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Machine learning for predicting psychotic relapse at 2 years in schizophrenia in the national FACE-SZ cohort
- 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.
- Subjects :
- Adult
Male
Low education
[SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology
media_common.quotation_subject
Illness duration
Anger
Machine learning
computer.software_genre
Sensitivity and Specificity
Cohort Studies
Machine Learning
03 medical and health sciences
0302 clinical medicine
Recurrence
medicine
Early warning signs
Humans
Diagnosis, Computer-Assisted
Biological Psychiatry
media_common
Pharmacology
Psychiatric Status Rating Scales
[SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior
business.industry
[SCCO.NEUR]Cognitive science/Neuroscience
[SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences
medicine.disease
Prognosis
030227 psychiatry
3. Good health
Aggression
Psychotic Disorders
Schizophrenia
Cohort
Acute Psychotic Episode
Female
Schizophrenic Psychology
Cognitive Assessment System
Artificial intelligence
business
computer
Follow-Up Studies
Subjects
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⟩