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ORIGINAL RESEARCH article

Front. Med. Eng.
Sec. Computational Medicine
Volume 2 - 2024 | doi: 10.3389/fmede.2024.1419786

A Stochastic Model-Based Control Methodology for Glycemic Management in the Intensive Care Unit

Provisionally accepted
Melike Sirlanci Melike Sirlanci 1,2*George Hripcsak George Hripcsak 3Cecilia C. Wang Cecilia C. Wang 4J.N. Stroh J.N. Stroh 1Yanran Wang Yanran Wang 5Tellen D. Bennett Tellen D. Bennett 1Andrew M. Stuart Andrew M. Stuart 6David Albers David Albers 1
  • 1 Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
  • 2 Department of Applied Mathematics, College of Arts and Sciences, University of Colorado Boulder, Boulder, Colorado, United States
  • 3 Department of Biomedical Informatics, Columbia University, New York, United States
  • 4 School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
  • 5 Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
  • 6 Department of Computing + Mathematical Sciences, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, United States

The final, formatted version of the article will be published soon.

    Intensive care unit (ICU) patients exhibit erratic blood glucose (BG) fluctuations, including hypoglycemic and hyperglycemic episodes, and require exogenous insulin delivery to keep their BG in healthy ranges. Glycemic control via glycemic management (GM) is associated with reduced mortality and morbidity in the ICU, but GM increases the cognitive load on clinicians.The availability of robust, accurate, and actionable clinical decision support (CDS) tools reduces this burden and assists in the decision-making process to improve health outcomes. Clinicians currently follow GM protocol flow charts for patient intravenous insulin delivery rate computations.We present a mechanistic model-based control algorithm that predicts the optimal intravenous insulin rate to keep BG within a target range; the goal is to develop this approach for eventual use within CDS systems. In this control framework, we employed a stochastic model representing BG dynamics in the ICU setting and used the linear quadratic Gaussian control methodology to develop a controller. We designed two experiments, one using virtual (simulated) patients and one using a real-world retrospective dataset. Using these, we evaluate the safety and efficacy of this model-based glycemic control methodology. The presented controller avoids hypoglycemia 1 Sirlanci et al.and hyperglycemia in virtual patients, maintaining BG levels in the target range more consistently than two existing GM protocols. Moreover, this methodology could theoretically prevent a large proportion of hypoglycemic and hyperglycemic events recorded in a real-world retrospective dataset.

    Keywords: personalized stochastic model, modeling blood glucose dynamics, glycemic management in the intensive care unit, Clinical decision support, reducing cognitive burden of healthcare professionals

    Received: 18 Apr 2024; Accepted: 10 Jul 2024.

    Copyright: © 2024 Sirlanci, Hripcsak, Wang, Stroh, Wang, Bennett, Stuart and Albers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Melike Sirlanci, Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.