Abstract
SummaryBackgroundYoung 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.MethodsWe 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.Findings651 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.InterpretationWe 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.FundingNational Institute for Health Research and Wellcome Trust.
Highlights
People with psychotic disorders such as schizophrenia have a life expectancy shortened by 10–15 years compared with the general population,[1] predominantly owing to a higher prevalence of physical conditions such as type 2 diabetes, obesity, and cardiovascular disease (CVD).[2]
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
Added value of this study We have developed and externally validated, to our knowledge, the first clinically useful and age-appropriate cardiometabolic risk prediction algorithm tailored for young people with psychosis— the Psychosis Metabolic Risk Calculator (PsyMetRiC)—using patient data from three geographically distinct UK National Health Service psychosis early intervention services
Summary
People with psychotic disorders such as schizophrenia have a life expectancy shortened by 10–15 years compared with the general population,[1] predominantly owing to a higher prevalence of physical conditions such as type 2 diabetes, obesity, and cardiovascular disease (CVD).[2] These comorbidities lead to a reduced quality of life and substantial health economic burden[3] and usually develop early in the course of the psychotic disorder. Prognostic risk prediction algorithms are a valuable means to encourage personalised, informed health-care decisions. Cardiometabolic risk prediction algorithms such as QRISK37 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.
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