Prandial Insulin Dosing Using Run-to-Run Control

Application of clinical data and medical expertise to define a suitable performance metric

  1. Cesar C. Palerm, PHD123,
  2. Howard Zisser, MD3,
  3. Wendy C. Bevier, PHD3,
  4. Lois Jovanovič, MD23 and
  5. Francis J. Doyle III, PHD123
  1. 1Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California
  2. 2Biomolecular Science and Engineering Program, University of California Santa Barbara, Santa Barbara, California
  3. 3Sansum Diabetes Research Institute, Santa Barbara, California
  1. Address correspondence and reprint requests to Francis J. Doyle III, Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA 93106-5080. E-mail: frank.doyle{at}icb.ucsb.edu

Abstract

OBJECTIVE—We propose a novel algorithm to adjust prandial insulin dose using sparse blood glucose measurements. The dose is adjusted on the basis of a performance measure for the same meal on the previous day. We determine the best performance measure and tune the algorithm to match the recommendations of experienced physicians.

RESEARCH DESIGN AND METHODS—Eleven subjects with type 1 diabetes, using continuous subcutaneous insulin infusion, were recruited (seven women and four men, aged 21–65 years with A1C of 7.1 ± 1.3%). Basal insulin infusion rates were optimized. Target carbohydrate content for the lunch meal was calculated on the basis of a weight-maintenance diet. Over a period of 2–4 days, subjects were asked to measure their blood glucose according to the algorithm's protocol. Starting with their usual insulin-to-carbohydrate ratio, the insulin bolus dose was titrated downward until postprandial glucose levels were high (180–250 mg/dl [10–14 mmol/l]). Subsequently, physicians made insulin bolus recommendations to normalize postprandial glucose concentrations. Graphical methods were then used to determine the most appropriate performance measure for the algorithm to match the physician's decisions. For the best performance measure, the gain of the controller was determined to be the best match to the dose recommendations of the physicians.

RESULTS—The correlation between the clinically determined dose adjustments and those of the algorithm is R2 = 0.95, P < 1e − 18.

CONCLUSIONS—We have shown how engineering methods can be melded with medical expertise to develop and refine a dosing algorithm. This algorithm has the potential of drastically simplifying the determination of correct insulin-to-carbohydrate ratios.

Footnotes

  • Published ahead of print at http://care.diabetesjournals.org on 15 February 2007. DOI: 10.2337/dc06-2115.

    A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

    The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C Section 1734 solely to indicate this fact.

    • Accepted January 31, 2007.
    • Received October 20, 2006.
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  1. Diabetes Care vol. 30 no. 5 1131-1136
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