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

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.

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

“…LASSO handles multicollinearity well, a consistent occurrence in clinical samples, and we took the further step of removing highly correlated variables before any analyses. LASSO regressions have been shown to work well in clinical data sets ( Kuhn and Johnson, 2013 ), and have good predictive accuracy when determining psychosis onset ( Ciarleglio et al, 2018 ), schizophrenia diagnosis ( Salvador et al, 2017 ), and response to clozapine ( Fonseca de Freitas et al, 2022 ).…”

Machine learning methods to predict outcomes of pharmacological treatment in psychosis

“…The sociodemographic and clinical characteristics of participants in included studies are presented in Table 1. Among the 28 included publications, the majority of them considered only subjects with the first episode of psychosis (FEP) [23,[28][29][30][31][32][33][34][35][36][37], while 10 studies considered patients with chronic SCZ [38][39][40][41][42][43][44][45][46][47]. The remaining selected studies included both FEP and SCZ patients [48][49][50][51][52][53][54].…”

“…Moreover, three studies used sMRI measures [31,49,50], three studies used EEG data [41,43,47], one study considered positron emission tomography (PET) data [54], and one study used proton magnetic resonance spectroscopy (MRS) data [36]. Conversely, eight studies used clinical and sociodemographic data as input features to the ML algorithms [23,35,37,38,40,42,44,51]. Among the multi-modality studies, different sets of features were used together as input to the ML algorithms.…”

“…Noteworthy, several studies used different sociodemographic and clinical measures as features for ML analysis. Many of them found that important predictors of antipsychotic treatment outcome were baseline severity of psychotic symptoms [35,37,42,44,51] and comorbidities [23,40], suggesting that ML models developed by including routinely available, patient-reportable information might present adequate predictive ability to be applied in clinical settings.…”

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment

“…Sarpal et al, using resting-state functional MRI, found that 91 regions that have functional connections with the striatum were an accurate prognostic tool for treatment response to antipsychotic in acutely psychotic patients [122]; however, the accuracy was 78%. A LASSO algorithm was also used to predict clozapine response at 3 months [123].…”