A clinical review of the t:slim X2 insulin pump

Addressing the Burden of Multiple Daily Insulin Injections in Type 2 Diabetes with Insulin Pump Technology: A Narrative Review

The growing prevalence of type 2 diabetes (T2D) remains a leading health concern in the US. Despite new medications and technologies, glycemic control in this population remains suboptimal, which increases the risk of poor outcomes, increased healthcare resource utilization, and associated costs. This article reviews the clinical and economic impacts of suboptimal glycemic control in patients on basal-bolus insulin or multiple daily injections (MDI) and discusses how new technologies, such as tubeless insulin delivery devices, referred to as "patch pumps", have the potential to improve outcomes in patients with T2D.

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.

New therapies towards a better glycemic control in youths with type 1 diabetes

Type 1 diabetes (T1D) is the most frequent form of diabetes in pediatric age, affecting more than 1.5 million people younger than age 20 years worldwide. Early and intensive control of diabetes provides continued protection against both microvascular and macrovascular complications, enhances growth, and ensures normal pubertal development. In the absence of definitive reversal therapy for this disease, achieving and maintaining the recommended glycemic targets is crucial. In the last 30 years, enormous progress has been made using technology to better treat T1D. In spite of this progress, the majority of children, adolescents and young adults do not reach the recommended targets for glycemic control and assume a considerable burden each day. The development of promising new therapeutic advances, such as more physiologic insulin analogues, pioneering diabetes technology including continuous glucose monitoring and closed loop systems as well as new adjuvant drugs, anticipate a new paradigm in T1D management over the next few years. This review presents insights into current management of T1D in youths.

Situational Awareness and Proactive Engagement Predict Higher Time in Range in Adolescents and Young Adults Using Hybrid Closed-Loop

Background

Adolescents and young adults with type 1 diabetes have high HbA1c levels and often struggle with self-management behaviors and attention to diabetes care. Hybrid closed-loop systems (HCL) like the t:slim X2 with Control-IQ technology (Control-IQ) can help improve glycemic control. The purpose of this study is to assess adolescents' situational awareness of their glucose control and engagement with the Control-IQ system to determine significant factors in daily glycemic control.

Methods

Adolescents (15-25 years) using Control-IQ participated in a 2-week prospective study, gathering detailed information about Control-IQ system engagements (boluses, alerts, and so on) and asking the participants' age and gender about their awareness of glucose levels 2-3 times/day without checking. Mixed models assessed which behaviors and awareness items correlated with time in range (TIR, 70-180 mg/dl, 3.9-10.0 mmol/L).

Results

Eighteen adolescents/young adults (mean age 18 ± 1.86 years and 86% White non-Hispanic) completed the study. Situational awareness of glucose levels did not correlate with time since the last glucose check (p = 0.8). In multivariable modeling, lower TIR was predicted on days when adolescents underestimated their glucose levels (r = -0.22), received more CGM alerts (r = -0.31), and had more pump engagements (r = -0.27). A higher TIR was predicted when adolescents responded to CGM alerts (r = 0.20) and entered carbohydrates into the bolus calculator (r = 0.49).

Conclusion

Situational awareness is an independent predictor of TIR and may provide insight into patterns of attention and focus that could positively influence glycemic outcomes in adolescents. Proactive engagements predict better TIR, whereas reactive engagement predicted lower TIR. Future interventions could be designed to train users to develop awareness and expertise in effective diabetes self-management.

Machine Learning Approach for Care Improvement of Children and Youth with Type 1 Diabetes Treated with Hybrid Closed-Loop System

Type 1 diabetes is a disease affecting beta cells of the pancreas and it’s responsible for a decreased insulin secretion, leading to an increased blood glucose level. The traditional method for glucose treatment is based on finger-stick measurement of the blood glucose concentration and consequent manual insulin injection. Nowadays insulin pumps and continuous glucose monitoring systems are replacing them, being simpler and automatized. This paper focuses on analyzing and improving the knowledge about which Machine Learning algorithms can work best with glycaemic data and tries to find out the relation between insulin pump settings and glycaemic control. The dataset is composed of 90 days of recordings taken from 16 children and adolescents. Three Machine Learning approaches, two for classification, Logistic Regression (LR) and Random Forest (RL), and one for regression, Multivariate Linear Regression (MLR), have been used for the purpose. Specifically, the pump settings analysis was performed based on the Time In Range (TIR) computation and comparison consequent to pump setting changes. RF and MLR have shown the best results, while, for the settings’ analysis, the data show a discrete correlation between changes and TIRs. This study provides an interesting closer look at the data recorded by the insulin pump and a suitable starting point for a thorough and complete analysis of them.

Closed-Loop Artificial Pancreas Therapy for Type 1 Diabetes

PURPOSE OF REVIEW

Closed-loop insulin pump systems (artificial pancreas) represent the cutting edge of insulin delivery technology. There are only a few systems currently approved for use in the USA: the MiniMed 670G/770G (which share an algorithm), t:slim X2 Control IQ, and the Omnipod 5. We review these systems and look into the future of the technology.

RECENT FINDINGS

All of the approved closed-loop insulin pump systems have demonstrated in multicenter prospective trials improvements in time in range, hemoglobin A1c, and time spent in hypoglycemia. The newer systems have also improved time spent in automation. Comparisons between the systems with regard to glycemic control are difficult to make due to differences in clinical trial design, but there are notable differences in the user experience between systems. The past few years have been a time of exponential development in the field of closed-loop insulin pump systems. However, more research is needed to provide full automation of these systems without any need for information from the user.

Meal and Physical Activity Detection from Free-Living Data for Discovering Disturbance Patterns of Glucose Levels in People with Diabetes

Objective: The interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID). Methods: Two challenges in time-series data collected in daily living are addressed: data quality improvement and the detection of unannounced disturbances of BGC. CGM data have missing values for varying periods of time and outliers. People may neglect reporting their meal and physical activity information. In this work, novel methods for preprocessing real-world data collected from people with T1D and the detection of meal and exercise events are presented. Four recurrent neural network (RNN) models are investigated to detect the occurrences of meals and physical activities disjointly or concurrently. Results: RNNs with long short-term memory (LSTM) with 1D convolution layers and bidirectional LSTM with 1D convolution layers have average accuracy scores of 92.32% and 92.29%, and outperform other RNN models. The F1 scores for each individual range from 96.06% to 91.41% for these two RNNs. Conclusions: RNNs with LSTM and 1D convolution layers and bidirectional LSTM with 1D convolution layers provide accurate personalized information about the daily routines of individuals. Significance: Capturing daily behavior patterns enables more accurate future BGC predictions in AID systems and improves BGC regulation.

Clinical Implementation of the Omnipod 5 Automated Insulin Delivery System: Key Considerations for Training and Onboarding People With Diabetes

Automated insulin delivery (AID) systems, which connect an insulin pump, continuous glucose monitoring system, and software algorithm to automate insulin delivery based on real-time glycemic data, hold promise for improving outcomes and reducing therapeutic burden for people with diabetes. This article reviews the features of the Omnipod 5 Automated Insulin Delivery System and how it compares to other AID systems available on or currently under review for the U.S. market. It also provides practical guidance for clinicians on how to effectively train and onboard people with diabetes on the Omnipod 5 System, including how to personalize therapy and optimize glycemia. Many people with diabetes receive their diabetes care in primary care settings rather than in a diabetes specialty clinic. Therefore, it is important that primary care providers have access to resources to support the adoption of AID technologies such as the Omnipod 5 System.

Switching from predictive low glucose suspend to advanced hybrid closed loop control: Effects on glucose control and patient reported outcomes

AIMS

Automated insulin delivery improves glucose control. Aim of this study was to compare in real life the effects on glucose control and patient reported outcomes of an advanced hybrid closed loop system (Control-IQ), versus a simpler system with predictive low glucose suspend function (Basal-IQ).

METHODS

Thirty-one type 1 diabetic subjects were studied during Basal-IQ and after switching to Control-IQ. Variables analyzed were time spent in range (70-180 mg/dL), in tight range (70-140 mg/dL), above range (>180 mg/dL), below range (<70 mg/dL), mean glucose, coefficient of variation and glycated hemoglobin. Questionnaires were administered regarding therapy satisfaction (Diabetes Treatment Satisfaction Questionnaire in status/change form), fear of hypoglycemia (Hypoglycemia Fear Survey), quality of sleep (Pittsburgh Sleep Quality Index).

RESULTS

After 12 weeks of Control-IQ, time in range increased from 62.7 to 74.0%, p < 0.0001, time in tight range increased from 37.1 to 44.6 %, p < 0.001, time above range decreased from 35.6 to 24.4% p < 0.0001. Improvements were observed in mean glucose and glucose variability. Glycated hemoglobin decreased from 7.0% (53 mmol/mol) to 6.6% (49 mmol/mol), p < 0.0001. Subjects using Control-IQ manifested greater satisfaction and less fear of hypoglycemia.

CONCLUSION

Compared to Basal-IQ, Control-IQ improves glucose control and therapy satisfaction.

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.

Emerging Treatment Strategies for Diabetes Mellitus and Associated Complications: An Update

The occurrence of diabetes mellitus (DM) is increasing rapidly at an accelerating rate worldwide. The status of diabetes has changed over the last three generations; whereas before it was deemed a minor disease of older people but currently it is now one of the leading causes of morbidity and mortality among middle-aged and young people. High blood glucose-mediated functional loss, insulin sensitivity, and insulin deficiency lead to chronic disorders such as Type 1 and Type 2 DM. Traditional treatments of DM, such as insulin sensitization and insulin secretion cause undesirable side effects, leading to patient incompliance and lack of treatment. Nanotechnology in diabetes studies has encouraged the development of new modalities for measuring glucose and supplying insulin that hold the potential to improve the quality of life of diabetics. Other therapies, such as β-cells regeneration and gene therapy, in addition to insulin and oral hypoglycemic drugs, are currently used to control diabetes. The present review highlights the nanocarrier-based drug delivery systems and emerging treatment strategies of DM.

Real-world performance of hybrid closed loop in youth, young adults, adults and older adults with type 1 diabetes: Identifying a clinical target for hybrid closed-loop use

AIM

To describe real-world hybrid closed loop (HCL) use and glycaemic outcomes across the lifespan and identify a clinical threshold for HCL use associated with meeting the internationally recommended target of 70% sensor glucose time in range (TIR; 70-180 mg/dL).

MATERIALS AND METHODS

Mixed models examined MiniMed 670G HCL use and glycaemic outcomes in 276 people with type 1 diabetes from four age groups: youth (aged <18 years), young adults (18-25 years), adults (26-49 years) and older adults (≥50 years) for 1 year. ROC analysis identified the minimum percentage HCL use associated with meeting the TIR goal of 70%.

RESULTS

HCL use at month 1 was 70.7% ± 2.9% for youth, 71.0% ± 3.8% for young adults, 78.9% ± 2.1% for adults and 84.7% ± 3.8% in older adults. HCL use declined significantly at 12 months to 49.3% ± 3.2% in youth (P < .001) and 55.7% ± 4.3% in young adults (P = .002). HCL use was sustained at 12 months in adults (76.4% ± 2.2%, P = .36) and older adults (80.4% ± 3.9%, P = .36). HCL use of 70.6% was associated with 70% TIR (sensitivity 58.3%, specificity 85%, AUC 0.77). Older age, 80% or higher continuous glucose monitor use and four or more blood glucose checks per day were associated with attaining the HCL-use threshold.

CONCLUSIONS

HCL use of 70% or higher may be a useful target for clinicians to use to assist people with diabetes in attaining glycaemic goals. Youth may struggle with HCL use more than adults and require clinical intervention to help sustain HCL use across time.

Initiating hybrid closed loop: A program evaluation of an educator-led Control-IQ follow-up at a large pediatric clinic

BACKGROUND

Control-IQ (Tandem Diabetes) is a hybrid closed-loop (HCL) system that users self-initiate after completing online training. Best practices for clinical follow-up are not known. Our quality improvement objective was to evaluate the usefulness of an educator-led follow-up program for new HCL users in a type 1 diabetes pediatric clinic.

METHODS

We implemented an ''HCLCheck-in'' program, first determining when users started HCL, then having diabetes educators contact them for a follow-up call 2-weeks after start. Educators used a Clinical Tool to inform insulin dose and behavior recommendations, and used four benchmarks to determine need for further follow-up: ≥71% HCL use, ≥71% CGM use, ≥60% Time-in-Range (TIR, 70-180 mg/dL), <5% below 70 mg/dL. Family and educator satisfaction were surveyed.

RESULTS

One-hundred-twenty-three youth [mean age 13.6 ± 3.7 y, 53.7% female, mean HbA1c 7.6 ± 1.4% (60 mmol/mol)] completed an HCLCheck-in call a median (IQR) of 18(15, 21) days post-HCL start. 74 users (60%) surpassed benchmarks with 94% HCL use and 71% TIR. Of the 49 who did not, 16 completed a second call, and improved median TIR 12.5% (p = 0.03). HCL users reported high satisfaction with the program overall [median 10 (9, 10) out of 10]. Educators spent a median of 45 (32,70) minutes per user and rated satisfaction with the program as 8 (7,9.5) and the Tool as 9 (9, 10).

CONCLUSION

Our HCLCheck-in program received high satisfaction ratings and resulted in improved TIR for those initially not meeting benchmarks, suggesting users may benefit from early follow-up. Similar programs may be beneficial for other new technologies.

Standardized Hybrid Closed-Loop System Reporting

The hybrid closed-loop (HCL) system has been shown to improve glycemic control and reduce hypoglycemia. Optimization of HCL settings requires interpretation of the glucose, insulin, and factors affecting glucose such as food intake and exercise. To the best of our knowledge, there is no published guidance on the standardized reporting of HCL systems. Standardization of HCL reporting would make interpretation of data easy across different systems. We reviewed the literature on patient and provider perspectives on downloading and reporting glucose metric preferences. We also incorporated international consensus on standardized reporting for glucose metrics. We describe a single-page HCL data reporting, referred to here as "artificial pancreas (AP) Dashboard." We propose seven components in the AP Dashboard that can provide detailed information and visualization of glucose, insulin, and HCL-specific metrics. The seven components include (A) glucose metrics, (B) hypoglycemia, (C) insulin, (D) user experience, (E) hyperglycemia, (F) glucose modal-day profile, and (G) insight. A single-page report similar to an electrocardiogram can help providers and patients interpret HCL data easily and take the necessary steps to improve glycemic outcomes. We also describe the optimal sampling duration for HCL data download and color coding for visualization ease. We believe that this is a first step in creating a standardized HCL reporting, which may result in better uptake of the systems. For increased adoption, standardized reporting will require input from providers, patients, diabetes device manufacturers, and regulators.