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Development of a predictive algorithm to identify pre-school children at risk for behavior changes associated with
Victor E Ezeugwu1, Darryl Adamko2, Charmaine van Eeden1
1Department of Pediatrics, University of Alberta, Edmonton, AB, Canada.
Insights
A new algorithm combining urine metabolites and sleep questionnaires can identify children at risk for sleep-related breathing disorder (SRBD) and associated behavior problems. This tool aids early detection in children with snoring history.
Area of Science:
- Pediatric Sleep Medicine
- Biomarker Discovery
- Child Behavior
Background:
- Sleep-related breathing disorder (SRBD) in children, particularly late-onset or persistent forms, is linked to increased behavioral issues.
- Early identification of at-risk children is crucial for timely intervention and management of potential behavioral consequences.
Purpose of the Study:
- To develop and validate a predictive algorithm for identifying children at risk of late-onset or persistent SRBD.
- To assess the utility of combining urinary metabolite profiles with sleep questionnaire data for SRBD risk prediction.
Main Methods:
- Analysis of urine metabolites and sleep data from 165 children in the CHILD birth cohort study at age three years.
- Utilized Random Forest with Boruta wrapper to identify key urinary metabolites associated with SRBD.
- Developed a predictive algorithm incorporating a metabolite composite score and the SDBeasy score.
Main Results:
- Seven urinary metabolites and the SDBeasy score were significant predictors of late/persistent SRBD (AUC = 0.87).
- The algorithm identified children with a history of snoring and a metabolite composite score ≥0, with an SDBeasy score ≥3, as over 13 times more likely to have late/persistent SRBD (OR 13.7).
- The algorithm demonstrated a sensitivity of 69.6%, specificity of 85.7%, and a positive likelihood ratio of 4.9.
Conclusions:
- A novel predictive algorithm combining urine metabolites and sleep questionnaires effectively identifies children at risk for late/persistent SRBD by age five.
- This approach offers a promising non-invasive method for early detection of SRBD and potential associated behavioral problems in children.
Study Objectives:
Children with late-onset (2-5 years) or persistent (3 months-5 years) sleep-related breathing disorder (SRBD) have an increased risk of behavior problems compared to children with no or early-onset SRBD. We sought to determine whether a combination of urine metabolites and sleep questionnaires could identify children at risk for SRBD-associated behavior problems.
Methods:
Urine and data were analyzed from the Edmonton site of the CHILD birth cohort study. We measured urine metabolites (random, mid-stream) at age three-years among a sub-cohort of participants (n = 165). Random Forest with a Boruta wrapper was used to identify important metabolites (creatinine-corrected, z-scores) for late/persistent SRBD versus no/early SRBD (reference). An algorithm was subsequently generated to predict late/persistent SRBD in children with a history of snoring using a metabolite composite score (z-scores < or ≥ 0) plus the SDBeasy score defined as [age (yrs.) of most recent positive SRBD]2 - [age (yrs.) first reported ever snoring]2.
Results:
Of the 165 children with SRBD data, 40 participants had late/persistent SRBD. Seven urinary metabolites in addition to the SDBeasy score were confirmed as important for late/persistent SRBD (AUC = 0.87). Among children with an ever-snoring history and a metabolite composite score ≥0, those with SDBeasy score ≥3 were over 13-fold more likely to have late/persistent SRBD (OR 13.7; 95%CI: 3.0, 62.1; p = 0.001). This algorithm has a Sensitivity of 69.6%, Specificity of 85.7% and a positive likelihood ratio (+LR) of 4.9.
Conclusions:
We developed a predictive algorithm using a combination of questionnaires and urine metabolites at age three-years to identify children with late/persistent SRBD by five-years of age.
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