Real-world characterization of blood glucose control and insulin use in the intensive care unit
Name
s41598-020-67864-z.pdf
Description
Published version
Size
3.79 MB
Format
Adobe PDF
Checksum (MD5)
2b55ea4dbeb94678f784e2305adcee2e
Author(s)
Baker, Lawrence
Maley, Jason H.
Arévalo, Aldo
DeMichele, Francis
Mateo-Collado, Roselyn
Finkelstein, Stan
Celi, Leo Anthony G.
Date Issued
July 2020
Journal
Scientific Reports
Publisher
Springer Science and Business Media LLC
Citation
Baker, Lawrence et al. "Real-world characterization of blood glucose control and insulin use in the intensive care unit." Scientific Reports 10, 1 (July 2020): 10718. © 2020 The Author(s)
Version
Final published version
Abstract
The heterogeneity of critical illness complicates both clinical trial design and real-world management. This complexity has resulted in conflicting evidence and opinion regarding the optimal management in many intensive care scenarios. Understanding this heterogeneity is essential to tailoring management to individual patients. Hyperglycaemia is one such complication in the intensive care unit (ICU), accompanied by decades of conflicting evidence around management strategies. We hypothesized that analysis of highly-detailed electronic medical record (EMR) data would demonstrate that patients vary widely in their glycaemic response to critical illness and response to insulin therapy. Due to this variability, we believed that hyper- and hypoglycaemia would remain common in ICU care despite standardised approaches to management. We utilized the Medical Information Mart for Intensive Care III v1.4 (MIMIC) database. We identified 19,694 admissions between 2008 and 2012 with available glucose results and insulin administration data. We demonstrate that hyper- and hypoglycaemia are common at the time of admission and remain so 1 week into an ICU admission. Insulin treatment strategies vary significantly, irrespective of blood glucose level or diabetic status. We reveal a tremendous opportunity for EMR data to guide tailored management. Through this work, we have made available a highly-detailed data source for future investigation.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41598-020-67864-z
