A novel combined nomogram for predicting severe acute lower respiratory tract infection in children hospitalized for RSV infection during the post-COVID-19 period

作者：Haifeng Liu, Xue-Zu Zhang, Cong-Yun Liu, Wang Li, Wenhong Li, Yayu Wang, He-Yun Li, Mei Xiang, Rui Lu, Ting-Yun Yuan, Hong-Min Fu · 发表于：Frontiers in Immunology · 年份：2024 · DOI：10.3389/fimmu.2024.1437834 · 被引用次数：10 · 研究领域：Respiratory viral infections research、COVID-19 Clinical Research Studies、COVID-19 Impact on Reproduction

Introduction: Off-season upsurge of respiratory syncytial virus (RSV) infection with changed characteristics and heightened clinical severity during the post-COVID-19 era are raising serious concerns. This study aimed to develop and validate a nomogram for predicting the risk of severe acute lower respiratory tract infection (SALRTI) in children hospitalized for RSV infection during the post-COVID-19 era using machine learning techniques. Methods: A multicenter retrospective study was performed in nine tertiary hospitals in Yunnan, China, enrolling children hospitalized for RSV infection at seven of the nine participating hospitals during January-December 2023 into the development dataset. Thirty-nine variables covering demographic, clinical, and laboratory characteristics were collected. Primary screening and dimension reduction of data were performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by identification of independent risk factors for RSV-associated SALRTI using Logistic regression, thus finally establishing a predictive nomogram model. Performance of the nomogram was internally evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) based on the development dataset. External validation of our model was conducted using same methods based on two independent RSV cohorts comprising pediatric RSV inpatients from another two participating hospitals between January-March 2024. ...