia in children. Methods : This study aimed to construct a diagnostic prediction model for severe RSV pneumonia in children using a machine learning algorithm. We analyzed data from the Gene Expression Omnibus (GEO) Series, including training dataset GSE246622 and testing dataset GSE105450, to identify differential genes between severe and mild-to-moderate RSV pneumonia in children. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the differential genes, followed by the construction of a protein-protein interaction network. An artificial neural network (ANN) algorithm was then used to develop and validate a diagnostic prediction model for severe RSV pneumonia in children. Results : We identified 34 differentially expressed genes between the severe and mild-to-moderate RSV pneumonia groups. Enrichment analysis revealed that these genes were primarily related to pathogenic infection and immune response. From the protein-protein interaction network, we identified 10 hub genes and, using the random forest algorithm, screened out 20 specific genes. The ANN-based diagnostic prediction model achieved an area under the curve value of 0.970 in the training group and 0.833 in the testing group, demonstrating the model's accuracy. Conclusions : This study identified specific biomarkers and developed a diagnostic model for severe RSV pneumonia in children. These findings provide a robust foundation for early identification and treatment of severe RSV pneumonia, offering new insights into its pathogenesis and improving pediatric care.
严重呼吸道合胞病毒（RSV）肺炎是婴幼儿住院和发病的主要原因。早期识别严重RSV肺炎对于儿科医生及时有效的治疗至关重要。目前，尚无用于识别儿童严重RSV肺炎的预测模型。方法：本研究旨在使用机器学习算法构建儿童严重RSV肺炎的诊断预测模型。我们分析了来自基因表达综合数据库（GEO）系列的数据，包括训练数据集GSE246622和测试数据集GSE105450，以识别儿童严重和轻度至中度RSV肺炎之间的差异基因。对差异基因进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析，随后构建蛋白质-蛋白质相互作用网络。然后使用人工神经网络（ANN）算法开发并验证儿童严重RSV肺炎的诊断预测模型。结果：我们在严重和轻度至中度RSV肺炎组之间鉴定出34个差异表达基因。富集分析表明，这些基因主要与病原体感染和免疫反应有关。从蛋白质-蛋白质相互作用网络中，我们鉴定出10个枢纽基因，并使用随机森林算法筛选出20个特定基因。基于ANN的诊断预测模型在训练组中的曲线下面积值为0.970，在测试组中为0.833，证明了该模型的准确性。结论：本研究鉴定了特定的生物标志物，并开发了儿童严重RSV肺炎的诊断模型。这些发现为早期识别和治疗严重RSV肺炎提供了有力的基础，为其发病机制提供了新的见解，并改善了儿科护理。
