Physiological parameters to support attention deficit hyperactivity disorder diagnosis in children: a multiparametric approach.

INTRODUCTION

Attention deficit hyperactivity disorder (ADHD) is a high-prevalent neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity, frequently co-occurring with other psychiatric and medical conditions. Current diagnosis is time-consuming and often delays effective treatment; to date, no valid biomarker has been identified to facilitate this process. Research has linked the core symptoms of ADHD to autonomic dysfunction resulting from impaired arousal modulation, which contributes to physiological abnormalities that may serve as useful biomarkers for the disorder. While recent research has explored alternative objective assessment tools, few have specifically focused on studying ADHD autonomic dysregulation through physiological parameters. This study aimed to design a multiparametric physiological model to support ADHD diagnosis.

METHODS

In this observational study we non-invasively analyzed heart rate variability (HRV), electrodermal activity (EDA), respiration, and skin temperature parameters of 69 treatment-naïve ADHD children and 29 typically developing (TD) controls (7-12 years old). To identify the most relevant parameters to discriminate ADHD children from controls, we explored the physiological behavior at baseline and during a sustained attention task and applied a logistic regression procedure.

RESULTS

ADHD children showed increased HRV and lower EDA at baseline. The stress-inducing task elicits higher reactivity for EDA, pulse arrival time (PAT), and respiratory frequency in the ADHD group. The final classification model included 4 physiological parameters and was adjusted by gender and age. A good capacity to discriminate between ADHD children and TD controls was obtained, with an accuracy rate of 85.5% and an AUC of 0.95.

DISCUSSION: Our findings suggest that a multiparametric physiological model constitutes an accurate tool that can be easily employed to support ADHD diagnosis in clinical practice. The discrimination capacity of the model may be analyzed in larger samples to confirm the possibility of generalization.