Prediction of illness remission in patients with Obsessive-Compulsive Disorder with supervised machine learning

Prediction of illness remission in patients with Obsessive-Compulsive
Disorder with supervised machine learning

Introduction: The course of OCD differs widely among OCD patients, varying from chronic symptoms to full
remission. No tools for individual prediction of OCD remission are currently available. This study aimed to
develop a machine learning algorithm to predict OCD remission after two years, using solely predictors easily
accessible in the daily clinical routine.
Methods: Subjects were recruited in a longitudinal multi-center study (NOCDA). Gradient boosted decision
trees were used as supervised machine learning technique. The training of the algorithm was performed with 227
predictors and 213 observations collected in a single clinical center. Hyper-parameter optimization was performed
with cross-validation and a Bayesian optimization strategy. The predictive performance of the algorithm
was subsequently tested in an independent sample of 215 observations collected in five different centers.
Between-center differences were investigated with a bootstrap resampling approach.
Results: The average predictive performance of the algorithm in the test centers resulted in an AUROC of
0.7820, a sensitivity of 73.42%, and a specificity of 71.45%. Results also showed a significant between-center
variation in the predictive performance. The most important predictors resulted related to OCD severity, OCD
chronic course, use of psychotropic medications, and better global functioning.
Limitations: All recruiting centers followed the same assessment protocol and are in The Netherlands. Moreover,
the sample of the data recruited in some of the test centers was limited in size.
Discussion: The algorithm demonstrated a moderate average predictive performance, and future studies will
focus on increasing the stability of the predictive performance across clinical settings.