Long-term antipsychotic polypharmacy prescribing in secondary mental health care : detection, predictors and outcomes

• Main Author: Kadra-Scalzo, Giouliana
• Other Authors: Hayes, Richard Derek ; MacCabe, James Hunter ; Stewart, Robert James
• Published: King's College London (University of London) 2017
• Subjects: 616.89
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.721680

Background: Investigating long-term antipsychotic polypharmacy is key to unpacking the associations between serious mental illnesses (SMI) and detrimental outcomes, such as premature death and frequent hospital readmissions, observed in this population. However, existing research is sparse and hampered by methodological problems such as examining small and homogeneous samples and residual confounding. Objectives: 1) To identify cases on long-term antipsychotic polypharmacy (≥ 6 months) prescribing in South London and Maudsley electronic health records (EHR); 2) To identify factors that predict long-term antipsychotic polypharmacy prescribing for SMI patients in secondary mental health care; 3) To investigate whether outcomes such as hospital readmission and mortality are associated with long-term antipsychotic polypharmacy prescribing in secondary mental health care. Methods: Antipsychotic medication information was derived from the Clinical Record Interactive Search (CRIS), a de-identified electronic patient records system, for the period between 2007 and 2014. Data on mortality were extracted using existing linkages between CRIS and death certification (Office of National Statistics). Information about antipsychotic co-prescribing was extracted using a bespoke algorithm. Multivariable logistic models were built to investigate predictors of antipsychotic polypharmacy. To investigate the impact of antipsychotic polypharmacy on hospital readmission and all-cause mortality, I constructed multivariable Cox proportion hazard models. To test the association between long-term antipsychotic polypharmacy and cause-specific mortality I used competing risk regression. Implications: On a clinical level, this thesis provides an insight into factors that can predict clinical decision-making regarding antipsychotic polypharmacy prescribing in real-life clinical settings. On a patient level, the findings highlight patient burden associated with this antipsychotic regimen. In the wider treatment, service and policy context, the lack of patient benefit from antipsychotic polypharmacy highlights the need for programmes that target prescribers, to reduce antipsychotic polypharmacy.