精神病の若者の身体健康リスクを予測するツール「PsyMetRiC」を開発（PsyMetRiC – a new tool to predict physical health risks in young people with psychosis）

2026-03-12 バーミンガム大学

＜関連情報＞

• https://www.birmingham.ac.uk/news/2026/psymetric-a-new-tool-to-predict-physical-health-risks-in-young-people-with-psychosis
• https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(25)00398-0/fulltext
• https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(21)00114-0/fulltext

英国における精神病スペクトラム障害を有する若年者を対象とした心血管代謝予測モデル（PsyMetRiC 2.0）：後向き多コホート臨床予測モデル研究 Cardiometabolic prediction models for young people with psychosis spectrum disorders in the UK (PsyMetRiC 2.0): a retrospective, multicohort clinical prediction model study

Benjamin I Perry, PhD ∙ Emanuele F Osimo, PhD ∙ Shuqing Si, PhD ∙ Karla V B Hitchins, MSc ∙ Clara Lewis, BSc ∙ Ben Laws, PhD ∙ et al.
Lancet Psychiatry  Published: March 11, 2026
DOI:https://doi.org/10.1016/S2215-0366(25)00398-0

Summary

Background

Young people with psychosis spectrum disorders are at a high risk of cardiometabolic morbidity and subsequent premature mortality, but there are no accurate clinic-ready prediction models for this group. We aimed to collaboratively refine, extend, and validate the Psychosis Metabolic Risk Calculator (PsyMetRiC) prediction models for accuracy, clinical usefulness, and acceptability, and to translate the models into a regulated, clinically available medical device.

Methods

In this retrospective, multicohort clinical prediction model study, we used primary care (Clinical Practice Research Datalink and QResearch) and secondary care (South London and Maudsley NHS Foundation Trust) datasets. Individuals from primary care sources were aged 16–35 years when they received a first recorded diagnosis of a psychosis-spectrum disorder between Jan 1, 2005, and Dec 31, 2015, with follow-up to Dec 31, 2020. Individuals from the secondary care source were enrolled in the psychosis early intervention service between Jan 1, 2012, and Dec 31, 2024. We developed models for a binary outcome of metabolic syndrome within 1–6 years using logistic regression; a time-to-event outcome of type 2 diabetes within 10 years using Weibull regression; and a binary outcome of clinically significant weight gain within 1 year using logistic regression. We revised existing predictors (hereafter referred to as the PsyMetRiC1 models) for finer detail and added new predictors: a family history of cardiometabolic disorder, antidepressant prescription, systolic blood pressure, and HbA_1C (hereafter PsyMetRiC2 models). Refinement and external validation were performed for metabolic syndrome models (PsyMetRiC1-MetS and PsyMetRiC2-MetS), and development and external validation were performed for the type 2 diabetes model (PsyMetRiC2-T2D). Development and internal validation were performed for the clinically significant weight gain model (PsyMetRiC2-WG), but external validation was not possible due to data availability. Partial versions without biochemical results were also developed for weight gain and metabolic syndrome models. We involved stakeholders including people with lived experience; and implemented the models in a web application compliant with regulatory standards in Great Britain.

Findings

In total, we included 25 850 individuals (male, n=13 614 [52·7%]; female, n=12 236 [47·3%]; White European, 16 445 [63·6%]; Black African or Caribbean, south Asian, mixed, and east Asian or other n=9405 [36·3%]; and mean age 26·7 years [SD=5·4]). For primary care, we included 3989 individuals for development and 4347 individuals for external validation of metabolic syndrome outcomes; and 9181 individuals for development and 7487 individuals for external validation of type 2 diabetes outcomes. For secondary care, we included 846 individuals for development and internal validation of weight gain outcomes. For metabolic syndrome, the performance of PsyMetRiC2-MetS at external validation was C=0·81 (95% CI 0·77–0·84) for the full model (with biochemical predictors) and C=0·79 (0·76–0·83) for the partial model (without biochemical predictors). For type 2 diabetes, discriminative performance at internal validation of PsyMetRiC2-T2D was C=0·86 (0·76–0·95) for the full model, and at external validation it was C=0·81 (0·71–0·88). For weight gain, discriminative performance at internal validation of PsyMetRiC2-WG was C=0·78 (0·73–0·82) for the full model and C=0·77 (0·72–0·80) for the partial model. Calibration plots were acceptable for all models. All models displayed evidence of clinical usefulness at all plausible thresholds. The PsyMetRiC web application is available at https://psymetric.app.

Interpretation

We developed prediction models for incident cardiometabolic disorders in young people with psychosis. The PsyMetRiC models are among the first in psychiatry to be available for routine clinical use. PsyMetRiC can support a shift toward collaborative, preventive physical health care for young people with psychosis.

Funding

National Institute for Health and Care Research.

精神病代謝リスク計算ツール（PsyMetRiC）の開発と外部検証：精神病を患う若年者のための心血管代謝リスク予測アルゴリズム Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis

Benjamin I Perry, MRCPsych ∙ Emanuele F Osimo, MRCPsych ∙ Prof Rachel Upthegrove, PhD ∙ Pavan K Mallikarjun, PhD ∙ Jessica Yorke, MBBS ∙ Jan Stochl, PhD ∙ et al.
Lancet Psychiatry  Published June 1, 2021
DOI:https://doi.org/10.1016/S2215-0366(21)00114-0

Summary

Background

Young 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.

Methods

We 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.

Findings

651 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.

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. 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.

Funding

National Institute for Health Research and Wellcome Trust.