Cross-sectional study comparing cognitive function in treatment responsive versus treatment non-responsive schizophrenia: Evidence from the STRATA study

Edward Millgate, Eugenia Kravariti, Alice Egerton, Oliver D. Howes, Robin M. Murray, Laura Kassoumeri, Jacek Donocik, Shôn Lewis, Richard Drake, Stephen Lawrie, Anna Murphy, Tracy Collier, Jane Lees, Charlotte Stockton-Powdrell, James Walters, Bill Deakin, James MacCabe

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Background 70%–84% of individuals with antipsychotic treatment resistance show non-response from the first episode. Emerging cross-sectional evidence comparing cognitive profiles in treatment resistant schizophrenia to treatment-responsive schizophrenia has indicated that verbal memory and language functions may be more impaired in treatment resistance. We sought to confirm this finding by comparing cognitive performance between antipsychotic non-responders (NR) and responders (R) using a brief cognitive battery for schizophrenia, with a primary focus on verbal tasks compared against other measures of cognition. Design Cross-sectional. Setting This cross-sectional study recruited antipsychotic treatment R and antipsychotic NR across four UK sites. Cognitive performance was assessed using the Brief Assessment of Cognition in Schizophrenia (BACS). Participants One hundred and six participants aged 18–65 years with a diagnosis of schizophrenia or schizophreniform disorder were recruited according to their treatment response, with 52 NR and 54 R cases. Outcomes Composite and subscale scores of cognitive performance on the BACS. Group (R vs NR) differences in cognitive scores were investigated using univariable and multivariable linear regressions adjusted for age, gender and illness duration. Results Univariable regression models observed no significant differences between R and NR groups on any measure of the BACS, including verbal memory (ß=−1.99, 95% CI −6.63 to 2.66, p=0.398) and verbal fluency (ß=1.23, 95% CI −2.46 to 4.91, p=0.510). This pattern of findings was consistent in multivariable models. Conclusions The lack of group difference in cognition in our sample is likely due to a lack of clinical distinction between our groups. Future investigations should aim to use machine learning methods using longitudinal first episode samples to identify responder subtypes within schizophrenia, and how cognitive factors may interact within this.

Original languageEnglish
Article numbere054160
Number of pages8
JournalBMJ Open
Issue number11
Publication statusPublished - 1 Nov 2021
Externally publishedYes


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