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

Benjamin Ian Perry, Emanuele Osimo, Pavan Mallikarjun, Rachel Upthegrove, Jesus Perez, Jan Stochl, Stan Zammit, Oliver Howes, Peter Jones, Golam Khandaker

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Background: Young people with psychosis are at high risk of developing cardiometabolic disorders. However, a suitable cardiometabolic risk prediction algorithm for this group is lacking. Therefore, we aimed to develop and externally validate a cardiometabolic risk prediction algorithm tailored for young people (aged 16-35 years) with psychosis.

Methods: We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) for young people (16-35y) with psychosis. From commonly recorded information, We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up-to six-year risk of incident metabolic syndrome 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, high-density lipoprotein and triglycerides) and a partial-model excluding biochemical results. PsyMetRiC was developed using data from two UK psychosis early-intervention services (EIS) (n=651); and externally validated in another EIS (n=510). Additionally, sensitivity analysis was conducted in 505 birth cohort participants (18y) who were at risk of developing psychosis. Algorithm performance was assessed primarily via discrimination (c-statistic) and calibration (calibration plots). We conducted decision curve analysis, and produced an online data-visualisation app.

Outcomes: PsyMetRiC performed well at internal (full-model: C=0·80, 95% C.I., 0·74-0·86; partial-model: C=0·79, 95% C.I., 0·73-0·84) and external validation (full-model: C=0·75, 95% C.I., 0·69-0·80; partial-model: C=0·74, 95% C.I., 0·67-0·79); calibration plots were adequate. At a cut-off score of 0·18, PsyMetRiC improved net benefit by 7·95% (sensitivity=0·75, 95% C.I., 0·66-0·82; specificity=0·74, 95% C.I., 0·71-0·78), 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/mortality, in young people with psychosis. PsyMetRiC has the potential to become a valuable resource for EIS clinicians and enable personalized, informed healthcare decisions regarding choice of antipsychotic medication and lifestyle interventions.
Original languageEnglish
Pages (from-to)589-598
JournalThe Lancet Psychiatry
Issue number7
Early online date1 Jun 2021
Publication statusE-pub ahead of print - 1 Jun 2021

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry


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