在急诊科验证糖尿病筛查资格的电子健康记录算法。

IF 1.8 3区 医学 Q2 EMERGENCY MEDICINE
Mary H Smart, Janet Y Lin, Brian T Layden, Yuval Eisenberg, Kirstie K Danielson, Ruth Pobee, Chuxian Tang, Brett Rydzon, Anjana Bairavi Maheswaran, A Simon Pickard, Lisa K Sharp, Angela Kong
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引用次数: 0

摘要

目的:虽然美国糖尿病协会(ADA)筛查指南已被广泛使用,但实施和适应特定环境的方式会影响其实际应用和使用。我们的主要目的是验证ADA指南所告知的最佳实践咨询(BPA)筛选算法,以确定急诊科(ED)有资格进行血红蛋白a1c (HbA1c)检测的患者。方法:这项横断面研究包括2021年5月在一家大型城市医疗中心急诊科就诊的成年人。我们使用敏感性、特异性、似然比和预测值来评估该算法正确识别符合糖尿病筛查条件的患者的能力,并将人工图表审查作为参考标准。入选标准针对的是可能不知道自身HbA1c升高的糖尿病高危患者。我们还计算了接收器工作特性曲线下的面积(AUC)。结果:2021年5月,3850名成年人中有2963人(77%)接受了常规实验室检查。其中,796人(27%)有BPA触发,其中631人(79%)完成了HbA1c测试。该算法具有可接受的灵敏度(0.69,95%可信区间[CI] 0.66 ~ 0.72)、特异性(0.91,CI 0.89 ~ 0.92)、阳性预测值(0.75,CI 0.72 ~ 0.78)和阴性预测值(0.88,CI 0.86 ~ 0.89)。阳性似然比(7.39,CI 6.35-8.42)是足够的,阴性似然比(0.34,CI 0.30-0.37)是有信息量的。AUC为0.74 (CI 0.72-0.77),表明该算法具有可接受的准确性。结论:研究结果表明,根据ADA指南,基于电子健康记录的算法是一种有效的工具,可以识别到急诊科就诊的患者,这些患者有资格进行HbA1c检测,但可能不知道自己患有糖尿病前期或糖尿病。工作流程集成的便捷性和潜在未诊断糖尿病和前驱糖尿病的高产量使BPA算法成为ED内糖尿病筛查的一种有吸引力的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validating an Electronic Health Record Algorithm for Diabetes Screening Eligibility in the Emergency Department.

Objective: While the American Diabetes Association (ADA) screening guidelines have been used widely, the way they are implemented and adapted to a particular setting can impact their practical application and usage. Our primary objective was to validate a best practice advisory (BPA) screening algorithm informed by the ADA guidelines to identify patients eligible for hemoglobin a1c (HbA1c) testing in the emergency department (ED).

Methods: This cross-sectional study included adults presenting to a large urban medical center's ED in May 2021. We used sensitivity, specificity, likelihood ratios, and predictive values to estimate the algorithm's ability to correctly identify patients eligible for diabetes screening, with manual chart review as the reference standard. Eligibility criteria targeted patients at risk for diabetes who were likely unaware of their elevated HbA1c. We also calculated the area under the receiver operating characteristic curve (AUC).

Results: In May 2021, 2,963 (77%) of the 3,850 adults admitted to the ED had a routine lab ordered. Among those, 796 (27%) had a BPA triggered, and of those 631 (79%) had an HbA1c test completed. The algorithm had acceptable sensitivity (0.69, 95% confidence interval [CI] 0.66-0.72), specificity (0.91, CI 0.89-0.92), positive predictive value (0.75, CI 0.72-0.78) and negative predictive value (0.88, CI 0.86-0.89). The positive likelihood ratio (7.39, CI 6.35-8.42) was adequate, and the negative likelihood ratio (0.34, CI 0.30-0.37) was informative. The AUC of 0.74 (CI 0.72-0.77) suggests that the algorithm had acceptable accuracy.

Conclusion: Findings suggest that an electronic health record-based algorithm informed by the ADA guidelines is a valid tool for identifying patients presenting to the ED who are eligible for HbA1c testing and may be unaware of having prediabetes or diabetes. The ease of workflow integration and high yield of potentially undiagnosed diabetes and prediabetes makes the BPA algorithm an appealing method for diabetes screening within the ED.

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来源期刊
Western Journal of Emergency Medicine
Western Journal of Emergency Medicine Medicine-Emergency Medicine
CiteScore
5.30
自引率
3.20%
发文量
125
审稿时长
16 weeks
期刊介绍: WestJEM focuses on how the systems and delivery of emergency care affects health, health disparities, and health outcomes in communities and populations worldwide, including the impact of social conditions on the composition of patients seeking care in emergency departments.
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