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开发用于预测急诊科连续护理过程中与药物相关问题的筛查工具:一项前瞻性多中心研究。

Development of Screening Tools to Predict Medication-Related Problems Across the Continuum of Emergency Department Care: A Prospective, Multicenter Study.

作者信息

Taylor Simone E, Mitri Elise A, Harding Andrew M, Taylor David McD, Weeks Adrian, Abbott Leonie, Lambros Pani, Lawrence Dona, Strumpman Dana, Senturk-Raif Reyhan, Louey Stephen, Crisp Hamish, Tomlinson Emily, Manias Elizabeth

机构信息

Pharmacy Department, Austin Health, Heidelberg, VIC, Australia.

Emergency Department, Austin Health, Heidelberg, VIC, Australia.

出版信息

Front Pharmacol. 2022 Jul 6;13:865769. doi: 10.3389/fphar.2022.865769. eCollection 2022.

Abstract

Medication-related problems (MRPs) occur across the continuum of emergency department (ED) care: they may contribute to ED presentation, occur in the ED/short-stay unit (SSU), at hospital admission, or shortly after discharge to the community. This project aimed to determine predictors for MRPs across the continuum of ED care and incorporate these into screening tools (one for use at ED presentation and one at ED/SSU discharge), to identify patients at greatest risk, who could be targeted by ED pharmacists. A prospective, observational, multicenter study was undertaken in nine EDs, between July 2016 and August 2017. Blocks of ten consecutive adult patients presenting at pre-specified times were identified. Within 1 week of ED discharge, a pharmacist interviewed patients and undertook a medical record review to determine a medication history, patient understanding of treatment, risk factors for MRPs and to manage the MRPs. Logistic regression was undertaken to determine predictor variables. Multivariable regression beta coefficients were used to develop a scoring system for the two screening tools. Of 1,238 patients meeting all inclusion criteria, 904 were recruited. Characteristics predicting MRPs related to ED presentation were: patient self-administers regular medications (OR = 7.95, 95%CI = 3.79-16.65), carer assists with medication administration (OR = 15.46, 95%CI = 6.52-36.67), or health-professional administers (OR = 5.01, 95%CI = 1.77-14.19); medication-related ED presentation (OR = 9.95, 95%CI = 4.92-20.10); age ≥80 years (OR = 3.63, 95%CI = 1.96-6.71), or age 65-79 years (OR = 2.01, 95%CI = 1.17-3.46); potential medication adherence issue (OR = 2.27, 95%CI = 1.38-3.73); medical specialist seen in past 6-months (OR = 2.02, 95%CI = 1.42-2.85); pharmaceutical benefit/pension/concession cardholder (OR = 1.89, 95%CI = 1.28-2.78); inpatient in previous 4-weeks (OR = 1.60, 95%CI = 1.02-2.52); being male (OR = 1.48, 95%CI = 1.05-2.10); and difficulties reading labels (OR = 0.63, 95%CI = 0.40-0.99). Characteristics predicting MRPs related to ED discharge were: potential medication adherence issue (OR = 6.80, 95%CI = 3.97-11.64); stay in ED > 8 h (OR = 3.23, 95%CI = 1.47-7.78); difficulties reading labels (OR = 2.33, 95%CI = 1.30-4.16); and medication regimen changed in ED (OR = 3.91, 95%CI = 2.43-6.30). For ED presentation, the model had a C-statistic of 0.84 (95% CI 0.81-0.86) (sensitivity = 80%, specificity = 70%). For ED discharge, the model had a C-statistic of 0.78 (95% CI 0.73-0.83) (sensitivity = 82%, specificity = 57%). Predictors of MRPs are readily available at the bedside and may be used to screen for patients at greatest risk upon ED presentation and upon ED/SSU discharge to the community. These screening tools now require external validation and implementation studies to evaluate the impact of using such tools on patient care outcomes.

摘要

与用药相关的问题(MRPs)在急诊科(ED)护理的全过程中都会出现:它们可能导致患者前来急诊科就诊,也可能出现在急诊科/短期住院单元(SSU)、入院时或出院后不久回到社区时。本项目旨在确定急诊科护理全过程中与用药相关问题的预测因素,并将其纳入筛查工具(一个用于患者前来急诊科就诊时,另一个用于在急诊科/SSU出院时),以识别风险最高的患者,这些患者可作为急诊科药剂师的目标对象。2016年7月至2017年8月期间,在9个急诊科开展了一项前瞻性、观察性、多中心研究。确定了在预先指定时间前来就诊的连续10名成年患者为一组。在急诊科出院后1周内,药剂师对患者进行访谈并查阅病历,以确定用药史、患者对治疗的理解、与用药相关问题的风险因素,并处理这些与用药相关的问题。采用逻辑回归确定预测变量。使用多变量回归β系数为两种筛查工具制定评分系统。在符合所有纳入标准的1238例患者中,招募了904例。预测与急诊科就诊相关的用药相关问题的特征包括:患者自行规律用药(比值比[OR]=7.95,95%置信区间[CI]=3.79 - 16.65)、护理人员协助用药(OR = 15.46,95%CI = 6.52 - 36.67)或医护人员给药(OR = 5.01,95%CI = 1.77 - 14.19);因用药相关问题前来急诊科就诊(OR = 9.95,95%CI = 4.92 - 20.10);年龄≥80岁(OR = 3.63,95%CI = 1.96 - 6.71)或年龄65 - 79岁(OR = 2.01,95%CI = 1.17 - 3.46);潜在的用药依从性问题(OR = 2.27,95%CI = 1.38 - 3.73);在过去6个月内看过医学专科医生(OR = 2.02,95%CI = 1.42 - 2.85);持有药品福利/养老金/优惠卡(OR = 1.89,95%CI = 1.28 - 2.78);在过去4周内曾住院(OR = 1.60,95%CI = 1.02 - 2.52);男性(OR = 1.48,95%CI = 1.05 - 2.10);以及阅读标签有困难(OR = 0.63,95%CI = 0.40 - 0.99)。预测与急诊科出院相关的用药相关问题的特征包括:潜在的用药依从性问题(OR = 6.80,95%CI = 3.97 - 11.64);在急诊科停留时间>8小时(OR = 3.23,95%CI = 1.47 - 7.78);阅读标签有困难(OR = 2.33,95%CI = 1.30 - 4.16);以及在急诊科改变了用药方案(OR = 3.91,95%CI = 2.43 - 6.30)。对于急诊科就诊时的情况,该模型的C统计量为0.84(95%CI 0.81 - 0.86)(敏感性=80%,特异性=70%)。对于急诊科出院时的情况,该模型的C统计量为0.78(95%CI 0.73 - 0.83)(敏感性=82%,特异性=57%)。与用药相关问题的预测因素在床边很容易获得,可用于在患者前来急诊科就诊时以及从急诊科/SSU出院回到社区时筛查出风险最高的患者。这些筛查工具现在需要进行外部验证和实施研究,以评估使用此类工具对患者护理结果的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34ee/9299090/03f137877763/fphar-13-865769-g001.jpg

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