Development and validation of a prediction model to predict school-age asthma in preschool children

Area of Science:

• Pediatric Pulmonology
• Clinical Epidemiology
• Biostatistics

Background:

• Asthma is a common chronic respiratory disease in children.
• Identifying children at high risk for persistent asthma is crucial for early intervention.
• Preschool asthma often poses diagnostic challenges, with many cases persisting into school age.

Purpose of the Study:

• To develop and validate a clinical prediction model for identifying school-age asthma in preschool children.
• To identify key prognostic variables associated with the transition from preschool to school-age asthma.
• To provide a tool for early risk assessment and potential intervention in young asthmatic children.

Main Methods:

• Retrospective prognosis cohort study involving preschool asthmatic children (3-5 years) with at least 2 years of follow-up.
• Logistic regression was used to develop the prediction model based on baseline variables.
• Model performance was evaluated using discrimination (Area Under the ROC Curve - AUC) and calibration (Brier score), with temporal validation.

Main Results:

• The prediction model included variables such as age, parental asthma, early wheezing, allergic rhinitis, eczema, allergic conjunctivitis, obesity, and dust mite allergy.
• The model demonstrated moderate discrimination (AUC 0.788) and good calibration (Brier score 0.169) in the development dataset.
• Temporal validation showed satisfactory performance with AUC 0.818 and Brier score 0.150.

Conclusions:

• A validated clinical prediction model can effectively identify preschool asthmatic children at risk for developing school-age asthma.
• The model, available as a web calculator and nomogram, facilitates clinical application for risk stratification.
• Early identification of high-risk children can potentially lead to timely interventions and improved asthma management.