Predicting maintenance lithium response for bipolar disorder from electronic health records—a retrospective study
Background Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited. Method We aimed to determine...
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PeerJ Inc.
2024-10-01
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| author | Joseph F. Hayes Fehmi Ben Abdesslem Sandra Eloranta David P. J. Osborn Magnus Boman |
| author_facet | Joseph F. Hayes Fehmi Ben Abdesslem Sandra Eloranta David P. J. Osborn Magnus Boman |
| author_sort | Joseph F. Hayes |
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| description | Background Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited. Method We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders. Results We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models. Discussion Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium vs. olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium vs. olanzapine responders. |
| format | Article |
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| publishDate | 2024-10-01 |
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| spelling | doaj-art-43cbb58a40ac472bb46d522a4501decb2025-08-20T02:17:10ZengPeerJ Inc.PeerJ2167-83592024-10-0112e1784110.7717/peerj.17841Predicting maintenance lithium response for bipolar disorder from electronic health records—a retrospective studyJoseph F. Hayes0Fehmi Ben Abdesslem1Sandra Eloranta2David P. J. Osborn3Magnus Boman4Department of Psychiatry, University College London, University of London, London, United KingdomDepartment of Psychiatry, University College London, University of London, London, United KingdomDivision of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, SwedenDepartment of Psychiatry, University College London, University of London, London, United KingdomDepartment of Psychiatry, University College London, University of London, London, United KingdomBackground Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited. Method We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders. Results We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models. Discussion Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium vs. olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium vs. olanzapine responders.https://peerj.com/articles/17841.pdfBipolar disorderLithiumMaintenance response predictionMachine learningRetrospective study |
| spellingShingle | Joseph F. Hayes Fehmi Ben Abdesslem Sandra Eloranta David P. J. Osborn Magnus Boman Predicting maintenance lithium response for bipolar disorder from electronic health records—a retrospective study PeerJ Bipolar disorder Lithium Maintenance response prediction Machine learning Retrospective study |
| title | Predicting maintenance lithium response for bipolar disorder from electronic health records—a retrospective study |
| title_full | Predicting maintenance lithium response for bipolar disorder from electronic health records—a retrospective study |
| title_fullStr | Predicting maintenance lithium response for bipolar disorder from electronic health records—a retrospective study |
| title_full_unstemmed | Predicting maintenance lithium response for bipolar disorder from electronic health records—a retrospective study |
| title_short | Predicting maintenance lithium response for bipolar disorder from electronic health records—a retrospective study |
| title_sort | predicting maintenance lithium response for bipolar disorder from electronic health records a retrospective study |
| topic | Bipolar disorder Lithium Maintenance response prediction Machine learning Retrospective study |
| url | https://peerj.com/articles/17841.pdf |
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