Related Experiment Video
Updated: Sep 7, 2025
An In vitro Model to Study Immune Responses of Human Peripheral Blood Mononuclear Cells to Human Respiratory Syncytial Virus Infection
Published on: December 10, 2013
Validation of a Classification Model Using Complete Blood Count to Predict Severe Human Adenovirus Lower Respiratory
Huifeng Fan1, Ying Cui2, Xuehua Xu3
1Department of Respiration, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
Insights
Predicting severe human adenovirus (HAdV) lower respiratory tract infections (LRTIs) in children is possible using complete blood count (CBC) data. A higher monocyte ratio (MONO%) can indicate early severity, with a four-factor model showing high accuracy.
Area of Science:
- Pediatric Infectious Diseases
- Hematology
- Clinical Diagnostics
Background:
- Human adenovirus (HAdV) lower respiratory tract infections (LRTIs) pose a significant risk of severe illness and mortality in children.
- Accurate prediction of HAdV LRTI severity is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a classification model for predicting the severity of HAdV LRTIs in pediatric patients.
- To identify key complete blood count (CBC) parameters that can serve as early indicators of severe HAdV LRTIs.
Main Methods:
- Retrospective analysis of CBC data from 1,069 pediatric patients diagnosed with HAdV LRTIs between 2013 and 2019.
- Utilized a random forest model to identify significant predictors of disease severity.
- Data split into discovery (2017-2019) and validation (2013-2016) cohorts.
Main Results:
- The monocyte ratio (MONO%) emerged as a key differentiator between mild and severe HAdV LRTIs, showing strong predictive value (AUROC: 0.843).
- A classification model incorporating MONO%, hematocrit (HCT), red blood cell count (RBC), and platelet count (PLT) demonstrated high accuracy in predicting severity (AUROC: 0.931 in discovery, 0.903 in validation).
Conclusions:
- The monocyte ratio (MONO%) is a valuable individual predictor for early identification of severe HAdV LRTIs in children.
- The four-factor risk assessment model provides a simple yet accurate tool for early prediction of severe HAdV LRTIs, aiding clinical decision-making.
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.
