TY - JOUR
T1 - Prediction of illness remission in patients with Obsessive-Compulsive Disorder with supervised machine learning
AU - Grassi, Massimiliano
AU - Rickelt, Judith
AU - Caldirola, Daniela
AU - Eikelenboom, Merijn
AU - van Oppen, Patricia
AU - Dumontier, Michel
AU - Perna, Giampaolo
AU - Schruers, Koen
N1 - Funding Information:
We are grateful to Alexander Malic and Vincent Emonet for technical support. We also wish to acknowledge to the Institute of Data Science (Maastricht University) for the computer resources to perform the study.
Funding Information:
The research infrastructure needed to complete the baseline measurements was financed almost exclusively by the participating organizations: Academic department VU Medical Centre /GGZinGeest, Amsterdam, The Netherlands; Innova Research Centre, Mental Health Care Institute GGZ Centraal, Marinade Wolf Anxiety Research Centre, Ermelo, The Netherlands; Institute of Integrated Mental Health Care “Pro Persona,” “Overwaal” Centre of Expertise for Anxiety Disorders OCD and PTSD Nijmegen, the Netherlands; Dimence, GGZ Overijssel; Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands; ‘Vincent van Gogh institute’ Mental Health Care Centre Noorden Midden-Limburg, Venray, The Netherlands; Academic Anxiety Center, PsyQ Maastricht University, Division Mental Health and Neuroscience, Maastricht, The Netherlands, except for the field work coordinator, which was financed by a research grant from ‘Stichting tot Steun VCVGZ’, awarded to Patricia van Oppen and Anton J. L. M. van Balkom.
Publisher Copyright:
© 2021
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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.
KW - Machine Learning
KW - Obsessive-Compulsive Disorder
KW - Personalized Medicine
KW - Prognosis
KW - Remission
UR - http://www.scopus.com/inward/record.url?scp=85116058497&partnerID=8YFLogxK
U2 - 10.1016/j.jad.2021.09.042
DO - 10.1016/j.jad.2021.09.042
M3 - Article
C2 - 34600172
VL - 296
SP - 117
EP - 125
JO - Journal of Affective Disorders
JF - Journal of Affective Disorders
SN - 0165-0327
ER -