Development and validation of a risk score to identify children at risk of life-threatening asthma. (2nd January 2022)

• Record Type: Journal Article
• Authors: Lee, Menolly; Bogdanova, Yulia; Chan, Mei; Owens, Louisa; Wales, Sandy; Gray, Melinda; Burns, Christine; Briggs, Nancy; Jaffe, Adam; Homaira, Nusrat
• Is Part Of: Journal of asthma. Volume 59:Number 1(2022)
• Page Start: 105
• Page End: 114
• Publication Date: 2022-01-02
• Subjects: Asthma -- paediatrics -- fatal outcome -- risk factors -- risk management; Asthma -- Periodicals; 616.238005
• DOI: 10.1080/02770903.2020.1841224
• Languages: English
• ISSNs: 0277-0903

Abstract

Abstract: Objective: To develop and validate a prediction risk score for identification of children at risk of developing life-threatening asthma (LTA). Methods: Our study utilized existing medical records and retrospective analysis to develop and validate a risk score. The study population included children aged 2-17 years, admitted with a primary diagnosis of asthma, to Sydney Children's Hospital between 2011-2016. Children admitted in the intensive care unit with asthma at risk of LTA (cases) and those admitted into general ward (comparison group), were randomly divided into a derivation and a validation cohort. Candidate predictors from derivation cohort were selected through multivariable regression, which were used to estimate each child's risk of developing LTA in the validation cohort. Predictive performance of the risk score was evaluated by the area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow goodness-of-fit test. Results: The study population comprised of 1171 children; 586 in the derivation and 585 in the validation cohort. Four independent candidate variables from derivation cohort (age at admission, socioeconomic status, a family history of asthma/atopy and previous asthma hospitalizations) were retained in the predictive model (AUROC 0.759; 95% CI, 0.694–0.823), with a sensitivity of 78.5% and specificity of 46.6%. Conclusions: Our risk algorithm based on routinely collected clinical data may be used to develop a user-friendly risk score for early identification and monitoring of children at risk of developing LTA.