Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach
Jessica de Nijs, Thijs Jan Burger, Ronald J. Janssen, Seyed Mostafa Kia, Daniël P. J. van Opstal, Mariken B. de Koning, Lieuwe de Haan, GROUP investigators, Behrooz Z. Alizadeh, Agna A. Bartels‐Velthuis, Nico J. van Beveren, Richard Bruggeman, Lieuwe de Haan, Philippe Delespaul, Jurjen J. Luykx, Inez Myin‐Germeys, René S. Kahn, Frederike Schirmbeck, Claudia J.P. Simons, Thérèse van Amelsvoort, Jim van Os, Ruud van Winkel, Wiepke Cahn, Hugo G. Schnack
Abstract
Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF ≥ 65) and poor (GAF < 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5-67.6% (global outcome). Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient's and clinicians' needs in prognostication.