Validation of a Classification Model Using Complete Blood Count to Predict Severe Human Adenovirus Lower Respiratory Tract Infections in Pediatric Cases

• Published in: Frontiers in pediatrics Vol. 10; p. 896606
• Main Authors: Fan, Huifeng, Cui, Ying, Xu, Xuehua, Zhang, Dongwei, Yang, Diyuan, Huang, Li, Ding, Tao, Lu, Gen
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 27.05.2022
• Subjects: complete blood count; human adenovirus; lower respiratory tract infection; pediatric; Pediatrics; severe; human adenovirus; lower respiratory tract infection; severe; complete blood count; pediatric
• ISSN: 2296-2360; 2296-2360
• DOI: 10.3389/fped.2022.896606

Human adenovirus (HAdV) lower respiratory tract infections (LRTIs) are prone to severe cases and even cause death in children. Here, we aimed to develop a classification model to predict severity in pediatric patients with HAdV LRTIs using complete blood count (CBC). The CBC parameters from pediatric patients with a diagnosis of HAdV LRTIs from 2013 to 2019 were collected during the disease's course. The data were analyzed as potential predictors for severe cases and were selected using a random forest model. We enrolled 1,652 CBC specimens from 1,069 pediatric patients with HAdV LRTIs in the present study. Four hundred and seventy-four patients from 2017 to 2019 were used as the discovery cohort, and 470 patients from 2013 to 2016 were used as the validation cohort. The monocyte ratio (MONO%) was the most obvious difference between the mild and severe groups at onset, and could be used as a marker for the early accurate prediction of the severity [area under the subject operating characteristic curve (AUROC): 0.843]. Four risk factors [MONO%, hematocrit (HCT), red blood cell count (RBC), and platelet count (PLT)] were derived to construct a classification model of severe and mild cases using a random forest model (AUROC: 0.931 vs. 0.903). Monocyte ratio can be used as an individual predictor of severe cases in the early stages of HAdV LRTIs. The four risk factors model is a simple and accurate risk assessment tool that can predict severe cases in the early stages of HAdV LRTIs.