Frontiers in Psychiatry (May 2019)

Individualized Prediction of Transition to Psychosis in 1,676 Individuals at Clinical High Risk: Development and Validation of a Multivariable Prediction Model Based on Individual Patient Data Meta-Analysis

  • Aaltsje Malda,
  • Aaltsje Malda,
  • Nynke Boonstra,
  • Nynke Boonstra,
  • Hans Barf,
  • Steven de Jong,
  • Andre Aleman,
  • Andre Aleman,
  • Jean Addington,
  • Marita Pruessner,
  • Marita Pruessner,
  • Dorien Nieman,
  • Lieuwe de Haan,
  • Anthony Morrison,
  • Anthony Morrison,
  • Anita Riecher-Rössler,
  • Erich Studerus,
  • Stephan Ruhrmann,
  • Frauke Schultze-Lutter,
  • Suk Kyoon An,
  • Shinsuke Koike,
  • Shinsuke Koike,
  • Shinsuke Koike,
  • Shinsuke Koike,
  • Kiyoto Kasai,
  • Kiyoto Kasai,
  • Kiyoto Kasai,
  • Kiyoto Kasai,
  • Barnaby Nelson,
  • Barnaby Nelson,
  • Patrick McGorry,
  • Patrick McGorry,
  • Stephen Wood,
  • Stephen Wood,
  • Stephen Wood,
  • Ashleigh Lin,
  • Alison Y. Yung,
  • Alison Y. Yung,
  • Alison Y. Yung,
  • Alison Y. Yung,
  • Magdalena Kotlicka-Antczak,
  • Marco Armando,
  • Marco Armando,
  • Stefano Vicari,
  • Masahiro Katsura,
  • Kazunori Matsumoto,
  • Kazunori Matsumoto,
  • Kazunori Matsumoto,
  • Sarah Durston,
  • Tim Ziermans,
  • Tim Ziermans,
  • Lex Wunderink,
  • Lex Wunderink,
  • Helga Ising,
  • Mark van der Gaag,
  • Mark van der Gaag,
  • Paolo Fusar-Poli,
  • Paolo Fusar-Poli,
  • Paolo Fusar-Poli,
  • Paolo Fusar-Poli,
  • Gerdina Hendrika Maria Pijnenborg,
  • Gerdina Hendrika Maria Pijnenborg

DOI
https://doi.org/10.3389/fpsyt.2019.00345
Journal volume & issue
Vol. 10

Abstract

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Background: The Clinical High Risk state for Psychosis (CHR-P) has become the cornerstone of modern preventive psychiatry. The next stage of clinical advancements rests on the ability to formulate a more accurate prognostic estimate at the individual subject level. Individual Participant Data Meta-Analyses (IPD-MA) are robust evidence synthesis methods that can also offer powerful approaches to the development and validation of personalized prognostic models. The aim of the study was to develop and validate an individualized, clinically based prognostic model for forecasting transition to psychosis from a CHR-P stage.Methods: A literature search was performed between January 30, 2016, and February 6, 2016, consulting PubMed, Psychinfo, Picarta, Embase, and ISI Web of Science, using search terms (“ultra high risk” OR “clinical high risk” OR “at risk mental state”) AND [(conver* OR transition* OR onset OR emerg* OR develop*) AND psychosis] for both longitudinal and intervention CHR-P studies. Clinical knowledge was used to a priori select predictors: age, gender, CHR-P subgroup, the severity of attenuated positive psychotic symptoms, the severity of attenuated negative psychotic symptoms, and level of functioning at baseline. The model, thus, developed was validated with an extended form of internal validation.Results: Fifteen of the 43 studies identified agreed to share IPD, for a total sample size of 1,676. There was a high level of heterogeneity between the CHR-P studies with regard to inclusion criteria, type of assessment instruments, transition criteria, preventive treatment offered. The internally validated prognostic performance of the model was higher than chance but only moderate [Harrell’s C-statistic 0.655, 95% confidence interval (CIs), 0.627–0.682].Conclusion: This is the first IPD-MA conducted in the largest samples of CHR-P ever collected to date. An individualized prognostic model based on clinical predictors available in clinical routine was developed and internally validated, reaching only moderate prognostic performance. Although personalized risk prediction is of great value in the clinical practice, future developments are essential, including the refinement of the prognostic model and its external validation. However, because of the current high diagnostic, prognostic, and therapeutic heterogeneity of CHR-P studies, IPD-MAs in this population may have an limited intrinsic power to deliver robust prognostic models.

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