Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Clinical Trial
. 2010 Jan 1;4(1):25-33.
doi: 10.1177/193229681000400104.

Hypoglycemia prediction with subject-specific recursive time-series models

Meriyan Eren-Oruklu  1 , Ali Cinar, Lauretta Quinn
Affiliations
Clinical Trial

Hypoglycemia prediction with subject-specific recursive time-series models

Meriyan Eren-Oruklu et al. J Diabetes Sci Technol. .

Abstract

Background: Avoiding hypoglycemia while keeping glucose within the narrow normoglycemic range (70-120 mg/dl) is a major challenge for patients with type 1 diabetes. Continuous glucose monitors can provide hypoglycemic alarms when the measured glucose decreases below a threshold. However, a better approach is to provide an early alarm that predicts a hypoglycemic episode before it occurs, allowing enough time for the patient to take the necessary precaution to avoid hypoglycemia.

Methods: We have previously proposed subject-specific recursive models for the prediction of future glucose concentrations and evaluated their prediction performance. In this work, our objective was to evaluate this algorithm further to predict hypoglycemia and provide early hypoglycemic alarms. Three different methods were proposed for alarm decision, where (A) absolute predicted glucose values, (B) cumulative-sum (CUSUM) control chart, and (C) exponentially weighted moving-average (EWMA) control chart were used. Each method was validated using data from the Diabetes Research in Children Network, which consist of measurements from a continuous glucose sensor during an insulin-induced hypoglycemia. Reference serum glucose measurements were used to determine the sensitivity to predict hypoglycemia and the false alarm rate.

Results: With the hypoglycemic threshold set to 60 mg/dl, sensitivity of 89, 87.5, and 89% and specificity of 67, 74, and 78% were reported for methods A, B, and C, respectively. Mean values for time to detection were 30 +/- 5.51 (A), 25.8 +/- 6.46 (B), and 27.7 +/- 5.32 (C) minutes.

Conclusions: Compared to the absolute value method, both CUSUM and EWMA methods behaved more conservatively before raising an alarm (reduced time to detection), which significantly decreased the false alarm rate and increased the specificity.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Reference (Ref) blood glucose measurements, CGMS sensor data, and 30-minute-ahead predicted glucose levels of a representative subject. Method A triggers an early alarm when the absolute value of the predicted glucose concentration drops below the threshold of 60 mg/dl. Shaded area represents the true positive alarm issued 20 minutes before the hypoglycemic event (time to detection).
Figure 2.
Figure 2.
(A) Alarm triggered with method B (CUSUM). Time to detection is 10 minutes. (B) CUSUM control chart used for alarm decision. An alarm is issued when the Ck− exceeds the limit H.
Figure 3.
Figure 3.
(A) Alarm triggered with method C (EWMA). Time to detection is 15 minutes. (B) EWMA control chart used for alarm decision. An alarm is issued when the zk crosses the LCL.
See this image and copyright information in PMC

References

    1. The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977–986. - PubMed
    1. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) Lancet. 1998;352(9131):837–853. - PubMed
    1. Eren-Oruklu M, Cinar A, Quinn L, Smith D. Estimation of future glucose concentrations with subject-specific recursive linear models. Diabetes Technol Ther. 2009;11(4):243–253. - PMC - PubMed
    1. Eren-Oruklu M, Cinar A, Quinn L, Smith D. Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes. J Process Control. 2009;19(8):1333–1346.
    1. Bequette BW. Optimal estimation applications to continuous glucose monitoring. Proceedings 2004 American Control Conference; 2004. pp. 958–962.

Publication types