Back to Search Start Over

Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort

Authors :
A. Schandrin
E. Bulzacka
Guillaume Fond
Pierre Vidailhet
Delphine Capdevielle
Fabrice Berna
Mohamed Boucekine
Isabelle Chereau
Franck Schürhoff
Caroline Dubertret
L. Boyer
Romain Rey
Ophélia Godin
Thierry d'Amato
Pierre-Michel Llorca
Christine Passerieux
Jasmina Mallet
Bruno Aouizerate
Julien Dubreucq
Mathieu Urbach
C. Lançon
David Misdrahi
Sylvain Leignier
Catherine Faget
Marion Leboyer
Fondation FondaMental [Créteil]
Institut Mondor de Recherche Biomédicale (IMRB)
Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)
Neuro-Psycho Pharmacologie des Systèmes Dopimanégiques sous-corticaux (NPsy-Sydo)
CHU Clermont-Ferrand-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])
Université Montpellier 1 (UM1)
Centre Hospitalier le Vinatier [Bron]
Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)
Departement de Psychiatrie
Hôpital de la Conception [CHU - APHM] (LA CONCEPTION)
Génétique moléculaire de la neurotransmission et des processus neurodégénératifs (LGMNPN)
Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)
Centre d'études et de recherche sur les services de santé et la qualité de vie (CEReSS)
Aix Marseille Université (AMU)
Source :
Progress in Neuro-Psychopharmacology and Biological Psychiatry, Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2019, 92 (8), pp.8-18. ⟨10.1016/j.pnpbp.2018.12.005⟩, Progress in Neuro-Psychopharmacology and Biological Psychiatry, Elsevier, 2019, 92 (8), pp.8-18. ⟨10.1016/j.pnpbp.2018.12.005⟩
Publication Year :
2018

Abstract

International audience; Background Predicting psychotic relapse is one of the major challenges in the daily care of schizophrenia. Objectives To determine the predictors of psychotic relapse and follow-up withdrawal in a non-selected national sample of stabilized community-dwelling SZ subjects with a machine learning approach. Methods Participants were consecutively included in the network of the FondaMental Expert Centers for Schizophrenia and received a thorough clinical and cognitive assessment, including recording of current treatment. Relapse was defined by at least one acute psychotic episode of at least 7 days, reported by the patient, her/his relatives or by the treating psychiatrist, within the 2-year follow-up. A classification and regression tree (CART) was used to construct a predictive decision tree of relapse and follow-up withdrawal. Results Overall, 549 patients were evaluated in the expert centers at baseline and 315 (57.4%) (mean age = 32.6 years, 24% female gender) were followed-up at 2 years. On the 315 patients who received a visit at 2 years, 125(39.7%) patients had experienced psychotic relapse at least once within the 2 years of follow-up. High anger (Buss&Perry subscore), high physical aggressiveness (Buss&Perry scale subscore), high lifetime number of hospitalization in psychiatry, low education level, and high positive symptomatology at baseline (PANSS positive subscore) were found to be the best predictors of relapse at 2 years, with a percentage of correct prediction of 63.8%, sensitivity 71.0% and specificity 44.8%. High PANSS excited score, illness duration Conclusion Machine learning can help constructing predictive score. In the present sample, aggressiveness appears to be a good early warning sign of psychotic relapse and follow-up withdrawal and should be systematically assessed in SZ subjects. The other above-mentioned clinical variables may help clinicians to improve the prediction of psychotic relapse at 2 years.

Details

ISSN :
18784216 and 02785846
Volume :
92
Database :
OpenAIRE
Journal :
Progress in neuro-psychopharmacologybiological psychiatry
Accession number :
edsair.doi.dedup.....357f36f98252728f0ffc3bee6d1eb0d4
Full Text :
https://doi.org/10.1016/j.pnpbp.2018.12.005⟩