PMID- 27569526 OWN - NLM STAT- MEDLINE DCOM- 20171113 LR - 20171113 IS - 2215-0374 (Electronic) IS - 2215-0366 (Linking) VI - 3 IP - 10 DP - 2016 Oct TI - Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. PG - 935-946 LID - S2215-0366(16)30171-7 [pii] LID - 10.1016/S2215-0366(16)30171-7 [doi] AB - 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. FINDINGS: 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. INTERPRETATION: 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. FUNDING: The European Group for Research in Schizophrenia. CI - Copyright (c) 2016 Elsevier Ltd. All rights reserved. FAU - Koutsouleris, Nikolaos AU - Koutsouleris N AD - Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany. Electronic address: Nikolaos.Koutsouleris@med.uni-muenchen.de. FAU - Kahn, Rene S AU - Kahn RS AD - Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, Utrecht, Netherlands. FAU - Chekroud, Adam M AU - Chekroud AM AD - Department of Psychology, Yale University, New Haven, CT, USA; Centre for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA. FAU - Leucht, Stefan AU - Leucht S AD - Department of Psychiatry and Psychotherapy, Technical University, Munich, Germany. FAU - Falkai, Peter AU - Falkai P AD - Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany. FAU - Wobrock, Thomas AU - Wobrock T AD - Centre of Mental Health, County Hospitals Darmstadt-Dieburg, Germany; Department of Psychiatry and Psychotherapy, Georg-August-University Gottingen, Gottingen, Germany. FAU - Derks, Eske M AU - Derks EM AD - Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, Utrecht, Netherlands. FAU - Fleischhacker, Wolfgang W AU - Fleischhacker WW AD - Department of Biological Psychiatry, Medical University Innsbruck, Innsbruck, Austria. FAU - Hasan, Alkomiet AU - Hasan A AD - Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany. LA - eng PT - Journal Article PT - Multicenter Study PT - Randomized Controlled Trial DEP - 20160825 PL - England TA - Lancet Psychiatry JT - The lancet. Psychiatry JID - 101638123 SB - IM CIN - Lancet Psychiatry. 2016 Oct;3(10):908-909. PMID: 27569527 EIN - Lancet Psychiatry. 2017 Feb;4(2):95. PMID: 28137386 CIN - AMA J Ethics. 2018 Sep 1;20(9):E804-811. PMID: 30242810 MH - Adult MH - Clinical Decision-Making MH - Female MH - Humans MH - Machine Learning MH - Male MH - Psychotic Disorders/*therapy MH - Treatment Outcome EDAT- 2016/08/30 06:00 MHDA- 2017/11/14 06:00 CRDT- 2016/08/30 06:00 PHST- 2016/04/08 00:00 [received] PHST- 2016/06/24 00:00 [revised] PHST- 2016/06/28 00:00 [accepted] PHST- 2016/08/30 06:00 [pubmed] PHST- 2017/11/14 06:00 [medline] PHST- 2016/08/30 06:00 [entrez] AID - S2215-0366(16)30171-7 [pii] AID - 10.1016/S2215-0366(16)30171-7 [doi] PST - ppublish SO - Lancet Psychiatry. 2016 Oct;3(10):935-946. doi: 10.1016/S2215-0366(16)30171-7. Epub 2016 Aug 25.