The impact of real-time alerting on appropriate prescribing in kidney disease: a cluster randomized controlled trial
The impact of real-time alerting on appropriate prescribing in kidney disease: a cluster randomized controlled trial
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DOI：
10.1093/jamia/ocv159
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发表时间：
2016-05-01
影响因子：
6.4
通讯作者：
El-Kareh, Robert
中科院分区：
文献类型：
--
作者：
Awdishu, Linda;Coates, Carrie R.;El-Kareh, Robert
背景肾脏疾病患者因用药不当而有发生不良事件的风险。临床决策支持(CDS)利用动态评估患者肾功能来改善肾脏疾病患者处方的随机对照试验还很少发表。方法我们开发了一个用于商业电子健康记录中20种药物的CDS工具。我们的系统检测到这样的情况：在最初开处方时，建议在门诊和急性环境中对肾功能受损的成年患者停药或调整剂量(“前瞻性”警报)，并通过监测已经接受研究药物之一的患者的肾功能变化(“回顾”警报)。我们进行了一项前瞻性、整群随机对照试验，比较接受肾脏剂量调整临床决策支持的内科医生和执行常规工作流程的内科医生。主要终点是在患者的情况需要根据警报逻辑进行更改时，针对患者的肾功能进行适当调整的研究处方的比例。我们使用多变量Logistic回归模型来调整肾小球滤过率、性别、年龄、住院状态、住院时间、警报类型、从研究开始的时间以及处方医生在主要终点上的聚集性。结果在1278名独特的患者中，共有4068个触发条件发生；其中1579个触发条件产生了医生在干预组中看到的警报，2489个这些触发条件被捕获但被抑制，从而不会为对照组的医生产生警报。干预组和对照组分别有17%的时间和5.7%的时间对处方顺序进行了适当的调整(优势比：1.89，95%可信区间，1.45~2.47，P
Background Patients with kidney disease are at risk for adverse events due to improper medication prescribing. Few randomized controlled trials of clinical decision support (CDS) utilizing dynamic assessment of patients' kidney function to improve prescribing for patients with kidney disease have been published.Methods We developed a CDS tool for 20 medications within a commercial electronic health record. Our system detected scenarios in which drug discontinuation or dosage adjustment was recommended for adult patients with impaired renal function in the ambulatory and acute settings both at the time of the initial prescription ("prospective" alerts) and by monitoring changes in renal function for patients already receiving one of the study medications ("look-back" alerts). We performed a prospective, cluster randomized controlled trial of physicians receiving clinical decision support for renal dosage adjustments versus those performing their usual workflow. The primary endpoint was the proportion of study prescriptions that were appropriately adjusted for patients' kidney function at the time that patients' conditions warranted a change according to the alert logic. We employed multivariable logistic regression modeling to adjust for glomerular filtration rate, gender, age, hospitalized status, length of stay, type of alert, time from start of study, and clustering within the prescribing physician on the primary endpoint.Results A total of 4068 triggering conditions occurred in 1278 unique patients; 1579 of these triggering conditions generated alerts seen by physicians in the intervention arm and 2489 of these triggering conditions were captured but suppressed, so as not to generate alerts for physicians in the control arm. Prescribing orders were appropriate adjusted in 17% of the time vs 5.7% of the time in the intervention and control arms, respectively (odds ratio: 1.89, 95% confidence interval, 1.45-2.47, P