Methods Inf Med
DOI: 10.1055/s-0039-1693731
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients

Akira A. Nair
1  Lakeside High School, Seattle, Washington, United States
,
Mihir Velagapudi
2  Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, United States
,
Lakshmana Behara
3  Perimatics LLC, Bellevue, Washington, United States
,
Ravitheja Venigandla
3  Perimatics LLC, Bellevue, Washington, United States
,
Christine T. Fong
4  Center for Perioperative & Pain Initiatives in Quality Safety Outcome (PPiQSO), Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington, United States
,
Mayumi Horibe
5  Department of Anesthesiology, VA Puget Sound Health Care System, Seattle, Washington, United States
,
Bala G. Nair
4  Center for Perioperative & Pain Initiatives in Quality Safety Outcome (PPiQSO), Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington, United States
› Author Affiliations
Funding None.
Further Information

Publication History

13 March 2019

14 June 2019

Publication Date:
09 August 2019 (eFirst)

Abstract

Background Hyperglycemia or high blood glucose during surgery is associated with poor postoperative outcome. Knowing in advance which patients may develop hyperglycemia allows optimal assignment of resources and earlier initiation of glucose management plan.

Objective To develop predictive models to estimate peak glucose levels in surgical patients and to implement the best performing model as a point-of-care clinical tool to assist the surgical team to optimally manage glucose levels.

Methods Using a large perioperative dataset (6,579 patients) of patient- and surgery-specific parameters, we developed and validated linear regression and machine learning models (random forest, extreme gradient boosting [Xg Boost], classification and regression trees [CART], and neural network) to predict the peak glucose levels during surgery. The model performances were compared in terms of mean absolute percentage error (MAPE), logarithm of the ratio of the predicted to actual value (log ratio), median prediction error, and interquartile error range. The best performing model was implemented as part of a web-based application for optimal decision-making toward glucose management during surgery.

Results Accuracy of the machine learning models were higher (MAPE = 17%, log ratio = 0.029 for Xg Boost) when compared with that of the linear regression model (MAPE = 22%, log ratio = 0.041). The Xg Boost model had the smallest median prediction error (5.4 mg/dL) and the narrowest interquartile error range (−17 to 24 mg/dL) as compared with the other models. The best performing model, Xg Boost, was implemented as a web application, Hyper-G, which the perioperative providers can use at the point of care to estimate peak glucose levels during surgery.

Conclusions Machine learning models are able to accurately predict peak glucose levels during surgery. Implementation of such a model as a web-based application can facilitate optimal decision-making and advance planning of glucose management strategies.

Supplementary Material