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Development and validation of a risk score to identify children at risk of life-threatening asthma
Menolly Lee1, Yulia Bogdanova1, Mei Chan1
1Discipline of Paediatrics, School of Women's and Children's Health, Faculty of Medicine, UNSW Sydney, Sydney, Australia.
Insights
This study developed a risk score to identify children at risk of life-threatening asthma (LTA). The score uses routine clinical data for early detection and monitoring of pediatric asthma patients.
Area of Science:
- Pediatric Pulmonology
- Clinical Risk Prediction
- Asthma Management
Background:
- Life-threatening asthma (LTA) poses a significant risk to children.
- Early identification of at-risk children is crucial for timely intervention.
- Existing methods for predicting LTA risk in pediatric populations require enhancement.
Purpose of the Study:
- To develop and validate a prediction risk score for identifying children at risk of developing life-threatening asthma (LTA).
- To create a tool for early identification and monitoring of pediatric asthma patients at high risk for severe outcomes.
Main Methods:
- Retrospective analysis of medical records from Sydney Children's Hospital (2011-2016).
- Inclusion of children aged 2-17 years with a primary asthma diagnosis.
- Development of a risk score using multivariable regression in a derivation cohort and validation in a separate cohort, evaluating performance with AUROC and Hosmer-Lemeshow tests.
Main Results:
- The study included 1171 children, divided into derivation (586) and validation (585) cohorts.
- Four predictors (age at admission, socioeconomic status, family history of asthma/atopy, previous asthma hospitalizations) were identified.
- The predictive model achieved an AUROC of 0.759, with 78.5% sensitivity and 46.6% specificity.
Conclusions:
- A risk algorithm utilizing routinely collected clinical data has been developed.
- This algorithm can be translated into a user-friendly risk score for clinical application.
- The risk score facilitates early identification and monitoring of children susceptible to life-threatening asthma.
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.
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