Abstract
Background: Cardiometabolic dysfunction is common in young people with psychosis. Recently, the Psychosis Metabolic Risk Calculator (PsyMetRiC) was developed and externally validated in the UK, predicting up-to six-year risk of metabolic syndrome (MetS) from routinely collected data. The full-model includes age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations; the partial-model excludes biochemical predictors.
Methods: To move toward a future internationally-useful tool, we externally validated PsyMetRiC in two independent European samples. We used data from the PsyMetab (Lausanne, Switzerland) and PAFIP (Cantabria, Spain) cohorts, including participants aged 16–35y without MetS at baseline who had 1–6y follow-up. Predictive performance was assessed primarily via discrimination (C-statistic), calibration (calibration plots), and decision curve analysis. Site-specific recalibration was considered.
Findings: We included 1024 participants (PsyMetab n=558, male=62%, outcome prevalence=19%, mean follow-up=2.48y; PAFIP n=466, male=65%, outcome prevalence=14%, mean follow-up=2.59y). Discrimination was better in the full- compared with partial-model (PsyMetab=full-model C=0.73, 95% C.I., 0.68–0.79, partial-model C=0.68, 95% C.I., 0.62–0.74; PAFIP=full-model C=0.72, 95% C.I., 0.66–0.78; partial-model C=0.66, 95% C.I., 0.60–0.71). As expected, calibration plots revealed varying degrees of miscalibration, which recovered following site-specific recalibration. PsyMetRiC showed net benefit in both new cohorts, more so after recalibration.
Interpretation: The study provides evidence of PsyMetRiC's generalizability in Western Europe, although further local and international validation studies are required. In future, PsyMetRiC could help clinicians internationally to identify young people with psychosis who are at higher cardiometabolic risk, so interventions can be directed effectively to reduce long-term morbidity and mortality.
Funding: NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); The Wellcome Trust (201486/Z/16/Z); Swiss National Research Foundation (320030-120686, 324730- 144064, and 320030-173211); The Carlos III Health Institute (CM20/00015, FIS00/3095, PI020499, PI050427, and PI060507); IDIVAL (INT/A21/10 and INT/A20/04); The Andalusian Regional Government (A1-0055-2020 and A1-0005-2021); SENY Fundacion Research (2005-0308007); Fundacion Marques de Valdecilla (A/02/07, API07/011); Ministry of Economy and Competitiveness and the European Fund for Regional Development (SAF2016-76046-R and SAF2013-46292-R).
For the Spanish and French translation of the abstract see Supplementary Materials section.
| Original language | English |
|---|---|
| Article number | 100493 |
| Number of pages | 14 |
| Journal | The Lancet Regional Health - Europe |
| Volume | 22 |
| Early online date | 19 Aug 2022 |
| DOIs | |
| Publication status | Published - Nov 2022 |
Bibliographical note
Funding Information:EFO acknowledges funding support from the Medical Research Council (MRC) and the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). This research was supported by the NIHR Cambridge Biomedical Research Centre ( BRC-1215-20014 ). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. GMK acknowledges funding support from the Wellcome Trust (Intermediate Clinical Fellowship; grant code: 201486/Z/16/Z ), The MQ: Transforming Mental Health (Data Science Award; grant code: MQDS17/40), the UK Medical Research Council (MICA: Mental Health Data Pathfinder; grant code: MC_PC_17213; and Therapeutic Target Validation in Mental Health; grant code: MR/S037675/1), and the BMA Foundation (J Moulton grant 2019). PBJ acknowledges funding from the MRC and MQ (as above), programmatic funding from NIHR (RP-PG- 0616-20003) and support from the Applied Research Collaboration East of England. RU acknowledges funding support from the NIHR (HTA grant code): 127700 and MRC (Therapeutic Target Validation in Mental Health grant code: MR/S037675/1). This work has been funded in part by the Swiss National Research Foundation (CE and PC: 320030-120686, 324730- 144064, and 320030-173211; CBE, PC and KJP: 320030_200602). NG-T acknowledges funding support from The Carlos III Health Institute (Rio Hortega contract: CM20/00015). JV-B acknowledges funding support from IDIVAL (grant codes: INT/A21/10 and INT/A20/04). MR-V acknowledges funding support from The Andalusian Regional Government (grant codes: A1-0055-2020 and A1-0005-2021). BC-F acknowledges the PAFIP researchers who have carried out a great number of outstanding investigations that have notably contributed to improving our knowledge in the field of early psychosis treatment. We would also like to thank the participants and their families for enrolling in these studies. The Santander (Spain) cohort was funded by the following grants to Dr Crespo-Facorro: Instituto de Salud Carlos III (grants FIS00/3095, PI020499, PI050427, and PI060507), Plan Nacional de Drogas Research (grant 2005-Orden sco/3246/2004), SENY Fundacion Research (grant 2005-0308007), Fundacion Marques de Valdecilla (grant A/02/07, API07/011) and Ministry of Economy and Competitiveness and the European Fund for Regional Development (grants SAF2016-76046-R and SAF2013-46292-R). The funding sources had no role in the writing of the manuscript or in the decision to submit it for publication.
Publisher Copyright:
© 2022 The Author(s)
Keywords
- Early Intervention
- International Validation
- Metabolic Syndrome
- PAFIP
- Psychosis
- PsyMetab
- Risk Prediction Algorithm
ASJC Scopus subject areas
- Internal Medicine
- Oncology
- Health Policy
