Development of a clinical prediction score for recurrent wheezing following acute lower respiratory tract infection in children
Abstract
BACKGROUND: Recurrent wheezing is a significant complication following acute lower respiratory tract infection (ALRTI) in children and is a known precursor to asthma development. Identifying high-risk children remains challenging, particularly in resource-limited settings.
AIM: To develop and evaluate a simplified clinical prediction score for recurrent wheezing following ALRTI in children.
METHODS: A retrospective cohort study was conducted at Naresuan University Hospital, Thailand, involving children under 15 years diagnosed with ALRTI from July 2020 to December 2021. Participants were followed for 12 months. Recurrent wheezing was defined as ≥ 2 physician-diagnosed episodes. Demographic, clinical, environmental, and laboratory variables were extracted from electronic medical records. Independent predictors were identified using multivariable logistic regression and used to construct a point-based clinical prediction score. Model performance was evaluated using discrimination, calibration, sensitivity, specificity, positive predictive value, negative predictive value and overall accuracy.
RESULTS: Of 280 eligible children, 52 (18.6%) developed recurrent wheezing. Independent predictors included allergic rhinitis, first-degree family history of asthma, eosinophilia, history of multiple LRTIs (≥ 2 prior episodes), food/drug allergy, and thalassemia. A point-based score (0-9) stratified patients into low-, moderate-, and high-risk groups. The model demonstrated good discrimination (area under the receiver operating characteristic curve: 0.79; 95% confidence interval: 0.71-0.88) and adequate calibration. Sensitivity decreased across increasing risk categories (48.57% for low, 34.29% for moderate, and 17.14% for high-risk groups), whereas specificity increased (12.75%, 87.25%, and 100%, respectively). Positive predictive values were 16.04%, 48.0%, and 100%, and negative predictive values were 41.94%, 79.46%, and 77.86%.
CONCLUSION: This clinical prediction score, based on readily available clinical variables, provides a practical tool for early risk stratification of recurrent wheezing in children following ALRTI. External validation is required to ensure the model’s robustness before widespread clinical adoption.
Related
- Guillain-Barré Syndrome in Late Pregnancy Following Respiratory Syncytial Virus Infection: A Diagnostic Dilemma.
- Development and Internal Validation of a Prediction Model Incorporating Respiratory Muscle Function for CAT Score Deterioration in Stable COPD
- The Trajectory of Gastrointestinal Symptom Experience Following Fecal Microbiota Transplant for Recurrent Clostridioides Difficile Infection
- Development and internal validation of a machine learning prediction model for postoperative hemorrhage following modified radical mastectomy for breast cancer
- Development and internal validation of a prediction model for postpartum haemorrhage following caesarean delivery in a resource-limited setting in Ethiopia
BibTeX
@article{dokkham2026development,
title = {{Development of a clinical prediction score for recurrent wheezing following acute lower respiratory tract infection in children}},
author = {Pandaree Dokkham and Thamonpan Kiatvitchukul and Kornthip Jeephet and Klaita Srisingh},
journal = {World Journal of Clinical Pediatrics},
year = {2026},
month = nov,
doi = {10.5409/wjcp.123752},
url = {https://doi.org/10.5409/wjcp.123752},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Development of a clinical prediction score for recurrent wheezing following acute lower respiratory tract infection in children
AU - Pandaree Dokkham
AU - Thamonpan Kiatvitchukul
AU - Kornthip Jeephet
AU - Klaita Srisingh
PY - 2026
DA - 2026/11/03
JO - World Journal of Clinical Pediatrics
DO - 10.5409/wjcp.123752
UR - https://doi.org/10.5409/wjcp.123752
AB - BACKGROUND: Recurrent wheezing is a significant complication following acute lower respiratory tract infection (ALRTI) in children and is a known precursor to asthma development. Identifying high-risk children remains challenging, particularly in resource-limited settings. AIM: To develop and evaluate a simplified clinical prediction score for recurrent wheezing following ALRTI in children. METHODS: A retrospective cohort study was conducted at Naresuan University Hospital, Thailand, involving children under 15 years diagnosed with ALRTI from July 2020 to December 2021. Participants were followed for 12 months. Recurrent wheezing was defined as ≥ 2 physician-diagnosed episodes. Demographic, clinical, environmental, and laboratory variables were extracted from electronic medical records. Independent predictors were identified using multivariable logistic regression and used to construct a point-based clinical prediction score. Model performance was evaluated using discrimination, calibration, sensitivity, specificity, positive predictive value, negative predictive value and overall accuracy. RESULTS: Of 280 eligible children, 52 (18.6%) developed recurrent wheezing. Independent predictors included allergic rhinitis, first-degree family history of asthma, eosinophilia, history of multiple LRTIs (≥ 2 prior episodes), food/drug allergy, and thalassemia. A point-based score (0-9) stratified patients into low-, moderate-, and high-risk groups. The model demonstrated good discrimination (area under the receiver operating characteristic curve: 0.79; 95% confidence interval: 0.71-0.88) and adequate calibration. Sensitivity decreased across increasing risk categories (48.57% for low, 34.29% for moderate, and 17.14% for high-risk groups), whereas specificity increased (12.75%, 87.25%, and 100%, respectively). Positive predictive values were 16.04%, 48.0%, and 100%, and negative predictive values were 41.94%, 79.46%, and 77.86%. CONCLUSION: This clinical prediction score, based on readily available clinical variables, provides a practical tool for early risk stratification of recurrent wheezing in children following ALRTI. External validation is required to ensure the model’s robustness before widespread clinical adoption.
SN - 2219-2808
ER -