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

•Machine Learning has been used to predict two-year remission of Obsessive-Compulsive Disorder.•Only predictors that are easily accessible in clinical practice have been used.•The generalized predictive performance has been tested in multiple clinical centers.•The performance variation among centers...

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Vydáno v:Journal of affective disorders Ročník 296; s. 117 - 125
Hlavní autoři: Grassi, Massimiliano, Rickelt, Judith, Caldirola, Daniela, Eikelenboom, Merijn, van Oppen, Patricia, Dumontier, Michel, Perna, Giampaolo, Schruers, Koen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier B.V 01.01.2022
Témata:
Bayes Theorem
Humans
Machine Learning
Obsessive-Compulsive Disorder
Obsessive-Compulsive Disorder - therapy
Personalized Medicine
Prognosis
Psychiatric/Mental Health
Remission
Remission Induction
Supervised Machine Learning
Remission
Prognosis
Personalized Medicine
Obsessive-Compulsive Disorder
Machine Learning
ISSN:0165-0327, 1573-2517, 1573-2517
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Shrnutí:•Machine Learning has been used to predict two-year remission of Obsessive-Compulsive Disorder.•Only predictors that are easily accessible in clinical practice have been used.•The generalized predictive performance has been tested in multiple clinical centers.•The performance variation among centers was observed, which is often uninvestigated. 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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0165-0327
1573-2517
1573-2517
DOI:10.1016/j.jad.2021.09.042