Using a statistical learning approach to identify sociodemographic and clinical predictors of response to clozapine

Daniela Fonseca de Freitas*, Giouliana Kadra-Scalzo, Deborah Agbedjro, Emma Francis, Isobel Ridler, Megan Pritchard, Hitesh Shetty, Aviv Segev, Cecilia Casetta, Sophie E. Smart, Johnny Downs, Søren Rahn Christensen, Nikolaj Bak, Bruce J. Kinon, Daniel Stahl, James H. MacCabe, Richard D. Hayes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)498-506
Number of pages9
JournalJournal of Psychopharmacology
Issue number4
StatePublished - Apr 2022


  • Refractory psychosis
  • clorazil
  • health records
  • machine learning
  • zaponex


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