Prediction models for childhood asthma: A systematic review. Issue 6 (13th April 2020)

• Record Type: Journal Article
• Authors: Kothalawala, Dilini M.; Kadalayil, Latha; Weiss, Veronique B. N.; Kyyaly, Mohammed Aref; Arshad, Syed Hasan; Holloway, John W.; Rezwan, Faisal I.
• Editors: Kalaycı, Ömer
• Is Part Of: Pediatric allergy and immunology. Volume 31:Issue 6(2020)
• Page Start: 616
• Page End: 627
• Publication Date: 2020-04-13
• Subjects: asthma -- childhood -- prediction model -- risk scores -- wheeze; Allergy in children -- Periodicals; Immunologic diseases in children -- Periodicals; 617
• DOI: 10.1111/pai.13247
• Languages: English
• ISSNs: 0905-6157

Abstract

Abstract: Background: The inability to objectively diagnose childhood asthma before age five often results in both under‐treatment and over‐treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school‐age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school‐age asthma. Methods: Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school‐age children (6‐13 years). Validation studies were evaluated as a secondary objective. Results: Twenty‐four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression‐based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression‐based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62‐0.83). Conclusion: Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school‐age asthma prediction. Abstract :