4.3 Article

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

Journal

JOURNAL OF PSYCHOPHARMACOLOGY
Volume 36, Issue 4, Pages 498-506

Publisher

SAGE PUBLICATIONS LTD
DOI: 10.1177/02698811221078746

Keywords

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

Funding

  1. H. Lundbeck A/S, as part of STRATA, a large multi-centre research collaborative programme - Medical Research Council (MRC) [MR/L011794]
  2. National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London
  3. NIHR Clinician Science Fellowship [CS-2018-18-ST2-014]
  4. MRC Clinical Research Training Fellowship [MR/L017105/1]
  5. Psychiatry Research Trust Peggy Pollak Research Fellowship in Developmental Psychiatry
  6. National Institutes of Health Research (NIHR) [CS-2018-18-ST2-014] Funding Source: National Institutes of Health Research (NIHR)

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In the treatment of treatment-resistant schizophrenia, some patients do not show improvement with clozapine treatment. This study found that patients with higher baseline illness severity, female gender, and comorbid mood disorders have a better response to clozapine.
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.

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