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dc.creatorPerry, Benjamin I.es
dc.creatorVandenberghe, Frederikes
dc.creatorGarrido Torres, Nathaliaes
dc.creatorOsimo, Emanuele F.es
dc.creatorPiras, Mariannaes
dc.creatorVázquez-Bourgon, Javieres
dc.creatorRuiz Veguilla, Migueles
dc.creatorCrespo Facorro, Benedictoes
dc.date.accessioned2023-05-29T14:46:29Z
dc.date.available2023-05-29T14:46:29Z
dc.date.issued2022-08-19
dc.identifier.citationPerry, B.I., Vandenberghe, F., Garrido Torres, N., Osimo, E.F., Piras, M., Vázquez-Bourgon, J.,...,Crespo Facorro, B. (2022). The psychosis metabolic risk calculator (PsyMetRiC) for young people with psychosis: International external validation and site-specific recalibration in two independent European samples. The Lancet Regional Health Europe, 22, 100493. https://doi.org/10.1016/j.lanepe.2022.100493.
dc.identifier.issn2666-7762es
dc.identifier.urihttps://hdl.handle.net/11441/146754
dc.description.abstractBackground 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.es
dc.formatapplication/pdfes
dc.format.extent14 p.es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofThe Lancet Regional Health Europe, 22, 100493.
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPsychosises
dc.subjectEarly Interventiones
dc.subjectRisk Prediction Algorithmes
dc.subjectMetabolic Syndromees
dc.subjectInternational Validationes
dc.subjectPsyMetabes
dc.subjectPAFIPes
dc.titleThe psychosis metabolic risk calculator (PsyMetRiC) for young people with psychosis: International external validation and site-specific recalibration in two independent European sampleses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Psiquiatríaes
dc.relation.projectIDBRC-1215-20014es
dc.relation.projectID201486/Z/16/Zes
dc.relation.projectID320030-120686es
dc.relation.projectID324730- 144064es
dc.relation.projectID320030-173211es
dc.relation.projectIDCM20/00015es
dc.relation.projectIDFIS00/3095es
dc.relation.projectIDPI020499es
dc.relation.projectIDPI050427es
dc.relation.projectIDPI060507es
dc.relation.projectIDINT/A21/10es
dc.relation.projectIDINT/A20/04es
dc.relation.projectIDA1-0055-2020es
dc.relation.projectIDA1-0005-2021es
dc.relation.projectID2005-0308007es
dc.relation.projectIDA/02/07es
dc.relation.projectIDAPI07/011es
dc.relation.projectIDSAF2016-76046-Res
dc.relation.projectIDSAF2013-46292-Res
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2666776222001892?via%3Dihubes
dc.identifier.doi10.1016/j.lanepe.2022.100493es
dc.journaltitleThe Lancet Regional Health Europees
dc.publication.volumen22es
dc.publication.initialPage100493es
dc.contributor.funderJunta de Andalucíaes
dc.contributor.funderInstituto de Salud Carlos IIIes
dc.contributor.funderFundación Marqués de Valdecillaes
dc.contributor.funderIDIVALes
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO). Españaes
dc.contributor.funderEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)es
dc.contributor.funderNIHR Cambridge Biomedical Research Centrees
dc.contributor.funderSENY Fundacion Researches
dc.contributor.funderSwiss National Research Foundationes
dc.contributor.funderWellcome Trustes

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