"Let the Algorithm Do the Work": Reduction of Hypoglycemia Using Sensor-Augmented Pump Therapy with Predictive Insulin Suspension (SmartGuard) in Pediatric Type 1 Diabetes Patients

Integration of a Safety Module to Prevent Rebound Hypoglycemia in Closed-Loop Artificial Pancreas Systems

BACKGROUND

With automated insulin delivery (AID) systems becoming widely adopted in the management of type 1 diabetes, we have seen an increase in occurrences of rebound hypoglycemia or generated hypoglycemia induced by the controller's response to rapid glucose rises following rescue carbohydrates. Furthermore, as AID systems aim to enable complete automation of prandial control, algorithms are designed to react even more strongly to glycemic rises. This work introduces a rebound hypoglycemia prevention layer (HypoSafe) that can be easily integrated into any AID system.

METHODS

HypoSafe constrains the maximum permissible insulin delivery dose based on the minimum glucose reading from the previous hour and the current glucose level. To demonstrate its efficacy, we integrated HypoSafe into the latest University of Virginia (UVA) AID system and simulated two scenarios using the 100-adult cohort of the UVA/Padova T1D simulator: a nominal case including three unannounced meals, and another case where hypoglycemia was purposely induced by an overestimated manual bolus.

RESULTS

In both simulation scenarios, rebound hypoglycemia events were reduced with HypoSafe (nominal: from 39 to 0, hypo-induced: from 55 to 7) by attenuating the commanded basal (nominal: 0.27U vs. 0.04U, hypo-induced: 0.27U vs. 0.03U) and bolus (nominal: 1.02U vs. 0.05U, hypo-induced: 0.43U vs. 0.02U) within the 30-minute interval after treating a hypoglycemia event. No clinically significant changes resulted for time in the range of 70 to 180 mg/dL or above 180 mg/dL.

CONCLUSION

HypoSafe was shown to be effective in reducing rebound hypoglycemia events without affecting achieved time in range when combined with an advanced AID system.

Evidence from clinical trials on high-risk medical devices in children: a scoping review

BACKGROUND

Meeting increased regulatory requirements for clinical evaluation of medical devices marketed in Europe in accordance with the Medical Device Regulation (EU 2017/745) is challenging, particularly for high-risk devices used in children.

METHODS

Within the CORE-MD project, we performed a scoping review on evidence from clinical trials investigating high-risk paediatric medical devices used in paediatric cardiology, diabetology, orthopaedics and surgery, in patients aged 0-21 years. We searched Medline and Embase from 1st January 2017 to 9th November 2022.

RESULTS

From 1692 records screened, 99 trials were included. Most were multicentre studies performed in North America and Europe that mainly had evaluated medical devices from the specialty of diabetology. Most had enrolled adolescents and 39% of trials included both children and adults. Randomized controlled trials accounted for 38% of the sample. Other frequently used designs were before-after studies (21%) and crossover trials (20%). Included trials were mainly small, with a sample size <100 participants in 64% of the studies. Most frequently assessed outcomes were efficacy and effectiveness as well as safety.

CONCLUSION

Within the assessed sample, clinical trials on high-risk medical devices in children were of various designs, often lacked a concurrent control group, and recruited few infants and young children.

IMPACT

In the assessed sample, clinical trials on high-risk medical devices in children were mainly small, with variable study designs (often without concurrent control), and they mostly enrolled adolescents. We provide a systematic summary of methodologies applied in clinical trials of medical devices in the paediatric population, reflecting obstacles in this research area that make it challenging to conduct adequately powered randomized controlled trials. In view of changing European regulations and related concerns about shortages of high-risk medical devices for children, our findings may assist competent authorities in setting realistic requirements for the evidence level to support device conformity certification.

Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations

BACKGROUND

The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual's longer term control.

METHODS

We introduce explainable machine learning to make predictions of hypoglycemia (<70 mg/dL) and hyperglycemia (>270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user.

RESULTS

Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model.

CONCLUSIONS

Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user's glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.

Tandem T:Slim X2 Insulin Pump Use in Clinical Practice Among Pregnant Individuals With Type 1 Diabetes: A Retrospective Observational Cohort Study

BACKGROUND

Insulin pump use is increasing in frequency among pregnant individuals with type 1 diabetes (T1D). Automated insulin delivery (AID) technologies have not been studied extensively in pregnancy.

METHOD

We present a retrospective case series of eight individuals with T1D who used the Tandem t:slim X2 insulin pump (Tandem Diabetes Care, Inc., CA, USA) during pregnancy. Weekly continuous glucose monitor and insulin pump data were analyzed from electronic medical records and data-sharing portals. Safety, glycemic control, and pregnancy outcomes were examined with both the control IQ (CIQ) and basal IQ (BIQ) algorithms.

RESULTS

Six CIQ and two BIQ users were studied. The mean glycated hemoglobin (A1C) during pregnancy was 6.1%, and the average time in pregnancy-recommended glycemic range (TIR; 63-140mg/dL) was 67.9%. There were no instances of diabetic ketoacidosis or severe hypoglycemia. CIQ users had a higher mean sensor glucose (127.6 mg/dL) compared to BIQ participants (118.4 mg/dL). However, the average time below range (<63 mg/dL) was 6.1% in BIQ participants compared to 1.5% in CIQ participants. CIQ participants used several strategies to achieve glycemic targets, including daytime use of sleep activity. An increased basal-to-bolus insulin ratio was negatively correlated with TIR (r=-0.415).

CONCLUSIONS

Tandem t:slim X2 insulin pumps were safely used during pregnancy in eight individuals with T1D, with variable success in achieving recommended glycemic targets. Further research is needed to understand differences in CIQ and BIQ use in pregnancy. AID device manufacturers must additionally develop further methods to target lower glucose for pregnant users.

The changing landscape of automated insulin delivery in the management of type 1 diabetes

Automated insulin delivery systems, also known as closed-loop or 'artificial pancreas' systems, are transforming the management of type 1 diabetes. These systems consist of an algorithm which responds to real-time glucose sensor levels by automatically modulating insulin delivery through an insulin pump. We review the rapidly changing landscape of automated insulin-delivery systems over recent decades, from initial prototypes to the different hybrid closed-loop systems commercially available today. We discuss the growing body of clinical trials and real-world evidence demonstrating their glycaemic and psychosocial benefits. We also address future directions in automated insulin delivery such as dual-hormone systems and adjunct therapy as well as the challenges around ensuring equitable access to closed-loop technology.

Rebound Hypoglycemia and Hyperglycemia in Type 1 Diabetes

AIMS

The aim was to investigate rebound hypoglycemic and hyperglycemic events, and describe their relation to other glycemic metrics.

METHODS

Data from intermittently scanned continuous glucose monitoring were downloaded for 90 days for 159 persons with type 1 diabetes. A hypoglycemic event was defined as glucose <3.9 mmol/l for at least two 15-minute periods. Rebound hypoglycemia (Rhypo) was a hypoglycemic event preceded by glucose >10.0 mmol/l within 120 minutes and rebound hyperglycemia (Rhyper) was hypoglycemia followed by glucose >10.0 mmol/l within 120 minutes.

RESULTS

A total of 10 977 hypoglycemic events were identified of which 3232 (29%) were Rhypo and 3653 (33%) were Rhyper, corresponding to a median frequency of 10.1, 2.5, and 3.0 events per person/14 days. For 1267 (12%) of the cases, Rhypo and Rhyper coexisted. The mean peak glucose was 13.0 ± 1.6 mmol/l before Rhypo; 12.8 ± 1.1 mmol/l in Rhyper. The frequency of Rhyper was significantly (P < .001) correlated with Rhypo (Spearman's rho 0.84), glucose coefficient of variation (0.78), and time below range (0.69) but not with time above range (0.12, P = .13).

CONCLUSIONS

The strong correlation between Rhyper and Rhypo suggests an individual behavioral characteristic toward intensive correction of glucose excursions.

Sustained heart rate-corrected QT prolongation during recovery from hypoglycaemia in people with type 1 diabetes, independently of recovery to hyperglycaemia or euglycaemia

AIM

To investigate changes in cardiac repolarization abnormalities (heart rate-corrected QT [QT_c ] [primary endpoint], T-wave abnormalities) and heart-rate variability measures in people with type 1 diabetes during insulin-induced hypoglycaemia followed by recovery hyperglycaemia versus euglycaemia.

METHODS

In a randomized crossover study, 24 individuals with type 1 diabetes underwent two experimental clamps with three steady-state phases during electrocardiographic monitoring: (1) a 45-minute euglycaemic phase (5-8 mmol/L), (2) a 60-minute insulin-induced hypoglycaemic phase (2.5 mmol/L), and (3) 60-minute recovery in either hyperglycaemia (20 mmol/L) or euglycaemia (5-8 mmol/L).

RESULTS

All measured markers of arrhythmic risk indicated increased risk during hypoglycaemia. These findings were accompanied by a decrease in vagal tone during both hyperglycaemia and euglycaemia clamps. Compared with baseline, the QT_c interval increased during hypoglycaemia, and 63% of the participants exhibited a peak QT_c of more than 500 ms. The prolonged QT_c interval was sustained during both recovery phases with no difference between recovery hyperglycaemia versus euglycaemia. During recovery, no change from baseline was observed in heart-rate variability measures.

CONCLUSIONS

In people with type 1 diabetes, insulin-induced hypoglycaemia prolongs cardiac repolarization, which is sustained during a 60-minute recovery period independently of recovery to hyperglycaemia or euglycaemia. Thus, vulnerability to serious cardiac arrhythmias and sudden cardiac death may extend beyond a hypoglycaemic event, regardless of hyperglycaemic or euglycaemic recovery.

The Advanced Diabetes Technologies for Reduction of the Frequency of Hypoglycemia and Minimizing the Occurrence of Severe Hypoglycemia in Children and Adolescents with Type 1 Diabetes

Hypoglycemia is an often-observed acute complication in the management of children and adolescents with type 1 diabetes. It causes inappropriate glycemic outcomes and may impair the quality of life in the patients. Severe hypoglycemia with cognitive impairment, such as a convulsion and coma, is a lethal condition and is associated with later-onset cognitive impairment and brain-structural abnormalities, especially in young children. Therefore, reducing the frequency of hypoglycemia and minimizing the occurrence of severe hypoglycemia are critical issues in the management of children and adolescents with type 1 diabetes. Advanced diabetes technologies, including continuous glucose monitoring and sensor-augmented insulin pumps with low-glucose suspension systems, can reduce the frequency of hypoglycemia and the occurrence of severe hypoglycemia without aggravating glycemic control. The hybrid closed-loop system, an automated insulin delivery system, must be the most promising means to achieve appropriate glycemic control with preventing severe hypoglycemia. The use of these advanced diabetes technologies could improve glycemic outcomes and the quality of life in children and adolescents with type 1 diabetes.

Insulin pumps in children - a systematic review

BACKGROUND

Insulin pump therapy is a real breakthrough in managing diabetes Mellitus, particularly in children. It can deliver a tiny amount of insulin and decreases the need for frequent needle injections. It also helps to maintain adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues. Children are suitable candidates for pump therapy as they need a more freestyle and proper metabolic control to ensure adequate growth and development. Therefore, children and their caregivers should have proper education and training and understand the proper use of insulin pumps to achieve successful pump therapy. The pump therapy continuously improves to enhance its performance and increase its simulation of the human pancreas. Nonetheless, there is yet a long way to reach the desired goal.

AIM

To review discusses the history of pump development, its indications, types, proper use, special conditions that may enface the children and their families while using the pump, its general care, and its advantages and disadvantages.

METHODS

We conducted comprehensive literature searches of electronic databases until June 30, 2022, related to pump therapy in children and published in the English language.

RESULTS

We included 118 articles concerned with insulin pumps, 61 were reviews, systemic reviews, and meta-analyses, 47 were primary research studies with strong design, and ten were guidelines.

CONCLUSION

The insulin pump provides fewer needles and can provide very tiny insulin doses, a convenient and more flexible way to modify the needed insulin physiologically, like the human pancreas, and can offer adequate and optimal glycemic control to reduce the risk of metabolic derangements in different tissues.

Predictors of time in target glucose range in real-world users of the MiniMed 780G system

AIM

Automated insulin delivery systems have improved glycaemic control in people with type 1 diabetes mellitus. The analysis investigated predictors of improved sensor glucose time-in-range (TIR; 70-180 mg/dl) based on real-world use of the MiniMed 780G advanced hybrid closed-loop (AHCL) system.

METHODS

Data uploaded by MiniMed 780G system users from August 2020-July 2021 were analysed using univariate and multivariable models to identify baseline, demographic and system use characteristics associated with TIR after AHCL initiation (post-AHCL). System settings associated with improved TIR post-AHCL were identified and their impact on time below range (TBR, <70 mg/dl) post-AHCL was explored.

RESULTS

In total, 12 870 users were included, of which 2977 had baseline sensor glucose data. Baseline TIR and time in AHCL (defined as the percentage of time the system was in Auto-mode) were positively associated with TIR post-AHCL with larger values predicting greater mean TIR post-AHCL. Characteristics inversely associated with TIR post-AHCL included the percentage of daily basal insulin dose, daily autocorrection dose, number of daily AHCL exits triggered by the system and number of daily alarms, wherein larger values of these characteristics predicted lower mean TIR post-AHCL. System settings that predicted the largest mean TIR post-AHCL were active insulin time of 2 h and glucose target of 100 mg/dl. Active insulin time was not associated with TBR post-AHCL.

CONCLUSION

Modifiable factors, including optimized pump settings, can allow users to achieve glycaemic targets with >80% TIR. The findings from this analysis will potentially guide the optimal use of the MiniMed 780G system and facilitate meaningful improvements in safe glycaemic control.

Insulin settings and their association with time in range in patients with type 1 diabetes users of predictive low glucose suspend (PLGS) augmented insulin pumps in Santiago, Chile

AIMS

Sensor augmented insulin pumps have become a powerful tool for managing type 1 diabetes (T1D). This study aimed to analyze the insulin pump configuration in users of predictive insulin suspension technology (PLGS).

METHODS

T1D patients on insulin pumps with PLGS (Medtronic 640G®) were enrolled. Data was obtained from medical records and pump data was downloaded for 30 days. Basal insulin, bolus calculator parameters, and PLGS operation parameters were analyzed and compared with Time in Range, Time Below Range, and Time Above Range.

RESULTS

112 patients were included, with average TIR of 73,96 % and HbA1c 7,0 % and 25 months of follow-up. Basal insulin remained similar to initial doses, with an increase of 27 % for the Dawn phenomenon. The Carbohydrate ratio was slightly more aggressive. Insulin sensitivity was 17 % less stringent than initially programmed. No differences were observed in Time in Rage according to the number of basal, ratio, and sensitivity segments. Time of insulin suspension correlated directly with Time Bellow Range.

CONCLUSIONS

Patients with good metabolic control have basal insulin programming similar to their initiation doses with less aggressive sensitivity factors. Excessive suspension time determined by PLGS could be an expression of excess insulin and increased hypoglycemia risk.

In-home use of a hybrid closed loop achieves time-in-range targets in preschoolers and school children: Results from a randomized, controlled, crossover trial

AIM

To obtain additional information on the incremental differences between using a sensor-augmented pump (SAP) without automated insulin delivery (AID), using it with predictive low-glucose management (PLGM) or as hybrid closed loop (HCL), in preschool and school children.

METHODS

We conducted a monocentric, randomized, controlled, two-phase crossover study in 38 children aged 2-6 and 7-14 years. The primary endpoint was the percentage of time in range (TIR) of 70-180 mg/dl. Other continuous glucose sensor metrics, HbA1c, patient-related outcomes (DISABKIDS questionnaire, Fear of Hypoglycaemia Survey) and safety events were also assessed. Results from 2 weeks of SAP, 8 weeks of PLGM and 8 weeks of HCL were compared using a paired t-test or Wilcoxon signed-rank test.

RESULTS

Overall, we found a high rate of TIR target (>70%) achievement with HCL in preschool (88%) and school children (50%), with average times in Auto Mode of 93% and 87%, respectively. Preschool children achieved a mean TIR of 73% ± 6% (+8% vs. SAP, +6% vs. PLGM) and school children 69% ± 8% (+15% vs. SAP and + 14% vs. PLGM). Overall, HbA1c improved from 7.4% ± 0.9% to 6.9% ± 0.5% (P = .0002). Diabetes burden and worries and fear of hypoglycaemia remained at low levels, without significant changes versus PLGM. No events of severe hypoglycaemia or diabetic ketoacidosis occurred.

CONCLUSIONS

Preschool children profit from AID at least as much as those aged 7 years and older. To ensure safe use and prescribing modalities, regulatory approval is also required for young children.

Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study

Background

For adolescents living with type 1 diabetes (T1D), completion of multiple daily self-management tasks, such as monitoring blood glucose and administering insulin, can be challenging because of psychosocial and contextual barriers. These barriers are hard to assess accurately and specifically by using traditional retrospective recall. Ecological momentary assessment (EMA) uses mobile technologies to assess the contexts, subjective experiences, and psychosocial processes that surround self-management decision-making in daily life. However, the rich data generated via EMA have not been frequently examined in T1D or integrated with machine learning analytic approaches.

Objective

The goal of this study is to develop a machine learning algorithm to predict the risk of missed self-management in young adults with T1D. To achieve this goal, we train and compare a number of machine learning models through a learned filtering architecture to explore the extent to which EMA data were associated with the completion of two self-management behaviors: mealtime self-monitoring of blood glucose (SMBG) and insulin administration.

Methods

We analyzed data from a randomized controlled pilot study using machine learning–based filtering architecture to investigate whether novel information related to contextual, psychosocial, and time-related factors (ie, time of day) relate to self-management. We combined EMA-collected contextual and insulin variables via the MyDay mobile app with Bluetooth blood glucose data to construct machine learning classifiers that predicted the 2 self-management behaviors of interest.

Results

With 1231 day-level SMBG frequency counts for 45 participants, demographic variables and time-related variables were able to predict whether daily SMBG was below the clinical threshold of 4 times a day. Using the 1869 data points derived from app-based EMA data of 31 participants, our learned filtering architecture method was able to infer nonadherence events with high accuracy and precision. Although the recall score is low, there is high confidence that the nonadherence events identified by the model are truly nonadherent.

Conclusions

Combining EMA data with machine learning methods showed promise in the relationship with risk for nonadherence. The next steps include collecting larger data sets that would more effectively power a classifier that can be deployed to infer individual behavior. Improvements in individual self-management insights, behavioral risk predictions, enhanced clinical decision-making, and just-in-time patient support in diabetes could result from this type of approach.

Erste Anwendungserfahrung eines neuen, Glukosesensor-unterstützten Pumpensystems mit vorausschauender Insulin-Abschaltung zum Hypoglykämieschutz bei pädiatrischen Patienten in Deutschland

Einleitung Die prädiktive Insulinabschaltung ist als System zur Prävention von Hypoglykämien in Deutschland etabliert (Smartguard). Seit 2020 ist in Deutschland ein zweites System verfügbar (Basal-IQ). Unterschiede betreffen eine nicht veränderbare prädiktive Abschaltgrenze von 80 mg/dl (vs. 50–90 mg/dl), eine Abschaltzeit von minimal 5 Minuten (vs. 30 Minuten) sowie die Festlegung der Wiedereinschaltung des Insulin bei einem höheren Wert als zuvor (vs. einem Abstand von 20 mg/dl über der Abschaltgrenze und höherer Prädiktion). Die Systeme wurden in einer Altersgruppe, die besonders von Unterzuckerungen bedroht ist, verglichen.

Methodik Pädiatrische Patienten (Alter 6–13 Jahre), mit Pumpen- und Sensorerfahrung (kein AID) wurde die Erprobung von Basal-IQ für eine Dauer von 3 Monaten angeboten. Betrachtet wurden die CGM-Parameter Zeit unter Zielbereich (TBR < 70mg/dl), im Zielbereich (TIR 70–180 mg/dl), glykämische Variabilität (Varianzkoeffizient CV%) und HbA1c. Patienten-bezogene Outcomes (PROʼs) wurden mit dem Diabskids-Elternfragebogen und einem Gerätefragebogen erfasst.

Ergebnisse Neun Teilnehmer (alle männlich, Mittelwerte: 9.7 Jahre, Diabetesdauer 6.1 Jahre, HbA1c 6.8%, Time in Range (TIR) 61.9%, Time below Range (TBR) 4.5%, mittlere Glukose (MW) 164 mg/dl, (CV) 40) wurden gefunden. Nach 3 Monaten konnten Verbesserungen der glykämischen Parameter beobachtet werden (HbA1c 6.5%, TIR 69.2%, TBR 2.8%, MW 159, CV 40; Kontrollen HbA1c 7.2%, TIR 64.9%, TBR 4.3%, MW 158, CV 39), die sich von einer zeitgleich mit Smartguard behandelten Kindern nicht unterschieden. Die Erfassung der PROʼs zeigte einen Rückgang der Diabetes- und Therapiebelastung, sowie eine Zufriedenheit mit dem System.

Diskussion Das neue System mit prädiktiver Abschaltung zeigte nach 3 Monaten eine Verbesserung der glykämischen Parameter und PROʼs. Ein statistischer Vergleich vorher/nachher ist aufgrund der geringen Patientenzahl nicht erfolgt, aber die Daten zeigen zumindest die Nichtunterlegenheit gegenüber dem Baseline-Zeitpunkt und den Daten, die aus einer Gruppe von Patienten mit kontinuierlicher Systemnutzung stammen. Somit stehen in Deutschland aktuell zwei verschiedene effiziente Systeme mit prädiktiver Insulinabschaltung für Kinder und Jugendliche mit Diabetes zur Verfügung, so dass diese nach fundierter Beratung auswählen können.

Safe and effective use of a hybrid closed-loop system from diagnosis in children under 18 months with type 1 diabetes

The management of type 1 diabetes in infancy presents significant challenges. Hybrid closed loop systems have been shown to be effective in a research setting and are now available for clinical use. There are relatively little reported data regarding their safety and efficacy in a real world clinical setting. We report two cases of very young children diagnosed with type 1 diabetes at ages 18 (Case 1) and 7 months (Case 2), who were commenced on hybrid closed-loop insulin delivery using the CamAPS FX™ system from diagnosis. At diagnosis, total daily dose (TDD) was 6 and 3.3 units for Case 1 and 2, respectively. Closed loop was started during the inpatient stay and weekly follow up was provided via video call on discharge. Seven months from diagnosis, Case 1 has an HbA1C of 49 mmol/mol, 61% time in range (TIR, 3.9-10 mmol/L) with 2% time in hypoglycemia (<3.9 mmol/L) with no incidents of very low blood glucose (BG; <3 mmol/L, 54 mg/dL) over 6 months. Given the extremely small TDD of insulin in Case 2, we elected to use diluted insulin (insulin aspart injection, NovoLog, Novo Nordisk Inc., Plainsboro, NJ, Diluting Medium for NovoLog®). Six months from diagnosis, the estimated HbA1c is 50 mmol/mol, TIR 76% with 1% hypoglycemia and no incidents of very low BG (<3 mmol/L, 54 mg/dL) over 6 months. We conclude that the use hybrid closed-loop can be safe and effective from diagnosis in children under 2 years of age with type 1 diabetes.

Long-term sensor-augmented pump therapy for neonatal diabetes mellitus: a case series

Neonatal diabetes mellitus (NDM) is a rare metabolic disorder that is mainly present in the first 6 months of life and necessitates insulin treatment. Sensor-augmented pump (SAP) therapy has been widely used in children with type 1 diabetes mellitus, but its use in patients with NDM is limited. We report three patients with NDM who received SAP therapy using the MiniMed™ 640G system starting in the neonatal period. Two patients were treated for 3 months, and one patient continued treatment up to an age of 22 mo. The MiniMed 640G system can automatically suspend insulin delivery (SmartGuard™ Technology) to avoid hypoglycemia when the sensor glucose level is predicted to approach the predefined threshold. We suggest that SmartGuard Technology is particularly useful for infants in whom hypoglycemia cannot be identified. The MiniMed 640G system automatically records the trends of sensor glucose levels and the total daily dose of insulin, which can make the management more accurate and reduce the family’s effort. SAP therapy for patients with NDM automatically prevents severe hypoglycemia and is useful for long-term management; however, attention should be paid to its application.

Intermittent Scanning Glucose Monitoring or Predicted Low Suspend Pump Treatment: Does It Impact Time in Glucose Target and Treatment Preference? The QUEST Randomized Crossover Study

OBJECTIVE

To compare glycemic control and treatment preference in children with type 1 diabetes (T1D) using sensor augmented pump (SAP) with predictive low glucose suspend (SmartGuard^®) or pump with independent intermittent scanning continuous glucose monitoring (iscCGM, Freestyle libre ^®).

METHODS

In this open label, cross-over study, children 6 to 14 years of age, treated with insulin pump for at least 6 months, were randomized to insulin pump and iscCGM (A) or SAP with SmartGuard^® (B) for 5 weeks followed by 5 additional weeks. The difference in percentages of time in glucose target (TIT), (3.9 - 8.0 mmol/l), <3 mmol/l, > 8 and 10 mmol/l, were analyzed using linear mixed models during the final week of each arm and were measured by blinded CGM (IPro2^®).

RESULTS

31 children (15 girls) finished the study. With sensor compliance > 60%, no difference in TIT was found, TIT: A 37.86%; 95% CI [33.21; 42.51]; B 37.20%; 95% CI [32.59; 41.82]; < 3 mmol/l A 2.27% 95% CI [0.71; 3.84] B 1.42% 95% CI [-0.13; 2.97]; > 8 mmol/l A 0.60% 95% CI [0.56, 0.67]; B 0.63% [0.56; 0.70]. One year after the study all participants were on CGM compared to 80.7% prior to the study, with a shift of 13/25 participants from iscCGM to SAP.

CONCLUSIONS

In this study, no significant difference in glycemic control was found whether treated with SAP (SmartGuard^®) or pump with iscCGM. The decision of all families to continue with CGM after the study suggests a positive impact, with preference for SmartGuard^®.

CLINICAL TRIAL REGISTRATION

[clinicaltrials.gov], identifier NCT03103867.

Empfehlungen zur Diabetes-Behandlung mit automatischen Insulin-Dosierungssystemen

Das Prinzip der automatischen Insulindosierung, kurz „AID“ genannt, zeigt in Zulassungsstudien und Real-World-Erfahrungen ausgezeichnete Behandlungsergebnisse. Beim AID wird eine Insulinpumpe mit einem System zur kontinuierlichen Glukosemessung zusammengeschaltet, während ein Rechenprogramm, der sogenannte Algorithmus, die Steuerung der Insulingabe nach Bedarf übernimmt. Idealerweise wäre das System ein geschlossener Kreis, bei dem die Menschen mit Diabetes keine Eingabe mehr machen müssten. Jedoch sind bei den heute verfügbaren Systemen verschiedene Grundeinstellungen und Eingaben erforderlich (insbesondere von Kohlenhydratmengen der Mahlzeiten oder körperlicher Aktivität), die sich von den bisherigen Empfehlungen der sensorunterstützten Pumpentherapie in einzelnen Aspekten unterscheiden. So werden die traditionellen Konzepte von „Basal“ und „Bolus“ mit AID weniger nützlich, da der Algorithmus beide Arten der Insulinabgabe verwendet, um die Glukosewerte dem eingestellten Zielwert zu nähern. Daher sollte bei diesen Systemen statt der Erfassung von „Basal“ und „Bolus“, zwischen einer „nutzerinitiierten“ und einer „automatischen“ Insulindosis unterschieden werden. Gemeinsame Therapieprinzipien der verschiedenen AID-Systeme umfassen die passgenaue Einstellung des Kohlenhydratverhältnisses, die Bedeutung des Timings der vom Anwender initiierten Insulinbolusgaben vor der Mahlzeit, den korrekten Umgang mit einem verzögerten oder versäumten Mahlzeitenbolus, neue Prinzipien im Umgang mit Sport oder Alkoholgenuss sowie den rechtzeitigen Umstieg von AID zu manuellem Modus bei Auftreten erhöhter Ketonwerte. Das Team vom Diabetes-Zentrum AUF DER BULT in Hannover hat aus eigenen Studienerfahrungen und der zugrunde liegenden internationalen Literatur praktische Empfehlungen zur Anwendung und Schulung der gegenwärtig und demnächst in Deutschland kommerziell erhältlichen Systeme zusammengestellt. Für den Erfolg der AID-Behandlung scheint das richtige Erwartungsmanagement sowohl beim Behandlungsteam und als auch beim Anwender von großer Bedeutung zu sein.

100 Years of Insulin: Lifesaver, immune target, and potential remedy for prevention

In this review, we bring our personal experiences to showcase insulin from its breakthrough discovery as a life-saving drug 100 years ago to its uncovering as the autoantigen and potential cause of type 1 diabetes and eventually as an opportunity to prevent autoimmune diabetes. The work covers the birth of insulin to treat patients, which is now 100 years ago, the development of human insulin, insulin analogues, devices, and the way into automated insulin delivery, the realization that insulin is the primary autoimmune target of type 1 diabetes in children, novel approaches of immunotherapy using insulin for immune tolerance induction, the possible limitations of insulin immunotherapy, and an outlook how modern vaccines could remove the need for another 100 years of insulin therapy.

Wearable Glucose Monitoring and Implantable Drug Delivery Systems for Diabetes Management

The global cost of diabetes care exceeds $1 trillion each year with more than $327 billion being spent in the United States alone. Despite some of the advances in diabetes care including continuous glucose monitoring systems and insulin pumps, the technology associated with managing diabetes has largely remained unchanged over the past several decades. With the rise of wearable electronics and novel functional materials, the field is well-poised for the next generation of closed-loop diabetes care. Wearable glucose sensors implanted within diverse platforms including skin or on-tooth tattoos, skin-mounted patches, eyeglasses, contact lenses, fabrics, mouthguards, and pacifiers have enabled noninvasive, unobtrusive, and real-time analysis of glucose excursions in ambulatory care settings. These wearable glucose sensors can be integrated with implantable drug delivery systems, including an insulin pump, glucose responsive insulin release implant, and islets transplantation, to form self-regulating closed-loop systems. This review article encompasses the emerging trends and latest innovations of wearable glucose monitoring and implantable insulin delivery technologies for diabetes management with a focus on their advanced materials and construction. Perspectives on the current unmet challenges of these strategies are also discussed to motivate future technological development toward improved patient care in diabetes management.

Newest Diabetes-Related Technologies for Pediatric Type 1 Diabetes and Its Impact on Routine Care: a Narrative Synthesis of the Literature

Purpose of Review

This review aims to address the actual state of the most advanced diabetes devices, as follows: continuous subcutaneous insulin infusions (CSII), continuous glucose monitoring systems (CGM), hybrid-closed loop (HCL) systems, and “Do-it-yourself” Artificial Pancreas Systems (DIYAPS) in children, adolescents, and young adults. This review has also the objective to assess the use of telemedicine for diabetes care across three different areas: education, social media, and daily care.

Recent Findings

Recent advances in diabetes technology after integration of CSII with CGM have increased the popularity of this treatment modality in pediatric age and shifted the standard diabetes management in many countries. We found an impressive transition from the use of CSII and/or CGM only to integrative devices with automated delivery systems. Although much has changed over the past 5 years, including a pandemic period that precipitated a broader use of telemedicine in diabetes care, some advances in technology may still be an additional burden of care for providers, patients, and caregivers. The extent of a higher rate of “auto-mode” use in diabetes devices while using the HCL/DIYAPS is essential to reduce the burden of diabetes treatment.

Summary

More studies including higher-risk populations are needed, and efforts should be taken to ensure proper access to cost-effective advanced technology on diabetes care.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40124-021-00248-7.

Predictive Low-Glucose Suspend Necessitates Less Carbohydrate Supplementation to Rescue Hypoglycemia: Need to Revisit Current Hypoglycemia Treatment Guidelines

Current guidelines recommend 15-20 g of carbohydrate (CHO) for treatment of mild to moderate hypoglycemia. However, these guidelines do not account for reduced insulin during suspensions with predictive low-glucose suspend (PLGS). We assessed insulin suspensions, hypoglycemic events, and CHO treatment during a 20-h inpatient evaluation of an investigational system with a PLGS feature, including an overnight basal up-titration period to activate the PLGS. Among 10 adults with type 1 diabetes, there were 59 suspensions; 7 suspensions were associated with rescue CHO and 5 with hypoglycemia. Rescue treatment consisted of median 9 g CHO (range: 5-16 g), with no events requiring repeat CHO. No rescue CHO were given during or after insulin suspension for the overnight basal up-titration. To minimize rebound hyperglycemia and needless calorie intake from hypoglycemia overtreatment, updated guidance for PLGS systems should reflect possible need to reduce CHO amounts for hypoglycemia rescue associated with an insulin suspension. The clinical trial was registered with ClinicalTrials.gov (NCT03890003).

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

Lower versus standard sucrose dose for treating hypoglycemia in patients with type 1 diabetes mellitus in therapy with predictive low glucose suspend (PLGS) augmented insulin pumps: A randomized crossover trial in Santiago, Chile

BACKGROUND AND AIMS

Recommended hypoglycemia treatment in adults with T1D consists of 15 g of rapid absorption carbohydrates. We aimed to evaluate the response to fewer carbohydrates for treating hypoglycemia in patients with T1D on insulin pumps with predictive suspension technology (PLGS).

METHODS

T1D patients on insulin pumps with PLGS were randomized to receive 10 or 15 g of sucrose per hypoglycemia for two weeks (S10 and S15 groups, respectively) when capillary blood glucose (BG) was <70 mg/dL, with crossover after two weeks. Evolution of capillary BG, active insulin, and suspension time were assessed.

RESULTS

59 hypoglycemic episodes were analyzed, 33 in S10 and 26 in S15. Baseline BG in S10 was 54.3 ± 7.7 mg/dL versus 56.9 ± 8.8 in S15 (p = 0,239). Active insulin, present in 85% of the episodes, and PLGS suspension time were similar between groups. BG at 15 min was 77 mg/dL in S10 and 95 mg/dL in S15 (p = 0.0007). In S10, 21% of the episodes required to repeat the treatment after 15 min compared with none on S15, with a RR of 0,79 (95% CI 0.66, 0.940, p = 0,014) for successfully treating the episode. Sensor glucose was significantly different from BG at the moment of the hypoglycemia and control 15 min after treatment. No severe hypoglycemia and no rebound hyperglycemia occurred in neither group.

CONCLUSIONS

A hypoglycemia treatment protocol with a lower dose of sucrose might be insufficient despite PLGS technology. Our data suggest that standard doses of sucrose should still be recommended.

Factors associated with clinically significant hypoglycemia in patients with type 1 diabetes using sensor-augmented pump therapy with predictive low-glucose management: A multicentric study on iberoamerica

BACKGROUND AND AIMS

Despite using sensor-augmented pump therapy (SAPT) with predictive low-glucose management (PLGM), hypoglycemia is still an issue in patients with type 1 Diabetes (T1D). Our aim was to determine factors associated with clinically significant hypoglycemia (<54 mg/dl) in persons with T1D treated with PLGM-SAPT.

METHOD

ology: This is a multicentric prospective real-life study performed in Colombia, Chile and Spain. Patients with T1D treated with PLGM-SAPT, using sensor ≥70% of time, were included. Data regarding pump and sensor use patterns and carbohydrate intake from 28 consecutive days were collected. A bivariate and multivariate Poisson regression analysis was carried out, to evaluate the association between the number of events of <54 mg/dl with the clinical variables and patterns of sensor and pump use.

RESULTS

188 subjects were included (41 ± 13.8 years-old, 23 ± 12 years disease duration, A1c 7.2% ± 0.9). The median of events <54 mg/dl was four events/patient/month (IQR 1-10), 77% of these events occurred during day time. Multivariate analysis showed that the number of events of hypoglycemia were higher in patients with previous severe hypoglycemia (IRR1.38; 95% CI 1.19-1.61; p < 0.001), high glycemic variability defined as Coefficient of Variation (CV%) > 36% (IRR 2.09; 95%CI 1.79-2.45; p < 0.001) and hypoglycemia unawareness. A protector effect was identified for adequate sensor calibration (IRR 0.77; 95%CI 0.66-0.90; p:0.001), and the use of bolus wizard >60% (IRR 0.74; 95%CI 0.58-0.95; p:0.017).

CONCLUSION

In spite of using advanced SAPT, clinically significant hypoglycemia is still a non-negligible risk. Only the identification and intervention of modifiable factors could help to prevent and reduce hypoglycemia in clinical practice.

Sustained Impact of Real-time Continuous Glucose Monitoring in Adults With Type 1 Diabetes on Insulin Pump Therapy: Results After the 24-Month RESCUE Study

OBJECTIVE

In recent years, a growing number of people with type 1 diabetes gained access to real-time continuous glucose monitoring (rtCGM). Long-term benefits of rtCGM are unclear because of a lack of large studies of long duration. We evaluated whether real-world rtCGM use up to 24 months offered benefits, particularly in those living with impaired awareness of hypoglycemia (IAH).

RESEARCH DESIGN AND METHODS

This 24-month, prospective, observational cohort study followed 441 adults with insulin pumps receiving full reimbursement for rtCGM. Forty-two percent had IAH. The primary end point was evolution of HbA_1c, with secondary end points change in acute hypoglycemia complications, diabetes-related work absenteeism, and quality of life scores. Additionally, we evaluated whether people could achieve glycemic consensus targets during follow-up.

RESULTS

After 24 months, HbA_1c remained significantly lower compared with baseline (7.64% [60 mmol/mol] vs. 7.37% [57 mmol/mol], P < 0.0001). Sustained benefits were also observed for the score on the hypoglycemia fear survey and hypoglycemia-related acute complications irrespective of hypoglycemia awareness level. People with IAH had the strongest improvement, especially for severe hypoglycemia (862 events in the year before vs. 119 events per 100 patient-years in the 2nd year, P < 0.0001). Over 24 months, more people were able to meet hypoglycemia consensus targets at the expense of slightly fewer people achieving hyperglycemia consensus targets. Furthermore, the number of people with HbA_1c <7% (<53 mmol/mol) without severe hypoglycemia events more than doubled (11.0% vs. 25.4%, P < 0.0001).

CONCLUSIONS

Use of rtCGM led to sustained improvements in hypoglycemia-related glucose control over 24 months. Lower fear of hypoglycemia, fewer acute hypoglycemia-related events, and fewer diabetes-related days off from work were observed, particularly in those with IAH.

Closed-loop insulin delivery systems in children and adolescents with type 1 diabetes

Introduction: Optimal glycemic control remains challenging in children and adolescents with type 1 diabetes due to highly variable day-to-day and night-to-night insulin requirements. This hurdle could be addressed by glucose-responsive insulin delivery based on real-time continuous glucose measurements.Areas covered: This review summaries recent advances of closed-loop systems in children and adolescents with type 1 diabetes, using both single- and dual-hormone closed-loop systems. The main outcomes, proportions of time spent in target range 70-180 mg/dl, and time spent in hypoglycemia below 70 mg/dl, are assessed particularly during unsupervised free-living randomized controlled trials.Expert opinion: Noteworthy and clinically meaningful translation of experimental investigations from controlled in-hospital settings to unrestricted home studies have been achieved over the past years, resulting in the regulatory approval of the first hybrid closed-loop system also in the pediatric population and with several other advanced devices in the pipeline. Large multinational and pivotal clinical trials including broad age populations are underway to facilitate the use of closed-loop systems in routine clinical practice.

Health Care Provider Knowledge and Perceptions of FDA-Approved and Do-It-Yourself Automated Insulin Delivery

BACKGROUND

Automated insulin delivery (AID) technology may reduce variability in blood glucose, resulting in lower risk for hypoglycemia and associated complications, and by extension improve quality of life. While clinical trials, research, and patient experience have consistently demonstrated the value of AID, this technology is still inaccessible to many patients. Patient-driven innovation has resulted in alternative do-it-yourself (DIY) solutions to available off-the-shelf AID devices.

METHOD

This two-phase cross-sectional observational study addressed health care provider (HCP) perceptions of AID as well as the perceived need for, development of, and evaluation of an AID fact sheet comparing the most commonly used Federal Drug Administration approved AID and DIY AID devices.

RESULTS

Negative attitudes toward the use of DIY AID were low. The majority of HCPs saw their lack of knowledge about how DIY AID work to be the greatest barrier to answering patient questions about what is available (74.4%). Additionally, the majority of HCPs (64.5%) indicated they were either "likely" or "very likely" to use the fact sheet when answering patient questions about AID options.

CONCLUSION

Increased awareness and utilization of AID technology offer hope to further reduce the burden of diabetes, but there is a need to bridge the knowledge gap about DIY AID. A fact sheet provides a way to facilitate discussions of this emerging technology between HCPs and patients. Next steps could investigate additional ways to put needed information in the hands of HCPs.

When Low Blood Sugars Cause High Anxiety: Fear of Hypoglycemia Among Parents of Youth With Type 1 Diabetes Mellitus

OBJECTIVES

Hypoglycemia is the most common acute complication of type 1 diabetes (T1D), and the potential short- and long-term sequelae can cause children and parents to develop significant fear of hypoglycemia (FOH). FOH and associated anxiety can be disruptive to activities of daily living and lead to reduced quality of life. We sought to determine the extent of FOH among parents of children with T1D within our clinic and to identify factors associated with greater FOH.

METHODS

Two hundred sixty-four parents of youth (2 to 18 years of age; mean ± standard deviation, 12.4±3.5 years) with T1D completed a survey that included demographic and disease-specific questions, the Spielberger State-Trait Anxiety Inventory and the Hypoglycemia Fear Survey---Parent version (HFS-P).

RESULTS

Of the 264 participants, 207 completed the full HFS-P, with a mean score of 67±19 (range, 31 to 119). The most frequent worries related to the child being hypoglycemic while alone or asleep. Higher HFS-P scores were also associated with more frequent and severe hypoglycemic episodes, higher state-trait anxiety scores, use of a continuous glucose monitor and more frequent blood glucose checks. Higher HFS-P scores were also associated with worse parental sleep quality and less parental engagement with treatment plans.

CONCLUSIONS

Parents of children with T1D experience FOH, especially during times of high vulnerability. Moreover, FOH could potentially impact clinical care (with parents being reluctant to administer suggested insulin doses) and quality of life (due to parental/child sleep disruption). Further studies are needed to develop and evaluate interventions aimed at reducing FOH in parents of youth with T1D.

Modern diabetes devices in the school setting: Perspectives from school nurses

OBJECTIVE

To explore the experiences, practices, and attitudes of school nurses related to modern diabetes devices (insulin pumps, continuous glucose monitors, and hybrid-closed loop systems).

RESEARCH DESIGN AND METHODS

Semistructured interviews were conducted with 40 public school nurses caring for children in elementary and middle schools. Developed with stakeholder input, the interview questions explored experiences working with devices and communicating with the health care system. Deidentified transcripts were analyzed through an iterative process of coding to identify major themes.

RESULTS

School nurses reported a range of educational backgrounds (58% undergraduate, 42% graduate), geographic settings (20% urban, 55% suburban, 25% rural), and years of experience (20% <5 years, 38%, 5-15 years, 42% >15 years). Four major themes emerged: (a) As devices become more common, school nurses must quickly develop new knowledge and skills yet have inconsistent training opportunities; (b) Enthusiasm for devices is tempered by concerns about implementation due to poor planning prior to the school year and potential disruptions by remote monitors; (c) Barriers exist to integrating devices into schools, including school/classroom policies, liability/privacy concerns, and variable staff engagement; and (d) Collaboration between school nurses and providers is limited; better communication may benefit children with diabetes.

CONCLUSIONS

Devices are increasingly used by school-aged children. School nurses appreciate device potential but share structural and individual-level challenges. Guiding policy is needed as the technology progressively becomes standard of care. Enhanced training and collaboration with diabetes providers may help to optimize school-based management for children in the modern era.

Nighttime Hypoglycemia in Children with Type 1 Diabetes after one Day of Football Tournament

The aim of the study was to investigate factors related to the occurrence of nighttime hypoglycemia after a football tournament in children with type 1 diabetes mellitus. The multicenter study (GoalDiab study) included 189 children and adolescents with type 1 diabetes mellitus, from 11 diabetes care centers in Poland. Hypoglycemia was defined according to the International Hypoglycemia Study Group Statement. We analyzed the data of 95 participants with completed protocols with regards to nighttime hypoglycemia (82% male), aged 11.6 (9.8-14.2) years, diabetes duration 5.0 (2.0-8.0) years. There were 47 episodes of nighttime Level 1 hypoglycemia (≤3.9 mmol/L). Occurrence of clinically important Level 2 hypoglycemia (<3.0 mmol/L) during a game period was positively associated with nighttime hypoglycemia (≤3.9 mmol/L) incident (Odds Ratio=10.7; 95% Confidence Interval: 1.1-100.2; p=0.04). Using Continuous Glucose Monitoring was negatively associated with the occurrence of nighttime hypoglycemia (≤3.9 mmol/L) compared with using glucose meters or Flash Glucose Monitoring (Odds Ratio=0.31; 95% Confidence Interval: 0.12-0.83; p=0.02). The occurrence of clinically important hypoglycemia related to physical activity is associated with the occurrence of hypoglycemia during the night. Continuous Glucose Monitoring is negatively associated with nighttime hypoglycemia after a day of competition.

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.

Evolution of Insulin Delivery Devices: From Syringes, Pens, and Pumps to DIY Artificial Pancreas

The year 2021 will mark 100 years since the discovery of insulin. Insulin, the first medication to be discovered for diabetes, is still the safest and most potent glucose-lowering therapy. The major challenge of insulin despite its efficacy has been the occurrence of hypoglycemia, which has resulted in sub-optimal dosages being prescribed in the vast majority of patients. Popular devices used for insulin administration are syringes, pens, and pumps. An artificial pancreas (AP) with a closed-loop delivery system with > 95% time in range is believed to soon become a reality. The development of closed-loop delivery systems has gained momentum with recent advances in continuous glucose monitoring (CGM) and computer algorithms. This review discusses the evolution of syringes, disposable, durable pens and connected pens, needles, tethered and patch insulin pumps, bionic pancreas, alternate controller-enabled infusion (ACE) pumps, and do-it-yourself artificial pancreas systems (DIY-APS).

New technical approach to the diabetes therapy

Optimal glycemic control remains challenging in individuals with type 1 diabetes. With the comprehensive clinical evidence on safety and efficiency, the adoption of continuous glucose monitoring (CGM), insulin pumps, and control algorithms merging the two into closed-loop systems is rapidly increasing. Particularly the CGM and intermittently scanned CGM improved diabetes management outcomes in large populations. A meaningful translation from clinical trials in highly controlled settings to numerous evaluations of closed-loop technology in the unrestricted home environment ended with its commercialization and use in routine clinical practice. Although it is still not a cure, the closed-loop currently seems to be the most promising advancement in the treatment of diabetes, with promising results also reported from routine clinical practice in children and adults with type 1 diabetes. We summarize different aspects of a technological approach to diabetes care, list currently available devices and systems in the pipeline, and the key supporting clinical evidence for their use. We consider human factors associated with technology use and the importance of health economics to support implementation and reimbursement.

Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon

Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, which commonly goes undetected. Continuous glucose monitoring (CGM) devices have enabled prediction of impending nocturnal hypoglycemia, however, prior efforts have been limited to a short prediction horizon (~ 30 minutes). To this end, a nocturnal hypoglycemia prediction model with a 6-hour horizon (midnight-6 am) was developed using a random forest machine- learning model based on data from 10,000 users with more than 1 million nights of CGM data. The model demonstrated an overall nighttime hypoglycemia prediction performance of ROC AUC = 0.84, with AUC = 0.90 for early night (midnight-3 am) and AUC = 0.75 for late night (prediction at midnight, looking at 3-6 am window). While instabilities and the absence of late-night blood glucose patterns introduce predictability challenges, this 6-hour horizon model demonstrates good performance in predicting nocturnal hypoglycemia. Additional study and specific patient-specific features will provide refinements that further ensure safe overnight management of glycemia.

How does a predictive low glucose suspend (PLGS) system tackle pediatric lifespan challenges in diabetes treatment? Real world data analysis

OBJECTIVES

The aim of the study was to assess the benefits of a predictive low glucose suspend (PLGS) system in real-life in children and adolescents with type 1 diabetes of different age and age-related clinical challenges.

METHODS

Real life retrospective and descriptive analysis included 44 children (26 girls) with type 1 diabetes who were introduced to PLGS system. We divided them in three age groups: I (3-6 years old, n = 12), II (7-10 y/o, n = 16), III (11-19 y/o, n = 16). All children and their caregivers received unified training in self-management during PLGS therapy. Patients' data included: age, HbA1C levels, sex. While from the CGM metric, we obtained: time of sensor use (SENSuse), time in range (TiR): in, below and over target range and average blood glycemia (AVG), insulin suspension time (INSsusp).

RESULTS

SENSuse was 93% in total, with 92%, 94%, and 87% in age groups I, II, III, respectively. In total the reduction of mean HbA1C from 7.61% to 6.88% (P < .05), while for the I, II, and III it was 7.46% to 6.72%, 6.91% to 6.41%, and 8.46 to 7.44%, respectively (P < .05). Although we observed a significant reduction of HbA1C, the time below target range was minimal. Specific findings included: group I-longest INSsusp (17%), group II-lowest glycemic variability (CV) (36%), and group III-highest AVG (169 mg/dL). There was a reverse correlation between suspend before low and age (-0.32, P < .05). In group I CV reduced TiR in target range (TiRin) (-0.82, P < .05), in group II use of complex boluses increased TiRin (0.52, P < .05). In group III higher CV increased HbA1C (0.64, P < .05) while reducing TiRin (-0.72, P < .05).

CONCLUSIONS

PLGS is a suitable and safe therapeutic option for children with diabetes of all age and it is effective in addressing age-specific challenges. PLGS improves glycemic control in children of all age, positively affecting its different parameters.

Mini-doses of glucagon to prevent hypoglycemia in children with type 1 diabetes refusing food: a case series

AIMS

Hypoglycemia in small children with type 1 diabetes is difficult to manage if nausea, vomit or food refusal occurs. If oral carbohydrate cannot be used, there is a hypothetical risk of severe hypoglycemia. The present article describes the effect on glucose of small doses of subcutaneous glucagon to revert hypoglycemia and prevent severe events in small children with type 1 diabetes using a continuous glucose monitoring.

METHODS

We analyzed 4 episodes of impending or mild hypoglycemia in 3 children with type 1 diabetes who refused to eat carbohydrates. Using a standard U-100 insulin syringe, children received one "unit" (10 μg) of glucagon subcutaneously for every year of age up to 15 units (150 μg). If the blood glucose did not increase within 30 min, the initial dosage was repeated at that time. Instructions were given by phone from the physician. At the following visit data from continuous glucose monitoring devices, insulin pump and glucometer were downloaded and reviewed retrospectively from the physician.

RESULTS

Blood glucose from continuous glucose monitoring after one and 2 h was 127 ± 80 mg/dl and 165 ± 78 mg/dl, respectively. After a glucagon injection, there was a single recurrence of hypoglycemia, requiring another shot. The glucagon was well tolerated, except for nausea, present before the injection. None of the children were taken to our hospital because of concerns for hypoglycemia.

CONCLUSION

Mini-doses of glucagon given subcutaneously were effective and safe in preventing frank or impending hypoglycemia in type 1 diabetes children refusing food.

Evolution of Diabetes Technology

Technological innovations have fundamentally changed diabetes care. Insulin pump use and continuous glucose monitoring are associated with improved glycemic control along with a better quality of life; automated insulin-dosing advisors facilitate and improve decision making. Glucose-responsive automated insulin delivery enables the highest targets for time in range, lowest rate and duration of hypoglycemia, and favorable quality of life. Clear targets for time in ranges and a standard visualization of the data will help the diabetes technology to be used more efficiently. Decision support systems within and integrated cloud environment will further simplify, unify, and improve modern routine diabetes care.

Effect of frequency of sensor use on glycaemic control in individuals on sensor-augmented pump therapy with and without Predictive Low Glucose Management System

Improved frequency of sensor use improves glycaemic control. Furthermore, there is no deterioration of glycaemic control with increased sensor use in individuals on Predictive Low Glucose Management (PLGM) system. Younger children are more likely to have better sensor uptake than older children.

A Novel CGM Metric-Gradient and Combining Mean Sensor Glucose Enable to Improve the Prediction of Nocturnal Hypoglycemic Events in Patients with Diabetes

Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, and it is often asymptomatic. A novel CGM metric-gradient was proposed in this paper, and a method of combining mean sensor glucose (MSG) and gradient was presented for the prediction of nocturnal hypoglycemia. For this purpose, the data from continuous glucose monitoring (CGM) encompassing 1,921 patients with diabetes were analyzed, and a total of 302 nocturnal hypoglycemic events were recorded. The MSG and gradient values were calculated, respectively, and then combined as a new metric (i.e., MSG+gradient). In addition, the prediction was conducted by four algorithms, namely, logistic regression, support vector machine, random forest, and long short-term memory. The results revealed that the gradient of CGM showed a downward trend before hypoglycemic events happened. Additionally, the results indicated that the specificity and sensitivity based on the proposed method were better than the conventional metrics of low blood glucose index (LBGI), coefficient of variation (CV), mean absolute glucose (MAG), lability index (LI), etc., and the complex metrics of MSG+LBGI, MSG+CV, MSG+MAG, and MSG+LI, etc. Specifically, the specificity and sensitivity were greater than 96.07% and 96.03% at the prediction horizon of 15 minutes and greater than 87.79% and 90.07% at the prediction horizon of 30 minutes when the proposed method was adopted to predict nocturnal hypoglycemic events in the aforementioned four algorithms. Therefore, the proposed method of combining MSG and gradient may enable to improve the prediction of nocturnal hypoglycemic events. Future studies are warranted to confirm the validity of this metric.

Severe Hypoglycemia: Is It Still a Threat for Children and Adolescents With Type 1 Diabetes?

Severe hypoglycemia is defined as a condition with serious cognitive dysfunction, such as a convulsion and coma, requiring external help from other persons. This condition is still lethal and is reported to be the cause of death in 4-10% in children and adolescents with type 1 diabetes. The incidence of severe hypoglycemia in the pediatric population was previously reported as high as more than 50-100 patient-years; however, there was a decline in the frequency of severe hypoglycemia during the past decades, and relationship with glycemic control became weaker than previously reported. A lot of studies have shown the neurological sequelae with severe hypoglycemia as cognitive dysfunction and abnormalities in brain structure. This serious condition also provides negative psychosocial outcomes and undesirable compensatory behaviors. Various possible factors, such as younger age, recurrent hypoglycemia, nocturnal hypoglycemia, and impaired awareness of hypoglycemia, are possible risk factors for developing severe hypoglycemia. A low HbA_1c level is not a predictable value for severe hypoglycemia. Prevention of severe hypoglycemia remains one of the most critical issues in the management of pediatric patients with type 1 diabetes. Advanced technologies, such as continuous glucose monitoring (CGM), intermittently scanned CGM, and sensor-augmented pump therapy with low-glucose suspend system, potentially minimize the occurrence of severe hypoglycemia without worsening overall glycemic control. Hybrid closed-loop system must be the most promising tool for achieving optimal glycemic control with preventing the occurrence of severe hypoglycemia in pediatric patients with type 1 diabetes.

Assessment of Safety and Glycemic Control During Football Tournament in Children and Adolescents With Type 1 Diabetes-Results of GoalDiab Study

PURPOSE

To assess glycemic control and safety of children and adolescents with type 1 diabetes participating in a 2-day football tournament.

METHODS

In total, 189 children with type 1 diabetes from 11 diabetes care centers, in Poland, participated in a football tournament in 3 age categories: 7-9 (21.2%), 10-13 (42.9%), and 14-17 (36%) years. Participants were qualified and organized in 23 football teams, played 4 to 6 matches of 30 minutes, and were supervised by a medical team. Data on insulin dose and glycemia were downloaded from personal pumps, glucose meters, continuous glucose monitoring, and flash glucose monitoring systems.

RESULTS

The median level of blood glucose before the matches was 6.78 (4.89-9.39) mmol/L, and after the matches, it was 7.39 (5.5-9.87) mmol/L (P = .001). There were no episodes of severe hypoglycemia or ketoacidosis. The number of episodes of low glucose value (blood glucose ≤3.9 mmol/L) was higher during the tournament versus 30 days before: 1.2 (0-1.5) versus 0.7 (0.3-1.1) event/person/day, P < .001. Lactate levels increased during the matches (2.2 [1.6-4.0] mmol/L to 4.4 [2.6-8.5] mmol/L after the matches, P < .001).

CONCLUSIONS

Large football tournaments can be organized safely for children with type 1 diabetes. For the majority of children, moderate mixed aerobic-anaerobic effort did not adversely affect glycemic results and metabolic safety.

A Review of Predictive Low Glucose Suspend and Its Effectiveness in Preventing Nocturnal Hypoglycemia

To evaluate the effectiveness of predictive low glucose suspend (PLGS) systems within sensor-augmented insulin infusion pumps at preventing nocturnal hypoglycemia in patients with type 1 diabetes (DM1), we performed a systematic review and meta-analysis of randomized crossover trials. Pubmed and Google Scholar were searched for randomized crossover trials, published between January 2013 and July 2018, in nonpregnant outpatients with DM1, which compared event rates during PLGS overnight periods and non-PLGS overnight periods. The primary outcome was the proportion of overnight periods with one or more hypoglycemic measurement. When available, individual patient data were used to assess the effect of clustering measurements within patients. Four studies (272 patients, 10,735 patient-nights: 5422 PLGS and 5313 non-PLGS) were included in the meta-analysis. Two studies reported patient-level data that permitted assessment of the effect of clustering measurements within patients. The effect on the risk difference was minimal. The proportion of overnight periods with one or more episodes of hypoglycemia was 19.6% for the PLGS periods and 27.8% for the non-PLGS periods. Based on the pooled estimate, PLGS overnight periods were associated with an 8.8% lower risk of hypoglycemia (risk difference -0.088; 95% CI -0.119 to -0.056, I² = 67.4%, τ² = 0.0006, 4 studies). PLGS systems can reduce nocturnal hypoglycemic events in patients with DM1.

[Insulin pump therapy in children, adolescents and adults, guidelines (Update 2019)]

This position statement is based on current evidence available on the safety and benefits of continuous subcutaneous insulin infusion therapy (CSII, pump therapy) in diabetes with an emphasis on the effects of CSII on glycemic control, hypoglycaemia rates, occurrence of ketoacidosis, quality of life and the use of insulin pump therapy in pregnancy. The current article represents the recommendations of the Austrian Diabetes Association for the clinical praxis of insulin pump treatment in children, adolescents and adults.

The hypoglycemia-prevention effect of sensor-augmented pump therapy with predictive low glucose management in Japanese patients with type 1 diabetes mellitus: a short-term study

Aims/introduction

The predictive low glucose management (PLGM) system was introduced in March 2018 in Japan. Although there are some reports demonstrating the benefit of PLGM in preventing hypoglycemia, no data are currently available in Japanese patients with type 1 diabetes mellitus (T1DM). The aim of the present study is to evaluate the effect of PLGM with sensor-augmented pump therapy in the prevention of hypoglycemia in Japanese patients.

Materials and methods

We included 16 patients with T1DM who used the MiniMed^®640G system after switching from the MiniMed^®620G system. We retrospectively analysed the data of the continuous glucose monitoring system in 1 month after switching to MiniMed^®640G.

Results

The area under the curve (AUC) of hypoglycemia of < 70 mg/dL was lowered from 0.42 ± 0.43 mg/dL day to 0.18 ± 0.18 mg/dL day (P = 0.012). Correspondingly, the duration of severe hypoglycemia (< 54 mg/dL) was reduced significantly from 15.3 ± 21.7 min/day to 4.8 ± 6.9 min/day (P = 0.019). The duration of hypoglycemia was reduced, but the reduction was not significant. Regarding the AUC for hyperglycemia > 180 mg/dL and the duration of hyperglycemia did not change. With the PLGM function, 79.3% of the predicted hypoglycemic events were avoided.

Conclusions

The hypoglycemia avoidance rate was comparable to those in previous reports. In addition, we demonstrated that PLGM can markedly suppress severe hypoglycemia without deteriorating glycemic control in Japanese T1DM patients. It is necessary to further investigate the effective use of the PLGM feature such as establishing a lower limit and the timing of resumption.

Current trends of digital solutions for diabetes management

Industry 4.0 is an updated concept of smart production, which is identified with the fourth industrial revolution and the emergence of cyber-physical systems. Industry 4.0 is the next stage in the digitization of productions and industries, where such technologies and concepts as the Internet of things, big data, predictive analytics, cloud computing, machine learning, machine interaction, artificial intelligence, robotics, 3D printing, augmented reality. As an area of therapy with the best market potential and one of the most expensive global diseases, diabetes attracts the best healthcare players, who use innovative technologies. Current trends in digitalization of diabetes management are presented.