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

Huifeng Fan; Ying Cui; Xuehua Xu; Dongwei Zhang; Diyuan Yang; Li Huang; Tao Ding; Gen Lu

doi:10.3389/fped.2022.896606

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

Front Pediatr. 2022 May 27:10:896606. doi: 10.3389/fped.2022.896606. eCollection 2022.

Authors

Huifeng Fan¹, Ying Cui², Xuehua Xu³, Dongwei Zhang³, Diyuan Yang¹, Li Huang³, Tao Ding^{2

4

5}, Gen Lu¹

Affiliations

¹ Department of Respiration, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
² Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
³ Pediatric Intensive Care Unit, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
⁴ Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, China.
⁵ Provincial Engineering Technology Research Center for Biological Vector Control, Guangzhou, China.

Abstract

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: 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.

Keywords: complete blood count; human adenovirus; lower respiratory tract infection; pediatric; severe.