Who needs AESOP? Predicting long‐term readmission rates from routine Early Intervention team discharge information

• Published in: Early intervention in psychiatry Vol. 12; no. 2; pp. 240 - 242
• Main Authors: Taylor, Matthew J., Pena, Tania Blanco, Perez‐Iglesias, Rocio
• Format: Journal Article
• Language: English
• Published: Melbourne Wiley Publishing Asia Pty Ltd 01.04.2018; Wiley Subscription Services, Inc
• Subjects: Clinical outcomes; Discharge; Early intervention; Early Medical Intervention; Female; Humans; Male; Mathematical models; Medical prognosis; Models, Statistical; Patient admissions; Patient Discharge; patient readmission; Patient Readmission - statistics & numerical data; Predictions; Prognosis; Psychosis; psychotic disorders; Psychotic Disorders - diagnosis; Recurrence; statistical models; recurrence; patient readmission; psychotic disorders; prognosis; statistical models
• ISSN: 1751-7885; 1751-7893
• DOI: 10.1111/eip.12413

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.