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Development and Validation of Inpatient Hypoglycemia Models Centered Around the Insulin Ordering Process.

Authors :
Wright AP
Embi PJ
Nelson SD
Smith JC
Turchin A
Mize DE
Source :
Journal of diabetes science and technology [J Diabetes Sci Technol] 2024 Mar; Vol. 18 (2), pp. 423-429. Date of Electronic Publication: 2022 Sep 01.
Publication Year :
2024

Abstract

Background: The insulin ordering process is an opportunity to provide clinicians with hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin ordering process exist.<br />Methods: We used data on adult patients, admitted in 2019 to non-ICU floors of a large teaching hospital, who had orders for subcutaneous insulin. Our outcome was hypoglycemia, defined as a blood glucose (BG) <70 mg/dL within 24 hours after ordering insulin. We trained and evaluated models to predict hypoglycemia at the time of placing an insulin order, using logistic regression, random forest, and extreme gradient boosting (XGBoost). We compared performance using area under the receiver operating characteristic curve (AUCs) and precision-recall curves. We determined recall at our goal precision of 0.30.<br />Results: Of 21 052 included insulin orders, 1839 (9%) were followed by a hypoglycemic event within 24 hours. Logistic regression, random forest, and XGBoost models had AUCs of 0.81, 0.80, and 0.79, and recall of 0.44, 0.49, and 0.32, respectively. The most significant predictor was the lowest BG value in the 24 hours preceding the order. Predictors related to the insulin order being placed at the time of the prediction were useful to the model but less important than the patient's history of BG values over time.<br />Conclusions: Hypoglycemia within the next 24 hours can be predicted at the time an insulin order is placed, providing an opportunity to integrate decision support into the medication ordering process to make insulin therapy safer.<br />Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Details

Language :
English
ISSN :
1932-2968
Volume :
18
Issue :
2
Database :
MEDLINE
Journal :
Journal of diabetes science and technology
Publication Type :
Academic Journal
Accession number :
36047538
Full Text :
https://doi.org/10.1177/19322968221119788