title: Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach.
abstract: BACKGROUND
  At present, no tools exist to estimate objectively the risk of poor treatment outcomes in patients with first-episode psychosis. Such tools could improve treatment by informing clinical decision-making before the commencement of treatment. We tested whether such a tool could be successfully built and validated using routinely available, patient-reportable information.
  
  
  METHODS
  By applying machine learning to data from 334 patients in the European First Episode Schizophrenia Trial (EUFEST; International Clinical Trials Registry Platform number, ISRCTN68736636), we developed a tool to predict poor versus good treatment outcome (Global Assessment of Functioning [GAF] score ≥65 vs GAF <65, respectively) after 4 weeks and 52 weeks of treatment. To enable the unbiased estimation of the predictive system's generalisability to new patients, we used repeated nested cross-validation to prevent information leaking between patients used for training and validating the models. In pursuit of everyday clinical applicability, we retrained the 4-week outcome predictor with only the top ten predictors of the pooled prediction system and then tested this tool in 108 independent patients with 4-week outcome labels. Discontinuation and readmission to hospital events in patients with predicted poor versus good outcomes were assessed with Kaplan-Meier log-rank analyses, whereas generalised linear mixed-effects models were used to investigate the GAF-based predictions against several clinically meaningful outcome indicators, including treatment adherence, symptom remission, and quality of life.
  
  
  RESULTS
  The generalisability of our outcome predictions were estimated with cross-validation (test-fold balanced accuracy [BAC] of 75·0% for 4-week outcomes and 73·8% for and 52-week outcomes), and leave-site-out validation across 44 European sites (BAC of 72·1% for 4-week outcomes and 71·1% for 52-week outcomes). We identified a smaller group of ten predictors still providing a BAC of 71·7% in 108 patients never used for model discovery. Unemployment, poor education, functional deficits, and unmet psychosocial needs predicted both endpoints, whereas previous depressive episodes, male sex, and suicidality additionally predicted poor 1-year outcomes. 52-week predictions identified patients at risk for symptom persistence, non-adherence to treatment, readmission to hospital and poor quality of life. Specifically among these patients, amisulpride and olanzapine showed superior efficacy versus haloperidol, quetiapine, and ziprasidone.
  
  
  CONCLUSIONS
  Our results suggest that prognostic models operating on brief, patient-reportable pre-treatment data might provide useful insight into individualised outcome trajectories, optimising treatment selection, and targeted clinical trial designs. To embed these tools into real-world care, replication is needed in external first-episode samples with overlapping variables, which are not available in the field at present.
  
  
  BACKGROUND
  The European Group for Research in Schizophrenia.
authors: Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany. Electronic address: Nikolaos.Koutsouleris@med.uni-muenchen.de.
authors: Nikolaos
authors: N
authors: Koutsouleris
authors: Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, Utrecht, Netherlands.
authors: René S
authors: RS
authors: Kahn
authors: Department of Psychology, Yale University, New Haven, CT, USA; Centre for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
authors: Adam M
authors: AM
authors: Chekroud
authors: Department of Psychiatry and Psychotherapy, Technical University, Munich, Germany.
authors: Stefan
authors: S
authors: Leucht
authors: Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.
authors: Peter
authors: P
authors: Falkai
authors: Centre of Mental Health, County Hospitals Darmstadt-Dieburg, Germany; Department of Psychiatry and Psychotherapy, Georg-August-University Göttingen, Göttingen, Germany.
authors: Thomas
authors: T
authors: Wobrock
authors: Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, Utrecht, Netherlands.
authors: Eske M
authors: EM
authors: Derks
authors: Department of Biological Psychiatry, Medical University Innsbruck, Innsbruck, Austria.
authors: Wolfgang W
authors: WW
authors: Fleischhacker
authors: Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.
authors: Alkomiet
authors: A
authors: Hasan
mesh_terms: D000328:Adult; D000066491:Clinical Decision-Making; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D011618:Psychotic Disorders / Q000628:therapy*; D016896:Treatment Outcome