How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

Abstract

Background: In type 1 diabetes (T1D) management, short-term glucose prediction can allow to anticipate therapeutic decisions when hypo/hyperglycemia is imminent. Literature prediction methods mainly use past continuous glucose monitoring (CGM) readings. Sophisticated algorithms can use information on insulin delivered and meal carbohydrate (CHO) content. The quantification of how much insulin and CHO information improves glucose prediction is missing in the literature and is investigated, in an open-loop setting, in this proof-of-concept study. Methods: We adopted a versatile literature prediction methodology able to utilize a variety of inputs. We compared predictors that use (1) CGM; (2) CGM and insulin; (3) CGM and CHO; and (4) CGM, insulin, and CHO. Data of 15 T1D subjects in open-loop setup were used. Prediction was evaluated via absolute error and temporal gain focusing on meal/night periods. The relative importance of each individual input of the predictor was evaluated with a sensitivity analysis. Results: For a prediction horizon (PH) ≥ 30 minutes, insulin and CHO information improves prediction accuracy of 10% and double the temporal gain during the 2 hours following the meal. During the night the 4 methods did not give statistically different results. When PH ≥ 45 minutes, the influence of CHO information on prediction is 5-fold that of insulin. Conclusions: In an open-loop setting, with PH ≥ 30 minutes, information on CHO and insulin improves short-term glucose prediction in the 2-hour time window following a meal, but not during the night. CHO information improves prediction significantly more than insulin.

Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review

“…Literature studies have shown that the performance of glucose prediction algorithms can significantly improve when additional inputs are considered apart from CGM, like insulin, meals, and physical activity, all factors that strongly impact glycemic excursions [50–52]. Some literature studies on basal insulin attenuation algorithms have explored the possibility of enhancing glucose prediction by taking into account insulin data [22, 26] and historical CGM data [25].…”

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors

“…While, others use CGM data plus external inputs, such as the amount of ingested carbohydrates, injected insulin and physical activity [ 52 ]. These have been shown to enhance the performance of predictions when compared to predictions using only CGM data [ 53 ].…”

“…This type of algorithm has recently been included in a prototype of a mobile DSS to support patients with T1D in daily management decisions [ 56 ]. More recently, Zecchin et al [ 53 ] have proposed using a jump NN, i.e., an NN with inputs directly connected to both the hidden layer and the output, and with four inputs: CGM reading, its first-order derivative, injected insulin and ingested carbohydrates.…”

Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges

“…It is worth noting that more sophisticated prediction algorithms, also exploiting other signals like the amount of insulin injected or physical activity, can be employed, e.g., those of Zhao et al [9,12], Zecchin et al [13,41], Turksoy et al [10], Zarkogianni et al [11], and Georga et al [42,43]. …”

“…From a clinical point of view, it has been widely demonstrated that the additional information provided by CGM sensors, when used in conjunction with SMBG data, improves the quality of glucose control [7,8]. From an academic point of view, the availability of CGM data stimulated, over the last 15 years, the development of several CGM-based applications, e.g., algorithms for the prediction of future glucose concentration to generate preventive hypo/hyperglycemic alerts [9,10,11,12,13], for the real-time modulation of the basal insulin administration [14,15,16], and for the detection of faults with glucose sensor–insulin pumps system [17,18,19,20,21]. Even more interesting is that CGM sensors enabled the realization of the artificial pancreas (AP), i.e., a device designed mainly for Type 1 diabetes (T1D), which is aimed at maintaining the BG concentration within the safety range by automatically injecting insulin via an insulin pump controlled by a closed-loop control algorithm [22,23,24,25].…”