Use of sensor-integrated pump therapy to reduce hypoglycaemia in people with Type 1 diabetes: a real-world study in the UK

The Tandem Control-IQ advanced hybrid system improves glycemic control in children under 18 years of age with type 1 diabetes and night rest in caregivers

OBJECTIVE

To determine the impact of switching from the predictive low glucose suspend (PLGS) system to the advanced hybrid Tandem Control-IQ system on glucometrics and glycosylated haemoglobin (HbA1c) at one year. To assess the impact on the quality of life perceived by parents.

METHOD

Prospective study in 71 patients aged 6-18 years with type 1 diabetes (DM1), in treatment with PLGS, who switched to an advanced hybrid system. Glucometric data were collected before the change, at 4 and 8 weeks, and at one year of use; HbA1c before the change and after one year. The Diabetes Impact and Devices Satisfaction (DIDS) questionnaire was used at weeks 4 and 8.

RESULTS

An increase in time in range (TIR) was observed with a median of 76% (P<.001) at 4 weeks, which was maintained after one year (+8% in the total group). Overall, 73.24% of patients achieved a TIR above 70%. The subgroup with an initial TIR of less than 56% increased it by 14.4%. After one year there was a 0.3% reduction in HbA1c. Level 1 hypoglycaemia, level 1 and level 2 hyperglycaemia, mean glucose (GM) and coefficient of variation (CV) decreased. Auto mode stayed on 97% of the time and no dropouts occurred. Caregivers had a perception of better glycaemic control and less need to monitor blood glucose variations during the night. None of them would switch back to the previous system and they feel safe with the new system.

CONCLUSIONS

The Tandem Control-IQ advanced hybrid system was shown to be effective one year after its implementation with improvement in all glucometric parameters and HbA1c, as well as night-time rest in caregivers.

The Medtronic 780G advanced hybrid closed-loop system achieves and maintains good glycaemic control in type 1 diabetes adults despite previous treatment

INTRODUCTION

Improvements in continuous glucose monitoring (CGM) in recent years have changed the treatment of type 1 diabetes (T1D) by permitting the automation of glucose control. The Minimed 780G advanced hybrid closed-loop (ACHL) system adapts basal infusion rates and delivers auto-correction boluses in order to achieve a user-decided glucose target (100, 110 or 120mg/dL). This study set out to evaluate the effectiveness of the Medtronic 780G system in real-life conditions over 6 months.

MATERIALS AND METHODS

Prospective study that included T1D subjects previously treated with insulin pump without CGM (pump group) or with sensor-augmented pump with predictive low-glucose suspend (SAP-PLGS group) who started with the Minimed 780G system. Sensor and pump data from baseline, and at 1, 3 and 6 months were downloaded and HbA1c was recorded at baseline and at 6 months.

RESULTS

Fifty T1D subjects were included; 25 were previous SAP-PLGS 640G users and 25 used 640G without CGM. 66% were female, 48.6 (40-57) years of age with 20 (12-31.5) years of diabetes duration. Time in range (TIR) improved in the total cohort from baseline to 6 months (69% (57.7-76) vs. 74% (70-82); p=0.01 as did HbA1c (7.6% (7.1-7.8) vs. 7.0% (6.8-7.5); p<0.001), with improvement in times <54, >180 and >250mg/dL. Outcomes at 6 months did not differ between groups, although the SAP-PLGS subjects were prone to hypoglycaemia and the pump group mainly presented suboptimal metabolic control.

CONCLUSION

The AHCL Medtronic Minimed 780G system achieves and maintains good glycaemic control over 6 months in real-life conditions in different profiles of T1D subjects.

Long-term efficacy of sensor-augmented pump therapy (Minimed 640G system) combined with a telemedicine follow-up in patients with type 1 diabetes: A real life study

Objective

Evaluate the efficacy of a new modality of insulin therapy associating both the sensor-augmented pump therapy with predictive low-glucose management (SAP-PLGM) and a telemedicine follow-up in patients with Type 1 diabetes (T1D) in a real-life setting.

Methods

T1D adults under Minimed 640G system with a telemedicine follow-up for glucose management were included in a retrospective study. The primary endpoint was HbA1c while continuous glucose monitoring parameters (CGM) and treatment compliance were the secondary endpoints. These parameters were analyzed according to the therapeutic indication, HbA1c ≥ 8 % (Group A) or severe hypoglycemic events (Group B) and in patients switched to SAP-PLGM therapy.

Results

62 patients were analyzed with a 28 ± 12 months of follow-up. In Group A, HbA1c decreased from 8.3 ± 0.4 % to 7.7 ± 0.7 % (p < 0.05) and to 7.9 ± 0.3 % (p < 0.05) after 2 and 3 years, respectively. In patients switched to SAP-PLGM therapy, HbA1c decreased from 7.7 ± 0.7 % to 7.2 ± 0.8 % (p < 0.05) at 2 years. After 6 months, the time-below-range (<70 mg/dL) decreased from 2.1 % [0.6-4] to 1.1 % [0.3-2.6] (p < 0.05). Severe hypoglycemic events decreased from 1.62 to 0.5 events/patient/year in Group B (p < 0.05). At 3 years, treatment compliance was 92 % [70-97] in the total population.

Conclusions

Long-term real-life treatment with the SAP-PLGM therapy combined with telemedicine was associated with improved glycemic control in T1D, along with high treatment compliance.

Sensor-Augmented Insulin Pump with Predictive Low-Glucose Suspend (PLGS): Determining Optimal Settings of Pump and Sensor in a Multicenter Cohort of Patients with Type 1 Diabetes

INTRODUCTION

The use of predictive low-glucose suspend (PLGS) sensor-augmented pumps has been shown to lead to a significant reduction in hypoglycemic episodes in patients with type 1 diabetes (T1D), but their effects on hyperglycemia exposure are heterogeneous. The aim of this study was to determine the settings of the Medtronic 640G system to obtain the optimal balance between occurrence of both hypoglycemia and hyperglycemia.

METHODS

The hypo- and hyperglycemia area under the curve (AUC), as well as system settings [hypoglycemic threshold, mean insulin total daily dose (TDD), mean basal insulin percentage, and mean daily duration of PLGS] were collected between 2 and 12 times during 1 year in patients from four university hospital centers. Univariate/multivariate analyses and receiver operating characteristics (ROC) curves were performed to determine factors associated with hyper- and hypoglycemia AUC.

RESULTS

A total of 864 observations were analyzed from 110 patients with T1D. Two preselected settings predictive of low hyperglycemia AUC were a basal insulin percentage < 52.0% [sensitivity (Se) = 0.66 and specificity (Sp) = 0.53] and a PLGS duration > 157.5 min/day (Se = 0.47 and Sp = 0.73). The preselected setting predictive of a low hypoglycemia AUC was a PLGS duration ≤ 174.4 min (Se = 0.83 and Sp = 0.51). Between-visit variation of PLGS and TDD was positively correlated (r = 0.61; p < 0.0001).

CONCLUSION

The most important Medtronic 640G setting was the mean daily PLGS duration, where a value between 157.5 and 174.4 min/day was associated with the best reduction in both hypo- and hyperglycemia AUC. In this study, we showed that PLGS duration could be indirectly modified through total daily insulin dose adaptation.

TRIAL REGISTRATION

This study is registered in clinicaltrials.gov (NCT03047486).

Real-world outcomes of two different sensor-augmented insulin pumps with predictive low glucose suspend function in type 1 diabetes patients

AIM

To analyse the real-life outcomes of two sensor-augmented pumps (SAP) with predictive low glucose suspend (PLGS) function, Medtronic Minimed 640G™ with SmartGuard (MM640G) and Tandem T Slim X2™ with Basal-IQ™ (TTSX2), in Type 1 Diabetes Mellitus (T1DM) patients.

METHODS

Observational cross-sectional study using data obtained from computerized clinical records. All T1DM patients on TTSX2 therapy were compared (1:1) with MM640G treated patients selected through stratified sampling. Primary efficacy outcome was to describe time in rage (TIR, 70-180 mg/dL, 3.9-10 mmol/L) interstitial glucose differences according to a non-inferiority hypothesis with TTSX2 compared to MM640G.

RESULTS

Forty-four patients were analyzed (female 66%). Mean age was 38.9 yrs. (range 23-59 yrs.) and mean diabetes duration was 23.4 ± 9.2 yrs. Patients treated with TTSX2 showed a numerically slightly lower, but non-statistically significantly different, TIR from the MM640G pump group (64.9 ± 16.4% vs. 72.4 ± 17.0%, P = 0.108). Similarly, we did no find differences in HbA1c between T1D patients treated with TTSX2 and MM640G (6.8 ± 1.0% vs. 7.0 ± 0.9%, 51 ± 11 mmol/mol vs. 53 ± 10 mmol/mol, P = 0.312). Moreover, rest of evaluated glycemic outcomes were similar between both treatment groups.

CONCLUSIONS

Patients using two different SAP with PLGS automatic function showed similar glycaemic control in a real-world scenario. NCT04741685.

A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning

The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system's performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system's improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.

Hypoglycaemia and its management in primary care setting

Hypoglycaemia is common in patients with type 1 diabetes and type 2 diabetes and constitutes a major limiting factor in achieving glycaemic control among people with diabetes. While hypoglycaemia is defined as a blood glucose level under 70 mg/dL (3.9 mmol/L), symptoms may occur at higher blood glucose levels in individuals with poor glycaemic control. Severe hypoglycaemia is defined as an episode requiring the assistance of another person to actively administer carbohydrate, glucagon, or take other corrective actions to assure neurologic recovery. Hypoglycaemia is the most important safety outcome in clinical studies of glucose lowering agents. The American Diabetes Association Standards of Medical Care recommends that a management protocol for hypoglycaemia should be designed and implemented by every hospital, along with a clear prevention and treatment plan. A tailored approach, using clinical and pathophysiologic disease stratification, can help individualize glycaemic goals and promote new therapies to improve quality of life of patients. Data from recent large clinical trials reported low risk of hypoglycaemic events with the use of newer anti-diabetic drugs. Increased hypoglycaemia risk is observed with the use of insulin and/or sulphonylureas. Vulnerable patients with T2D at dual risk of severe hypoglycaemia and cardiovascular outcomes show features of "frailty." Many of such patients may be better treated by the use of GLP-1 receptor agonists or SGLT2 inhibitors rather than insulin. Continuous glucose monitoring (CGM) should be considered for all individuals with increased risk for hypoglycaemia, impaired hypoglycaemia awareness, frequent nocturnal hypoglycaemia and with history of severe hypoglycaemia. Patients with impaired awareness of hypoglycaemia benefit from real-time CGM. The diabetes educator is an invaluable resource and can devote the time needed to thoroughly educate the individual to reduce the risk of hypoglycaemia and integrate the information within the entire construct of diabetes self-management. Conversations about hypoglycaemia facilitated by a healthcare professional may reduce the burden and fear of hypoglycaemia among patients with diabetes and their family members. Optimizing insulin doses and carbohydrate intake, in addition to a short warm up before or after the physical activity sessions may help avoiding hypoglycaemia. Several therapeutic considerations are important to reduce hypoglycaemia risk during pregnancy including administration of rapid-acting insulin analogues rather than human insulin, pre-conception initiation of insulin analogues, and immediate postpartum insulin dose reduction.

Real-World Improvements in Hypoglycemia in an Insulin-Dependent Cohort With Diabetes Mellitus Pre/Post Tandem Basal-Iq Technology Remote Software Update

OBJECTIVE

Software updatable insulin pumps, such as the t:slim X2 pump from Tandem Diabetes Care, enable access to new technology as soon as it is commercialized. The remote software update process allows for minimal interruption in therapy compared to purchasing a new pump; however, little quantitative data exist on the software update process or on pre/post therapeutic outcomes. We examined real-world usage and impact of a remote software updatable predictive low-glucose suspend (PLGS) technology designed to reduce hypoglycemic events in people with insulin-dependent diabetes.

METHODS

Approximately 15,000 U.S. Tandem pump users remotely updated their t:slim X2 software to Basal-IQ PLGS technology since its commercial release. We performed a retrospective analysis of users who uploaded at least 21 days of pre/post PLGS update usage data to the Tandem t:connect web application between August 28, 2018, and October 21, 2019 (N = 6,170). Insulin delivery and sensor-glucose values were analyzed per recent international consensus and American Diabetes Association guidelines. Software update performance was also assessed.

RESULTS

Median software update time was 5.36 minutes. Overall glycemic outcomes for pre and post software update showed a decrease in sensor time <70 mg/dL from 2.14 to 1.18% (-1.01; 95% confidence interval [CI], -0.97, -1.05; P<.001), with overall sensor time 70 to 180 mg/dL increasing from 57.8 to 58.5% (0.64; 95% CI, 0.04, 1.24; P<.001). These improvements were sustained at 3, 6, and 9 months after the update.

CONCLUSION

Introduction of a software updatable PLGS algorithm for the Tandem t:slim X2 insulin pump resulted in sustained reductions of hypoglycemia.

ABBREVIATIONS

ADA = American Diabetes Association; CGM = continuous glucose monitoring; CI = confidence interval; PLGS = predictive low-glucose suspend; SG = sensor glucose; T1D = type 1 diabetes; T2D = type 2 diabetes; TIR = time-in-range.

Hypoglycemic Confidence in the Partners of Adults with Type 1 Diabetes

Background: Hypoglycemic confidence (HC) represents the degree to which an individual feels secure regarding his or her ability to stay safe from hypoglycemia-related problems. Self-report scales assessing HC in adults with type 1 diabetes (T1D) have found that greater HC is associated with better glycemic control and that HC rises significantly after real-time continuous glucose monitoring is introduced. To determine whether HC might be similarly meaningful in the partners of T1D adults, we developed the Hypoglycemic Confidence Scale for Partners (Partner-HCS). This article describes the construction and validation of the Partner-HCS and examines how HC in T1D partners is related to hypoglycemia-related experience and key psychosocial constructs. Methods: Items were developed from interviews with seven T1D partners, resulting in 12 self-report items. Exploratory factor analysis (EFA) was then conducted on data collected from T1D partners (n = 218). Variables to establish construct validity for the Partner-HCS included partner-reported diabetes distress, hypoglycemic fear, generalized anxiety, and confidence regarding glucagon use, as well as frequency of recent severe hypoglycemia in the T1D adult. Hierarchical regression analyses examined the unique contribution of Partner-HCS scores, independent of hypoglycemic fear, to key psychosocial constructs and hypoglycemia-related factors. Results: EFA of the 12 items yielded a single-factor solution, accounting for 51.2% of the variance. Construct validity was demonstrated by significant univariate associations with key psychosocial constructs. Importantly, Partner-HCS total score was, independent of hypoglycemic fear, significantly associated with diabetes distress (P < 0.05), overall relationship satisfaction (P = 0.004), number of severe hypoglycemic episodes in the last 6 months (P < 0.05), and confidence using glucagon (P = 0.007). In total, 38.5% of T1D partners indicated relatively low HC. Conclusions: HC is an important facet of the experiences of T1D partners. It is related to, yet distinct from, hypoglycemic fear. The Partner-HCS is a reliable, valid method for assessing HC in partners of T1D adults.

Diabetes Technology Use in Adults with Type 1 and Type 2 Diabetes

In the last 2 decades, diabetes technology has emerged as a branch of diabetes management thanks to the advent of continuous glucose monitoring (CGM) and increased availability of continuous subcutaneous insulin infusion systems, or insulin pumps. These tools have progressed from rudimentary instruments to sophisticated therapeutic options for advanced diabetes management. This article discusses the available CGM and insulin pump systems and the clinical benefits of their use in adults with type 1 diabetes, intensively insulin-treated type 2 diabetes, and pregnant patients with preexisting diabetes.