Hias Julie, Van der Linden Lorenz, Spriet Isabel, Vanbrabant Peter, Willems Ludo, Tournoy Jos, De Winter Sabrina
Pharmacy Department, University Hospitals Leuven, Leuven, Belgium.
Department of Pharmaceutical and Pharmacological Sciences, Clinical Pharmacology and Pharmacotherapy, University of Leuven, Leuven, Belgium.
Eur J Clin Pharmacol. 2017 Nov;73(11):1355-1377. doi: 10.1007/s00228-017-2308-1. Epub 2017 Jul 25.
Discrepancies in preadmission medication (PAM) are common and potentially harmful. Medication reconciliation is able to reduce the discrepancy rate, yet implementation is challenging. In order for reconciliation efforts to be more cost-effective, patients at high risk for reconciliation errors should be identified. The purpose of this systematic review is to identify predictors for unintentional discrepancies in PAM.
Medline and Embase were searched systematically until June 2017. Only studies concerning adult subjects were retained. Quantitative studies were included if predictors for unintentional discrepancies in the PAM had been determined on hospital admission. Variables were divided into patient-, medication-, and setting-related predictors based on a thematic analysis. Studies on identification of predictors for discrepancies and potentially harmful discrepancies were handled separately.
Thirty-five studies were eligible, of which 5 studies focused on potentially harmful discrepancies. The following 16 significant variables were identified using multivariable prediction models: number of preadmission drugs, patient's age, availability of a drug list, patients' understanding of medication, usage of different outpatient pharmacies, number of high-risk drugs, discipline for which the patient is admitted, admitting physician's experience, number and type of consulted sources, patient's gender, type of care before admission, number of outpatient visits during the past year, class of medication, number of reimbursements, use of an electronic prescription system, and type of admission (elective vs emergency). The number of preadmission drugs was identified as a predictor in 20 studies. Potentially harmful discrepancies were ascertained in 5 studies with age found to have a predictive value in all 5 studies.
Multiple suitable predictors for PAM-related discrepancies were identified of which higher age and polypharmacy were reported most frequently.
入院前用药（PAM）存在差异很常见，且可能有害。用药核对能够降低差异率，但实施具有挑战性。为了使核对工作更具成本效益，应识别出存在用药核对错误高风险的患者。本系统评价的目的是识别PAM中无意差异的预测因素。
系统检索Medline和Embase直至2017年6月。仅纳入涉及成年受试者的研究。如果在入院时已确定PAM中无意差异的预测因素，则纳入定量研究。基于主题分析，将变量分为与患者、药物和环境相关的预测因素。关于差异预测因素识别以及潜在有害差异的研究分别进行处理。
35项研究符合条件，其中5项研究关注潜在有害差异。使用多变量预测模型识别出以下16个显著变量：入院前用药数量、患者年龄、是否有药物清单、患者对药物的了解程度、是否使用不同的门诊药房、高风险药物数量、患者入院科室、主治医生经验、咨询来源的数量和类型、患者性别、入院前的护理类型、过去一年的门诊就诊次数、药物类别、报销次数、是否使用电子处方系统以及入院类型（择期与急诊）。入院前用药数量在20项研究中被确定为预测因素。在5项研究中确定了潜在有害差异，年龄在所有5项研究中均具有预测价值。
识别出多个与PAM相关差异的合适预测因素，其中年龄较大和用药种类较多最为常见。
