Who needs AESOP? predicting long-term readmission rates from routine Early Intervention team discharge information
<p><strong>AIM:</strong> 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 progno...
| Autors principals: | , , |
|---|---|
| Format: | Journal article |
| Idioma: | English |
| Publicat: |
Wiley
2017
|
| _version_ | 1826261382442516480 |
|---|---|
| author | Taylor, MJ Pena, TB Perez-Iglesias, R |
| author_facet | Taylor, MJ Pena, TB Perez-Iglesias, R |
| author_sort | Taylor, MJ |
| collection | OXFORD |
| description | <p><strong>AIM:</strong> 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.</p>
<p><strong>METHODS:</strong> 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.</p>
<p><strong>RESULTS:</strong> 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.</p>
<p><strong>CONCLUSIONS:</strong> 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.</p> |
| first_indexed | 2024-03-06T19:20:32Z |
| format | Journal article |
| id | oxford-uuid:19ee99f4-ab31-442b-b7d2-088d07ea3adb |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:20:32Z |
| publishDate | 2017 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:19ee99f4-ab31-442b-b7d2-088d07ea3adb2022-03-26T10:51:50ZWho needs AESOP? predicting long-term readmission rates from routine Early Intervention team discharge informationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:19ee99f4-ab31-442b-b7d2-088d07ea3adbEnglishSymplectic ElementsWiley2017Taylor, MJPena, TBPerez-Iglesias, R<p><strong>AIM:</strong> 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.</p> <p><strong>METHODS:</strong> 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.</p> <p><strong>RESULTS:</strong> 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.</p> <p><strong>CONCLUSIONS:</strong> 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.</p> |
| spellingShingle | Taylor, MJ Pena, TB Perez-Iglesias, R Who needs AESOP? predicting long-term readmission rates from routine Early Intervention team discharge information |
| title | Who needs AESOP? predicting long-term readmission rates from routine Early Intervention team discharge information |
| title_full | Who needs AESOP? predicting long-term readmission rates from routine Early Intervention team discharge information |
| title_fullStr | Who needs AESOP? predicting long-term readmission rates from routine Early Intervention team discharge information |
| title_full_unstemmed | Who needs AESOP? predicting long-term readmission rates from routine Early Intervention team discharge information |
| title_short | Who needs AESOP? predicting long-term readmission rates from routine Early Intervention team discharge information |
| title_sort | who needs aesop predicting long term readmission rates from routine early intervention team discharge information |
| work_keys_str_mv | AT taylormj whoneedsaesoppredictinglongtermreadmissionratesfromroutineearlyinterventionteamdischargeinformation AT penatb whoneedsaesoppredictinglongtermreadmissionratesfromroutineearlyinterventionteamdischargeinformation AT pereziglesiasr whoneedsaesoppredictinglongtermreadmissionratesfromroutineearlyinterventionteamdischargeinformation |