Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis: does one size fit all?
Abstract The heterogeneity in recovery outcomes for individuals with First Episode Psychosis (FEP) calls for a strong evidence base to inform practice at an individual level. Between 19–89% of young people with FEP have an incomplete recovery despite gold-standard evidence-based treatments, suggesti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2022-11-01
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| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-022-02256-7 |
| _version_ | 1811319585549320192 |
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| author | Siân Lowri Griffiths Paris Alexandros Lalousis Stephen J. Wood Rachel Upthegrove |
| author_facet | Siân Lowri Griffiths Paris Alexandros Lalousis Stephen J. Wood Rachel Upthegrove |
| author_sort | Siân Lowri Griffiths |
| collection | DOAJ |
| description | Abstract The heterogeneity in recovery outcomes for individuals with First Episode Psychosis (FEP) calls for a strong evidence base to inform practice at an individual level. Between 19–89% of young people with FEP have an incomplete recovery despite gold-standard evidence-based treatments, suggesting current service models, which adopt a ‘one-size fits all’ approach, may not be addressing the needs of many young people with psychosis. The lack of consistent terminology to define key concepts such as recovery and treatment resistance, the multidimensional nature of these concepts, and common comorbid symptoms are some of the challenges faced by the field in delineating heterogeneity in recovery outcomes. The lack of robust markers for incomplete recovery also results in potential delay in delivering prompt, and effective treatments to individuals at greatest risk. There is a clear need to adopt a stratified approach to care where interventions are targeted at subgroups of patients, and ultimately at the individual level. Novel machine learning, using large, representative data from a range of modalities, may aid in the parsing of heterogeneity, and provide greater precision and sophistication in identifying those on a pathway to incomplete recovery. |
| first_indexed | 2024-04-13T12:45:34Z |
| format | Article |
| id | doaj.art-6d7bf43b5fe540a29943d8dd1e51c324 |
| institution | Directory Open Access Journal |
| issn | 2158-3188 |
| language | English |
| last_indexed | 2024-04-13T12:45:34Z |
| publishDate | 2022-11-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Translational Psychiatry |
| spelling | doaj.art-6d7bf43b5fe540a29943d8dd1e51c3242022-12-22T02:46:23ZengNature Publishing GroupTranslational Psychiatry2158-31882022-11-011211610.1038/s41398-022-02256-7Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis: does one size fit all?Siân Lowri Griffiths0Paris Alexandros Lalousis1Stephen J. Wood2Rachel Upthegrove3Institute for Mental Health, University of BirminghamInstitute for Mental Health, University of BirminghamInstitute for Mental Health, University of BirminghamInstitute for Mental Health, University of BirminghamAbstract The heterogeneity in recovery outcomes for individuals with First Episode Psychosis (FEP) calls for a strong evidence base to inform practice at an individual level. Between 19–89% of young people with FEP have an incomplete recovery despite gold-standard evidence-based treatments, suggesting current service models, which adopt a ‘one-size fits all’ approach, may not be addressing the needs of many young people with psychosis. The lack of consistent terminology to define key concepts such as recovery and treatment resistance, the multidimensional nature of these concepts, and common comorbid symptoms are some of the challenges faced by the field in delineating heterogeneity in recovery outcomes. The lack of robust markers for incomplete recovery also results in potential delay in delivering prompt, and effective treatments to individuals at greatest risk. There is a clear need to adopt a stratified approach to care where interventions are targeted at subgroups of patients, and ultimately at the individual level. Novel machine learning, using large, representative data from a range of modalities, may aid in the parsing of heterogeneity, and provide greater precision and sophistication in identifying those on a pathway to incomplete recovery.https://doi.org/10.1038/s41398-022-02256-7 |
| spellingShingle | Siân Lowri Griffiths Paris Alexandros Lalousis Stephen J. Wood Rachel Upthegrove Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis: does one size fit all? Translational Psychiatry |
| title | Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis: does one size fit all? |
| title_full | Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis: does one size fit all? |
| title_fullStr | Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis: does one size fit all? |
| title_full_unstemmed | Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis: does one size fit all? |
| title_short | Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis: does one size fit all? |
| title_sort | heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis does one size fit all |
| url | https://doi.org/10.1038/s41398-022-02256-7 |
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