Who needs AESOP? Predicting long‐term readmission rates from routine Early Intervention team discharge information. Issue 2 (26th January 2017)

• Record Type: Journal Article
• Authors: Taylor, Matthew J.; Pena, Tania Blanco; Perez‐Iglesias, Rocio
• Is Part Of: Early intervention in psychiatry. Volume 12:Issue 2(2018:Apr.)
• Page Start: 240
• Page End: 242
• Publication Date: 2017-01-26
• Subjects: psychotic disorders -- patient readmission -- prognosis -- recurrence -- statistical models; Mental health -- Periodicals; Psychiatry -- Periodicals; Psychiatry -- Research -- Periodicals; Mental illness -- Prevention -- Research -- Periodicals; Mental illness -- Treatment -- Research -- Periodicals; 616.89
• DOI: 10.1111/eip.12413
• Languages: English
• ISSNs: 1751-7885

Abstract

Abstract : Aim: Prognosis following early psychosis is highly variable. Long‐term prognostic information from research studies is available in only a few areas. We sought to understand how well routine discharge information allows prediction of long‐term readmission prognosis. Methods: We reviewed the records of 239 people leaving Early Intervention services, after an average of 2.5 years, and counted the number of relapses. The distribution was modelled and extrapolated to a predicted 10 year outcome. Model predictions were compared with published data. Results: Numbers of relapses varied substantially, with 59% having no relapses before discharge, and 5% having 4 or more. Model predictions for 10‐year outcome were close to the observed data. Conclusions: A simple model can describe the distribution of numbers of relapses among people discharged from EI services, and predict long‐term outcomes matching those observed in formal research. This low‐cost approach could allow EI services to develop locale‐specific prognostic information.