Risk factor screening and predictive modeling of time-in-range in patients with T2DM undergoing SIIT therapy
This study, based on real-world data, developed a machine learning model (including logistic regression and random forest) to predict the achievement of TIR during hospitalization. The model not only identifies key clinical factors influencing blood glucose fluctuations, but also provides quantifiable decision support for personalized glucose management. This model has the potential to offer new insights and methods for early identification of high-risk patients and optimization of SIIT treatment strategies in clinical practice.
Authors
- Lingyong Cao
- Nengjuan Li (ORCID: https://orcid.org/0009-0002-6155-0391)
- Shu‐Yuan Lin (ORCID: https://orcid.org/0000-0001-5402-4014)
- Hu Jiang (ORCID: https://orcid.org/0000-0003-2711-9995)
- Yujia Liu (ORCID: https://orcid.org/0000-0002-0406-8569)
- Jin Zhang (ORCID: https://orcid.org/0000-0003-2460-0396)
- Wanting Liu (ORCID: https://orcid.org/0000-0001-5565-4747)
Institutions
- Zhejiang Chinese Medical University (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-12-04
- DOI
- https://doi.org/10.3389/fendo.2025.1664366
- Primary Topic
- Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- State Administration of Traditional Chinese Medicine of the People's Republic of China
- Science and Technology Department of Zhejiang Province