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Published on: December 10, 2013
Development and Validation of a Predictive Model for Wheezing Illness Following Human Bocavirus 1 Infection in
Ri De1, Zeng Li1, Kexiang Zhang1
1Laboratory of Virology, Capital Center for Children's Health, Capital Institute of Pediatrics, Capital Medical University, Beijing 100020, China.
Insights
This study identifies key predictors for wheezing illness in children with Human Bocavirus 1 (HBoV1) infection. The findings enable early risk stratification for timely clinical intervention in pediatric patients.
Area of Science:
- Pediatric Infectious Diseases
- Respiratory Illnesses in Children
- Clinical Predictive Modeling
Background:
- Human Bocavirus 1 (HBoV1) is a significant pathogen linked to pediatric wheezing illnesses.
- Effective clinical indicators for predicting wheezing in HBoV1-infected children are currently lacking.
Purpose of the Study:
- To develop and validate a predictive model for identifying children at high risk of wheezing illness following HBoV1 infection.
- To identify key clinical predictors associated with wheezing in pediatric HBoV1 cases.
Main Methods:
- A retrospective cohort study of 330 pediatric patients with single HBoV1 infection.
- Univariate and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression for predictor screening.
- Multivariate logistic regression model construction and validation using training (80%) and testing (20%) datasets.
Main Results:
- Abnormal NK cell percentage, preterm birth, and personal history of allergy were identified as independent predictors of wheezing illness.
- The predictive model demonstrated high performance with Area Under the Curve (AUC) values of 0.904 (training) and 0.876 (testing).
- The model accurately stratified patients into high-risk (85.3% wheezing rate) and low-risk (18.7% wheezing rate) groups in the test set.
Conclusions:
- A validated predictive model incorporating NK cell percentage, preterm birth, and allergy history effectively stratifies wheezing illness risk in children with HBoV1 infection.
- This model facilitates early clinical intervention for pediatric patients diagnosed with HBoV1.
- The findings contribute to improved management strategies for HBoV1-associated respiratory symptoms in children.
Abstract:
Human Bocavirus 1 (HBoV1) is one major pathogen that has been associated with wheezing illnesses. However, there is still a lack of effective clinical predictive indicators for wheezing illnesses in children infected with HBoV1. A retrospective cohort study was conducted among pediatric patients with single-HBoV1 infection from September 2016 to August 2023. Then, univariate logistic regression was used to screen potential predictors for wheezing illness, and Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to minimize overfitting and select key predictors. Finally, a multivariate logistic regression model was constructed in a training dataset comprising 80% of patients and validated in an independent test dataset comprising 20% of patients. Then, its performance was evaluated using the Area Under the Curve (AUC). A total of 330 pediatric patients were enrolled, including 228 in the wheezing-illness group and 102 in the non-wheezing group. Three independent predictors, including abnormal NK cell percentage (OR = 1.101, 95 %CI 1.03-1.27), preterm birth (OR = 1.65, 95 %CI 1.49-1.82) and personal history of allergy (OR = 1.25, 95 %CI 1.11-1.41), were identified. The model achieved AUCs of 0.904 and 0.876 in the training and test sets, respectively. Using a Youden-derived threshold (0.382), the high-risk group in the test set had an observed wheezing rate of 85.3%, compared with 18.7% in the low-risk group (p < 0.001). Calibration was satisfactory (Hosmer-Lemeshow p = 0.324 and 0.576). A validated predictive model incorporating abnormal NK cell percentage, preterm birth and personal history of allergy accurately stratifies the risk of wheezing illness after HBoV1 infection in children, facilitating early clinical intervention.
