Department of Practice and Policy UCL School of Pharmacy PhD Thesis – University College London Development of the Medicines Optimisation Assessment Tool (MOAT) Targeting hospital pharmacists' input to reduce risks and improve patient outcomes Cathy Anne Geeson NIHR Clinical Doctoral Research Fellow Pharmacy Department Luton and Dunstable University Hospital 1 Declaration I, Cathy Geeson confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Signature: Date: 2 Abstract Abstract Background Medicines optimisation is a key role for hospital pharmacists, but with ever-increasing demands on services, there is a need to increase efficiency while maintaining patient safety. The aim of this study was to use prognostic modelling to develop a prediction tool, the Medicines Optimisation Assessment Tool (MOATTM), to target patients most in need of pharmacists’ input while in hospital. Methods and analysis Patients from adult medical wards at two UK hospitals were prospectively included into this cohort study between April and November 2016. Data on medication related problems (MRPs) were collected by pharmacists at the study sites as part of their routine daily clinical assessment of patients. Data on potential risk factors, such as polypharmacy and use of ‘high-risk’ medicines, were collected retrospectively from the information departments at the study sites, laboratory reporting systems and patient medical records. Multivariable logistic regression was used to determine the relationship between potential risk factors and the study outcome, namely preventable MRPs that were at least moderate in severity. A simplified electronic scoring system (the MOAT) was then developed. Results Among 1,503 eligible patient admissions, 610 (40.6%) experienced the study outcome. Eighteen risk factors were pre-selected for MOAT development, with 11 variables retained in the final model. The MOAT demonstrated fair predictive performance (concordance index 0.66), and good calibration. The decision threshold between ‘low’ and ‘medium-risk’ patients has a sensitivity of 90% (specificity 30%). The sensitivity for the threshold between ‘medium’ and ‘high-risk’ patients is 66% (specificity 61%). Decision curve analysis suggests that the MOAT has potential to be clinically useful across a wide range of predicted risk probabilities (from approximately 15% to 70%). Conclusions The MOAT has potential to predict those patients most at risk of moderate or severe preventable MRPs. External validation will be required to establish predictive accuracy in a new group of patients. 3 Impact statement Impact statement The work presented in this thesis has the potential to be put to beneficial use both outside and inside academia. Each of these is discussed below, together with proposed timescales and methods for impact realisation. Impact on clinical practice In terms of potential impact of this research outside academia, the main output is the Medicines Optimisation Assessment Tool (MOATTM) itself, which was developed with the aim of increasing the efficiency of hospital pharmacy services, reducing risks and improving patient outcomes. Once fully validated, the MOAT has the potential to impact on professional practice and patient safety. The intention was for the MOAT to be adopted widely into clinical practice, therefore if generalisability and clinical effectiveness are demonstrated, the MOAT has the potential to be used across the UK, and potentially more widely; I have already received an expression of interest from a pharmacist in Australia regarding external validation of the MOAT within her clinical setting. Impact within this research area This study has also created knowledge that may inform future research in this field. This includes: consensus views on potential risk factors associated with medication related problems (MRPs) in hospitalised patients; data on MRP occurrence in adult medical patients in UK hospitals. More specifically, the prevalence of MRPs and moderate or severe preventable MRPs, and their breakdown by MRP sub-categories; quantification of the potential variability in MRP identification by hospital pharmacists; the views of practising pharmacy clinicians on the requirements of a predictive tool, including presentation and usability. My academic supervisors at UCL have offered a place to a prospective PhD candidate to progress work in this area; if accepted, I have been asked to be a clinical advisor. Realisation of impact While the academic knowledge created by this research has potential to be used immediately, further research will be required prior to implementation of the MOAT into 4 Impact statement routine practice. This will include external validation to assess predictive accuracy in a new sample of patients, and impact and implementation studies. I estimate this will take three to four years, dependant on funding opportunities. Initially I intend to raise awareness of the findings of this study through dissemination via presentations at professional, academic and scientific meetings and conferences, and submission for publication in peer-reviewed journals. I also plan to present at relevant patient / public meetings at the study sites, and to work with the patient and public members of the project steering group to develop a wider public dissemination strategy. I hope to secure further funding to validate the MOAT, and once fully validated I would aim to work with key decision makers, such as the Head of Research at the Royal Pharmaceutical Society, and Medication Safety Team at NHS England to advise on further dissemination and adoption of the MOAT into practice. There is also potential to work with software developers to integrate the MOAT into automated systems such as electronic health records systems, permitting automated risk assessments in ‘real-time’, further supporting implementation into clinical environments. 5 Acknowledgments Acknowledgments First, I would like to give my heartfelt thanks to Professor Bryony Dean Franklin and Dr Li Wei for their unwavering support, guidance and encouragement. It has been an absolute privilege to work under your supervision. In particular, I would like to thank Professor Bryony Dean Franklin who is a truly inspirational role model! I am sincerely grateful to Dr Mary Evans and Lindsay Smith for their clinical supervision, and for permitting this research to take place within their organisations. I would also like to thank the pharmacy staff involved in data collection; your dedication and combined enthusiasm were a tremendous support. Additional thanks go to Jack Glendenning, Colin Merrill, Andy Finch and Alan Osman for their support with technical aspects of data collection. I am also particularly grateful to Sehrush Hussain and Shahnaz Begum for their assistance with the simulated medication related problem (MRP) identification exercise, and to members of the expert panel, Sue Lee, Dr Siva Puthrasingam and Ann Williams; your expertise and experience were greatly appreciated. My sincere thanks also go to Aneesh Khurana for his technical input into the development of the electronic Medicines Optimisation Assessment Tool (MOATTM) scoring system. Similarly, I am indebted to the pharmacists and clinical pharmacy technicians who participated in the assessment of the MOAT’s clinical credibility. I would also like to recognise the patient and public members of the MOAT project steering group: Helen Clothier, Marie-France Capon, Brian Smith, Derek Wright and Tom Drabble. These amazingly supportive people ensured that a patient / carer perspective remained prominent during the research, while providing encouragement and valuable advice throughout. I would also like to thank the National Institute for Health Research for the opportunity to undertake a Clinical Doctoral Research Fellowship. This enabled me to fully concentrate on my academic and clinical development, and afforded access to outstanding training and development opportunities, for which I feel truly fortunate. Finally, to my friends and family, your unquestioning support and understanding has enabled me to maintain my passion, my vision and my focus. I am particularly grateful to my wonderful parents, who helped mould me into the person I have become, while always encouraging me to follow my dreams. I also want to thank my fantastic children, Toby and Ellie; you brighten my life more than you could know! I owe and dedicate this thesis to you all. 6 Contents Table of contents Abstract ........................................................................................................................ 3 Impact statement .......................................................................................................... 4 Acknowledgments ......................................................................................................... 6 Table of contents .......................................................................................................... 7 List of tables ............................................................................................................... 14 List of figures .............................................................................................................. 17 Abbreviations .............................................................................................................. 18 Chapter 1: Overview ..................................................................................................