TY - JOUR
T1 - Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC)
T2 - a cardiometabolic risk prediction algorithm for young people with psychosis
AU - Perry, Benjamin Ian
AU - Osimo, Emanuele
AU - Mallikarjun, Pavan
AU - Upthegrove, Rachel
AU - Perez, Jesus
AU - Stochl, Jan
AU - Zammit, Stan
AU - Howes, Oliver
AU - Jones, Peter
AU - Khandaker, Golam
PY - 2021/6/1
Y1 - 2021/6/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85108114706&partnerID=8YFLogxK
U2 - 10.1016/S2215-0366(21)00114-0
DO - 10.1016/S2215-0366(21)00114-0
M3 - Article
SN - 2215-0366
VL - 8
SP - 589
EP - 598
JO - The Lancet Psychiatry
JF - The Lancet Psychiatry
IS - 7
ER -