Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach

J. de Nijs, T.J. Burger, R.J. Janssen, S.M. Kia, D.P.J. van Opstal, M.B. de Koning, L. de Haan, B.Z. Alizadeh, A.A. Bartels-Velthuis, N.J. van Beveren, R. Bruggeman, P. Delespaul, J.J. Luykx, I. Myin-Germeys, R.S. Kahn, F. Schirmbeck, C.J.P. Simons, T. van Amelsvoort, J. van Os, R. van WinkelW. Cahn, H.G. Schnack*, GRP Investigators

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Web of Science)

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.

Original languageEnglish
Article number34
Number of pages11
Journalnpj Schizophrenia
Volume7
Issue number1
DOIs
Publication statusPublished - 2 Jul 2021

Keywords

  • 1ST-EPISODE PSYCHOSIS
  • MENTAL-HEALTH
  • CAMBERWELL ASSESSMENT
  • PERSONAL RECOVERY
  • HIGH-RISK
  • SCHIZOPHRENIA
  • DIAGNOSIS
  • MODEL
  • RELIABILITY
  • INSTRUMENT

Cite this