Real-world data analysis for the association of glucose control and mortality in critically ill patients.
Ilias KalamarasGeorge E DafoulasAlexandra BargiotaKonstantinos VotisPublished in: Health informatics journal (2023)
Existing results regarding the usage of glycemic control in critically ill patients for reduced morbidity and mortality have been based on clinical studies but could not be reproduced in large prospective studies. Current guidelines for glycemic control suggest a target blood glucose of 140-180 mg/dL, with lower targets being appropriate for some patients. The current study aims to provide additional evidence to this area, through the usage of real-world retrospective data of everyday clinical practice. We have used the large, credentialed access database MIMIC-IV to assess the effect of glycemic control to patient mortality. Glycemic control has been characterized by the percentage of time that the glucose measurements fall within pre-specified glucose bands. Results from logistic regression and survival analysis are reported, along with visualizations based on methods from the machine learning literature, which all suggest that increased time in low and high glucose values is related to increased ICU mortality and decreased survival.
Keyphrases
- glycemic control
- blood glucose
- type diabetes
- data analysis
- clinical practice
- weight loss
- cardiovascular events
- high glucose
- machine learning
- end stage renal disease
- insulin resistance
- endothelial cells
- ejection fraction
- chronic kidney disease
- newly diagnosed
- intensive care unit
- big data
- prognostic factors
- electronic health record
- patient reported outcomes
- deep learning
- cross sectional
- blood pressure
- mechanical ventilation
- skeletal muscle