Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists’ input to prevent medication-related problems

Abstract

BackgroundMedicines 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.ObjectiveTo develop a prediction tool, the Medicines Optimisation Assessment Tool (MOAT), to target patients most in need of pharmacists’ input in hospital.MethodsPatients 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.ResultsAmong 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.ConclusionThe 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.

High‐risk medicines associated with clinically relevant medication‐related problems in UK hospitals: A prospective observational study

“…Multivariable analysis was used to identify the high‐risk medicines that were independently associated with the study outcome after adjusting for other potential risk factors: use of other high‐risk medicines, age, socioeconomic status, previous allergy, body mass index, number of hospital admissions in previous 6 months, primary diagnosis, number of comorbidities, history of dementia, number of regular medicines prescribed on the first day of admission, parenteral medicines administration, renal function, liver disease, serum albumin, serum potassium, white cell count and platelet count. The extent of missing data has been reported previously . In summary, of 1503 included admissions, 387 (25.7%) had 1 or more missing data points, accounting for 1.6% of total risk factor data.…”

“…While risk of medication‐related harm associated with medicines such as anticoagulants and antidiabetic medicines is well recognised, the present study suggests such risk is likely to be multifactorial and subject to residual confounding, with use of high‐risk medicines alone being unlikely to accurately predict the occurrence of moderate or severe preventable MRPs. The present study therefore suggests that, while identification of high‐risk medicines associated with clinically relevant MRPs has potential to inform targeting of patients for medicines optimisation activities, additional risk factors also need to be considered . Further studies in different settings are needed to confirm these findings.…”

“…An overview of the 1503 included patient admissions has been described in detail elsewhere . A summary is provided in Appendix S2.…”

“…This prospective observational study involved adults admitted to the medical wards of 2 hospitals in South East England, described in detail elsewhere . A summary is provided in Appendix S1.…”

“…A second check was performed by the principal investigator. Examples of MRPs, classified by severity and preventability, have been published previously . MRP data were identified and recorded by pharmacists at the study sites as part of their routine daily clinical assessment of patients.…”