A clinical model for highly accurate prediction of blood glucose depression after continuous intravenous insulin therapy in hyperglycemic emergencies, a multicenter retrospective cohort study.

ABSTRACT:

Background

Hyperglycemic emergencies are broadly classified into diabetic ketoacidosis and hyperosmotic hyperglycemic state. The purpose of this study was to develop a clinical model for predicting treatment of hyperglycemic emergencies.

Methods

This study is a multicenter, retrospective cohort study. We used information on patients admitted to four medical institutions for treatment for hyperglycemic emergencies by diabetologists between April 1, 2010, and March 31, 2024, as the machine learning's training data. Multiple regression analysis was performed to find parameters that correlated with the difference between blood glucose levels before and after treatment initiation (ΔGlu), and a gradient boosting decision tree (GBDT) was created to predict ΔGlu.

Results

Pati

SUBMITTER: Iwamoto Y 

PROVIDER: S-EPMC12489326 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature