Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Algorithms for basal insulin suspension <s> Background:Hypoglycemia presents a significant risk for patients with insulin-dependent diabetes mellitus. We propose a predictive hypoglycemia detection algorithm that uses continuous glucose monitor (CGM) data with explicit certainty measures to enable early corrective action.Method:The algorithm uses multiple statistical linear predictions with regression windows between 5 and 75 minutes and prediction horizons of 0 to 20 minutes. The regressions provide standard deviations, which are mapped to predictive error distributions using their averaged statistical correlation. These error distributions give confidence levels that the CGM reading will drop below a hypoglycemic threshold. An alarm is generated if the resultant probability of hypoglycemia from our predictions rises above an appropriate, user-settable value. This level trades off the positive predictive value against lead time and missed events.Results:The algorithm was evaluated using data from 26 inpatient admissions of Navigator® 1-minute read... <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 suspension <s> Abstract Background: Nocturnal hypoglycemia is a significant problem. From 50% to 75% of hypoglycemia seizures occur at night. Despite the development of real-time glucose sensors (real-time continuous glucose monitor [CGM]) with hypoglycemic alarms, many patients sleep through these alarms. The goal of this pilot study was to assess the feasibility using a real-time CGM to discontinue insulin pump therapy when hypoglycemia was predicted. Methods: Twenty-two subjects with type 1 diabetes had two daytime admissions to a clinical research center. On the first admission their basal insulin was increased until their blood glucose level was <60 mg/dL. On the second admission hypoglycemic prediction algorithms were tested to determine if hypoglycemia was prevented by a 90-min pump shutoff and to determine if the pump shutoff resulted in rebound hyperglycemia. Results: Using a statistical prediction algorithm with an 80 mg/dL threshold and a 30-min projection horizon, hypoglycemia was prevented 60% of the time. ... <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 suspension <s> OBJECTIVE ::: The aim of this study was to develop a partial closed-loop system to safely prevent nocturnal hypoglycemia by suspending insulin delivery when hypoglycemia is predicted in type 1 diabetes. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: Forty subjects with type 1 diabetes (age range 12-39 years) were studied overnight in the hospital. For the first 14 subjects, hypoglycemia (<60 mg/dl) was induced by gradually increasing the basal insulin infusion rate (without the use of pump shutoff algorithms). During the subsequent 26 patient studies, pump shutoff occurred when either three of five (n = 10) or two of five (n = 16) algorithms predicted hypoglycemia based on the glucose levels measured with the FreeStyle Navigator (Abbott Diabetes Care). ::: ::: ::: RESULTS ::: The standardized protocol induced hypoglycemia on 13 (93%) of the 14 nights. With use of a voting scheme that required three algorithms to trigger insulin pump suspension, nocturnal hypoglycemia was prevented during 6 (60%) of 10 nights. When the voting scheme was changed to require only two algorithms to predict hypoglycemia to trigger pump suspension, hypoglycemia was prevented during 12 (75%) of 16 nights. In the latter study, there were 25 predictions of hypoglycemia because some subjects had multiple hypoglycemic events during a night, and hypoglycemia was prevented for 84% of these events. ::: ::: ::: CONCLUSIONS ::: Using algorithms to shut off the insulin pump when hypoglycemia is predicted, it is possible to prevent hypoglycemia on 75% of nights (84% of events) when it would otherwise be predicted to occur. <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 suspension <s> OBJECTIVE ::: The purpose of this study was to develop an advanced algorithm that detects pending hypoglycemia and then suspends basal insulin delivery. This approach can provide a solution to the problem of nocturnal hypoglycemia, a major concern of patients with diabetes. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: This real-time hypoglycemia prediction algorithm (HPA) combines five individual algorithms, all based on continuous glucose monitoring 1-min data. A predictive alarm is issued by a voting algorithm when a hypoglycemic event is predicted to occur in the next 35 min. The HPA system was developed using data derived from 21 Navigator studies that assessed Navigator function over 24 h in children with type 1 diabetes. We confirmed the function of the HPA using a separate dataset from 22 admissions of type 1 diabetic subjects. During these admissions, hypoglycemia was induced by gradual increases in the basal insulin infusion rate up to 180% from the subject's own baseline infusion rate. RESULTS Using a prediction horizon of 35 min, a glucose threshold of 80 mg/dl, and a voting threshold of three of five algorithms to predict hypoglycemia (defined as a FreeStyle plasma glucose readings <60 mg/dl), the HPA predicted 91% of the hypoglycemic events. When four of five algorithms were required to be positive, then 82% of the events were predicted. ::: ::: ::: CONCLUSIONS ::: The HPA will enable automated insulin-pump suspension in response to a pending event that has been detected prior to severe immediate complications. <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 suspension <s> Abstract Objective: Nocturnal hypoglycemia is a common problem with type 1 diabetes. In the home setting, we conducted a pilot study to evaluate the safety of a system consisting of an insulin pump and continuous glucose monitor communicating wirelessly with a bedside computer running an algorithm that temporarily suspends insulin delivery when hypoglycemia is predicted. Research Design and Methods: After the run-in phase, a 21-night randomized trial was conducted in which each night was randomly assigned 2:1 to have either the predictive low-glucose suspend (PLGS) system active (intervention night) or inactive (control night). Three predictive algorithm versions were studied sequentially during the study for a total of 252 intervention and 123 control nights. The trial included 19 participants 18–56 years old with type 1 diabetes (hemoglobin A1c level of 6.0–7.7%) who were current users of the MiniMed Paradigm® REAL-Time Revel™ System and Sof-sensor® glucose sensor (Medtronic Diabetes, Northridge, CA). R... <s> BIB005 </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 suspension <s> Aerobic exercise can lower blood glucose levels and alter insulin sensitivity both during and several hours after exercise, creating challenges for a closed-loop artificial pancreas. Predictive low glucose suspend (PLGS) algorithms are a first step toward an artificial pancreas, but few of these have been successfully applied to exercise. This study incorporates physical activity measurements from a combined accelerometer/heart rate monitor (HRM) to improve the performance of an existing PLGS algorithm at mitigating exercise-associated hypoglycemia in participants with type 1 diabetes. In all, 22 subjects with type 1 diabetes on insulin pump therapy were provided a combined accelerometer/HRM and (if not already using one) a continuous glucose monitor (CGM), then instructed to go about their everyday lives while wearing the devices. After the monitoring period, each subject's insulin pump, CGM, and accelerometer/HRM were downloaded and the data were used to augment an existing PLGS algorithm to incorporate activity. Using a computer simulator, the accelerometer-augmented algorithm was compared to the HRM-augmented algorithm to determine which was most effective at mitigating hypoglycemia. Mean length of monitoring was 4.9 days. Across all subjects, 11 061 CGM readings were recorded during the monitoring period. In the simulator analysis, the PLGS algorithm reduced hypoglycemia by 62%, compared to 71% and 74% reductions for the HRM-augmented and accelerometer-augmented algorithms, respectively; combined accelerometer and HRM augmentation provided a 76% reduction. In a simulated setting, the accelerometer-augmented pump suspension algorithm decreases the incidence of exercise-related hypoglycemia by a meaningful amount compared to the PLGS algorithm alone. Results also failed to justify the additional user burden of a HRM. <s> BIB006
The first attempt to produce an algorithm to suspend the basal insulin infusion rate based on CGM values is due to the research group of Dr. Bruce Buckingham. Indeed, in 2009 Buckingham and colleagues developed and tested a method to discontinue insulin pump injection when hypoglycemia is predicted from real-time CGM BIB002 . The algorithm used in this study consisted in suspending basal insulin delivery for 90 min when glucose was predicted to fall below 80 mg/dl in the next 30 min. In particular, two methods were proposed for glucose prediction: a simple linear prediction algorithm that predicts future glycemia by linear extrapolation of CGM data; and a method using multiple empirical statistical models to estimate probability of future hypoglycemia BIB001 . The algorithms were assessed in a daytime 7-h in clinic experiment in which hypoglycemia was induced by controlled increase of basal insulin infusion rate. After the encouraging results obtained in the first pilot study (56% of hypoglycemic events were prevented), Buckingham et al. further developed their method and tested a second prediction-based suspension algorithm for prevention of nocturnal hypoglycemia in clinic BIB003 . In this second study, hypoglycemia was predicted by using 5 different prediction algorithms and basal insulin infusion was suspended if 2 or 3 out of 5 prediction algorithms predicted a glucose value below 80 mg/dl in the next 35 min BIB004 . The 5 prediction algorithms considered were: linear extrapolation, Kalman filtering  , adaptive hybrid infinite impulse filter, statistical prediction BIB001 and numerical logical algorithm. Instead of using a fixed suspension time of 90 min as in their previous study, the authors proposed a new criterion for early restart of basal insulin in case (i) at least 30 min passed since suspension started, (ii) CGM rate of change is more than 0.5 mg/dl/min, (iii) CGM value is greater than 80 mg/dl. The early restart criterion allowed to reduce the mean peak glucose after pump suspension from 158 ± 50 mg/dl to 149 ± 32 mg/dl. Among the 5 prediction algorithms, the only algorithm that did not play a significant role in the generation of hypoglycemia alarms was the linear extrapolation, result that justifies the investigation of more complex prediction algorithms. In their subsequent work, Buckingham and coauthors abandoned the idea of having multiple prediction algorithms in favor of using a single Kalman filter prediction algorithm with a PH of 70 min  . This choice was motivated on one hand by the need to reduce computational complexity, on the other hand by the necessity to further anticipate the insulin suspension in order to increase the proportion of nights with prevented hypoglycemia, which was about 75% in their previous study BIB003 . For this purpose, the criteria to restart the basal insulin were also updated. In work by Cameron et al.  , basal insulin delivery was restarted if glucose was predicted to rise above 100 mg/dl in the next 70 min. Moreover, the algorithm allowed a maximum suspension time of 120 min every 150 min and 180 min per night. The new algorithm was tested in clinic in 16 subjects. Mean suspension time was 89 min, hypoglycemia was prevented in 11/15 subjects, but 13/15 subjects had a peak glucose > 180 mg/dl after basal suspension. Nevertheless, the authors stated their study was not designed to rigorously assess the rebound in hyperglycemia, because hypoglycemia was artificially induced by increasing basal insulin delivery, and no control subjects were studied. In 2013, Buckingham et al. BIB005 conducted an outpatient study to test the safety and effectiveness of the algorithm proposed by Cameron et al.  under real-life conditions. The trial revealed that the proposed algorithm was able to reduce overnight hypoglycemia (occurring in 19% of intervention nights vs 26% of control nights), but at the cost of significantly increasing mean morning glycemia (158 ± 52 mg/dl in intervention nights vs 125 ± 53 mg/dl in control nights). The authors then decided to revise their algorithm to reduce the occurrence of pump suspension. Specifically, the PH was reduced to 30 min, pump suspension was not allowed if CGM value was > 230 mg/dl or a drop of ≥ 40 mg/dl was present in consecutive CGM readings (suspected sensor compression artifact), and insulin delivery was resumed as soon as CGM values started rising. With the revised algorithm, overnight hypoglycemia occurred in 16% of intervention nights vs 30% of control nights and a smaller increase in mean morning glucose concentration was observed (144 ± 48 mg/dl vs 133 ± 57 mg/dl). The algorithm by Buckingham et al. BIB005 was extended in work by Stenerson et al. BIB006 to incorporate physical activity measurements derived from accelerometer and/or heart rate monitor to improve the algorithm's performance in mitigating exercise-induced hypoglycemia. Specifically, the extended algorithm suspended basal insulin delivery when CGM reading was below 180 mg/dl and decreasing and one of these two conditions was satisfied: (i) > 0.1 vector magnitude units on the accelerometer; (ii) > 90 beats per minute on the heart rate monitor. The efficacy of the algorithm was first tested in a simulated experiment. Results suggested that adding accelerometer data allowed to improve the performance of the pump suspension algorithm, while further addition of heart rate monitor data did not significantly improve the performance of the system.