Who needs AESOP? Predicting long term readmission rates from routine EI team discharge information

Matthew John Taylor; Tania Blanco Pena; Rocio Perez-Iglesias

doi:10.1111/eip.12413

Who needs AESOP? Predicting long term readmission rates from routine EI team discharge information

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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 four or more. Model predictions for ten 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 longer-term outcomes matching those observed in formal research. This low-cost approach could allow EI services to develop locale-specific prognostic information.

Original language	English
Number of pages	3
Journal	Early Intervention in Psychiatry
Early online date	26 Jan 2017
DOIs	https://doi.org/10.1111/eip.12413
Publication status	E-pub ahead of print - 26 Jan 2017

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Cite this

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title = "Who needs AESOP? Predicting long term readmission rates from routine EI team discharge information",

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 four or more. Model predictions for ten 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 longer-term outcomes matching those observed in formal research. This low-cost approach could allow EI services to develop locale-specific prognostic information.",

author = "Taylor, \{Matthew John\} and \{Blanco Pena\}, Tania and Rocio Perez-Iglesias",

year = "2017",

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doi = "10.1111/eip.12413",

language = "English",

journal = "Early Intervention in Psychiatry",

issn = "1751-7893",

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TY - JOUR

T1 - Who needs AESOP? Predicting long term readmission rates from routine EI team discharge information

AU - Taylor, Matthew John

AU - Blanco Pena , Tania

AU - Perez-Iglesias, Rocio

PY - 2017/1/26

Y1 - 2017/1/26

N2 - 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 four or more. Model predictions for ten 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 longer-term outcomes matching those observed in formal research. This low-cost approach could allow EI services to develop locale-specific prognostic information.

AB - 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 four or more. Model predictions for ten 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 longer-term outcomes matching those observed in formal research. This low-cost approach could allow EI services to develop locale-specific prognostic information.

U2 - 10.1111/eip.12413

DO - 10.1111/eip.12413

M3 - Article

SN - 1751-7893

JO - Early Intervention in Psychiatry

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