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