Liu Hai-Feng, Zhang Xue-Zu, Liu Cong-Yun, Li Wang, Li Wen-Hong, Wang Ya-Yu, Li He-Yun, Xiang Mei, Lu Rui, Yuan Ting-Yun, Fu Hong-Min
Department of Pulmonary and Critical Care Medicine, Yunnan Key Laboratory of Children's Major Disease Research, Yunnan Medical Center for Pediatric Diseases, Kunming Children's Hospital, Kunming Medical University, Kunming, Yunnan, China.
Department of Pediatrics, The People's Hospital of Lincang, Lincang, Yunnan, China.
Front Immunol. 2024 Jul 24;15:1437834. doi: 10.3389/fimmu.2024.1437834. eCollection 2024.
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.
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.
The development dataset included 1102 patients, 239 (21.7%) of whom developed SALRTI; while the external validation dataset included 249 patients (142 in Lincang subset and 107 in Dali subset), 58 (23.3%) of whom were diagnosed as SALRTI. Nine variables, including age, preterm birth, underlying condition, seizures, neutrophil-lymphocyte ratio (NLR), interleukin-6 (IL-6), lactate dehydrogenase (LDH), D-dimer, and co-infection, were eventually confirmed as the independent risk factors of RSV-associated SALRTI. A predictive nomogram was established via integrating these nine predictors. In both internal and external validations, ROC curves indicated that the nomogram had satisfactory discrimination ability, calibration curves demonstrated good agreement between the nomogram-predicted and observed probabilities of outcome, and DCA showed that the nomogram possessed favorable clinical application potential.
A novel nomogram combining several common clinical and inflammatory indicators was successfully developed to predict RSV-associated SALRTI. Good performance and clinical effectiveness of this model were confirmed by internal and external validations.
在新冠疫情后时代，呼吸道合胞病毒（RSV）感染出现非流行季高峰，其特征发生改变，临床严重程度增加，这引发了严重担忧。本研究旨在利用机器学习技术开发并验证一种列线图，以预测新冠疫情后时代因RSV感染住院儿童发生严重急性下呼吸道感染（SALRTI）的风险。
在中国云南的9家三级医院进行了一项多中心回顾性研究，将2023年1月至12月期间9家参与研究医院中7家医院因RSV感染住院的儿童纳入开发数据集。收集了涵盖人口统计学、临床和实验室特征的39个变量。使用最小绝对收缩和选择算子（LASSO）回归进行数据的初步筛选和降维，随后使用逻辑回归确定RSV相关SALRTI的独立危险因素，最终建立预测列线图模型。基于开发数据集，通过受试者操作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）对列线图的性能进行内部评估。基于2024年1月至3月期间来自另外两家参与医院的儿科RSV住院患者组成的两个独立RSV队列，使用相同方法对我们的模型进行外部验证。
开发数据集包括1102例患者，其中239例（21.7%）发生SALRTI；而外部验证数据集包括249例患者（临沧子集142例，大理子集107例），其中58例（23.3%）被诊断为SALRTI。最终确定年龄、早产、基础疾病、惊厥、中性粒细胞与淋巴细胞比值（NLR）、白细胞介素-6（IL-6）、乳酸脱氢酶（LDH）、D-二聚体和合并感染这9个变量为RSV相关SALRTI的独立危险因素。通过整合这9个预测因素建立了预测列线图。在内部和外部验证中，ROC曲线均表明列线图具有令人满意的区分能力，校准曲线显示列线图预测的结果概率与观察到的结果概率之间具有良好的一致性，DCA表明列线图具有良好的临床应用潜力。
成功开发了一种结合多种常见临床和炎症指标的新型列线图，用于预测RSV相关SALRTI。内部和外部验证证实了该模型具有良好的性能和临床有效性。
