Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <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> BIB001 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> Background: Sensor-augmented insulin pumps may be programmed to suspend insulin delivery in response to hypoglycemia. The Medtronic Paradigm ® Veo™ pump with automatic low glucose suspend (LGS) was released in 2009. Data from 7 months of real-world use of the system were analyzed to assess usage patterns and effectiveness of LGS. Method: Data from 935 patients totaling 49,867 patient days were collected; the LGS feature was on for 82% of these days. A subset of 278 subjects who used the pump for ≥3 months was analyzed separately; these subjects provided 28,401 patient days of data, with LGS used for 92% of the time. Results: <s> BIB002 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <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> Conclusion <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> Conclusion <s> IMPORTANCE ::: Hypoglycemia is a critical obstacle to the care of patients with type 1 diabetes. Sensor-augmented insulin pump with automated low-glucose insulin suspension has the potential to reduce the incidence of major hypoglycemic events. ::: ::: ::: OBJECTIVE ::: To determine the incidence of severe and moderate hypoglycemia with sensor-augmented pump with low-glucose suspension compared with standard insulin pump therapy. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: A randomized clinical trial involving 95 patients with type 1 diabetes, recruited from December 2009 to January 2012 in Australia. ::: ::: ::: INTERVENTIONS ::: Patients were randomized to insulin pump only or automated insulin suspension for 6 months. ::: ::: ::: MAIN OUTCOMES AND MEASURES ::: The primary outcome was the combined incidence of severe (hypoglycemic seizure or coma) and moderate hypoglycemia (an event requiring assistance for treatment). In a subgroup, counterregulatory hormone responses to hypoglycemia were assessed using the hypoglycemic clamp technique. ::: ::: ::: RESULTS ::: Of the 95 patients randomized, 49 were assigned to the standard-pump (pump-only) therapy and 46 to the low-glucose suspension group. The mean (SD) age was 18.6 (11.8) years; duration of diabetes, 11.0 (8.9) years; and duration of pump therapy, 4.1 (3.4) years. The baseline rate of severe and moderate hypoglycemic events in the pump-only group was 20.7 vs 129.6 events per 100 patient months in the low-glucose suspension group. After 6 months of treatment, the event rates decreased from 28 to 16 in the pump-only group vs 175 to 35 in the low-glucose suspension group. The adjusted incidence rate per 100 patient-months was 34.2 (95% CI, 22.0-53.3) for the pump-only group vs 9.5 (95% CI, 5.2-17.4) for the low-glucose suspension group. The incidence rate ratio was 3.6 (95% CI, 1.7-7.5; P <.001). There was no change in glycated hemoglobin in either group: mean, 7.4 (95% CI, 7.2-7.6) to 7.4 (95% CI, 7.2-7.7) in the pump-only group vs mean, 7.6 (95%, CI, 7.4-7.9) to 7.5 (95% CI, 7.3-7.7) in the low-glucose suspension group. Counterregulatory hormone responses to hypoglycemia were not changed. There were no episodes of diabetic ketoacidosis or hyperglycemia with ketosis. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Sensor-augmented pump therapy with automated insulin suspension reduced the combined rate of severe and moderate hypoglycemia in patients with type 1 diabetes. ::: ::: ::: TRIAL REGISTRATION ::: anzctr.org.au Identifier: ACTRN12610000024044. <s> BIB005 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <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 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> Abstract Background: The automatic Threshold Suspend (TS) feature of the MiniMed 530G system (Medtronic MiniMed, Inc., Northridge, CA), when enabled, suspends insulin delivery for up to 2 h when the sensor glucose (SG) value reaches a preset threshold. Materials and Methods: SG data from 20,973 patients who enabled the TS feature at their discretion and uploaded pump and sensor data to CareLink® (Medtronic MiniMed, Inc.) from October 15, 2013 to July 21, 2014 were analyzed. Comparisons between 758,382 patient-days wherein the TS feature was enabled at any time and 166,791 patient-days in which it was not enabled were made. Further comparisons were made between data collected during daytime (8:00 a.m. to 10:00 p.m.) and nighttime (10:00 p.m. to 8:00 a.m.) hours. Data from subsets of patients who enabled the TS feature all of the time (n=14,673) versus those who never enabled the TS feature (n=2,249) were also compared. Recovery from hypoglycemia during and after 2-h pump suspension events was also assessed... <s> BIB007 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> OBJECTIVE Nocturnal hypoglycemia can cause seizures and is a major impediment to tight glycemic control, especially in young children with type 1 diabetes. We conducted an in-home randomized trial to assess the efficacy and safety of a continuous glucose monitor–based overnight predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS In two age-groups of children with type 1 diabetes (11–14 and 4–10 years of age), a 42-night trial for each child was conducted wherein each night was assigned randomly to either having the PLGS system active (intervention night) or inactive (control night). The primary outcome was percent time RESULTS Median time at P n = 45) and by 50% from 6.2% to 3.1% ( P n = 36). Mean overnight glucose was lower on control versus intervention nights in both age-groups (144 ± 18 vs. 152 ± 19 mg/dL [ P P = 0.004], respectively). Mean morning blood glucose was 159 ± 29 vs. 176 ± 28 mg/dL ( P P = 0.11) in the 4–10-year-olds, respectively. No differences were found between intervention and control in either age-group in morning blood ketosis. CONCLUSIONS In 4–14-year-olds, use of a nocturnal PLGS system can substantially reduce overnight hypoglycemia without an increase in morning ketosis, although overnight mean glucose is slightly higher. <s> BIB008 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> 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. <s> BIB009 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> PURPOSE OF REVIEW ::: The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems. ::: ::: ::: RECENT FINDINGS ::: A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep. The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems. <s> BIB010 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> OBJECTIVE ::: To investigate whether predictive low glucose management (PLGM) of the MiniMed 640G system significantly reduces the rate of hypoglycemia compared with the sensor-augmented insulin pump in children with type 1 diabetes. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: This randomized, two-arm, parallel, controlled, two-center open-label study included 100 children and adolescents with type 1 diabetes and glycated hemoglobin A1c ≤10% (≤86 mmol/mol) and using continuous subcutaneous insulin infusion. Patients were randomly assigned to either an intervention group with PLGM features enabled (PLGM ON) or a control group (PLGM OFF), in a 1:1 ratio, all using the same type of sensor-augmented insulin pump. The primary end point was the number of hypoglycemic events below 65 mg/dL (3.6 mmol/L), based on sensor glucose readings, during a 14-day study treatment. The analysis was performed by intention to treat for all randomized patients. ::: ::: ::: RESULTS ::: The number of hypoglycemic events below 65 mg/dL (3.6 mmol/L) was significantly smaller in the PLGM ON compared with the PLGM OFF group (mean ± SD 4.4 ± 4.5 and 7.4 ± 6.3, respectively; P = 0.008). This was also true when calculated separately for night (P = 0.025) and day (P = 0.022). No severe hypoglycemic events occurred; however, there was a significant increase in time spent above 140 mg/dL (7.8 mmol/L) in the PLGM ON group (P = 0.0165). ::: ::: ::: CONCLUSIONS ::: The PLGM insulin suspension was associated with a significantly reduced number of hypoglycemic events. Although this was achieved at the expense of increased time in moderate hyperglycemia, there were no serious adverse effects in young patients with type 1 diabetes. <s> BIB011 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> OBJECTIVE ::: The objective of this study was to determine the safety, feasibility, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system compared with predictive low-glucose insulin suspension (PLGS) alone in overnight glucose control. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: A 42-night trial was conducted in 30 individuals with type 1 diabetes in the age range 15-45 years. Participants were randomly assigned each night to either PHHM or PLGS and were blinded to the assignment. The system suspended the insulin pump on both the PHHM and PLGS nights for predicted hypoglycemia but delivered correction boluses for predicted hyperglycemia on PHHM nights only. The primary outcome was the percentage of time spent in a sensor glucose range of 70-180 mg/dL during the overnight period. ::: ::: ::: RESULTS ::: The addition of automated insulin delivery with PHHM increased the time spent in the target range (70-180 mg/dL) from 71 ± 10% during PLGS nights to 78 ± 10% during PHHM nights (P < 0.001). The average morning blood glucose concentration improved from 163 ± 23 mg/dL after PLGS nights to 142 ± 18 mg/dL after PHHM nights (P < 0.001). Various sensor-measured hypoglycemic outcomes were similar on PLGS and PHHM nights. All participants completed 42 nights with no episodes of severe hypoglycemia, diabetic ketoacidosis, or other study- or device-related adverse events. ::: ::: ::: CONCLUSIONS ::: The addition of a predictive hyperglycemia minimization component to our existing PLGS system was shown to be safe, feasible, and effective in overnight glucose control. <s> BIB012 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> Abstract Background: The safety and effectiveness of the in-home use of a hybrid closed-loop (HCL) system that automatically increases, decreases, and suspends insulin delivery in response to continuous glucose monitoring were investigated. Methods: Adolescents (n = 30, ages 14–21 years) and adults (n = 94, ages 22–75 years) with type 1 diabetes participated in a multicenter (nine sites in the United States, one site in Israel) pivotal trial. The Medtronic MiniMed® 670G system was used during a 2-week run-in phase without HCL control, or Auto Mode, enabled (Manual Mode) and, thereafter, with Auto Mode enabled during a 3-month study phase. A supervised hotel stay (6 days/5 nights) that included a 24-h frequent blood sample testing with a reference measurement (i-STAT) occurred during the study phase. Results: Adolescents (mean ± standard deviation [SD] 16.5 ± 2.29 years of age and 7.7 ± 4.15 years of diabetes) used the system for a median 75.8% (interquartile range [IQR] 68.0%–88.4%) of the time (2977 pati... <s> BIB013 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> In this paper we consider the problem of predicting future values of glucose in type-1 diabetes. In particular, we investigate the benefit of including physical activity, measured by an off-the-shelf wearable device, to other physiologic signals frequently used to predict blood-glucose concentration, namely injected insulin, carbohydrates intake, and past glucose samples measured by a Continuous Glucose Monitoring (CGM) sensor. Derivation of individualized predictors is crucial to cope with the wide inter- and intra-subject variability: learning and updating patient-specific models of the glucose-insulin system and using them to design personalized control actions has the potential to improve substantially patients' quality oflife. On data collected by 6 subjects for 5 days, we identify a black-box liner model that uses insulin and meal as inputs and glucose as output. Prediction Error Method (PEM) is used for parameter estimation. The personalized model is employed to derive patient-tailored predictors. This procedure is then repeated using a further physiological input, accounting for physical activity. The prediction accuracy of the two models, including or not physical activity, was compared on the basis of two metrics commonly used in system identification, namely Coefficient of Determination (COD) and Root Mean Squared Error. The models identified with physical activity have better performance, increasing the 3-hr prediction COD by mean ± standard deviation of 18.5% ± 30.1%. <s> BIB014 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> OBJECTIVE:The primary objective of this trial was to evaluate the feasibility, safety, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system vs predictive low glucose suspension (PLGS) alone in optimizing overnight glucose control in children 6 to 14 years old. RESEARCH DESIGN AND METHODS:Twenty-eight participants 6 to 14 years old with T1D duration ≥1 year with daily insulin therapy ≥12 months and on insulin pump therapy for ≥6 months were randomized per night into PHHM mode or PLGS-only mode for 42 nights. The primary outcome was percentage of time in sensor-measured range 70 to 180 mg/dL in the overnight period. RESULTS:The addition of automated insulin delivery with PHHM increased time in target range (70-180 mg/dL) from 66 ± 11% during PLGS nights to 76 ± 9% during PHHM nights (P<.001), without increasing hypoglycemia as measured by time below various thresholds. Average morning blood glucose improved from 176 ± 28 mg/dL following PLGS nights to 154 ± 19 mg/dL following PHHM nights (P<.001). CONCLUSIONS:The PHHM system was effective in optimizing overnight glycemic control, significantly increasing time in range, lowering mean glucose, and decreasing glycemic variability compared to PLGS alone in children 6 to 14 years old. <s> BIB015 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> Real-world data from the first 3141 patients who completed 3 months of SmartGuard™ Auto Mode-enabled MiniMed™ 670G system use during the MiniMed 670G System Commercial Launch are reported. CareLink™ system data uploaded by real-world patients in the Commercial Launch from March 17, 2017 to December 31, 2017 were deidentified and analyzed. Comparisons of overall and night (10:00 PM-07:00 AM) time spent below, within, and above target glucose range (TIR) (70-180 mg/dL) between the baseline Manual Mode and closed-loop Auto Mode periods were made. These were evaluated alongside data from the 124 patients (aged 14-75 years) who completed the 3-month MiniMed 670G system pivotal trial (NCT 2463097), from June 2, 2015 to March 7, 2016. Real-world patients used Auto Mode a median 80.8% of the time (19 h and 24 min of the day). The overall mean of time spent in TIR was 66.0% during baseline Manual Mode versus 73.3% during Auto Mode (P < 0.001); the mean percentage of sensor glucose values <70 mg/dL was 2.7% versus 2.1% (P < 0.001); and that >180 mg/dL was 31.4% versus 24.6% (P < 0.001). The nighttime and early morning (03:00 AM-06:00 AM) TIR during Auto Mode was greater than that during baseline Manual Mode (nighttime: 77.2% vs. 67.4% [P < 0.001], early morning: 70.9% vs. 84.6% [P < 0.001]). Similar differences between Manual Mode and Auto Mode TIR were observed across different age groups. A slight increase in total insulin delivered was also observed. Consistent with improved glycemic control demonstrated in the pivotal trial, analysis of CareLink system data from >3000 real-world patients who completed 3 months of Auto Mode-enabled MiniMed 670G system use demonstrated increased TIR and decreased time below and above TIR compared with baseline. These improved clinical outcomes were observed across a broad age range of patients with type 1 diabetes. <s> BIB016 </s> Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review <s> Conclusion <s> Achievement of well-controlled blood glucose is essential for preventing complications in patients with type 1 diabetes. Since the inception of continuous subcutaneous insulin infusion, the aim has been to develop an artificial pancreas, with the ability to use an automated algorithm to deliver one or more hormones in response to blood glucose with the intent to keep blood sugar as close to a prespecified target as possible. Development and rapid improvement of continuous glucose sensor technology has recently allowed swift progress toward a fully closed-loop insulin delivery system. In 2017, Medtronic began marketing the 670G insulin pump with Guardian 3 sensor. When in auto mode, this is a hybrid closed-loop insulin delivery system that automatically adjusts basal insulin delivery every 5 min based on sensor glucose to maintain blood glucose levels as close to a specific target as possible. Patients receive prandial insulin by entering carbohydrate amount into the bolus calculator. Early studies show improvement in HbA1c in both adults and adolescents with this technology. Initial safety trials showed no occurrence of diabetic ketoacidosis or hypoglycemia. The utility of this device is limited by blood glucose targets of 120 and 150 mg/dL that are unacceptably high for some patients. Notwithstanding recent advances, we are far from a system that is able to replicate islet function in the form of a fully automated, multihormonal blood glucose control device. <s> BIB017
The availability of insulin pumps communicating with CGM sensors has stimulated the development of algorithms for the automatic suspension or attenuation of basal insulin delivery when hypoglycemia is either detected or predicted from CGM readings. Some algorithms were also implemented in commercial integrated systems produced by Medtronic. The commercially available algorithms are all suspension algorithms, i.e. they can only turn on or off basal insulin delivery, and rely only on CGM measurements for the prediction of future hypoglycemia BIB002 BIB007  . 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 BIB009 BIB010 BIB014 . Some literature studies on basal insulin attenuation algorithms have explored the possibility of enhancing glucose prediction by taking into account insulin data BIB001 BIB003 and historical CGM data BIB004 . However, such algorithms have been tested only in short in silico experiments and a more extensive evaluation is needed to understand the real benefits of using insulin and historical glucose data as additional inputs for glucose prediction. A first step in this direction could be the assessment of these algorithms using a more advanced simulation framework including a description of physiological, technological and behavioral variability, which allows the simulation of realistic multiple-day scenarios   . In another literature study, physical activity data were used to enhance the performance of an algorithm for basal insulin suspension in mitigation of exercise-induced hypoglycemia BIB006 . However, in the proposed algorithm, physical activity could trigger basal insulin suspension independently on glucose prediction. A more robust approach would be to use physical activity data to enhance glucose prediction and then use the prediction derived from CGM, physical activity and possibly other input data to trigger basal insulin suspension. Another interesting idea that has been proposed in the literature is the use of a smoothed attenuation function BIB001 BIB004 BIB003 . Further assessments are needed to understand the real benefits of using a smooth attenuation function instead of a threshold-based suspension strategy, like that implemented in commercial devices. Indeed, the plot of Fig. 2 shows that the attenuation function proposed by Hughes et al. BIB001 is very steep, thus the effect on glucose concentration of using such an attenuation function may actually be very similar to that obtained with a step function. The assessment of basal insulin suspension algorithms in clinical trials showed that these methods are effective in reducing hypoglycemia outcomes, except the rate of events of profound hypoglycemia (e.g. below 50 mg/dl), whose incidence and duration did not change significantly with or without the use of pump suspension BIB001 BIB011 . The price is a slightly increase of time spent in the high glucose range (e.g. above 140 mg/dl)  BIB008 BIB005 BIB011 , although no serious adverse event (e.g. diabetes ketoacidosis) associated with the use of suspension algorithms was observed. As Hughes et al. shown in a simulated case study BIB001 , the simple attenuation of basal insulin when hypoglycemia is predicted may not be sufficient to prevent episodes of profound hypoglycemia caused by large insulin boluses, unless this is combined with carbohydrate intake. Therefore, a possible effective solution for the prevention of profound hypoglycemia could be the combination of basal insulin attenuation algorithms with algorithms for the suggestion of preventive hypotreatments in presence of elevated hypoglycemia risk. Such preventive hypotreatment advisory systems could be also applied to mitigate hypoglycemia in patients on traditional multiple daily injections therapy. To minimize the undesired rebound in the high glucose range following a suspension event, basal insulin attenuation algorithms can be coupled with algorithms allowing automatic basal insulin increase in response to high glucose concentration. This strategy was supported, for example, by Patek et al. that showed the performance of their SSM increased when this was coupled with the Range Correction Module allowing positive insulin corrections BIB003 . In recent work by Spaic et al., the prediction-based suspension algorithm developed by Buckingham et al. BIB008 was combined with an automatic insulin-dosing component, forming the Predictive Hyperglycemia and Hypoglycemia Minimization system for overnight control BIB012 . Two recent randomized clinical trials showed that the addition of the insulin-dosing component allowed the system to increase time in target and reduce mean morning glycemia, without deteriorating the performance in hypoglycemia mitigation BIB012 BIB015 . Systems like these that allow realtime automatic positive and negative adjustments of basal insulin delivery are examples of hybrid closed-loop systems, which have been object of intense research activity in the last 10 years and recently entered the market, with the Medtronic MiniMed 670G being the first commercial hybrid closed-loop system BIB013 BIB016 BIB017 . In conclusion, basal insulin attenuation algorithms are a promising technique for the mitigation of hypoglycemia in SAP therapy and represent the first step towards a fully closed-loop system. Although some of these algorithms have already been implemented in commercial devices, there are still many margins for improving their performance. Interesting research topics in this area include the enhancement of prediction algorithms by adopting multivariable approaches, the use of different smooth attenuation functions, and the combination of basal insulin suspension algorithms with preventive hypotreatment advisory systems and/or automatic insulin dosing algorithms. Abbreviations T1D: Type 1 diabetes; BG: blood glucose; CGM: continuous glucose monitoring; HbA1c: glycated hemoglobin; SMBG: self-monitoring of blood glucose; PH: prediction horizon; SSM: safety supervision module; IOB: insulin-on-board; LGS: low glucose suspend; TS: threshold suspend; PLGM: Prediction Low Glucose Management; AUC : area under the curve; SAP: sensor-augmented pump.