Abstract
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. 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 18years 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. 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. 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.
Highlights
Cardiometabolic disorders broadly include cardiovascular diseases (CVD), disorders of adiposity such as obesity and disorders of glucose-insulin homeostasis such as type 2 diabetes mellitus (T2DM) [1]
We performed a systematic review of cardiometabolic risk prediction algorithms developed either for the general or psychiatric populations and considered their potential suitability for young people with psychosis
We used data from a sample of relatively young adults to first explore whether existing cardiometabolic risk prediction algorithms may be suitable for young people with or at risk of psychosis and second to explore the impact of the manner in which age is weighted in existing cardiometabolic risk prediction algorithms
Summary
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. A large number of cardiometabolic risk prediction algorithms have been developed, but only three algorithms (QRISK3, QDiabetes and PRIMROSE) included psychiatric predictors. From our exploratory analysis, we show that all three algorithms may underpredict cardiometabolic risk in young adults with or at risk of developing psychosis, which may be a function of the way age is modelled in the algorithms. No existing cardiometabolic risk prediction algorithm can be recommended for use in young adults with or at risk of developing psychosis, yet the population remains at higher risk of cardiometabolic disorders than their age-matched peers
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