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

Abstract

Summary Background Young people with psychosis are at high risk of developing cardiometabolic disorders; however, there is no suitable cardiometabolic risk prediction algorithm for this group. We aimed to develop and externally validate a cardiometabolic risk prediction algorithm for young people with psychosis. Methods We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk of incident metabolic syndrome in young people (aged 16–35 years) with psychosis from commonly recorded information at baseline. We developed two PsyMetRiC versions using the forced entry method: a full model (including age, sex, ethnicity, body-mass index, smoking status, prescription of a metabolically active antipsychotic medication, HDL concentration, and triglyceride concentration) and a partial model excluding biochemical results. PsyMetRiC was developed using data from two UK psychosis early intervention services (Jan 1, 2013, to Nov 4, 2020) and externally validated in another UK early intervention service (Jan 1, 2012, to June 3, 2020). A sensitivity analysis was done in UK birth cohort participants (aged 18 years) who were at risk of developing psychosis. Algorithm performance was assessed primarily via discrimination (C statistic) and calibration (calibration plots). We did a decision curve analysis and produced an online data-visualisation app. Findings 651 patients were included in the development samples, 510 in the validation sample, and 505 in the sensitivity analysis sample. PsyMetRiC performed well at internal (full model: C 0·80, 95% CI 0·74–0·86; partial model: 0·79, 0·73–0·84) and external validation (full model: 0·75, 0·69–0·80; and partial model: 0·74, 0·67–0·79). Calibration of the full model was good, but there was evidence of slight miscalibration of the partial model. At a cutoff score of 0·18, in the full model PsyMetRiC improved net benefit by 7·95% (sensitivity 75%, 95% CI 66–82; specificity 74%, 71–78), equivalent to detecting an additional 47% of metabolic syndrome cases. Interpretation We have developed an age-appropriate algorithm to predict the risk of incident metabolic syndrome, a precursor of cardiometabolic morbidity and mortality, in young people with psychosis. PsyMetRiC has the potential to become a valuable resource for early intervention service clinicians and could enable personalised, informed health-care decisions regarding choice of antipsychotic medication and lifestyle interventions. Funding National Institute for Health Research and Wellcome Trust.

Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England

“…However, comparison of the predicted sensitivities and specificities from these prediction models indicates that the improvement in detection of depression/CMDs relative to the primary care record data based on diagnosis, symptoms and treatment is minimal. We also acknowledge that these prediction models were only validated internally using ALSPAC data; before being used more widely, these models should be calibrated and validated externally using independent datasets from different populations (see, eg, Perry et al 48 ).…”

Cardiovascular disease risk in people with severe mental disorders: an update and call for action

“…Cardiovascular risk scores validated for the general population do not appear to be suitable for young people with SMDs and may underestimate their cardiometabolic risk [70]. Thus, a prediction model tailored to young people with psychosis is developed that integrates established risk factors (age, sex, BMI, smoking status and blood lipid profile) along with antipsychotics with metabolic side effects [71 ▪▪ ]. The results indicate that the risk calculator can reliably predict metabolic syndrome, an important CVD risk factor, in young people with psychosis and those at risk of developing psychosis [71 ▪▪ ].…”

“…Thus, a prediction model tailored to young people with psychosis is developed that integrates established risk factors (age, sex, BMI, smoking status and blood lipid profile) along with antipsychotics with metabolic side effects [71 ▪▪ ]. The results indicate that the risk calculator can reliably predict metabolic syndrome, an important CVD risk factor, in young people with psychosis and those at risk of developing psychosis [71 ▪▪ ]. Still, further validation in larger samples is necessary before such tools can be implemented in the clinic to improve management of CVD [71 ▪▪ ].…”

“…Increasing research suggests that prediction tools including genotyping help identify people who are more likely to benefit from certain pharmacologic and nonpharmacological interventions [77 ▪▪ ]. Thus, improving the current CVD algorithms for people with SMDs is likely to promote the management of their cardiovascular health by highlighting modifiable risk factors and guide the selection of medication and lifestyle interventions [71 ▪▪ ].…”

Interaction Between Race, Ethnicity, Severe Mental Illness, and Cardiovascular Disease

“…It is notable that cardiovascular risk screening tools in routine practice, such as the QRisk3 tool, may miss risk in younger individuals, even after inclusion of antipsychotic medication as a factor, and latterly cardiometabolic risk predictor tools, especially for people with first‐episode psychosis, have been developed to address this unmet need. 39 …”