超重和肥胖退伍军人糖尿病前期识别算法的实现。

Q Medicine
Tannaz Moin, Laura J Damschroder, Bradley Youles, Fatima Makki, Charles Billington, William Yancy, Matthew L Maciejewski, Linda S Kinsinger, Jane E Weinreb, Nanette Steinle, Caroline Richardson
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引用次数: 6

摘要

预防2型糖尿病是退伍军人健康管理局(VHA)的一个重要国家目标:四分之一的退伍军人患有糖尿病。我们在退伍军人事务部(VA)医疗中心(VAMCs)实施了一种前驱糖尿病识别算法,以估计超重和肥胖退伍军人的前驱糖尿病患病率,为糖尿病预防计划(DPP)向患有前驱糖尿病的退伍军人提供的实用研究做准备。本项目嵌入于2012 - 2015年开展的VA DPP临床示范项目。退伍军人参加了VHA减肥计划(MOVE!)的培训课程,使用现有的推荐流程从具有不同地理和种族人口的VAMCs中招募退伍军人。每个站点都实施并适应了糖尿病前期识别算法,以最适合当地的临床环境。网站依赖于现有的转诊流程,其中糖尿病前期识别算法与现有的临床流程并行实施;这种方法限制了接受评估和筛选的超重和肥胖退伍军人的数量。我们通过图表回顾、访谈和/或实验室测试评估了1,830名患者。在这个队列中,我们估计正常血糖状态、前驱糖尿病和糖尿病的患病率分别为29% (n = 530)、28% (n = 504)和43% (n = 796)。实施有针对性的糖尿病前期识别计划需要仔细考虑如何进行糖尿病前期评估和筛查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of a prediabetes identification algorithm for overweight and obese Veterans.

Type 2 diabetes prevention is an important national goal for the Veteran Health Administration (VHA): one in four Veterans has diabetes. We implemented a prediabetes identification algorithm to estimate prediabetes prevalence among overweight and obese Veterans at Department of Veterans Affairs (VA) medical centers (VAMCs) in preparation for the launch of a pragmatic study of Diabetes Prevention Program (DPP) delivery to Veterans with prediabetes. This project was embedded within the VA DPP Clinical Demonstration Project conducted in 2012 to 2015. Veterans who attended orientation sessions for an established VHA weight-loss program (MOVE!) were recruited from VAMCs with geographically and racially diverse populations using existing referral processes. Each site implemented and adapted the prediabetes identification algorithm to best fit their local clinical context. Sites relied on an existing referral process in which a prediabetes identification algorithm was implemented in parallel with existing clinical flow; this approach limited the number of overweight and obese Veterans who were assessed and screened. We evaluated 1,830 patients through chart reviews, interviews, and/or laboratory tests. In this cohort, our estimated prevalence rates for normal glycemic status, prediabetes, and diabetes were 29% (n = 530), 28% (n = 504), and 43% (n = 796), respectively. Implementation of targeted prediabetes identification programs requires careful consideration of how prediabetes assessment and screening will occur.

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