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An International External Validation and Revision of the PsyMetRiC Cardiometabolic Risk Prediction Algorithm for Young People with Psychosis

Published online by Cambridge University Press:  01 September 2022

B. Perry*
Affiliation:
University of Cambridge, Dept Of Psychiatry, Cambridge, United Kingdom
F. Vandenberghe
Affiliation:
Lausanne University Hospital, Chuv, Lausanne, Switzerland
E.F. Osimo
Affiliation:
University of Cambridge, Dept Of Psychiatry, Cambridge, United Kingdom Imperial College London, Institute Of Clinical Sciences, London, United Kingdom
C. Grosu
Affiliation:
Lausanne University Hospital, Chuv, Lausanne, Switzerland
M. Piras
Affiliation:
Lausanne University Hospital, Chuv, Lausanne, Switzerland
P. Jones
Affiliation:
University of Cambridge, Dept Of Psychiatry, Cambridge, United Kingdom
P. Mallikarjun
Affiliation:
University of Birmingham, Institute Of Clinical Sciences, Birmingham, United Kingdom
J. Stochl
Affiliation:
University of Cambridge, Dept Of Psychiatry, Cambridge, United Kingdom
R. Upthegrove
Affiliation:
University of Birmingham, Institute Of Clinical Sciences, Birmingham, United Kingdom
G. Khandaker
Affiliation:
University of Cambridge, Dept Of Psychiatry, Cambridge, United Kingdom
C. Eap
Affiliation:
Lausanne University Hospital, Chuv, Lausanne, Switzerland
*
*Corresponding author.

Abstract

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Introduction

The comorbidity between cardiometabolic and psychotic disorders develops early. This is a crucial window of opportunity to reduce excess morbidity and mortality. Recently, a cardiometabolic risk prediction algorithm for young people with psychosis, the psychosis metabolic risk calculator (PsyMetRiC) was developed and externally validated in the UK. However, its international transportability is unknown.

Objectives

We performed the first international validation study of PsyMetRiC in Lausanne, Switzerland, and examined whether additional variables (clinical and/or genetic) may improve the predictive performance of the algorithm

Methods

We included people aged 16-35y with psychosis from the PsyMetab cohort, who did not have MetS at baseline, and who had 1-6y follow-up data. The PsyMetRiC partial (age, sex, ethnicity, body mass index, smoking status, and prescription of a metabolically-active antipsychotic) and full (also including high-density lipoprotein and triglycerides) algorithms were applied. Predictive performance was assessed using measures of discrimination (C-statistic) and calibration (calibration plots). Recalibration steps included refitting the intercept and/or slope if necessary. Additional variables (e.g. speed of weight gain, polygenic risk scores) were added to the model and predictive performance was reassessed.

Results

We included 545 participants. The discrimination performance of both PsyMetRiC algorithms was good (C>0.75), and calibration plots showed good agreement between observed and predicted risk. Additional analyses to be conducted.

Conclusions

PsyMetRiC is likely to be generalizable for use in Switzerland, suggesting that PsyMetRiC may also be suitable for use in other European populations. While additional international validations are required, these findings are an encouraging step toward an international cardiometabolic risk prediction algorithm for young people with psychosis.

Disclosure

No significant relationships.

Type
Abstract
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the European Psychiatric Association
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