Department of General and Specialized Nursing, University of São Paulo at Ribeirão Preto College of Nursing, Ribeirão Preto, São Paulo, Brazil.
Centre for Health Systems and Safety Research, Australian Institute for Health Innovation, Macquarie University, Sydney, NSW, Australia.
PLoS One. 2019 Jul 31;14(7):e0220248. doi: 10.1371/journal.pone.0220248. eCollection 2019.
The primary aims were to determine the rate of potential drug-drug interactions (pDDIs) in patients with nasally placed feeding tubes (NPFT) and the factors significantly associated with pDDIs. The secondary aim was to assess the change in pDDIs for patients between admission and discharge.
This multicentre study applied a cross-sectional design and was conducted in six Brazilian hospitals, from October 2016 to July 2018. Data from patients with NPFT were collected through electronic forms. All regular medications prescribed were recorded. Medications were classified according to the World Health Organization (WHO) Anatomical Therapeutic Chemical code. Drug-drug interaction screening software was used to screen patients' medications for pDDIs. Negative binomial regression was used to account for the over dispersed nature of the pDDI count. Since the number of pDDIs was closely related to the number of prescribed medications, we modelled the rate of pDDIs with the count of pDDIs as the numerator and the number of prescribed medications as the denominator; six variables were considered for inclusion: time (admission or discharge), patient age, patient gender, age-adjusted Charlson Comorbidity Index (CCI) score, type of prescription (electronic or handwritten) and patient care complexity. To account for correlation within the two time points (admission and discharge) for each patient a generalised estimating equations approach was used to adjust the standard error estimates. To test the change in pDDI rate between admission and discharge a full model of six variables was fitted to generate an adjusted estimate.
In this study, 327 patients were included. At least one pDDI was found in more than 91% of patients on admission and discharge and most of these pDDIs were classified as major severity. Three factors were significantly associated with the rate of pDDIs per medication: patient age, patient care complexity and prescription type (handwritten vs electronic). There was no evidence of a difference in pDDI rate between admission and discharge.
Patients with a NPFT are at high risk of pDDIs. Drug interaction screening tools and computerized clinical decision support systems could be effective risk mitigation strategies for this patient group.
本研究旨在确定接受鼻饲管（nasally placed feeding tubes，NPFT）治疗的患者中潜在药物-药物相互作用（potential drug-drug interactions，pDDIs）的发生率，以及与 pDDIs 显著相关的因素。次要目的是评估患者在入院和出院时的 pDDIs 变化。
本多中心研究采用横断面设计，于 2016 年 10 月至 2018 年 7 月在巴西的 6 家医院进行。通过电子表格收集 NPFT 患者的数据。记录所有常规处方药物。根据世界卫生组织（World Health Organization，WHO）解剖治疗化学分类法对药物进行分类。使用药物相互作用筛查软件对患者的药物进行 pDDI 筛查。采用负二项回归来解释 pDDI 计数的过离散性质。由于 pDDI 的数量与处方药物的数量密切相关，我们将 pDDI 的发生率作为分子，处方药物的数量作为分母，建立 pDDI 发生率模型；纳入了 6 个变量：时间（入院或出院）、患者年龄、患者性别、年龄调整 Charlson 合并症指数（age-adjusted Charlson Comorbidity Index，CCI）评分、处方类型（电子或手写）和患者护理复杂性。为了考虑每个患者在两个时间点（入院和出院）之间的相关性，使用广义估计方程方法调整标准误差估计值。为了测试入院和出院时 pDDI 发生率的变化，我们拟合了一个包含 6 个变量的全模型，生成调整后的估计值。
本研究共纳入 327 例患者。入院和出院时，超过 91%的患者至少存在一种 pDDI，其中大多数 pDDI 被归类为严重程度较大。有 3 个因素与每一种药物的 pDDI 发生率显著相关：患者年龄、患者护理复杂性和处方类型（手写与电子）。入院和出院时 pDDI 发生率无差异。
接受 NPFT 治疗的患者存在发生 pDDIs 的高风险。药物相互作用筛查工具和计算机化临床决策支持系统可能是针对这一患者群体的有效风险缓解策略。
