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dc.contributor.authorLeighton, Samuel P
dc.contributor.authorKrishnadas, Rajeev
dc.contributor.authorUpthegrove, Rachel
dc.contributor.authorMarwaha, Steven
dc.contributor.authorSteyerberg, Ewout W
dc.contributor.authorGkoutos, Georgios V
dc.contributor.authorBroome, Matthew R
dc.contributor.authorLiddle, Peter F
dc.contributor.authorEverard, Linda
dc.contributor.authorSingh, Swaran P
dc.contributor.authorFreemantle, Nicholas
dc.contributor.authorFowler, David
dc.contributor.authorJones, Peter B
dc.contributor.authorSharma, Vimal
dc.contributor.authorMurray, Robin
dc.contributor.authorWykes, Til
dc.contributor.authorDrake, Richard J
dc.contributor.authorBuchan, Iain
dc.contributor.authorRogers, Simon
dc.contributor.authorCavanagh, Jonathan
dc.contributor.authorLewis, Shon W
dc.contributor.authorBirchwood, Max
dc.contributor.authorMallikarjun, Pavan K
dc.date.accessioned2021-10-29T02:12:50Z
dc.date.available2021-10-29T02:12:50Z
dc.date.issued2021-01-01
dc.identifier.otherPMC8458108
dc.identifier.other34568827
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330023
dc.description.abstractPsychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom nonremission in first-episode psychosis. Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 and 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 and 2009 from a further 11 English early intervention services. The one-year nonremission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for nonremission, which was externally validated. The prediction model showed good discrimination C-statistic of 0.73 (0.71, 0.75) and adequate calibration with intercept alpha of 0.12 (0.02, 0.22) and slope beta of 0.98 (0.85, 1.11). Our model improved the net-benefit by 15% at a risk threshold of 50% compared to the strategy of treating all, equivalent to 15 more detected nonremitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases. Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of nonremission at initial clinical contact.
dc.languageeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2632-7899
dc.sourcenlmid: 101770329
dc.subjectSchizophrenia
dc.subjectPrognosis
dc.subjectLogistic regression
dc.subjectearly intervention
dc.subjectPsychotic Disorders
dc.subjectPrecision Medicine
dc.titleDevelopment and Validation of a Nonremission Risk Prediction Model in First-Episode Psychosis: An Analysis of 2 Longitudinal Studies.
dc.typeArticle
dc.date.updated2021-10-29T02:12:49Z
prism.issueIdentifier1
prism.publicationNameSchizophrenia bulletin open
prism.volume2
dc.identifier.doi10.17863/CAM.77467
rioxxterms.versionofrecord10.1093/schizbullopen/sgab041
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidGkoutos, Georgios V [0000-0002-2061-091X]
dc.contributor.orcidDrake, Richard J [0000-0003-0220-4835]
dc.contributor.orcidBuchan, Iain [0000-0003-3392-1650]
dc.contributor.orcidRogers, Simon [0000-0003-3578-4477]
pubs.funder-project-idUK Department of Health (RDD/ARF2)
pubs.funder-project-idNational Institute of Health Research (RP-PG-0109-10074)
pubs.funder-project-idUK Medical Research Council and Department of Health (G0300610)
pubs.funder-project-idChief Scientist Office, Scotland (CAF/19/04)
pubs.funder-project-idChief Scientist Office (CAF/19/04)
pubs.funder-project-idWellcome Trust (104025/Z/14/Z)


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International