Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis

Research output: Contribution to journalReview articlepeer-review

Authors

  • B. I. Perry
  • O. Crawford
  • S. Jang
  • E. Lau
  • I. McGill
  • E. Carver
  • P. B. Jones
  • G. M. Khandaker

Abstract

Objective: Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis. Methods: We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance. Results: We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved. Conclusion: Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.

Bibliographic note

Funding Information: This report is independent research supported by the National Institute for Health Research (NIHR Doctoral Research Fellowship, Dr Benjamin Ian Perry, DRF-2018-11-ST2-018). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. GMK acknowledges funding support from the Wellcome Trust (Intermediate Clinical Fellowship; grant code: 201486/Z/16/Z); the BMA Foundation (J Moulton Grant 2019); the MQ: Transforming Mental Health (grant code: MQDS17/40 [with PBJ]); the Medical Research Council (grant code: MC_PC_17213) and the Medical Research Council (grant code: MR/S037675/1 [with RU]). PBJ receives grant support from the NIHR Applied Research Collaboration East of England. This publication is the work of the authors who will serve as guarantors for the contents of this paper. The authors report no conflicts of interest. Publisher Copyright: © 2020 The Authors. Acta Psychiatrica Scandinavica published by John Wiley & Sons Ltd Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

Details

Original languageEnglish
Pages (from-to)215-232
Number of pages18
JournalActa Psychiatrica Scandinavica
Volume142
Issue number3
Publication statusPublished - 12 Jul 2020

Keywords

  • algorithms, ALSPAC, cardiometabolic risk, prediction, psychosis, systematic review

ASJC Scopus subject areas