A Guideline-Based Decision Tree Achieves Better Glucose Control with Less Hypoglycemia at 3 Months in Chinese Diabetic Patients.

Abstract

INTRODUCTION:

China has the world's largest diabetes epidemic and has been facing a serious shortage of primary care providers for chronic diseases including diabetes. To help primary care physicians follow guidelines and mitigate the workload in primary care communities in China, we developed a guideline-based decision tree. This study aimed to validate it at 3 months with real-world data.

METHODS:

The decision tree was developed based on the 2017 Chinese Type 2 Diabetes (T2DM) guideline and 2018 guideline for primary care. It was validated with the data from two registry studies: the NEW2D and ORBIT studies. Patients' data were divided into two groups: the compliance and non-compliance group, depending on whether the physician's prescription was consistent with the decision tree or not. The primary outcome was the difference of change in HbA1c from baseline to 3 months between the two groups. The secondary outcomes included the difference in the proportion of patients achieving HbA1c < 7% at 3 months between the two groups, the incidence of self-reported hypoglycemia at 3 months, and the proportion of patients (baseline HbA1c ≥ 7%) with a HbA1c reduction ≥ 0.3%. The statistical analysis was performed using linear or logistic regression with inverse probability of treatment weighting with adjustments of confounding factors.

RESULTS:

There was a 0.9% reduction of HbA1c in the compliance group and a 0.8% reduction in the non-compliance group (P < 0.001); 61.1% of the participants in the compliance group and 44.3% of the participants in the non-compliance group achieved a HbA1c level < 7% at 3 months (P < 0.001). The hypoglycemic events occurred in 7.1% of patients in the compliance group vs. 9.4% in the non-compliance group (P < 0.001).

CONCLUSION:

The decision tree can help physicians to treat their patients so that they achieve their glycemic targets with fewer hypoglycemic risks. ( http://www.clinicaltrials.gov NCT01525693 & NCT01859598).

• Authors: Yingying Luo, Hong Wu, Xiyang Liao, Tingting Zhao, Nan Cui, Aihua Li, Xingzhi Sun, Puhong Zhang, Yahua Huang, Xia Zhang, Huiqiu Yin, Linong Ji
• Journal: Diabetes therapy : research, treatment and education of diabetes and related disorders (Diabetes Ther) Vol. 12 Issue 7 Pg. 1887-1899 (07 2021) ISSN: 1869-6953 [Print] United States
• PMID: 34050897 (Publication Type: Journal Article)