Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice
Year: 2020
Type: review
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.
Source: Schizophrenia Bulletin
Institutions: King's College London, Hospital General Universitario Gregorio Marañón, Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, University of Basel, University of the Basque Country, BioCruces Health research Institute, Hospital de Basurto, National Health Service, South London and Maudsley NHS Foundation Trust, University of Oxford, Leiden University Medical Center, Erasmus MC, University of Pavia, NIHR Maudsley Biomedical Research Centre
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,981
Funders: Fundación Alicia Koplowitz, King’s College London, South London and Maudsley NHS Foundation Trust, Wellcome Trust, NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research, National Institute for Health and Care Research, Department of Health and Social Care, King's College London, Medical Research Council
OpenAlex ID: W3086470062