FASDetect as a machine learning-based screening app for FASD in youth with ADHD

• Published in: NPJ digital medicine Vol. 6; no. 1; p. 130
• Main Authors: Ehrig, Lukas, Wagner, Ann-Christin, Wolter, Heike, Correll, Christoph U, Geisel, Olga, Konigorski, Stefan
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 19.07.2023; Nature Publishing Group UK; Nature Portfolio
• Subjects: Age groups; Attention deficit hyperactivity disorder; Fetal alcohol syndrome; Machine learning; Medical screening; Pediatrics; Germany
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-023-00864-1

Fetal alcohol-spectrum disorder (FASD) is underdiagnosed and often misdiagnosed as attention-deficit/hyperactivity disorder (ADHD). Here, we develop a screening tool for FASD in youth with ADHD symptoms. To develop the prediction model, medical record data from a German University outpatient unit are assessed including 275 patients aged 0-19 years old with FASD with or without ADHD and 170 patients with ADHD without FASD aged 0-19 years old. We train 6 machine learning models based on 13 selected variables and evaluate their performance. Random forest models yield the best prediction models with a cross-validated AUC of 0.92 (95% confidence interval [0.84, 0.99]). Follow-up analyses indicate that a random forest model with 6 variables - body length and head circumference at birth, IQ, socially intrusive behaviour, poor memory and sleep disturbance - yields equivalent predictive accuracy. We implement the prediction model in a web-based app called FASDetect - a user-friendly, clinically scalable FASD risk calculator that is freely available at https://fasdetect.dhc-lab.hpi.de .