CHARACTERISTICS, COMORBIDITIES, COMEDICATION AND TREATMENT TRANSITIONS IN CHINESE PATIENTS WITH TYPE-2 DIABETES MELLITUS AND CHRONIC KIDNEY DISEASES WHO INITIATED ANTIHYPERTENSIVE AND HYPOGLYCEMIC DRUGS BETWEEN 2012-2022

8 Feb 2025 12 a.m. 12 a.m.
WCN25-AB-680, Poster Board= SAT-255

Introduction:

Chronic kidney disease (CKD) is a major complication of type 2 diabetes mellitus (T2D). Patients with both conditions require medications to control blood pressure and blood glucose to reduce the risk of cardiovascular disease and death. The clinical landscape for the treatment of patients with CKD and T2D is rapidly evolving with the introduction of new treatments. Therefore, more research is warranted to describe how treatment patterns evolved in previous years and to provide context for clinical applications of new drugs.

Methods:

Using real-world data based on electronic health records from Yinzhou (an eastern region in China), we described demographics, comorbidities, drug utilization, and temporal changes for CKD and T2D patients during 2012 and 2022. We categorized patients into four “new-user” cohorts of index drug initiation, including sodium-glucose cotransporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP-1 RA), renin-angiotensin system inhibitors (RASi) and traditional Chinese medicine (TCM) cohorts. For each cohort, new-users were defined as patients who were newly prescribed one of the certain classes of drug and had no prescriptions for that class of drug during the previous 12 months. The date of first prescription was defined as baseline, and then the patients were followed until death, loss to follow-up, or 29th June,2022, whichever came first. The four medication-specific cohorts were not mutually exclusive; thus, the same patients might be included in different cohorts.

Results:

The number of patients in SGLT2i, GLP-1 RA, RASi, and TCM cohorts were 1849, 102, 7305, and 2270, respectively, with the GLP1 RA cohort having the smallest mean age (58.06 years) and the RASi cohort having the highest mean age (64.61years). The average durations of T2D and CKD ranged from 5.64 to 8.74 years and 1.48 to 3.03 years, respectively, with metformin and sulfonylureas being the most commonly hypoglycemic medications. In SGLT2i and GLP-1 RA cohorts, approximately 99% of patients were followed until the end of the study, while the proportions were 85.6%, and 88.3% in RASi, and TCM cohorts, respectively. Most patients were on monotherapy at baseline, and the proportion was highest in the RASi cohort (91.0%) and was lowest in the GLP-1 RA cohort (57.8%). As for the longitudinal prescribing patterns, more than 80% of patients in the other three cohorts started to take RASi during the follow up. The RASi cohort had the highest short- and long-term compliance rate among the four drug classes. The RASi cohort had a highest compliance rate within 90 days, with 67.7% of patients still being exposed, while that of the TCM cohort was only 42.0%, which was the smallest among the four cohorts. The proportion of not being currently exposed to TCM in the TCM cohort first increased and subsequently decreased between baseline and three years later.

Table 1. Demographic, comorbidities recorded and healthcare resource utilization recorded of new users of study medication

Table 1. Demographic, comorbidities recorded and healthcare resource utilization recorded of new users of study medication (continue)

Table 1. Demographic, comorbidities recorded and healthcare resource utilization recorded of new users of study medication (continue)

Table 2. Medications use recorded of new users of study medication

Table 2. Medications use recorded of new users of study medication (continue)

Table 2. Medications use recorded of new users of study medication (continue)

Conclusions:

RASi was the most commonly used medication in CKD and T2D patients, while SGLT2i and GLP-1 RA were gradually being used in clinical after launch. However, the overall drug adherence rates were not satisfactory across the four medication classes.

I have potential conflict of interest to disclose.
This research is funded and supported by Bayer Healthcare Co., Ltd.

I did not use generative AI and AI-assisted technologies in the writing process.