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Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists’ input to prevent medication-related problems
  1. Cathy Geeson1,
  2. Li Wei2,
  3. Bryony Dean Franklin2,3
  1. 1 Pharmacy, Luton and Dunstable University Hospital NHS Foundation Trust, Luton, UK
  2. 2 Research Department of Practice and Policy, UCL School of Pharmacy, London, UK
  3. 3 Pharmacy Department, Imperial College Healthcare NHS Trust, Charing Cross Hospital, London, UK
  1. Correspondence to Cathy Geeson, Pharmacy, Luton and Dunstable University Hospital NHS Foundation Trust, Luton LU4 0DZ, UK; cathy.geeson{at}ldh.nhs.uk

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.

Objective To develop a prediction tool, the Medicines Optimisation Assessment Tool (MOAT), to target patients most in need of pharmacists’ input in hospital.

Methods Patients from adult medical wards at two UK hospitals were prospectively included into this cohort study. Data on medication-related problems (MRPs) were collected by pharmacists at the study sites as part of their routine daily clinical assessments. Data on potential risk factors, such as number of comorbidities and use of ‘high-risk’ medicines, were collected retrospectively. Multivariable logistic regression modelling was used to determine the relationship between risk factors and the study outcome: preventable MRPs that were at least moderate in severity. The model was internally validated and a simplified electronic scoring system developed.

Results Among 1503 eligible admissions, 610 (40.6%) experienced the study outcome. Eighteen risk factors were preselected for MOAT development, with 11 variables retained in the final model. The MOAT demonstrated fair predictive performance (concordance index 0.66) and good calibration. Two clinically relevant decision thresholds (ie, the minimum predicted risk probabilities to justify pharmacists’ input) were selected, with sensitivities of 90% and 66% (specificity 30% and 61%); these equate to positive predictive values of 47% and 54%, respectively. Decision curve analysis suggests that the MOAT has potential value in clinical practice in guiding decision-making.

Conclusion The MOAT has potential to predict those patients most at risk of moderate or severe preventable MRPs, experienced by 41% of admissions. External validation is now required to establish predictive accuracy in a new group of patients.

  • medication safety
  • adverse events
  • epidemiology and detection
  • decision support
  • clinical
  • health services research
  • pharmacists

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Footnotes

  • Contributors CG was the principal investigator and was responsible for the initial concept, study design and analysis plan. BDF and LW refined the design and analysis plan. CG applied for National Institute for Health Research (NIHR) fellowship funding, with the support and guidance of BDF and LW. CG carried out the analysis and drafted the manuscript. The manuscript was then critically reviewed by BDF and LW. All authors approved the final version.

  • Funding This work was supported by a Clinical Doctoral Research Fellowship award from Health Education England (HEE) and the NIHR (CDRF-2014-05-033). This article represents independent research supported by the NIHR Imperial Patient Safety Translational Research Centre and the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Imperial College.

  • Disclaimer The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Care. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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