Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis.
Acta psychiatrica Scandinavica
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Perry, B., Upthegrove, R., Crawford, O., Jang, S., Lau, E., McGill, I., Carver, E., et al. (2020). Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis.. Acta psychiatrica Scandinavica, 142 (3), 215-232. https://doi.org/10.1111/acps.13212
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 18years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3, 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 7,820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high-risk of bias. Three studies (QDiabetes, QRISK3, 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 under-predict 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.
Humans, Cardiovascular Diseases, Risk Factors, Psychotic Disorders, Algorithms, Adolescent, Infant, Newborn
Wellcome Trust (201486/Z/16/Z)
Medical Research Council (MR/S037675/1)
MQ: Transforming Mental Health (MQDS17\40)
External DOI: https://doi.org/10.1111/acps.13212
This record's URL: https://www.repository.cam.ac.uk/handle/1810/307728
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