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Title: Clinical and genetic predictors of treatment resistant psychosis
Author: Smart, Sophie
ISNI:       0000 0004 9350 6114
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2020
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Treatment resistance (TR), affecting approximately 20-30% of patients with psychosis, has a high burden both for patients and healthcare services. I provided a general introduction to TR in Chapter 6 and highlighted the need to identify TR earlier in the course of the illness, so that an effective treatment, such as clozapine, can be offered promptly. The purpose of this thesis was to identify clinical, demographic, and genetic predictors of TR, using information identified when a patient first presents to clinical services with psychosis. First, I reported the results of a systemic literature review, which synthesised predictors of TR identified in longitudinal, observational studies (Chapter 8). Younger age of onset was the most consistent predictor of TR, but this review also indicated that, to date, studies have not used statistical methods specifically designed to identify predictors that have a high chance of predicting TR in future patients. Existing literature has primarily used methods designed to capture the magnitude of association, between predictors and TR, within the study sample. Second, I reported the results of my own analysis using a dataset created by combining existing longitudinal, first episode psychosis cohorts. This dataset, known as STRATA-G (and described in Chapter 7), included patients who had a minimum of one year follow-up, provided a DNA sample, could be classified as either TR or non-treatment resistant (NTR). I found significant associations between TR and both younger age of onset and fewer years in education (Chapter 9). I created a predictive model of TR that selected nine first episode variables and could, after internal validation, correctly classify 59% of TR patients and 65% of NTR patients (Chapter 9). Third, I reported the results of a genome-wide association study (GWAS) of treatment resistance, which was used to create a polygenic risk score for TR (Chapter 10). This polygenic risk score was significantly associated with TR in the STRATA-G sample. Fourth, I investigated whether age of psychosis onset mediated the relationship between genetic burden for TR and subsequent TR, using STRATA-G (Chapter 11). Age of onset was not associated with genetic risk for TR and there was no evidence it mediated the relationship between the polygenic risk score for TR and subsequent TR. Finally, I expanded on the discussion of the findings in the preceding chapters (Chapter 12). Here, I placed my findings in the context of the wider literature and discussed the limitations of my data. I discussed the future of prediction modelling in TR; the potential uses of a prediction model, in both clinical practice and research studies, and the importance of future work to validate the findings reporting in this thesis.
Supervisor: Maccabe, James Hunter ; Murray, Robin MacGregor ; O'Reilly, Paul Francis Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available