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.

Reporting of effect sizes is standard practice in psychology and psychiatry research. However, interpretation of these effect sizes can be meaningless or misleading – in particular, the evaluation of specific effect sizes as ‘small’, ‘medium’ and ‘large’ can be inaccurate depending on the research context. A real‐world example of this is research into the mental health of children and young people during the COVID‐19 pandemic. Evidence suggests that clinicians and services are struggling with increased demand, yet population studies looking at the difference in mental health before and during the pandemic report effect sizes that are deemed ‘small’. In this short review, we utilise simulations to demonstrate that a relatively small shift in mean scores on mental health measures can indicate a large shift in the number of cases of anxiety and depression when scaled up to an entire population. This shows that ‘small’ effect sizes can in some contexts be large and impactful.

Reporting of effect sizes is standard practice in psychology and psychiatry research. However, interpretation of these effect sizes can be meaningless or misleading – in particular, the evaluation of specific effect sizes as “small”, “medium” and “large” can be inaccurate depending on the research context. A real-world example of this is research into the mental health of children and young people during the Covid-19 pandemic. Evidence suggests that clinicians and services are struggling with increased demand, yet population studies looking at the difference in mental health before and during the pandemic report effect sizes that are deemed “small”. In this short review we utilise simulations to demonstrate that a relatively small shift in mean scores on mental health measures can indicate a large shift in the number of cases of anxiety and depression when scaled up to an entire population. This shows that “small” effect sizes can in some contexts be large and impactful.

Background: There is evidence of heterogeneity within treatment-resistant schizophrenia (TRS), with some people not responding to antipsychotic treatment from illness onset and others becoming treatment-resistant after an initial response period. These groups may have different aetiologies. Aim: This study investigates sociodemographic and clinical correlates of early onset of TRS. Method: Employing a retrospective cohort design, we do a secondary analysis of data from a cohort of people with TRS attending the South London and Maudsley. Regression analyses were conducted to identify the correlates of the length of treatment to TRS. Predictors included the following: gender, age, ethnicity, problems with positive symptoms, problems with activities of daily living, psychiatric comorbidities, involuntary hospitalisation and treatment with long-acting injectable antipsychotics. Results: In a cohort of 164 people with TRS (60% were men), the median length of treatment to TRS was 3 years and 8 months. We observed no cut-off on the length of treatment until TRS presentation differentiating between early and late TRS (i.e. no bimodal distribution). Having mild to very severe problems with hallucinations and delusions at the treatment start was associated with earlier TRS (~19 months earlier). In sensitivity analyses, including only complete cases (subject to selection bias), treatment with a long-acting injectable antipsychotic was additionally associated with later TRS (~15 months later). Conclusion: Our findings do not support a clear separation between early and late TRS but rather a continuum of the length of treatment before TRS onset. Having mild to very severe problems with positive symptoms at treatment start predicts earlier onset of TRS.

Objectives To develop a prognostic tool of treatment resistant schizophrenia (TRS) in a large and diverse clinical cohort, with comprehensive coverage of patients using mental health services in four London boroughs. Methods We used the Least Absolute Shrinkage and Selection Operator (LASSO) for time-to-event data, to develop a risk prediction model from the first antipsychotic prescription to the development of TRS, using data from electronic health records. Results We reviewed the clinical records of 1,515 patients with a schizophrenia spectrum disorder and observed that 253 (17%) developed TRS. The Cox LASSO survival model produced an internally validated Harrel’s C index of 0.60. A Kaplan-Meier curve indicated that the hazard of developing TRS remained constant over the observation period. Predictors of TRS were: having more inpatient days in the three months before and after the first antipsychotic, more community face-to-face clinical contact in the three months before the first antipsychotic, minor cognitive problems, and younger age at the time of the first antipsychotic. Conclusions Routinely collected information, readily available at the start of treatment, gives some indication of TRS but is unlikely to be adequate alone. These results provide further evidence that earlier onset is a risk factor for TRS.