Prediction of Treatment Response and Adverse Events in Pediatric Attention-Deficit/Hyperactivity Disorder Treated
Yang Li1, Zilong Ma2, Walter Falchi2
1The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China.
Insights
Machine learning models accurately predict treatment response and adverse events in children with attention-deficit/hyperactivity disorder (ADHD) receiving acupuncture. These models aid in personalized risk assessment for ADHD treatment.
Area of Science:
- Pediatric Neurology
- Integrative Medicine
- Computational Psychiatry
Background:
- Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children.
- Conventional first-line treatments for ADHD may have limitations in efficacy and side effects.
- Acupuncture is explored as an adjunctive therapy to conventional care for pediatric ADHD.
Purpose of the Study:
- To develop and externally validate machine learning (ML) models for predicting treatment response in children with ADHD.
- To develop and externally validate ML models for predicting adverse events in children with ADHD.
- To assess the clinical utility and interpretability of these ML models.
Main Methods:
- A multicentre retrospective study involving 809 children (aged 6-12 years) with ADHD.
- Development and evaluation of ten ML models using demographic, clinical, behavioral, and laboratory data.
- Models were validated internally and in two external cohorts using 10-fold cross-validation.
Main Results:
- An extreme gradient boosting model showed the best performance for predicting treatment response (AUROC 0.78-0.87).
- Moderate discriminative performance was observed for predicting adverse events (external AUROCs 0.73-0.85).
- Interpretability analyses indicated that multiple clinical features contribute to model predictions.
Conclusions:
- ML models using routinely collected data can reliably predict treatment response and adverse events in pediatric ADHD patients receiving acupuncture.
- These models may facilitate individualized benefit-risk assessments in integrative care settings.
- Prospective studies are needed to confirm the clinical impact of these ML models.
Introduction:
The aim of the study was to develop and externally validate machine learning (ML) models for predicting treatment response and adverse events in children with attention-deficit/hyperactivity disorder (ADHD) receiving acupuncture combined with conventional first-line care.
Methods:
In this multicentre retrospective study, demographic, clinical, behavioral, and laboratory data from 809 children with ADHD aged 6-12 years were used to develop and evaluate ten ML models. Treatment response was defined as a ≥30% reduction in the Swanson, Nolan, and Pelham-IV total score 8-16 weeks after treatment initiation, and adverse events were recorded from parent reports. Model development used repeated 10-fold cross-validation, with evaluation in an internal testing cohort and two external cohorts. Discrimination, calibration, and clinical utility were assessed, and model interpretability was examined using SHapley Additive exPlanations.
Results:
The extreme gradient boosting model demonstrated the best discriminative performance for predicting treatment response and was retained for further analyses. For treatment response prediction, it achieved an area under the receiver operating characteristic curve (AUROC) of 0.83 in the internal testing cohort and showed stable external performance, with AUROCs ranging from 0.78 to 0.87 across both external cohorts. For adverse event prediction, discriminative performance was moderate, with external AUROCs ranging from 0.73 to 0.85. Model interpretability analyses suggested that multiple clinical feature domains contributed to model predictions.
Conclusion:
Using routinely collected clinical data, ML-based models showed reproducible discrimination for predicting treatment response and adverse events in children with ADHD receiving acupuncture as adjunctive therapy. They may support individualized benefit-risk stratification in integrative treatment settings, although prospective assessment of clinical impact is warranted.
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