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Who needs AESOP ? Predicting long‐term readmission rates from routine Early Intervention team discharge information
Author(s) -
Taylor Matthew J.,
Pena Tania Blanco,
PerezIglesias Rocio
Publication year - 2018
Publication title -
early intervention in psychiatry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.087
H-Index - 45
eISSN - 1751-7893
pISSN - 1751-7885
DOI - 10.1111/eip.12413
Subject(s) - intervention (counseling) , term (time) , psychology , medicine , nursing , quantum mechanics , physics
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.