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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Abstract

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
JournalThe Lancet Psychiatry
Early online date1 Jun 2021
DOIs
Publication statusE-pub ahead of print - 1 Jun 2021

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