eprintid: 10167753 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/16/77/53 datestamp: 2023-04-05 08:23:24 lastmod: 2023-04-05 08:23:24 status_changed: 2023-04-05 08:23:24 type: article metadata_visibility: show sword_depositor: 699 creators_name: Fonseca de Freitas, Daniela creators_name: Kadra-Scalzo, Giouliana creators_name: Agbedjro, Deborah creators_name: Francis, Emma creators_name: Ridler, Isobel creators_name: Pritchard, Megan creators_name: Shetty, Hitesh creators_name: Segev, Aviv creators_name: Casetta, Cecilia creators_name: Smart, Sophie E creators_name: Downs, Johnny creators_name: Christensen, Søren Rahn creators_name: Bak, Nikolaj creators_name: Kinon, Bruce J creators_name: Stahl, Daniel creators_name: MacCabe, James H creators_name: Hayes, Richard D title: Using a statistical learning approach to identify sociodemographic and clinical predictors of response to clozapine ispublished: pub divisions: UCL divisions: B02 divisions: C07 divisions: D05 divisions: D79 divisions: FH4 keywords: Refractory psychosis, health records, machine learning, zaponex, clorazil note: © The Author(s) 2022. Creative Commons License (CC BY 4.0) This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). abstract: BACKGROUND: A proportion of people with treatment-resistant schizophrenia fail to show improvement on clozapine treatment. Knowledge of the sociodemographic and clinical factors predicting clozapine response may be useful in developing personalised approaches to treatment. METHODS: This retrospective cohort study used data from the electronic health records of the South London and Maudsley (SLaM) hospital between 2007 and 2011. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression statistical learning approach, we examined 35 sociodemographic and clinical factors' predictive ability of response to clozapine at 3 months of treatment. Response was assessed by the level of change in the severity of the symptoms using the Clinical Global Impression (CGI) scale. RESULTS: We identified 242 service-users with a treatment-resistant psychotic disorder who had their first trial of clozapine and continued the treatment for at least 3 months. The LASSO regression identified three predictors of response to clozapine: higher severity of illness at baseline, female gender and having a comorbid mood disorder. These factors are estimated to explain 18% of the variance in clozapine response. The model's optimism-corrected calibration slope was 1.37, suggesting that the model will underfit when applied to new data. CONCLUSIONS: These findings suggest that women, people with a comorbid mood disorder and those who are most ill at baseline respond better to clozapine. However, the accuracy of the internally validated and recalibrated model was low. Therefore, future research should indicate whether a prediction model developed by including routinely collected data, in combination with biological information, presents adequate predictive ability to be applied in clinical settings. date: 2022-04 date_type: published publisher: SAGE Publications official_url: https://doi.org/10.1177/02698811221078746 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1942701 doi: 10.1177/02698811221078746 medium: Print-Electronic lyricists_name: Francis, Emma lyricists_name: Ridler, Isobel lyricists_id: ERFRA78 lyricists_id: IRIDL50 actors_name: Ridler, Isobel actors_id: IRIDL50 actors_role: owner funding_acknowledgements: MR/L017105/1 [Medical Research Council] full_text_status: public publication: Journal of Psychopharmacology volume: 36 number: 4 pagerange: 498-506 event_location: United States issn: 0269-8811 citation: Fonseca de Freitas, Daniela; Kadra-Scalzo, Giouliana; Agbedjro, Deborah; Francis, Emma; Ridler, Isobel; Pritchard, Megan; Shetty, Hitesh; ... Hayes, Richard D; + view all <#> Fonseca de Freitas, Daniela; Kadra-Scalzo, Giouliana; Agbedjro, Deborah; Francis, Emma; Ridler, Isobel; Pritchard, Megan; Shetty, Hitesh; Segev, Aviv; Casetta, Cecilia; Smart, Sophie E; Downs, Johnny; Christensen, Søren Rahn; Bak, Nikolaj; Kinon, Bruce J; Stahl, Daniel; MacCabe, James H; Hayes, Richard D; - view fewer <#> (2022) Using a statistical learning approach to identify sociodemographic and clinical predictors of response to clozapine. Journal of Psychopharmacology , 36 (4) pp. 498-506. 10.1177/02698811221078746 <https://doi.org/10.1177/02698811221078746>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10167753/1/02698811221078746.pdf