Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Algorithms for basal insulin attenuation <s> OBJECTIVE ::: To introduce a data transformation that enhances the power of blood glucose data analyses. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: In the standard blood glucose scale, hypoglycemia (blood glucose, < 3.9 mmol/l) and hyperglycemia (blood glucose, > 10 mmol/l) have very different ranges, and euglycemia is not central in the entire blood glucose range (1.1-33.3 mmol/l). Consequently, the scale is not symmetric and its clinical center (blood glucose, 6-7 mmol/l) is distant from its numerical center (blood glucose, 17 mmol/l). As a result, when blood glucose readings are analyzed, the assumptions of many parametric statistics are routinely violated. We propose a logarithmic data transformation that matches the clinical and numerical center of the blood glucose scale, thus making the transformed data symmetric. ::: ::: ::: RESULTS ::: The transformation normalized 203 out of 205 data samples containing 13,584 blood glucose readings of 127 type 1 diabetic individuals. An example illustrates that the mean and standard deviation based on transformed, rather than on raw, data better described subject's blood glucose distribution. Based on transformed data: 1) the low blood glucose index predicted the occurrence of severe hypoglycemia, while the raw blood glucose data (and glycosylated hemoglobin levels) did not; 2) the high blood glucose index correlated with the subjects' glycosylated hemoglobin (r = 0.63, P < 0.001); and 3) the low plus high blood glucose index was more sensitive than the raw data to a treatment (blood glucose awareness training) designed to reduce the range of blood glucose fluctuations. ::: ::: ::: CONCLUSIONS ::: Using symmetrized, instead of raw, blood glucose data strengthens the existing data analysis procedures and allows for the development of new statistical techniques. It is proposed that raw blood glucose data should be routinely transformed to a symmetric distribution before using parametric statistics. <s> BIB001 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Algorithms for basal insulin attenuation <s> Background:Hypoglycemia has been identified as a primary barrier to optimal management of diabetes. This observation, in conjunction with the introduction of continuous glucose monitoring (CGM) devices, has set the stage for achieving tight glycemic control with systems that adjust the insulin pump settings based on measured glucose concentrations. Because system safety and system reliability are key considerations, there is a need for algorithms that reduce the risk of hypoglycemia in closed-loop, open-loop, and advisory-mode systems. More specifically, the algorithm presented here is formulated as a component of the independent safety system module proposed in the modular control-to-range architecture.Methods:We developed two algorithms for attenuating insulin pump injections, which we refer to as Brakes and Power Brakes: Brakes is a pump attenuation function computed using CGM information only, while Power Brakes is an attenuation function in which a metabolic state observer with insulin input is used ... <s> BIB002 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Algorithms for basal insulin attenuation <s> Modularity plays a key role in many engineering systems, allowing for plug-and-play integration of components, enhancing flexibility and adaptability, and facilitating standardization. In the control of diabetes, i.e., the so-called “artificial pancreas,” modularity allows for the step-wise introduction of (and regulatory approval for) algorithmic components, starting with subsystems for assured patient safety and followed by higher layer components that serve to modify the patient's basal rate in real time. In this paper, we introduce a three-layer modular architecture for the control of diabetes, consisting in a sensor/pump interface module (IM), a continuous safety module (CSM), and a real-time control module (RTCM), which separates the functions of insulin recommendation (postmeal insulin for mitigating hyperglycemia) and safety (prevention of hypoglycemia). In addition, we provide details of instances of all three layers of the architecture: the APS© serving as the IM, the safety supervision module (SSM) serving as the CSM, and the range correction module (RCM) serving as the RTCM. We evaluate the performance of the integrated system via in silico preclinical trials, demonstrating 1) the ability of the SSM to reduce the incidence of hypoglycemia under nonideal operating conditions and 2) the ability of the RCM to reduce glycemic variability. <s> BIB003 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Algorithms for basal insulin attenuation <s> Safety measures to prevent or mitigate hypoglycemia are an important component of open loop, closed loop, and advisory mode insulin therapy control settings in type 1 diabetes. In recent work, we introduce a method for the automatic, gradual attenuation of the insulin pump delivery rate when a risk of hypoglycemia is detected, a method that we refer to as brakes. In the methods presented here, we demonstrate the use of historical glucose measurement data to inform and enhance the ability of the brakes to prevent hypoglycemia in real-time. The updated brakes are based on a patient-specific, time-varying model that reflects the typical trajectory of glycemic fluctuations throughout the day. Historical heightened risk of hypoglycemia throughout the day prompts an increase in the aggressiveness of insulin attenuation as compared to the original brakes that are based on real-time data alone. Through the use of available real-time data supplemented with historical glucose information to assess hypoglycemic risk, we are able to better anticipate and prevent hypoglycemia. <s> BIB004 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Algorithms for basal insulin attenuation <s> BACKGROUND ::: A simulation methodology based on the net effect, a signal estimated from continuous glucose monitoring (CGM) and insulin data accounting for sources of glucose variability, for example, meals and exercise, has been proposed. This method has been recently used to "replay" real-life treatment scenarios and determine the minimal level of CGM sensor accuracy required for nonadjunctive use. Given the potential of the net effect method, it is important to assess its domain of validity. ::: ::: ::: METHODS ::: The UVA/Padova type 1 diabetes simulator is used to generate glucose and insulin data. The net effect signal is estimated and used to predict the glucose profiles resulting from the following therapy modifications: (1) basal insulin increase/decrease, (2) bolus reduction to prevent hypoglycemia, (3) bolus addition after CGM hyperalarms, (4) hypotreatment addition after CGM hypoalarms. Results of the net effect method are compared with the reference provided by the UVA/Padova simulator. ::: ::: ::: RESULTS ::: The net effect method (1) well predicts the effect of small basal insulin adjustments (±10%), but overestimates time in hypo/hyperglycemia for larger adjustments (±50%); (2) underestimates the bolus reduction required to prevent hypoglycemia; (3) underestimates time in hyperglycemia when introducing correction boluses; and (4) overestimates time in hypoglycemia when introducing hypotreatments. ::: ::: ::: CONCLUSIONS ::: The net effect method is reliable for small adjustments of basal insulin, while outside this domain of validity it can provide inaccurate results. <s> BIB005
Starting from 2010, the research group led by Prof. Boris Kovatchev at the University of Virginia has been working on a different approach, i.e. algorithms to attenuate, rather than suspend, the basal insulin delivery according to the risk of hypoglycemia. The two main approaches that they developed were called Brakes and Power Brakes BIB002 . Brakes is a detection-based algorithm which attenuates basal insulin delivery when the 15-min unweighted moving average of CGM measurements, ȳ , goes below T = 120 mg/ dl with negative derivative. The attenuation factor φ is calculated according to the following formula: where R(ȳ) is the glucose risk function introduced by Kovatchev et al. BIB001 and Ŵ is a patient-specific aggressiveness parameter derived from total daily insulin and correction factor. As visible in Fig. 2 , where φ(R(ȳ)) is plotted for two different values of Ŵ , the attenuation factor rapidly decreases as the hypoglycemia risk increases and this decrease is more rapid for higher Ŵ values. Basal insulin delivery is restored as soon as the derivative of ȳ becomes positive. Power Brakes is a prediction-based algorithm that uses both CGM and insulin data to forecast glucose concentration in the next 15 min and attenuate basal insulin delivery accordingly. Specifically, glucose prediction is performed by the Kalman filter using as underlying model the metabolic state observer, i.e. a linearized and discretized version of the subcutaneous oral glucose minimal model BIB005 with population parameters. The inputs of the model are CGM measurements and insulin pump delivery rate. The basal insulin attenuation factor is calculated as in Brakes (Eq. 1 replacing ȳ with ŷ , i.e. the 15-min ahead predicted glucose concentration returned by the Kalman filter). Basal insulin delivery is restored as soon as the derivative of ŷ becomes positive. The method of Power Brakes was further developed in work by Hughes et al. BIB004 by adding in input to the algorithm information extracted from historical CGM data collected in the previous 30 days. The goal was to let the algorithm anticipate routine behavioral events that can cause hypoglycemia, like exercise or consistent over-delivery of insulin. In the version of Power Brakes enhanced with historical CGM data, the Kalman filter and the metabolic state observer are used in real-time to filter the CGM signal and obtain a clean estimate of current glucose concentration. This is then projected in the next 30 min using a simple linear regression model with patient-specific parameters that depends on the time of the day (updated every 30 min) and have been estimated from historical CGM data collected in the previous 30 days. Historical data are also used to tune the threshold T for activation of basal insulin attenuation, which was set to either 120 mg/dl or 140 mg/dl depending on the probability of being in hypoglycemia in the next 60 min estimated from historical CGM data. Another variation of the Power Brakes algorithm was proposed by Patek et al. BIB003 as safety supervision module (SSM) of a modular closed-loop system. In this variant, glucose prediction is performed by a simple linear regression in time with PH = 17 min, intercept parameter equal to the unweighted average of CGM measurements collected in the last 10 min and slope parameter equal to the least-square linear fit of CGM samples in the last 30 min. Predicted glucose is then corrected to account for the effect of insulin-on-board (IOB), i.e. previously injected insulin that is still active in the body. This is done by subtracting to the predicted glucose concentration a term proportional  . In particular, the function is plotted for the subject with maximum aggressiveness parameter (max Ŵ , red line) and the subject with minimum aggressiveness parameter (min Ŵ 18:37 to an estimate of current IOB, obtained using insulin action curves of different duration (4-8 h) in different glucose ranges. The glucose value obtained after IOB correction is used to calculate the risk of hypoglycemia using the same function of the original Power Brakes formulation, but with different parameters. Particularly, in the SSM variant, basal insulin attenuation is triggered only if CGM is lower than 112.5 mg/dl with decreasing trend. Finally, the attenuation factor is calculated as in Eq. 1, but without applying the aggressiveness parameter ( Ŵ = 1).