Disclosures: Perry reports receiving a fellowship grant from the National Institute for Health Research during the conduct of this study. Please see the study for all other authors’ relevant financial disclosures.
June 04, 2021
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Algorithm predicts risk for incident metabolic syndrome in young people with psychosis

Disclosures: Perry reports receiving a fellowship grant from the National Institute for Health Research during the conduct of this study. Please see the study for all other authors’ relevant financial disclosures.
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Researchers have developed an algorithm tailored to age that can predict the risk for incident metabolic syndrome among young people with psychosis, according to study results published in The Lancet Psychiatry.

“A recent systematic review explored the suitability of existing cardiometabolic risk prediction algorithms for young people with psychosis,” Benjamin I. Perry, MRCPsych, of the department of psychiatry at the University of Cambridge in the U.K., and colleagues wrote. “However, all algorithms were developed in samples of adults with a mean age across included studies of 50.5 years, and no studies included participants younger than 35 years. Most included studies did not include relevant predictors, such as antipsychotic medication, so the authors of the review concluded that none are likely to be suitable for young people with psychosis.”

infographic showing percentage of metabolic syndrome cases detected
Infographic data derived from: Perry BI, et al. Lancet Psychiatry. 2021;doi:10.1016/S2215-0366(21)00114-0.

Moreover, an exploratory analysis that accompanied the prior systematic review demonstrated that existing algorithms significantly underpredict cardiometabolic risk among young people with or at risk for developing psychosis.

In the current study, Perry and colleagues developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk for incident metabolic syndrome among young people aged 16 to 35 years with psychosis using data commonly recorded at baseline. They used the forced entry method to develop a full model that included age, sex, ethnicity, BMI, smoking status, prescription of a metabolically active antipsychotic HDL concentration and triglyceride concentration, as well as a partial model that excluded biochemical results. Further, they used data from two U.K. psychosis early intervention services between Jan. 1, 2013, and Nov. 4, 2020, to develop PsyMetRiC and externally validated it in another U.K. early intervention service between Jan. 1, 2013, and Nov. 4, 2020. They conducted a sensitivity analysis in U.K. birth cohort participants aged 18 years who were at risk for developing psychosis.

The researchers included 651 patients in the development samples, 510 in the validation sample and 505 in the sensitivity analysis sample. Results showed a high level of performance for PsyMetRiC at internal and external validation. The researchers noted “good” calibration of the full model but found evidence of slight miscalibration of the partial model. PsyMetRiC improved net benefit by 7.95% with a sensitivity of 75% and a specificity of 74% at a cutoff score of 0.18. This improvement was equivalent to detecting an additional 47% of metabolic syndrome cases.

“PsyMetRiC has the potential to become a valuable resource for health-care professionals working in [early intervention services] by aiding the informed choice of antipsychotic medication, prescription of cardioprotective drugs and non-pharmacological interventions, including lifestyle adjustments to prevent the future development of cardiometabolic comorbidities and consequent years of life lost,” Perry and colleagues wrote.