Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical information
Introduction 23% of people experiencing a first episode of psychosis (FEP) develop treatment resistant schizophrenia (TRS). At present, there are no established methods to accurately identify who will develop TRS from baseline. Objectives In this study we used patient data from three UK early inte...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2022-06-01
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| Series: | European Psychiatry |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0924933822003030/type/journal_article |
| _version_ | 1797617596042838016 |
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| author | E.F. Osimo B. Perry P. Mallikarjun G. Murray O. Howes P. Jones R. Upthegrove G. Khandaker |
| author_facet | E.F. Osimo B. Perry P. Mallikarjun G. Murray O. Howes P. Jones R. Upthegrove G. Khandaker |
| author_sort | E.F. Osimo |
| collection | DOAJ |
| description |
Introduction
23% of people experiencing a first episode of psychosis (FEP) develop treatment resistant schizophrenia (TRS). At present, there are no established methods to accurately identify who will develop TRS from baseline.
Objectives
In this study we used patient data from three UK early intervention services (EIS) to investigate the predictive potential of routinely recorded sociodemographic, lifestyle and biological data at FEP baseline for the risk of TRS up to six years later.
Methods
We developed two risk prediction algorithms to predict the risk of TRS at 2-8 years from FEP onset using commonly recorded information at baseline. Using the forced-entry method, we created a model including age, sex, ethnicity, triglycerides, alkaline phosphatase levels and lymphocyte counts. We also produced a machine-learning-based model, including an additional four variables. The models were developed using data from two and externally validated in another UK psychosis EIS.
Results
The development samples included 785 patients, and 1,110 were included in the validation sample. The models discriminated TRS well at internal validation (forced-entry: C 0.70, 95%CI 0.63-0.76; LASSO: C 0.69, 95%CI 0.63-0.77). At external validation, discrimination performance attenuated (forced-entry: C 0.63, 0.58-0.69; LASSO: C 0.64, 0.58-0.69) but recovered for the forced entry model after recalibration and revision of the lymphocyte predictor (C: 0.67, 0.62-0.73).
Conclusions
The use of commonly recorded clinical information including biomarkers taken at FEP onset could help to predict TRS. These measures should be considered in future studies modelling psychiatric outcomes.
Disclosure
No significant relationships.
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| first_indexed | 2024-03-11T07:58:01Z |
| format | Article |
| id | doaj.art-b6ff8344b59b4de68d0d677aa1a098ee |
| institution | Directory Open Access Journal |
| issn | 0924-9338 1778-3585 |
| language | English |
| last_indexed | 2024-03-11T07:58:01Z |
| publishDate | 2022-06-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | European Psychiatry |
| spelling | doaj.art-b6ff8344b59b4de68d0d677aa1a098ee2023-11-17T05:05:20ZengCambridge University PressEuropean Psychiatry0924-93381778-35852022-06-0165S107S10710.1192/j.eurpsy.2022.303Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical informationE.F. Osimo0B. Perry1P. Mallikarjun2G. Murray3O. Howes4P. Jones5R. Upthegrove6G. Khandaker7University of Cambridge, Dept Of Psychiatry, Cambridge, United Kingdom Imperial College London, Institute Of Clinical Sciences, London, United KingdomUniversity of Cambridge, Dept Of Psychiatry, Cambridge, United KingdomUniversity of Birmingham, Institute Of Clinical Sciences, Birmingham, United KingdomUniversity of Cambridge, Dept Of Psychiatry, Cambridge, United KingdomKing’s College London, Psychiatry, London, United KingdomUniversity of Cambridge, Dept Of Psychiatry, Cambridge, United KingdomUniversity of Birmingham, Institute Of Clinical Sciences, Birmingham, United KingdomUniversity of Cambridge, Dept Of Psychiatry, Cambridge, United Kingdom Introduction 23% of people experiencing a first episode of psychosis (FEP) develop treatment resistant schizophrenia (TRS). At present, there are no established methods to accurately identify who will develop TRS from baseline. Objectives In this study we used patient data from three UK early intervention services (EIS) to investigate the predictive potential of routinely recorded sociodemographic, lifestyle and biological data at FEP baseline for the risk of TRS up to six years later. Methods We developed two risk prediction algorithms to predict the risk of TRS at 2-8 years from FEP onset using commonly recorded information at baseline. Using the forced-entry method, we created a model including age, sex, ethnicity, triglycerides, alkaline phosphatase levels and lymphocyte counts. We also produced a machine-learning-based model, including an additional four variables. The models were developed using data from two and externally validated in another UK psychosis EIS. Results The development samples included 785 patients, and 1,110 were included in the validation sample. The models discriminated TRS well at internal validation (forced-entry: C 0.70, 95%CI 0.63-0.76; LASSO: C 0.69, 95%CI 0.63-0.77). At external validation, discrimination performance attenuated (forced-entry: C 0.63, 0.58-0.69; LASSO: C 0.64, 0.58-0.69) but recovered for the forced entry model after recalibration and revision of the lymphocyte predictor (C: 0.67, 0.62-0.73). Conclusions The use of commonly recorded clinical information including biomarkers taken at FEP onset could help to predict TRS. These measures should be considered in future studies modelling psychiatric outcomes. Disclosure No significant relationships. https://www.cambridge.org/core/product/identifier/S0924933822003030/type/journal_articletreatment resistant schizophreniabiomarkersFirst Episode Psychosisrisk prediction |
| spellingShingle | E.F. Osimo B. Perry P. Mallikarjun G. Murray O. Howes P. Jones R. Upthegrove G. Khandaker Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical information European Psychiatry treatment resistant schizophrenia biomarkers First Episode Psychosis risk prediction |
| title | Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical information |
| title_full | Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical information |
| title_fullStr | Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical information |
| title_full_unstemmed | Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical information |
| title_short | Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical information |
| title_sort | predicting treatment resistance in people with a first episode of psychosis using commonly recorded clinical information |
| topic | treatment resistant schizophrenia biomarkers First Episode Psychosis risk prediction |
| url | https://www.cambridge.org/core/product/identifier/S0924933822003030/type/journal_article |
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