Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756195
Title: Development of the Medicines Optimisation Assessment Tool (MOAT) : targeting hospital pharmacists' input to reduce risks and improve patient outcomes
Author: Geeson, Cathy Anne
ISNI:       0000 0004 7429 1488
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.756195  DOI: Not available
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