A prediction nomogram for sodium-glucose co-transporter 2 inhibitors-induced diabetes ketoacidosis in non-surgical patients: a retrospective multicenter cohort study.
Sodium-glucose co-transporter 2 inhibitors (SGLT2i) are associated with a rare but serious risk of diabetic ketoacidosis (S-DKA). This study aimed to identify risk factors for S-DKA, develop a tool for its early detection, and assess the associated in-hospital severity.
A retrospective, multicenter cohort study was conducted across four hospitals from September 2019 to June 2024. After screening 35,633 SGLT2i-prescribed inpatients, a 1:3 propensity score matching was applied. The study included 432 patients in the development cohort (2019-2023) and 128 in a validation cohort (2023-2024). Lasso regression and logistic regression were used to identify S-DKA risk factors and construct a nomogram. The primary endpoint for severity was the need for intensive care.
Six independent predictive factors for S-DKA were identified: female, indications with diabetes, high HbA1c levels, fasting, infection and low BMI. The prediction nomogram demonstrated excellent discrimination, with an area under the curve (AUC) of 0.920(95% CI: 0.888 -0.952) in the development cohort and 0.735 (95% CI: 0.644 -0.826) upon validation. The model was well-calibrated. Furthermore, patients with S-DKA required intensive care at a significantly higher rate compared to those without.
S-DKA is associated with greater in-hospital severity, necessitating early identification. The developed nomogram provides a clinically valuable tool for predicting S-DKA risk, potentially aiding physicians in proactive patient management and improving outcomes.
A retrospective, multicenter cohort study was conducted across four hospitals from September 2019 to June 2024. After screening 35,633 SGLT2i-prescribed inpatients, a 1:3 propensity score matching was applied. The study included 432 patients in the development cohort (2019-2023) and 128 in a validation cohort (2023-2024). Lasso regression and logistic regression were used to identify S-DKA risk factors and construct a nomogram. The primary endpoint for severity was the need for intensive care.
Six independent predictive factors for S-DKA were identified: female, indications with diabetes, high HbA1c levels, fasting, infection and low BMI. The prediction nomogram demonstrated excellent discrimination, with an area under the curve (AUC) of 0.920(95% CI: 0.888 -0.952) in the development cohort and 0.735 (95% CI: 0.644 -0.826) upon validation. The model was well-calibrated. Furthermore, patients with S-DKA required intensive care at a significantly higher rate compared to those without.
S-DKA is associated with greater in-hospital severity, necessitating early identification. The developed nomogram provides a clinically valuable tool for predicting S-DKA risk, potentially aiding physicians in proactive patient management and improving outcomes.
Authors
Chen Chen, Gao Gao, Wang Wang, Zhu Zhu, Wan Wan, Tang Tang, Jiao Jiao, Ma Ma, Zhu Zhu, Fan Fan
View on Pubmed