Individual treatment effects of sodium-glucose co-transporter-2 inhibitors on the risk of chronic kidney disease in patients with type 2 diabetes: A counterfactual prediction model based on real-world data
Issued Date
2024-01-01
Resource Type
ISSN
14628902
eISSN
14631326
Scopus ID
2-s2.0-85199167766
Journal Title
Diabetes, Obesity and Metabolism
Rights Holder(s)
SCOPUS
Bibliographic Citation
Diabetes, Obesity and Metabolism (2024)
Suggested Citation
Siriyotha S., Lukkunaprasit T., Looareesuwan P., Kunakorntham P., Anothaisintawee T., Nimitphong H., McKay G.J., Attia J., Thakkinstian A. Individual treatment effects of sodium-glucose co-transporter-2 inhibitors on the risk of chronic kidney disease in patients with type 2 diabetes: A counterfactual prediction model based on real-world data. Diabetes, Obesity and Metabolism (2024). doi:10.1111/dom.15793 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/100035
Title
Individual treatment effects of sodium-glucose co-transporter-2 inhibitors on the risk of chronic kidney disease in patients with type 2 diabetes: A counterfactual prediction model based on real-world data
Corresponding Author(s)
Other Contributor(s)
Abstract
Aim: To estimate individual treatment effects (ITEs) of sodium-glucose co-transporter-2 inhibitors (SGLT2is) on lowering the risk of developing chronic kidney disease (CKD) in patients with type 2 diabetes (T2D) and to identify those most probable to benefit from treatment. Methods: This study followed a T2D cohort from Ramathibodi Hospital, Thailand, from 2015 to 2022. A counterfactual model was constructed to predict factual and counterfactual risks of CKD if patients did/did not receive SGLT2is. ITEs were estimated by subtracting the factual risk from the counterfactual risk of CKD. Results: There were 1619 and 15 879 patients included in the SGLT2i and non-SGLT2i groups, respectively. The estimated ITEs varied from −1.19% to −17.51% with a median of −4.49%, that is, 50% of patients had a 4.49% or greater lower CKD risk if they received an SGLT2i. Patients who gained the greatest benefit from SGLT2is were more probable to be male, aged at least 60 years, with a history of diabetes duration of at least 3 months, hypertension, peripheral arterial disease, diabetic retinopathy and low high-density lipoprotein cholesterol. Conclusions: Our prediction model provides individualized information that helps target T2D patients who may benefit more from SGLT2is. This could help clinical decision making and implementation of personalized medicine in clinical practice, especially in resource-limited settings.