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Prediction of transition to psychosis in patients with a clinical high risk for psychosis: a systematic review of methodology and reporting

Published online by Cambridge University Press:  16 January 2017

E. Studerus ,
A. Ramyead  and
A. Riecher-Rössler
E. Studerus
Affiliation:
University of Basel Psychiatric Hospital, Center for Gender Research and Early Detection, Basel, Switzerland
A. Ramyead
Affiliation:
Department of Psychiatry, Weill Institute for Neurosciences, University of California (UCSF), San Francisco, CA, USA
A. Riecher-Rössler*
Affiliation:
University of Basel Psychiatric Hospital, Center for Gender Research and Early Detection, Basel, Switzerland
*
*Address for correspondence: A. Riecher-Rössler, M.D., University of Basel Psychiatric Clinics, Center for Gender Research and Early Detection, Kornhausgasse 7, CH-4051 Basel, Switzerland. (Email: [email protected])
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Abstract

Background

To enhance indicated prevention in patients with a clinical high risk (CHR) for psychosis, recent research efforts have been increasingly directed towards estimating the risk of developing psychosis on an individual level using multivariable clinical prediction models. The aim of this study was to systematically review the methodological quality and reporting of studies developing or validating such models.

Method

A systematic literature search was carried out (up to 14 March 2016) to find all studies that developed or validated a clinical prediction model predicting the transition to psychosis in CHR patients. Data were extracted using a comprehensive item list which was based on current methodological recommendations.

Results

A total of 91 studies met the inclusion criteria. None of the retrieved studies performed a true external validation of an existing model. Only three studies (3.5%) had an event per variable ratio of at least 10, which is the recommended minimum to avoid overfitting. Internal validation was performed in only 14 studies (15%) and seven of these used biased internal validation strategies. Other frequently observed modeling approaches not recommended by methodologists included univariable screening of candidate predictors, stepwise variable selection, categorization of continuous variables, and poor handling and reporting of missing data.

Conclusions

Our systematic review revealed that poor methods and reporting are widespread in prediction of psychosis research. Since most studies relied on small sample sizes, did not perform internal or external cross-validation, and used poor model development strategies, most published models are probably overfitted and their reported predictive accuracy is likely to be overoptimistic.

Keywords

Clinical high riskpredictionprognostic modelspsychosisschizophrenia
Type
Review Article
Information
Psychological Medicine , Volume 47 , Issue 7 , May 2017 , pp. 1163 - 1178
Copyright
Copyright © Cambridge University Press 2017 

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Table S1

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