Time to Treatment Intensification to Reduce Diabetes-Related Complications: A Post Hoc Study
Open accessAbstract
Patients with type 2 diabetes mellitus (T2DM) can be affected by clinical inertia, leading to abysmal results. Studies on a suitable timeframe for treatment intensification remain scarce—especially outside of developed countries. This study aimed to explore the association between time to treatment intensification and diabetes-related complications. A database from a tertiary care hospital in Thailand was retrieved in order to conduct a retrospective cohort study for the years 2011–2017. This study comprised outpatients with T2DM presenting an HbA1c of ≥7.0%. Eligible patients were divided into three groups based on the time of treatment intensification: no delayed treatment intensification, treatment intensification within 6 months, and treatment intensification after 6 months. A Cox proportional hazards model was used to investigate the association between time to treatment intensification and diabetes-related complications. A total of 686 patients were included in the final analysis. During 6.5 years of median follow-up, the group with treatment intensification within 6 months was more strongly associated with diabetic nephropathy compared to the group with no delayed treatment intensification (adjusted HR 2.35; 95%CI 1.35–4.09). Our findings reveal that delaying treatment intensification by even 6 months can increase the likelihood of diabetic nephropathy compared to no delayed treatment intensification. We suggest that patients with T2DM whose blood glucose levels are outside the target range promptly receive treatment intensification.
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Models constructed with clinical data such as electronic health records, laboratory tests, and imaging are the most widely used due to their high accessibility.
“At the feature level, models constructed with clinical data such as EHR [148, 149], laboratory tests [150, 151], and imaging [152, 153] are the most widely used due to their high accessibility.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [151]
The Data Collection and Statistical Analysis sections describe using laboratory results, including HbA1c and LDL-C, in a multivariable regression model. They do not establish that models using clinical data are the most widely used or that accessibility explains their use. (LLM, conf=0.78)
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- Authors
- Kaewbut, P., Kosachunhanun, N., Phrommintikul, A., Chinwong, D., Hall, J. J., & Chinwong, S.
- Affiliations
- University of Phayao (TH)
- Chiang Mai University (TH)
- UNSW Sydney (AU)
- Corresponding author
- Chinwong, Surarong
- ORCID
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2022-09-01
- Volume
- 10
- Issue
- 9
- Pages
- 1673
- ISSN
- 2227-9032 (ISSN-L)
- PMC
- PMC9498838
- OpenAlex
- W4294151453
- Cited by
- 11
- References
- 31
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 1
- FWCI
- 1.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Graduate School, Chiang Mai University
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Keywords
- type 2 diabetes, treatment intensification, clinical inertia, diabetes complications, diabetic nephropathy, glycemic control, HbA1c
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