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一种用于分配糖尿病教育和支持服务的风险分层方法。

A Risk Stratification Approach to Allocating Diabetes Education and Support Services.

机构信息

University of Pittsburgh Division of Endocrinology and Metabolism, Pittsburgh, Pennsylvania, USA.

University of Pittsburgh Diabetes Institute, Division of Endocrinology and Metabolism, Pittsburgh, Pennsylvania, USA.

出版信息

Diabetes Technol Ther. 2022 Jan;24(1):75-78. doi: 10.1089/dia.2021.0253. Epub 2021 Dec 14.

Abstract

The objective of this study was to describe a predictive modeling approach to risk stratify people with type 2 diabetes for diabetes self-management education and support (DSMES) services. With data from a large health system, a predictive model including age, glycated hemoglobin (HbA1c), and insulin use among other factors, was developed to assess risk of future high HbA1c. The model was retrospectively applied to a cohort of people who received DSMES over a 2-year period to assess the impact of DSMES on glycemia by risk strata. Of 6934 eligible people, 4014 (58%) were in the composite low-risk group and 2604 (38%) were in the composite high-risk group. Mean HbA1c change after DSMES was -0.38% in the low-risk group and -0.84% in the high-risk group. This analysis demonstrates the potential application of predictive modeling as one approach to target DSMES resources to people who will benefit most.

摘要

本研究旨在描述一种预测建模方法,以对 2 型糖尿病患者进行糖尿病自我管理教育和支持 (DSMES) 服务的风险分层。该研究使用来自大型医疗系统的数据,开发了一个包含年龄、糖化血红蛋白 (HbA1c) 和胰岛素使用等因素的预测模型,以评估未来 HbA1c 升高的风险。该模型被回顾性地应用于接受 DSMES 治疗的患者队列中,以根据风险分层评估 DSMES 对血糖的影响。在 6934 名符合条件的患者中,4014 名 (58%) 为复合低危组,2604 名 (38%) 为复合高危组。在低危组和高危组中,DSMES 后 HbA1c 的平均变化分别为 -0.38%和-0.84%。这项分析表明,预测建模作为一种将 DSMES 资源靶向最受益人群的方法具有潜在的应用前景。

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