Diabetes technology & therapeutics | 2019

A Real-Time Continuous Glucose Monitoring Based Algorithm to Trigger Hypotreatments to Prevent/Mitigate Hypoglycemic Events.

 
 
 
 
 
 

Abstract


BACKGROUND\nThe standard treatment for hypoglycemia recommended by the American Diabetes Association (ADA) suggests patients with diabetes to take small amounts of carbohydrates, the so-called hypotreatments (HTs), as soon as blood glucose concentration goes below 70 mg/dl. However, prevention, or at least mitigation, of hypoglycemic events could be achieved by triggering HTs ahead of time thanks to the use of the predictive capabilities of suitable real-time algorithms fed by continuous glucose monitoring (CGM) sensors data.\n\n\nMETHODS\nThe algorithm proposed in this paper to trigger HTs for preventing forthcoming hypoglycemic events is based on the computation of the dynamic risk (DR), i.e., a non-linear function combining current glycemia with its rate-of-change, both provided by CGM. A comparison of performance of the proposed algorithm against the ADA guidelines is made, in-silico, on datasets of 100 virtual patients undergoing a single-meal experiment, with induced post-meal hypoglycemia, generated by the UVA/Padova T1D simulator.\n\n\nRESULTS\nOn noise-free CGM data, the proposed algorithm reduces the time spent in hypoglycemia, on median [25th - 75th percentiles] from 36 [29 - 43] to 0 [0 - 11] min (p < 0.0001), with a concomitant decrease of the post-treatment rebound in glucose concentration, on median [25th - 75th percentiles] from 136 [121 - 148] to 121 [116 - 127] mg/dl (p < 0.0001). On noisy CGM data, there is still a reduction of both time spent in hypoglycemia from 41 [28 - 49] min to 25 [0 - 41] min (p < 0.0001), and post-treatment rebound from 176 [151 - 196] mg/dl to 137 [123 - 151] mg/dl (p < 0.0001).\n\n\nCONCLUSIONS\nThe potentiality of the new algorithm in generating preventive HTs, which can allow significant reduction of hypoglycemia without concomitant increase of hyperglycemia, suggests its further development and test in-silico, e.g., simulating both insulin pump and multiple-daily-injections therapies.

Volume None
Pages None
DOI 10.1089/dia.2019.0139
Language English
Journal Diabetes technology & therapeutics

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