Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach

Publication date: Available online 25 August 2016 Source:The Lancet Psychiatry Author(s): Nikolaos Koutsouleris, René S Kahn, Adam M Chekroud, Stefan Leucht, Peter Falkai, Thomas Wobrock, Eske M Derks, Wolfgang W Fleischhacker, Alkomiet Hasan 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. Discontinu...
Source: The Lancet Psychiatry - Category: Psychiatry Source Type: research