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

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.

Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis

“…Ethnicity, smoking, and BMI are among the most commonly included predictors in existing algorithms 8 and are well known contributors to cardiometabolic risk, 19 so we included them in PsyMetRiC. Sex is also frequently considered in existing algorithms, 8 and we included it in PsyMetRiC. We found that male sex was a risk factor for incident metabolic syndrome, which aligns with meta-analytic reports that male sex is a risk factor for antipsychotic-induced metabolic dysfunction.…”

“…Ethnicity, smoking, and BMI are among the most commonly included predictors in existing algorithms 8 and are well known contributors to cardiometabolic risk, 19 so we included them in PsyMetRiC. Sex is also frequently considered in existing algorithms, 8 and we included it in PsyMetRiC.…”

“…In the general population, cardiometabolic risk prediction algorithms such as QRISK3 7 are commonly used to predict CVD risk from baseline demographic, lifestyle, and clinical information, to identify higher-risk individuals for tailored interventions. A recent systematic review 8 explored the suitability of existing cardiometabolic risk prediction algorithms for young people with psychosis. However, all algorithms were developed in samples of adults with a mean age across included studies of 50·5 years, and no studies included participants younger than 35 years.…”

“…Most included studies did not include relevant predictors such as antipsychotic medication, so the authors of the review concluded that none are likely to be suitable for young people with psychosis. 8 Furthermore, an accompanying exploratory analysis found that existing algorithms significantly underpredict cardiometabolic risk in young people with or at risk of developing psychosis. 8 …”

“… 8 Furthermore, an accompanying exploratory analysis found that existing algorithms significantly underpredict cardiometabolic risk in young people with or at risk of developing psychosis. 8 …”