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dc.contributor.authorLiddle, Peter F.
dc.date.accessioned2021-10-14T11:42:57Z
dc.date.available2021-10-14T11:42:57Z
dc.date.issued2021
dc.identifier.citationLeighton, S. P., Krishnadas, R., Upthegrove, R., Marwaha, S., Steyerberg, E. W., Gkoutos, G. V., Broome, M. R., Liddle, P. F., Everard, L., Singh, S. P., et al. (2021). Development and validation of a nonremission risk prediction model in first-episode psychosis: An analysis of 2 longitudinal studies. Schizophrenia Bulletin Open, 2(1), pp.sgab041.en_US
dc.identifier.other10.1093/schizbullopen/sgab041
dc.identifier.urihttp://hdl.handle.net/20.500.12904/14919
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.description.urihttps://academic.oup.com/schizbullopen/article/2/1/sgab041/6360456en_US
dc.language.isoenen_US
dc.subjectSchizophreniaen_US
dc.subjectPsychotic disordersen_US
dc.titleDevelopment and validation of a nonremission risk prediction model in first-episode psychosis: An analysis of 2 longitudinal studiesen_US
dc.typeArticleen_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.versionNAen_US
rioxxterms.typeJournal Article/Reviewen_US
refterms.panelUnspecifieden_US
refterms.dateFirstOnline31/08/2021
html.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.
rioxxterms.funder.project94a427429a5bcfef7dd04c33360d80cden_US


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