Dexcom (United States)

Continuous glucose monitoring (CGM) technology is becoming increasingly available to people with diabetes using insulin therapy; however, availability for people with type 2 diabetes (T2D) not on insulin remains limited. For people with T2D, there is strong evidence of glycemic benefit with CGM use for those treated with insulin, and CGM is accepted as standard of care. This review explores the impact of CGM use on glycemic and patient-reported outcomes in noninsulin treated populations with T2D, reporting outcomes from 10 identified randomized controlled trials and 15 nonrandomized studies. We report evidence that supports the use of this technology in people with T2D not using insulin.

Gestational diabetes mellitus (GDM) is a frequent metabolic complication during pregnancy that significantly impacts both maternal and neonatal health outcomes regularly resulting in NH. Exploring the interactions between maternal characteristics, neonatal outcomes, and data collected from wearable technologies, such as continuous glucose monitoring (CGM) could potentially enable the development of predictive models and support personalized care. This study employed probabilistic modeling, using Bayesian networks (BNs), to analyze data from the STEADY SUGAR clinical trial (N = 118 women with GDM) with the aim of discovering interactions between maternal characteristics, CGM-derived features calculated in the 90 days preceding delivery, and neonatal outcomes, particularly NH. The final BN returns a graph and conditional probability tables between inputs and outputs, whose statistical relevance has been quantified via odds ratios (ORs). Direct associations were identified between NH and maternal hypertension (OR: 2.13 [1.02, 4.46]), family history for diabetes (OR: 1.43 [0.57, 3.57]), and elevated maternal body mass index (BMI) (OR: 3.59 [1.42, 9.08] comparing lower vs higher BMI categories). Cesarean delivery also influenced NH risk (OR: 2.05 [0.98, 4.28]). Indirect associations involving medication regimens and delivery type were significant. Ethnic disparities emerged, notably higher hyperglycemia among Afro-American patients (OR: 2.91 [1.19, 7.11]), highlighting ethnicity-related variations in glycemic control. Notably, CGM-derived metrics were associated with multiple neonatal outcomes. Bayesian network allowed to explore the complex interactions between variables in pregnancies affected by GDM. This framework will be extended with wider data sets to provide valuable insights for clinical decision-making able to mitigate maternal and neonatal risks.

There is a growing trend toward AI systems interacting with humans to revolutionize a range of application domains such as healthcare and transportation. However, unsafe human-machine interaction can lead to catastrophic failures. We propose a novel approach that predicts future states by accounting for the uncertainty of human interaction, monitors whether predictions satisfy or violate safety requirements, and adapts control actions based on the predictive monitoring results. Specifically, we develop a new quantitative predictive monitor based on Signal Temporal Logic with Uncertainty (STL-U) to compute a robustness degree interval, which indicates the extent to which a sequence of uncertain predictions satisfies or violates an STL-U requirement. We also develop a new loss function to guide the uncertainty calibration of Bayesian deep learning and a new adaptive control method, both of which leverage STL-U quantitative predictive monitoring results. We apply the proposed approach to two case studies: Type 1 Diabetes management and semi-autonomous driving. Experiments show that the proposed approach improves safety and effectiveness in both case studies.

This study evaluated whether the combined use of continuous glucose monitoring (CGM) and semaglutide, a glucagon-like peptide-1 receptor agonist, was associated with larger hemoglobin A1c (HbA1c) improvements compared with use of semaglutidealone. Using US health care claims data from the Optum Clinformatics database, this retrospective analysis identified adults with type 2 diabetes (T2D) usingsemaglutide. The CGM cohort had at least 1 CGM-related claim between January 1, 2019, and September 30, 2022. Random index dates were used in the control (non-CGM) cohort. At least 1 laboratory HbA1c value was required during baseline and follow-up periods. Outcomes included change in HbA1c and the proportion of people who reached American Diabetes Association (ADA) or Healthcare Effectiveness Data and Information Set (HEDIS) HbA1c targets of less than 7.0% or less than 8.0%, respectively. A total of 21,247 people with T2D were identified, with 18,488 in the control group and 2759 using CGM. Overall, a significantly greater reduction in HbA1c was observed in the CGM cohort compared with the control group (difference-in-differences, -0.55%; 95% CI, -0.64% to -0.47%; P < .0001). Among CGM users, the proportion meeting the ADA target of HbA1c less than 7.0% nearly doubled, and the proportion achieving the HEDIS target of HbA1c less than 8.0% increased by more than 50%. The results suggest that CGM provides an additive benefit to semaglutide, leading to greater decreases in HbA1c. Expanded use of these complementary therapies in the primary care setting could enable more people with T2D to achieve their glycemic goals.

This article explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the integration of E-commerce platforms with Enterprise Resource Planning (ERP) systems. As E-commerce experiences explosive growth and ERP systems become increasingly complex, businesses face significant challenges in maintaining scalability and efficiency. We examine how AI and ML can optimize various aspects of these integrated systems, from intelligent automation and predictive analytics to anomaly detection and decision support. Through case studies and analysis of current trends, we demonstrate the tangible benefits of AI/ML implementation, including reduced costs, improved accuracy, and enhanced customer experiences. The article also addresses key challenges such as data quality, scalability, ethical considerations, and the skills gap. Finally, we explore future research directions in explainable AI, edge computing, blockchain integration, and natural language processing, highlighting their potential impacts on the E-commerce and ERP landscape.

Background:We evaluated accuracy and safety of a seventh-generation real-time continuous glucose monitoring (CGM) system during pregnancy.Materials and Methods:Evaluable data for accuracy analysis were obtained from 96 G7 sensors (Dexcom, Inc.) worn by 96 of 105 enrolled pregnant women with type 1 (n = 59), type 2 (n = 21), or gestational diabetes (n = 25). CGM values were compared with arterialized venous glucose values from the YSI comparator instrument during 6-h clinic sessions at different time points throughout the sensors' 10-day wear period. The primary endpoint was the proportion of CGM values in the 70–180 mg/dL range within 15% of comparator glucose values. Secondary endpoints included the proportion of CGM values within 20% or 20 mg/dL of comparator values ≥ or <100 mg/dL, respectively (the %20/20 agreement rate).Results:Of the 1739 pairs with CGM in the 70–180 mg/dL range, 83.2% were within 15% of comparator values. The lower bound of the 95% confidence interval was 79.8%. Of the 2102 pairs with CGM values in the 40–400 mg/dL range, the %20/20 agreement rate was 92.5%. Of the 1659 pairs with comparator values in the 63–140 mg/dL range, the %20/20 agreement rate was 92.3%. The %20/20 agreement rates on days 1, 4 and 7, and 10 were 78.6%, 96.3%, and 97.3%, respectively. Consensus error grid analysis showed 99.8% of pairs in the clinically acceptable A and B zones. There were no serious adverse events. The sensors' 10-day survival rate was 90.3%.Conclusion:The G7 system is accurate and safe during pregnancies complicated by diabetes and does not require confirmatory fingerstick testing.Clinical Trial Registration:clinicaltrials.gov NCT04905628

There is a heightened risk of glycemic variability in patients with diabetes and chronic kidney disease (CKD). This glycemic variability could lead to hypoglycemic or hyperglycemic crises. We hypothesized that initiation of continuous glucose monitoring (CGM), which provides a glucose measurement every 1-5 minutes, could reduce the incidence of hospitalizations for patients with type 2 diabetes (T2D) and CKD. A retrospective analysis of US administrative claims data from the Optum Clinformatics© Database was conducted. People with T2D, using insulin, not receiving dialysis, and living with stage 3-5 CKD who initiated CGM between 1/1/2016 and 3/31/2022 were identified. NDC and HCPCS codes were used to identify CGM device use and ICD-10 codes were used to identify CKD diagnosis and categorize healthcare encounters. Rates of diabetes-related hospitalizations were obtained, and multivariable logistic regression analyses revealed predictors of hypoglycemic and hyperglycemic encounters. A total of 8,959insulin-using patients with T2D and CKD were identified. Most were White (72.3%), had Medicare insurance coverage (82.2%), were using intensive insulin (91.3%), and had Stage 3 CKD (86.0%). After CGM initiation, rates of hospitalizations for hyperglycemia or hypoglycemia decreased by 18.2% and 17.0%, respectively (p<0.0001 for both). The proportion hospitalized with at least one hypoglycemic or hyperglycemic event also significantly decreased after CGM initiation. Significant predictors of both hypoglycemic and hyperglycemic encounters included a previous encounter of that type, age 30-59 years and depression (for hypoglycemia), and age 30-49 and neuropathy (for hyperglycemia). Use of CGM or glucagon-like peptide-1 receptor agonists was significantly protective against hypoglycemic encounters. Initiation of CGM was associated with significant reductions in diabetes-related hospitalizations among insulin-using individuals with T2D and moderate-to-severe CKD. CGM could help patients with T2D and CKD control their glucose and avoid potentially dangerous glycemic events.

Inclusion of advocates as partners in research is now required by numerous international funding agencies. The role of advocates has expanded in recent years to include all areas of research, including basic cancer research, translational research, and clinical trial design and development. The involvement of advocates as partners in cancer research can be challenging for the advocate and for the researchers, but this collaboration is beneficial to all involved. Herein, we will define patient advocacy, explore advocate engagement, and share information on programs that train advocates and researchers to work together as partners.