University of Pittsburgh Division of Endocrinology and Metabolism, Pittsburgh, Pennsylvania, USA.
University of Pittsburgh Diabetes Institute, Division of Endocrinology and Metabolism, Pittsburgh, Pennsylvania, USA.
Diabetes Technol Ther. 2022 Jan;24(1):75-78. doi: 10.1089/dia.2021.0253. Epub 2021 Dec 14.
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.
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。