Clinical Classification Model for Human Adenovirus Infection in the Respiratory Tract of Children Based on Complete Blood Cell Count
10.13471/j.cnki.j.sun.yat-sen.univ（med.sci）.2025.0519
- VernacularTitle:基于全血细胞计数构建儿童呼吸道人腺病毒感染临床分类模型
- Author:
Junyan ZHONG
1
;
Junxiang LI
2
;
Mei HUANG
3
;
Yuejuan WANG
1
;
Luohui LIU
1
;
Xiaohui CHEN
1
;
Min CAO
1
Author Information
1. Department of Emergency， Shenzhen Maternity and Child Healthcare Hospital， Southern Medical University， Shenzhen 518028， China
2. Department of Laboratory Medicine， Shenzhen Maternity and Child Healthcare Hospital， Southern Medical University， Shenzhen 518028， China
3. Department of Pediatrics， Shenzhen Maternity and Child Healthcare Hospital， Southern Medical University， Shenzhen 518028， China
- Publication Type:Journal Article
- Keywords:
Human adenovirus;
complete blood count;
pediatrics;
pneumonia;
fever
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2025;46(5):889-898
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo develop a classification model based on complete blood count （CBC） parameters combined with clinical factors to predict severe respiratory infections caused by Human adenovirus （HAdV） in pediatric patients. MethodsFrom September 2023 to September 2024， the CBC parameters and related clinical data from pediatric patients diagnosed with HAdV infection were collected. Principal component analysis and random forest models were used to identify potential predictors of severe cases. ResultsA total of 668 pediatric patients were included， with 564 cases assigned to the training cohort and 104 cases to the validation cohort. Severe cases were defined as pneumonia and/or fever lasting ≥5 days （pneumonia or prolonged fever， PorPF）. Principal component analysis and feature importance analysis （Mean Decrease Gini value） identified the monocytosis ratio （PMono）， red blood cell count （RBC）， and platelet count （PLT） as the most critical CBC parameters. Logistic regression analysis revealed that oxygen therapy （OR = 4.367， 95% CI： 1.568–12.161） and increased work of breathing （OR = 3.904， 95% CI： 2.146–7.101） were relative risk factors for PorPF. Meanwhile， higher PMono （OR = 0.696， 95% CI： 0.640–0.757）， RBC （OR = 0.201， 95% CI： 0.124–0.325）， and PLT （OR = 0.990， 95% CI： 0.987–0.994） were protective factors. When PMono was used as a predictive marker for PorPF， the area under the receiver operating characteristic curve （AUC） was 0.648 and 0.705， respectively. A random forest model incorporating four risk factors ［PMono， RBC， PLT， and hematocrit （HCT）］ was constructed to classify PorPF and general cases， achieving AUCs of 0.688 and 0.768， respectively. ConclusionsPMono， RBC， and PLT may serve as characteristic CBC indicators for predicting pneumonia or prolonged fever in children with HAdV infection. A risk factor model built using PMono， RBC， PLT， and HCT offers a relatively simple and accurate approach to predicting severe cases in pediatric HAdV infections.
