Optimizing Electronic Alerts for Acute Kidney Injury

项目摘要 该提案的总体目标是确定如何对急性肾损伤（阿基）进行警报， 设计为患者提供益处，以及是否可以通过智能靶向增强这种益处。 阿基是住院患者的常见并发症，并且伴随着显著增加的 发病率和死亡率。国际指南建议及时进行诊断评估并避免 肾毒素，但多项研究和我们的初步数据已经证明， 阿基的诊断通常被延迟或完全错过。即使被识别，诊断操作（如 尿分析）和治疗措施（如停用潜在的肾毒性药物）很少 满员你未能采用最佳实践与阿基发病率和死亡率增加相关 患者缺乏对阿基及其直接临床后果的认识，已经促使一些健康 美国和整个英国国民健康服务系统建立自动化，电子化 阿基警报然而，我们的初步随机试验的基本警报系统并没有表现出显着的 在阿基进展、透析或死亡等临床结局上警示阿基的益处。这项建议 描述了基于我们先前经验的研究，以确定阿基警报如何与临床最佳 实践行动，以及这些警报的智能定位，可以改善护理和患者的结果， 多中心 我们设计了一系列关于阿基警报的大型多中心随机试验，以确定 提供和定位，这将最好地改善病人的护理和临床结果。在6家医院， 每年总共有超过10，000例阿基病例，我们将评估1）阿基警报与 临床最佳实践顺序集，2）是否针对已接受特定药物类别的患者发出警报 可以修改处方行为并改善患者结局，以及3）警报的渐进式目标是否 通过使用提升建模可以增加有效性，同时减少警报疲劳。 为了支持这一申请，我们收集了有关以下方面的初步数据： 研究医院的阿基。我们还记录了令人惊讶的低比率的潜在肾毒性药物 在阿基患者中停药。此外，我们重新分析了原始试点警报试验的数据， 证明提升建模可用于识别最有可能从警报中受益的个人。这 预测建模方法是个性化医疗的一个例子，其中大量的患者数据被 用于将警报目标锁定在狭窄的人群中，避免不必要的警报，从而减少警报疲劳。 除了为未来阿基警报的使用提供信息外，这些研究还将提供基础设施和统计数据， 它是严格评估和针对多个医学领域最佳实践警报的基础。

Project Summary The overarching goal of this proposal is to determine how alerts for acute kidney injury (AKI) can be engineered to provide benefit to patients and whether that benefit can be enhanced with intelligent targeting. AKI is a common complication in hospitalized patients, and carries with it a substantially increased risk of morbidity and mortality. International guidelines suggest prompt diagnostic evaluation and avoidance of nephrotoxins once AKI develops, but multiple studies and our preliminary data have documented that the diagnosis of AKI is often delayed or missed altogether. Even when recognized, diagnostic actions (such as urinalysis) and therapeutic actions (such as discontinuing potentially nephrotoxic agents) are infrequently taken. Failure to engage in best practices is associated with increased morbidity and mortality among AKI patients. The lack of recognition of AKI, and its direct clinical consequences, has prompted several health systems in the US and the entire National Health Service of Great Britain to institute automated, electronic alerts for AKI. However, our pilot randomized trial of a basic alert system did not demonstrate a significant benefit of alerting for AKI on clinical outcomes such as progression of AKI, dialysis, or death. This proposal describes studies that build on our prior experience to determine how AKI alerts coupled with clinical best practice actions, and intelligent targeting of those alerts, can improve care and patient outcomes across multiple centers. We have designed a series of large, multi-center randomized trials of AKI alerts to identify modes of delivery and targeting that will best improve the care and clinical outcomes of patients. Across 6 hospitals, with a total of more than 10,000 AKI cases per year, we will evaluate 1) the clinical efficacy of an AKI alert tied to a clinical best practices order set, 2) whether targeting alerts to patients who have received specific drug-classes can modify prescription behavior and improve patient outcome and 3) whether progressive targeting of alerts through the use of uplift modeling can increase effectiveness while decreasing alert fatigue. In support of this application, we have collected preliminary data regarding the rates and outcomes of AKI at the study hospitals. We have also documented surprisingly low rates of potentially nephrotoxic-drug discontinuation among those with AKI. In addition, we have reanalyzed data from our original pilot alert trial to demonstrate that uplift modeling can be used to identify individuals most likely to benefit from alerts. This predictive modeling approach is an example of personalized medicine, where large amounts of patient data are used to target alerts to a narrow population, avoiding unnecessary alerting and thus reducing alert fatigue. Beyond informing the use of AKI alerts in the future, these studies will provide an infrastructure and a statistical foundation for the rigorous assessment and targeting of best-practice alerts across multiple fields of medicine.