Pharmacy, Luton and Dunstable University Hospital NHS Foundation Trust, Luton, UK
Research Department of Practice and Policy, UCL School of Pharmacy, London, UK.
BMJ Qual Saf. 2019 Aug;28(8):645-656. doi: 10.1136/bmjqs-2018-008335. Epub 2019 Mar 7.
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.
To develop a prediction tool, the Medicines Optimisation Assessment Tool (MOAT), to target patients most in need of pharmacists' input in hospital.
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.
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.
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.
药物优化是医院药师的关键角色，但随着服务需求的不断增加，需要在保证患者安全的同时提高效率。
开发一种预测工具，即药物优化评估工具（MOAT），以针对最需要药师投入的医院患者。
本队列研究前瞻性纳入来自英国两家医院成人内科病房的患者。药师在研究现场作为其日常临床评估的一部分，收集与药物相关的问题（MRP）的数据。回顾性收集潜在风险因素的数据，如合并症数量和使用“高风险”药物。多变量逻辑回归模型用于确定风险因素与研究结果之间的关系：至少中度严重的可预防 MRP。对模型进行内部验证并开发简化的电子评分系统。
在 1503 名符合条件的入院患者中，有 610 名（40.6%）发生了研究结果。对 MOAT 开发进行了 18 项风险因素的预筛选，最终模型保留了 11 个变量。MOAT 表现出良好的预测性能（一致性指数 0.66）和良好的校准度。选择了两个临床相关的决策阈值（即，证明药师投入合理的最小预测风险概率），敏感性分别为 90%和 66%（特异性分别为 30%和 61%）；这相当于阳性预测值分别为 47%和 54%。决策曲线分析表明，MOAT 在指导临床实践中的决策方面具有潜在价值。
MOAT 有可能预测出 41%的入院患者中最有可能发生中度或重度可预防 MRP 的患者。现在需要进行外部验证，以确定新患者群体的预测准确性。
