Kadra-Scalzo, Giouliana;
Fonseca de Freitas, Daniela;
Agbedjro, Deborah;
Francis, Emma;
Ridler, Isobel;
Pritchard, Megan;
Shetty, Hitesh;
... MacCabe, James H; + view all
(2022)
A predictor model of treatment resistance in schizophrenia using data from electronic health records.
PLoS One
, 17
(9)
, Article e0274864. 10.1371/journal.pone.0274864.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | A predictor model of treatment resistance in schizophrenia using data from electronic health records |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pone.0274864 |
| Publisher version: | https://doi.org/10.1371/journal.pone.0274864 |
| Language: | English |
| Additional information: | Copyright © 2022 Kadra-Scalzo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| Keywords: | Antipsychotic Agents, Schizophrenia, Inpatient, Medical Risk Factors, Forecasting, Electronic Health Records, Psychoses, Mental Health and Psychiatry |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry > Mental Health Neuroscience |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10167755 |
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