Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

Gonzalo Salazar De Pablo, Erich Studerus, Julio Vaquerizo-Serrano, Jessica Irving, Ana Catalan, Dominic Oliver, Helen Baldwin, Andrea Danese, Seena Fazel, Ewout W. Steyerberg, Daniel Stahl, Paolo Fusar-Poli*

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

52 Citations (Scopus)
13 Downloads (Pure)

Abstract

Background: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. Methods: PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. Findings: Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β =. 29, P =. 03) and diagnostic compared to prognostic (β =. 84, p <. 0001) and predictive (β =. 87, P =. 002) models were associated with increased accuracy. Interpretation: To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

Original languageEnglish
Pages (from-to)284-297
Number of pages14
JournalSchizophrenia Bulletin
Volume47
Issue number2
Early online date11 Sep 2020
DOIs
Publication statusPublished - 1 Mar 2021

Bibliographical note

Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

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