De Winter Sabrina, Vanbrabant Peter, Laeremans Pieter, Foulon Veerle, Willems Ludo, Verelst Sandra, Spriet Isabel
Department of Pharmaceutical and Pharmacological Sciences, Clinical Pharmacology and Pharmacotherapy, KU Leuven, University of Leuven, University Hospitals Leuven, Leuven, Belgium.
Department of General Internal Medicine, University Hospitals Leuven, Leuven, Belgium.
Emerg Med J. 2017 Aug;34(8):502-508. doi: 10.1136/emermed-2016-205804. Epub 2017 May 10.
The process of obtaining a complete medication history for patients admitted to the hospital from the ED at hospital admission, without discrepancies, is error prone and time consuming.
The goal of this study was the development of a clinical decision rule (CDR) with a high positive predictive value in detecting ED patients admitted to hospital at risk of at least one discrepancy during regular medication history acquisition, along with favourable feasibility considering time and budget constraints.
Data were based on a previous prospective study conducted at the ED in Belgium, describing discrepancies in 3592 medication histories. Data were split into a training and a validation set. A model predicting the number of discrepancies was derived from the training set with negative binomial regression and was validated on the validation set. The performance of the model was assessed. Several CDRs were constructed and evaluated on positive predictive value and alert rate.
The following variables were retained in the prediction model: (1) age, (2) gender, (3) medical discipline for which the patient was admitted, (4) degree of physician training, (5) season of admission, (6) type of care before admission, number of (7) drugs, (8) high-risk drugs, (9) drugs acting on alimentary tract and metabolism, (10) antithrombotics, antihaemorrhagics and antianaemic preparations, (11) cardiovascular drugs, (12) drugs acting on musculoskeletal system and (13) drugs acting on the nervous system; all recorded by the ED physician on admission. The final CDR resulted in an alert rate of 29% with a positive predictive value of 74%.
The final CDR allows identification of the majority of patients with a potential discrepancy within a feasible workload for the pharmacy staff. Our CDR is a first step towards a rule that could be incorporated into electronic medical records or a scoring system.
在医院入院时从急诊科获取完整且无差异的患者用药史的过程容易出错且耗时。
本研究的目标是制定一种临床决策规则（CDR），该规则在检测急诊科入院患者常规用药史采集过程中至少存在一处差异的风险时具有较高的阳性预测值，同时在考虑时间和预算限制的情况下具有良好的可行性。
数据基于先前在比利时急诊科进行的一项前瞻性研究，该研究描述了3592份用药史中的差异情况。数据被分为训练集和验证集。通过负二项回归从训练集中得出一个预测差异数量的模型，并在验证集上进行验证。评估该模型的性能。构建并评估了多个CDR的阳性预测值和警报率。
预测模型中保留了以下变量：（1）年龄，（2）性别，（3）患者入院的医学学科，（4）医生培训程度，（5）入院季节，（6）入院前的护理类型，（7）药物数量，（8）高风险药物，（9）作用于消化道和代谢的药物，（10）抗血栓、抗出血和抗贫血制剂，（11）心血管药物，（12）作用于肌肉骨骼系统的药物，（13）作用于神经系统的药物；所有这些均由急诊科医生在入院时记录。最终的CDR警报率为29%，阳性预测值为74%。
最终的CDR能够在药房工作人员可行的工作量范围内识别出大多数存在潜在差异的患者。我们的CDR是朝着可纳入电子病历或评分系统的规则迈出的第一步。
