Objective: Mitigation of the risk of prolonged hypoglycemia in T1D management requires patients to assume small doses of fast-acting carbohydrates, the so-called hypotreatments (HTs), as soon as hypoglycemia is detected. The present work evaluates, in an ideal noise-free simulated environment, the margins of improvement in HT administration granted by the possibility of predicting the occurrence of hypoglycemia ahead of time using CGM sensors. Method: A simulation framework relying on a well-established mathematical model of T1D metabolism has been devised to generate hypoglycemic events with different severity classes in 100 virtual patients. An algorithm to administer preventive HTs was developed by resorting to the “dynamic risk” non-linear function, which combines current glycaemia with its rate-of-change provided by CGM, adapted to distinguish the severity of the about-to-happen hypoglycemia. Time in hypoglycemia and rebound post-treatment (which penalizes huge HTs that could bring patients in hyperglycemia) were chosen as metrics to evaluate the performance of the new algorithm vs. the standard protocol. Result: Mild-class patients spent, in median [5th, 95th percentiles], 29 [20-39.5] min in hypoglycemia with the standard protocol and 0 [0-17] min (p<0.0001) with the proposed algorithm. For the severe-class patients time in hypoglycemia was reduced from 54.5 [38.0-100.0] min to 0 [0-51.0] min (p<0.0001). Rebound post-treatment was reduced too: from 127.8 [114.5-171.3] mg/dL to 123.1 [112.0-140.2] mg/dL (p<0.0001) for mild hypoglycemia; from 128.2 [110.6-176.0] mg/dL to 121.5 [109.7-138.0] mg/dL (p<0.0001) for severe hypoglycemia. Conclusion: The proposed algorithm seems able to efficiently handle the timing of HTs, resulting in a reduction of the time spent in hypoglycemia, without a rebound into the hyperglycemic range. However, further investigations in a noisy scenario are needed to fairly assess the proposed algorithm.
A new CGM-based algorithm to generate preventive hypotreatments: an in-silico assessment
Nunzio Camerlingo;Martina Vettoretti;Giacomo Cappon;Simone Del Favero;Andrea Facchinetti;Giovanni Sparacino
2019
Abstract
Objective: Mitigation of the risk of prolonged hypoglycemia in T1D management requires patients to assume small doses of fast-acting carbohydrates, the so-called hypotreatments (HTs), as soon as hypoglycemia is detected. The present work evaluates, in an ideal noise-free simulated environment, the margins of improvement in HT administration granted by the possibility of predicting the occurrence of hypoglycemia ahead of time using CGM sensors. Method: A simulation framework relying on a well-established mathematical model of T1D metabolism has been devised to generate hypoglycemic events with different severity classes in 100 virtual patients. An algorithm to administer preventive HTs was developed by resorting to the “dynamic risk” non-linear function, which combines current glycaemia with its rate-of-change provided by CGM, adapted to distinguish the severity of the about-to-happen hypoglycemia. Time in hypoglycemia and rebound post-treatment (which penalizes huge HTs that could bring patients in hyperglycemia) were chosen as metrics to evaluate the performance of the new algorithm vs. the standard protocol. Result: Mild-class patients spent, in median [5th, 95th percentiles], 29 [20-39.5] min in hypoglycemia with the standard protocol and 0 [0-17] min (p<0.0001) with the proposed algorithm. For the severe-class patients time in hypoglycemia was reduced from 54.5 [38.0-100.0] min to 0 [0-51.0] min (p<0.0001). Rebound post-treatment was reduced too: from 127.8 [114.5-171.3] mg/dL to 123.1 [112.0-140.2] mg/dL (p<0.0001) for mild hypoglycemia; from 128.2 [110.6-176.0] mg/dL to 121.5 [109.7-138.0] mg/dL (p<0.0001) for severe hypoglycemia. Conclusion: The proposed algorithm seems able to efficiently handle the timing of HTs, resulting in a reduction of the time spent in hypoglycemia, without a rebound into the hyperglycemic range. However, further investigations in a noisy scenario are needed to fairly assess the proposed algorithm.
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