Portrait for Type 2 Diabetes with Goal-Achieved HbA1c Using Digital Diabetes Care Model: A Real-World 12-Month Study from China

Abstract

Mingzhen Li,1 Bing Zhang,2 Lichuan Guo,2 Yuan Zhang,2 Xiaoyan Du,3 Bingyi Wang,3 Zheng Xu,4 Ning Sun,4 Ruibin Chen,4 Wanwen Han,4 Liming Chen,1 Zhenqiang Song1 1Department of Endocrinology, NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, People’s Republic of China; 2Department of Information Management, NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, People’s Republic of China; 3Department of Medical, Happy Life Technology Co Ltd, Beijing, People’s Republic of China; 4Department of Yutang, Andon Health Co., Ltd, Tianjin, People’s Republic of ChinaCorrespondence: Zhenqiang Song, Department of Endocrinology, NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, No. 6 North Huanrui Road, Beichen District, Tianjin, People’s Republic of China, Tel [phone], Fax [phone], Email [email protected]: Our previous study demonstrated that digital diabetes care model (DDCM) created by multidisciplinary care team (MDCT) can improve glycemic control for patients with diabetes than usual care. Therefore, we aimed to explore long-term glycemic control with DDCM and influencing factors in type 2 diabetic cohort, in order to make a portrait for diabetes with goal-achieved HbA1c in clinics.Methods: A total of 1198 outpatients with type 2 diabetes using DDCM for at least 12 months were recruited as a cohort. Medical records and specific DDCM indexes were collected. The influencing factors for glycemic control were explored by multivariate logistic regression analysis, followed by an internal and external validation.Results: A total of 887 patients were finally included. HbA1c target-achieving rate was increased from 39.83% at baseline to 71.79% after 3-month follow-up. A shorter duration of diabetes, more frequent self-monitoring of blood glucose, lower HbA1c level at baseline, and less frequent emergency out-of-hospital follow-ups were influencing factors for HbA1c < 7% at 12-month follow-up. AUC of the prediction model was 0.790, with a sensitivity of 69.7% and specificity of 76.1%. Internal and external validation in patients using the DDCM monitored by MDCT indicated that the DDCM was robust (AUC =0.783 and 0.723, respectively).Conclusion: Our findings made a portrait for T2DM with goal-achieved HbA1c in our DDCM. It is important to recognize associated factors for health providers to make personalized intervention in clinical practice.Keywords: digital diabetes care model, glycemic control, type 2 diabetes management

Keywords

• digital diabetes care model
• glycemic control
• type 2 diabetes management