Validation of a Classification Model Using Complete Blood Count to Predict Severe Human Adenovirus Lower Respiratory Tract Infections in Pediatric Cases - Fan, Huifeng; Cui, Ying; Xu, Xuehua; Zhang, Dongwei; Yang, Diyuan; Huang, Li; Ding, Tao; Lu, Gen (Frontiers in Pediatrics, 2022)
Background: 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). Methods: 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. Results: 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). Conclusion: MONO% 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 LRITs.