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A predictor model of treatment resistance in schizophrenia using data from electronic health records

Kadra-Scalzo, Giouliana, Fonseca de Freitas, Daniela, Agbedjro, Deborah, Francis, Emma, Ridler, Isobel, Pritchard, Megan, Shetty, Hitesh, Segev, Aviv, Casetta, Cecilia, Smart, Sophie E. ORCID:, Morris, Anna, Downs, Johnny, Christensen, Søren Rahn, Bak, Nikolaj, Kinon, Bruce J., Stahl, Daniel, Hayes, Richard D. and MacCabe, James H. 2022. A predictor model of treatment resistance in schizophrenia using data from electronic health records. PLoS ONE 17 (9) , e0274864. 10.1371/journal.pone.0274864

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Additional Information: License information from Publisher: LICENSE 1: URL:
Publisher: Public Library of Science
Date of First Compliant Deposit: 20 September 2022
Date of Acceptance: 7 September 2022
Last Modified: 23 May 2023 14:40

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