Continuous Glucose Monitoring Devices Research Articles

Generalization of a Deep Learning Model for Continuous Glucose Monitoring-Based Hypoglycemia Prediction: Algorithm Development and Validation Study.

Predicting hypoglycemia while maintaining a low false alarm rate is a challenge for the wide adoption of continuous glucose monitoring (CGM) devices in diabetes management. One small study suggested that a deep learning model based on the long short-term memory (LSTM) network had better performance in hypoglycemia prediction than traditional machine learning algorithms in European patients with type 1 diabetes. However, given that many well-recognized deep learning models perform poorly outside the training setting, it remains unclear whether the LSTM model could be generalized to different populations or patients with other diabetes subtypes. The aim of this study was to validate LSTM hypoglycemia prediction models in more diverse populations and across a wide spectrum of patients with different subtypes of diabetes. We assembled two large data sets of patients with type 1 and type 2 diabetes. The primary data set including CGM data from 192 Chinese patients with diabetes was used to develop the LSTM, support vector machine (SVM), and random forest (RF) models for hypoglycemia prediction with a prediction horizon of 30 minutes. Hypoglycemia was categorized into mild (glucose=54-70 mg/dL) and severe (glucose<54 mg/dL) levels. The validation data set of 427 patients of European-American ancestry in the United States was used to validate the models and examine their generalizations. The predictive performance of the models was evaluated according to the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). For the difficult-to-predict mild hypoglycemia events, the LSTM model consistently achieved AUC values greater than 97% in the primary data set, with a less than 3% AUC reduction in the validation data set, indicating that the model was robust and generalizable across populations. AUC values above 93% were also achieved when the LSTM model was applied to both type 1 and type 2 diabetes in the validation data set, further strengthening the generalizability of the model. Under different satisfactory levels of sensitivity for mild and severe hypoglycemia prediction, the LSTM model achieved higher specificity than the SVM and RF models, thereby reducing false alarms. Our results demonstrate that the LSTM model is robust for hypoglycemia prediction and is generalizable across populations or diabetes subtypes. Given its additional advantage of false-alarm reduction, the LSTM model is a strong candidate to be widely implemented in future CGM devices for hypoglycemia prediction.

Assessing Patterns of CGM use and Metrics of Glycemic Control in T1D and T2D Patients in the Veterans Healthcare System: Integrating CGM Device Data with EHR Data.

To integrate long-term daily continuous glucose monitoring (CGM) device data with electronic health records (EHR) for type 1 and type 2 diabetes patients in the Veterans Affairs Healthcare System to assess real-world patterns of CGM use and the reliability of EHR-based CGM information. This observational study used Dexcom CGM device data linked with EHR (from 2015-2020) for a large national cohort of diabetes patients. We tracked the initiation and consistency of CGM use, assessed concordance of CGM use and measures of glucose control between CGM device data and EHR records, and examined results by age, ethnicity, and diabetes type. The time from pharmacy release of CGM to patients to initiation of uploading CGM data to Dexcom servers averaged three weeks but demonstrated wide variation among individuals; importantly, this delay decreased markedly over the later years. The average daily wear time of CGM exceeded 22 hours over nearly three years of follow-up. Patterns of CGM use were generally consistent across age, race/ethnicity groups and diabetes type. There was strong concordance between EHR-based estimates of CGM use and Dexcom CGM wear time and between estimates of glucose control from both sources. The study demonstrates our ability to reliably integrate CGM devices and EHR data to provide valuable insights into CGM use patterns. The results indicate in the real-world environment that CGM is worn consistently over many years for both type 1 and type 2 patients within the Veterans Affairs Healthcare System and is similar across major race/ethnicity and age groups.

Development of a Multivariable Risk Prediction Tool to Predict Adverse Outcomes among Children with Type 1 Diabetes: A Pilot Study

Background. Children and adolescents with type 1 diabetes mellitus (T1DM) are frequently hospitalised for severe hypoglycaemia, hyperglycaemia, and diabetic ketoacidosis (DKA). While several risk factors have been recognised, clinically identifying these children at high risk of acute decompensation remains challenging. Objective. To develop a risk prediction model to accurately estimate the risk of acute healthcare utilisation due to severe hypoglycaemia, hyperglycaemia, and DKA in children and adolescents with T1DM. Materials and Methods. Using a retrospective dataset, baseline demographic and clinical data were collected from patients (&lt;18 years) seen at a regional paediatric diabetes clinic from 1 January 2018 to 1 January 2020. The outcome was the number of emergency department presentations or hospital admissions for severe hypoglycaemia, hyperglycaemia, and DKA across the study period. Variables that were significant in univariate analysis were entered into a multivariable model. Receiver operator characteristic (ROC) curves assessed the model’s discrimination and generated cut-offs for risk group stratification (low, medium, and high). Kaplan–Meier survival analysis measured time to acute healthcare utilisation across the risk groups. Results. Our multivariable risk prediction model consisted of five predictors (continuous glucose monitoring device, previous acute healthcare utilisation, missed appointments, and child welfare services involvement and socioeconomic status). The model exhibited good discrimination (area under the ROC = 0.81), accurately stratified children into low-, medium-, and high-risk groups, and demonstrated significant differences between median time to healthcare utilisation. Conclusion. Our model identified patients at an increased risk of acute healthcare utilisation due to severe hypoglycaemia, hyperglycaemia, and DKA.

Expert Panel Recommendations for a Standardized Ambulatory Glucose Profile Report for Connected Insulin Pens.

Connected insulin pens capture data on insulin dosing/timing and can integrate with continuous glucose monitoring (CGM) devices with essential insulin and glucose metrics into a single platform. Standardization of connected insulin pen reports is desirable to enhance clinical utility with a single report. An international expert panel was convened to develop a standardized connected insulin pen report incorporating insulin and glucose metrics into a single, clinically useful report. An extensive literature review and identification of examples of current connected insulin pen reports was performed serving as the basis for creation of a draft of a standardized connected insulin pen report. The expert panel participated in three virtual standardization meetings and online surveys. The AGP Report: Connected Insulin Pen brings all clinically relevant CGM-derived glucose and connected insulin pen metrics into a single simplified two-page report. The first page contains the time in ranges bar, summary of key insulin and glucose metrics, the ambulatory glucose profile (AGP) curve, and detailed basal (long-acting) insulin assessment. The second page contains the bolus (mealtime and correction) insulin assessment periods with information on meal timing, insulin-to-carbohydrate ratio (ICR), average bolus insulin dose and number of days with bolus doses recorded. The report's second page contains daily glucose profiles with an overlay of the timing and amount of basal and bolus insulin administered. The AGP Report: Connected Insulin Pen is a standardized clinically useful report that should be considered by companies developing connected pen technology as part of their system reporting/output.

Glucose Targets Using Continuous Glucose Monitoring Metrics in Older Adults With Diabetes: Are We There Yet?

The older population is increasing worldwide and up to 30% of older adults have diabetes. Older adults with diabetes are at risk of glucose-related acute and chronic complications. Recently, mostly in type 1 diabetes (T1D), continuous glucose monitoring (CGM) devices have proven beneficial in improving time in range (TIR glucose, 70-180 mg/dL or glucose 3.9-10 mmol/L), glycated hemoglobin (HbA1c), and in lowering hypoglycemia (time below range [TBR] glucose <70 mg/dL or glucose <3.9 mmol/L). The international consensus group formulated CGM glycemic targets relating to older adults with diabetes based on very limited data. Their recommendations, based on expert opinion, were aimed at mitigating hypoglycemia in all older adults. However, older adults with diabetes are a heterogeneous group, ranging from healthy to very complex frail individuals based on chronological, biological, and functional aging. Recent clinical trial and real-world data, mostly from healthy older adults with T1D, demonstrated that older adults often achieve CGM targets, including TIR recommended for non-vulnerable groups, but less often meet the recommended TBR <1%. Existing data also support that hypoglycemia avoidance may be more strongly related to minimization of glucose variability (coefficient of variation [CV]) rather than lower TIR. Very limited data are available for glucose goals in older adults adjusted for the complexity of their health status. Herein, we review the bidirectional associations between glucose and health status in older adults with diabetes; use of diabetes technologies, and their impact on glucose control; discuss current guidelines; and propose a new set of CGM targets for older adults with insulin-treated diabetes that are individualized for health and living status.

Budget impact analysis of continuous glucose monitoring in individuals with type 2 diabetes on insulin treatment in England

IntroductionIn 2022, updated guidance from NICE expanded the options for self-monitoring of blood glucose for patients with type 2 diabetes (T2DM), to include continuous glucose monitoring (CGM). In this budget impact analysis, the cost impact of CGM was compared with traditional self-monitoring of blood glucose (SMBG) in adults with T2DM over 1 year from the commissioner perspective in England.Research Design and methodsThe NICE-eligible T2DM cohort was split into 4 subgroups to enable nuanced costing by insulin administration frequency: basal human insulin, premixed insulin, basal-bolus insulin and bolus insulin. The model’s cost components comprised mild and severe hypoglycaemia (SH), diabetic ketoacidosis (DKA), consumables and healthcare resource utilisation in primary and secondary care.ResultsThe introduction of CGM is estimated to be cost additive by approximately £4.6 million in the basecase, driven by increased spending on the CGM device. Overall, healthcare activity was reduced by approximately 20,000 attendances, due to fewer SH and DKA episodes in the CGM arm. General Practitioner (GP) practice-based activity is expected to drop after the first year as patients requiring CGM training is reduced. The budget impact could be neutralised if the CGM sensor was discounted by 13.2% (£29.76 to £25.83).ConclusionsCGM may result in increased spending in the NICE-eligible T2DM cohort but is expected to reduce demand on secondary care services and GP time. These findings may be of interest to local decision-makers who wish to resolve the COVID-19 backlog with transformational investment in primary care to reduce secondary care activity.

Importance of FDA-Integrated Continuous Glucose Monitors to Ensure Accuracy of Continuous Glucose Monitoring.

Continuous glucose monitoring (CGM) has been shown to improve glycemic control and self-monitoring, as well as to reduce the risk of hypoglycemia. Integrated CGM (iCGM) FDA-cleared systems with published performance data are established nonadjunctive and accurate CGM tools that can directly inform decision-making in the treatment of diabetes (i.e., insulin dosing). Studies have assessed accuracy and safety data of CGMs that were eventually cleared for iCGM by the FDA and that informed the recommendation for their nonadjunctive use. Subsequent robust clinical trials and real-world studies demonstrated clinical effectiveness with improvements in a range of patient outcomes. In recent years, a number of non-iCGM-approved CGM devices have entered the market outside the United States worldwide. Some of these non-iCGM-approved CGM devices require additional user verification of blood glucose levels to be performed for making treatment decisions, termed adjunctive. Moreover, in many non-iCGM-approved CGM devices, accuracy studies published in peer-reviewed journals are scarce or have many limitations. Consequently, non-iCGM-approved CGM devices cannot be automatically perceived as having the same performance or quality standards than those approved for iCGM by the FDA. As a result, although these devices tend to cost less than iCGMs that carry FDA clearance and could therefore be attractive from the point of view of a health care payer, it must be emphasized that evaluation of costs should not be limited to the device (such as the usability preference that patients have for nonadjunctive sensors compared to adjunctive sensors) but to the wider value of the total benefit that the product provides to the patient.

Multifunctional nanomaterials for smart wearable diabetic healthcare devices

Wearable diabetic healthcare devices have attracted great attention for real-time continuous glucose monitoring (CGM) using biofluids such as tears, sweat, saliva, and interstitial fluid via noninvasive ways. In response to the escalating global demand for CGM, these devices enable proactive management and intervention of diabetic patients with incorporated drug delivery systems (DDSs). In this context, multifunctional nanomaterials can trigger the development of innovative sensing and management platforms to facilitate real-time selective glucose monitoring with remarkable sensitivity, on-demand drug delivery, and wireless power and data transmission. The seamless integration into wearable devices ensures patient's compliance. This comprehensive review evaluates the multifaceted roles of these materials in wearable diabetic healthcare devices, comparing their glucose sensing capabilities with conventionally available glucometers and CGM devices, and finally outlines the merits, limitations, and prospects of these devices. This review would serve as a valuable resource, elucidating the intricate functions of nanomaterials for the successful development of advanced wearable devices in diabetes management.

The effect of closed-loop glucose control on C-peptide secretion in youth with newly diagnosed type 1 diabetes: the CLOuD RCT

Background We assessed whether a sustained period of intensive glucose control with hybrid closed-loop for 12 months following diagnosis of type 1 diabetes in children and adolescents can preserve C-peptide secretion compared to standard insulin therapy. Methods In an open-label, multicentre, randomised, parallel trial, youth aged 10–16.9 years were randomised within 21 days of type 1 diabetes diagnosis to hybrid closed-loop or standard insulin therapy (control). Primary end point was the difference in mixed-meal C-peptide area under the curve 12 months post diagnosis. Key secondary end points included time spent in target glucose range, glycated haemoglobin and time spent below target glucose range at 12 months. Analysis was by intention to treat. The Closed Loop from Onset in Type 1 Diabetes consortium secured external funding for participants to continue on beyond 12 months, but the funding by National Institute for Health and Care Research and the results reported here refer only to the 12 months follow-up. Results We randomised 97 participants (mean ± standard deviation age 12 ± 2 years), 51 to closed-loop and 46 to control therapy. There was no difference in C-peptide area under the curve at 12 months between groups [geometric mean (interquartile range) closed-loop (n = 46): 0.35 pmol/ml (0.16, 0.49) vs. control (n = 37): 0.46 pmol/ml (0.22, 0.69); mean adjusted difference –0.06 (95% confidence interval –0.14 to 0.03); p = 0.19]. The proportion of time in target range 3.9–10.0 mmol/l based on 14-day masked LibrePro (Abbott Diabetes Care, Maidenhead, UK) sensor glucose data at 12 months was 10 percentage points (95% confidence interval 2 to 17) higher in the closed-loop group (64 ± 14%) compared to control group (54 ± 23%). Arithmetic mean glycated haemoglobin A1c was lower in the closed-loop group by 4 mmol/mol (0.4%) [95% confidence interval 0 to 8 mmol/mol (0.0% to 0.7%)] at 12 months. The mean difference in time spent &lt; 3.9 mmol/l between groups was 0.9 percentage points (95% confidence interval –1.0 to 2.8). Three severe hypoglycaemic events occurred in the closed-loop group (two participants), and one in the control group; one diabetic ketoacidosis occurred in the closed-loop group. Conclusions A sustained period of hybrid closed-loop glucose control following diagnosis of type 1 diabetes in children and adolescents does not slow down the decline in residual C-peptide secretion compared with standard insulin therapy. Despite the lack of effect on C-peptide, glycaemic control was sustained in the closed-loop group, whereas glycaemic control deteriorated in the control group 6 to 9 months after diagnosis and closed-loop safely accommodated the variability in exogenous insulin requirements which occur with beta-cell recovery post diagnosis. Limitations of the study included no central measurement of auto-antibodies at diagnosis. There was imbalance in the rate of diabetic ketoacidosis at diagnosis which is associated with a more rapid decline in C-peptide secretion, but this was adjusted for in the analyses. This highlights the need for improved therapies to allow youth to achieve recommended glycaemic targets from onset of type 1 diabetes irrespective of the lack of effect on residual C-peptide secretion. Future work includes ongoing follow-up of the study population for up to 4 years after diagnosis to observe how any differences in glycaemic control between treatment groups develop over time. Trial registration This trial is registered as Clinicaltrials.gov NCT02871089. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 14/23/09), the Helmsley Trust (2016PG-T1D045 and 2016PG-T1D046), and JDRF (22-2013-266 and 2-RSC-2019-828-M-N), and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 8. See the NIHR Funding and Awards website for further award information. Additional support for the artificial pancreas work was from the NIHR Cambridge Biomedical Research Centre and NIHR Oxford Biomedical Research Centre. Abbott Diabetes Care supplied free glucose monitoring devices, and Dexcom supplied discounted continuous glucose monitoring devices. Medtronic supplied discounted insulin pumps, phone enclosures, continuous glucose monitoring devices, and pump consumables.

Basal Hyperglycemia Contributes More Than Fifty Percent to Time in Range in Pregnant Women with Type 1 Diabetes.

To evaluate the relative contribution of basal hyperglycemia (BHG) and postprandial hyperglycemia (PHG) to the time in range (TIR) categories and adverse pregnancy outcomes in pregnant women with type 1 diabetes mellitus (T1DM). This observational study included 112 pregnancies with T1DM from the CARNATION study who wore continuous glucose monitoring (CGM) devices during pregnancy. The data from CGM were analyzed for TIR (range, 3.5-7.8 mmol/L), areas under the curve (AUC) of PHG, AUC of BHG, basal and postprandial hyperglycemia contribution rates. The contribution rates of BHG and PHG to the different levels of TIR(＜60%, 60-78%, ≥78%) and adverse pregnancy outcomes were analyzed. The participants' average age was 28.8±3.9 years with a diabetes duration of 8.4±6.2 years. All women experienced a mean TIR of 75.6±19.0% and a mean HbA1c of 6.2±1.1% during pregnancy. The BHG contribution accounted for 74.9(36.8, 100)%, 69.2(13.4, 100)%, and 66.5(10.0, 100)% (P＜0.001) and PHG accounted for 25.1(0, 63.2)% and 30.8(0, 86.6)% and 33.5(0, 90.0)% (P＜0.001) when participants experienced the TIR＜60%, 60-78%, ≥78%, respectively. Participants with higher BHG contribution rates tended to have more adverse pregnancy outcomes. Basal hyperglycemia was the major contributor to TIR during pregnancy. Along with controlling the postprandial hyperglycemia, pregnant women with T1DM who did not reach the target of TIR may benefit more from the optimization of insulin regimens focusing on reducing basal glucose.

Glycemic variability is associated with sural nerve conduction velocity in outpatients with type2diabetes: Usefulness of a new point-of-care device for nerve conduction studies.

Although several studies have shown the association between continuous glucose monitoring (CGM)-derived glycemic variability (GV) and diabetic peripheral neuropathy, no studies have focused on outpatients or used NC-stat®/DPNCheck™, a new point-of-care device for nerve conduction study (NCS). We investigated the association between CGM-derived GV and NCS using DPNCheck™ in outpatients with type 2 diabetes, and further analyzed the difference in results between patients with and without well-controlled HbA1c levels. All outpatients with type 2 diabetes using the CGM device (FreeStyle Libre Pro®) between 2017 and 2022 were investigated. Sural nerve conduction was evaluated by sensory nerve action potential (SNAP) amplitude and sensory conduction velocity (SCV) using DPNCheck™. Associations of CGM-derived GV metrics with SNAP amplitude and SCV were investigated. In total, 304 outpatients with type 2 diabetes were included. In a linear regression model, most CGM-derived GV metrics except for the mean amplitude of glucose excursion and low blood glucose index were significantly associated with SCV, but not with SNAP amplitude. The significant associations of most CGM-derived GV metrics with SCV remained after adjustment for possible confounding factors, but not after adjustment for glycated hemoglobin (HbA1c). Most CGM-derived GV metrics were significantly associated with SCV after adjustment for HbA1c in patients with a HbA1c ≤ 6.9%, but not in those with a HbA1c ≥ 7.0%. In outpatients with type 2 diabetes, multiple CGM-derived GV metrics were significantly associated with SCV obtained by DPNCheck™. GVmay have independent impacts on peripheral nerve function, particularly in patients with well-controlled HbA1c levels.

Practical considerations for continuous glucose monitoring in elite athletes with type 1 diabetes mellitus: A narrative review.

Type 1 diabetes mellitus (T1DM) refers to a metabolic condition where a lack of insulin impairs the usual homeostatic mechanisms to control blood glucose levels. Historically, participation in competitive sport has posed a challenge for those with T1DM, where the dynamic changes in blood glucose during exercise can result in dangerously high (hyperglycaemia) or low blood glucoses (hypoglycaemia) levels. Over the last decade, research and technological development has enhanced the methods of monitoring and managing blood glucose levels, thus reducing the chances of experiencing hyper- or hypoglycaemia during exercise. The introduction of continuous glucose monitoring (CGM) systems means that glucose can be monitored conveniently, without the need for frequent fingerpick glucose checks. CGM devices include a fine sensor inserted under the skin, measuring levels of glucose in the interstitial fluid. Readings can be synchronized to a reader or mobile phone app as often as every 1-5min. Use of CGM devices is associated with lower HbA1c and a reduction in hypoglycaemic events, promoting overall health and athletic performance. However, there are limitations to CGM, which must be considered when being used by an athlete with T1DM. These limitations can be addressed by individualized education plans, using protective equipment to prevent sensor dislodgement, as well as further research aiming to: (i) account for disparities between CGM and true blood glucose levels during vigorous exercise; (ii) investigate the effects of temperature and altitude on CGM accuracy, and (iii) explore of the sociological impact of CGM use amongst sportspeople without diabetes on those with T1DM.

First Accuracy and User-Experience Evaluation of New Continuous Glucose Monitoring System for Hypoglycemia Due to Hyperinsulinism.

Patients with congenital hyperinsulinism (HI) require constant glucose monitoring to detect and treat recurrent and severe hypoglycemia. Historically, this has been achieved with intermittent self-monitoring blood glucose (SMBG), but patients are increasingly using continuous glucose monitoring (CGM). Given the rapidity of CGM device development, and increasing calls for CGM use from HI families, it is vital that new devices are evaluated early. We provided two months of supplies for the new Dexcom G7 CGM device to 10 patients with HI who had recently finished using the Dexcom G6. Self-monitoring blood glucose was performed concurrently with paired readings providing accuracy calculations. Patients and families completed questionnaires about device use at the end of the two-month study period. Compared to the G6, the G7 showed a significant reduction in mean absolute relative difference (25%-18%, P < .001) and in the over-read error (Bland Altman +1.96 SD; 3.54 mmol/L to 2.95 mmol/L). This resulted in an improvement in hypoglycemia detection from 42% to 62% (P < .001). Families reported an overall preference for the G7 but highlighted concerns about high sensor failure rates. The reduction in mean absolute relative difference and over-read error and the improvement in hypoglycemia detection implies that the G7 is a safer and more useful device in the management of hypoglycemia for patients with HI. Accuracy, while improved from previous devices, remains suboptimal with 40% of hypoglycemia episodes not detected.

Continuous glucose monitoring and intrapersonal variability in fasting glucose.

Plasma fasting glucose (FG) levels play a pivotal role in the diagnosis of prediabetes and diabetes worldwide. Here we investigated FG values using continuous glucose monitoring (CGM) devices in nondiabetic adults aged 40-70 years. FG was measured during 59,565 morning windows of 8,315 individuals (7.16 ± 3.17 days per participant). Mean FG was 96.2 ± 12.87 mg dl-1, rising by 0.234 mg dl-1 per year with age. Intraperson, day-to-day variability expressed as FG standard deviation was 7.52 ± 4.31 mg dl-1. As there are currently no CGM-based criteria for diabetes diagnosis, we analyzed the potential implications of this variability on the classification of glycemic status based on current plasma FG-based diagnostic guidelines. Among 5,328 individuals who would have been considered to have normal FG based on the first FG measurement, 40% and 3% would have been reclassified as having glucose in the prediabetes and diabetes ranges, respectively, based on sequential measurements throughout the study. Finally, we revealed associations between mean FG and various clinical measures. Our findings suggest that careful consideration is necessary when interpreting FG as substantial intraperson variability exists and highlight the potential impact of using CGM data to refine glycemic status assessment.

Continuous glucose monitoring for the routine care of type 2 diabetes mellitus.

Although continuous glucose monitoring (CGM) devices are now considered the standard of care for people with type 1 diabetes mellitus, the uptake among people with type 2 diabetes mellitus (T2DM) has been slower and is focused on those receiving intensive insulin therapy. However, increasing evidence now supports the inclusion of CGM in the routine care of people with T2DM who are on basal insulin-only regimens or are managed with other medications. Expanding CGM to these groups could minimize hypoglycaemia while allowing efficient adaptation and escalation of therapies. Increasing evidence from randomized controlled trials and observational studies indicates that CGM is of clinical value in people with T2DM on non-intensive treatment regimens. If further studies confirm this finding, CGM could soon become a part of routine care for T2DM. In this Perspective we explore the potential benefits of widening the application of CGM in T2DM, along with the challenges that must be overcome for the evidence-based benefits of this technology to be delivered for all people with T2DM.

Quality of Life and Its Association With Time in Range Among People With Type 2 Diabetes Mellitus Following Different Dietary Interventions: A Crossover Clinical Trial.

Background Quality of Life (QoL) is an essential consideration in healthcare. Numerous studies have examined QoL in India; however, data on QoL following different dietary interventions are lacking. Similarly, the use of technology such as continuous glucose monitoring (CGM) for diabetes care has independently demonstrated improvements in glycemic control; however, its association with QoL remains limited. Purpose The purpose was to study the role of different dietary interventions on QoL and its association with Time in Range (TIR), Time Above Range (TAR), and Time Below Range (TBR) among the Type 2 Diabetes Mellitus (T2DM) population. Methodology A crossover interventional clinical trial (CTRI/2022/07/044356) was conducted among participants with T2DM of less than 5 years' duration, aged between 25 and 55 years, with an HbA1c level of less than 8%, and who were on Metformin only. Their QoL was assessed after following two diet patterns: the Continuous Calorie Restricted Diet (CCRD) - calorie reduction with small frequent meals, and Time Restricted Intermittent Fasting (TRIF) - calorie reduction with only two meals a day, using the Modified QoL (MDQOL-17) questionnaire. The association between post-dietary interventions QoL and TIR was studied using a 14-day CGM device. Results The overall QoL of 51 participants at the end of the dietary interventions was significantly better compared to their QoL before any dietary intervention (85.6±19.0% and 63.1±13.0%, respectively, p = 0.000). Decreased TIR correlated with increased role limitations due to physical functioning (p = 0.002) and decreased energy levels (p = 0.00). As TBR increased, role limitation due to emotional well-being increased, and energy levels decreased significantly (p = 0.01). As TAR increased, energy levels decreased (p = 0.01). A simple linear regression model was statistically significant for role limitations due to physical functioning (p = 0.003) and energy fatigue (p = 0.000), suggesting that higher TIR is associated with higher scores in these domains. Conclusion Dietary interventions that improve the TIR and reduce the TAR and TBR can enhance the QoL of individuals with T2DM.

Feasibility and Performance of Continuous Glucose Monitoring to Guide Computerized Insulin Infusion Therapy in Cardiovascular Intensive Care Unit.

We evaluated the feasibility of real-time continuous glucose monitoring (CGM) for titrating continuous intravenous insulin infusion (CII) to manage hyperglycemia in postoperative individuals in the cardiovascular intensive care unit and assessed their accuracy, nursing acceptance, and postoperative individual satisfaction. Dexcom G6 CGM devices were applied to 59 postsurgical patients with hyperglycemia receiving CII. A hybrid approach combining CGM with periodic point-of-care blood glucose (POC-BG) tests with two phases (initial-ongoing) of validation was used to determine CGM accuracy. Mean and median absolute relative differences and Clarke Error Grid were plotted to evaluate the CGM accuracy. Surveys of nurses and patients on the use of CGMs experience were conducted and results were analyzed. In this cohort (mean age 64, 32% female, 32% with diabetes) with 864 paired POC-BG and CGM values analyzed, mean and median absolute relative difference between POC-BG and CGM values were 13.2% and 9.8%, respectively. 99.7% of paired CGM and POC-BG were in Zones A and B of the Clarke Error Grid. Responses from nurses reported CGMs being very or quite convenient (n = 28; 93%) and it was favored over POC-BG testing (n = 28; 93%). Majority of patients (n = 42; 93%) reported their care process using CGM as being good or very good. This pilot study demonstrates the feasibility, accuracy, and nursing convenience of adopting CGM via a hybrid approach for insulin titration in postoperative settings. These findings provide robust rationale for larger confirmatory studies to evaluate the benefit of CGM in postoperative care to improve workflow, enhance health outcomes, and cost-effectiveness.

Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study with a Cohort Group in South Africa.

This study aims to explore the potential of Internet of Things (IoT) devices and explainable Artificial Intelligence (AI) techniques in predicting biomarker values associated with GDM when measured 13 - 16 weeks prior to diagnosis. We developed a system that forecasts biomarkers such as LDL, HDL, triglycerides, cholesterol, HbA1c, and results from the Oral Glucose Tolerance Test (OGTT) including fasting glucose, 1-hour, and 2-hour post-load glucose values. These biomarker values are predicted based on sensory measurements collected around week 12 of pregnancy, including continuous glucose levels, short physical movement recordings, and medical background information. To the best of our knowledge, this is the first study to forecast GDM-associated biomarker values 13 to 16 weeks prior to the GDM screening test, using continuous glucose monitoring devices, a wristband for activity detection, and medical background data. We applied machine learning models, specifically Decision Tree and Random Forest Regressors, along with Coupled-Matrix Tensor Factorisation (CMTF) and Elastic Net techniques, examining all possible combinations of these methods across different data modalities. The results demonstrated good performance for most biomarkers. On average, the models achieved Mean Squared Error (MSE) between 0.29 and 0.42 and Mean Absolute Error (MAE) between 0.23 and 0.45 for biomarkers like HDL, LDL, cholesterol, and HbA1c. For the OGTT glucose values, the average MSE ranged from 0.95 to 2.44, and the average MAE ranged from 0.72 to 0.91. Additionally, the utilisation of CMTF with Alternating Least Squares technique yielded slightly better results (0.16 MSE and 0.07 MAE on average) compared to the well-known Elastic Net feature selection technique. While our study was conducted with a limited cohort in South Africa, our findings offer promising indications regarding the potential for predicting biomarker values in pregnant women through the integration of wearable devices and medical background data in the analysis. Nevertheless, further validation on a larger, more diverse cohort is imperative to substantiate these encouraging results.

Evaluation of a community-based pharmacy resident-led continuous glucose monitoring program within a family medicine clinic

BackgroundPharmacist-driven continuous glucose monitoring (CGM) is associated with reduced hemoglobin A1c (HbA1c) and achievement of daily glycemic goals. Community-based pharmacists are well-positioned to improve CGM uptake among patients with diabetes due to their accessibility and expertise. However, little data exists evaluating the outcomes of CGM services led by a community-based pharmacist. ObjectiveTo evaluate the impact of a community-based pharmacy resident-driven CGM service on HbA1c, revenue, and patient satisfaction. Practice DescriptionIndependent community pharmacy sharing a clinical services agreement with a primary care clinic for Postgraduate Year One (PGY1) Community-based Pharmacy Residents to provide patient care under general supervision of the physician. Practice innovationPatients were offered CGM services if they were 18+ years with an HbA1c > 7.0% and had insurance coverage for CGM. Enrolled patients engaged in three months of pharmacist-led appointments for CGM application, data interpretation, diabetes education, and lifestyle management. Current Procedural Terminology (CPT) codes 99211, 95250, or 95251 were billed based on each encounter. HbA1c values were collected at program enrollment and conclusion. Patients completed a satisfaction survey at program conclusion. Evaluation methodsDemographics and billed CPT codes were collected from the electronic health record. Descriptive statistics were used to analyze data. ResultsEighteen patients were included. A mean reduction of 1.2% occurred in HbA1c (n = 12; 9.7%−8.5%). Forty CPT codes were billed, generating $3671.40 of revenue. Satisfaction surveys were collected for 50% of participants (n = 9). Most were satisfied with the CGM service and its individual components (n = 8, 89%). Most were willing to continue using CGM devices and receive diabetes education from a pharmacist (n = 8, 89%). ConclusionA community-based pharmacist-led CGM service demonstrated a reduction in HbA1c and generated revenue for the clinic. Patients reported satisfaction and willingness to continue the service.

The Discrepancy Between Hemoglobin A1c and Glucose Management Indicators in 26 Patients Treated With Continuous Glucose Monitoring in an Internal Medicine Residency Clinic.

We conducted a retrospective observational cohort study between 2020 and 2023 in 26 patients with type 1 and type 2 diabetes mellitus (DM) who were using 3-4 injections per day of insulin and were monitored by continuous glucose monitoring (CGM). The goal of this retrospective observational cohort study is to compare these two metrics in an internal medicine community primary care residency clinic. We used CGM devices, Dexcom G6 and G7, and Freestyle Libre 3. The goal was to compare the patient's hemoglobin A1c (HbA1c) taken during their clinic visit by phlebotomy as a marker for diabetic control with an estimated HbA1c glucose management indicator (GMI) derived from the 30-day CGM readings. HbA1c is derived from the blood, while the GMI value is derived from the interstitial fluid. Both parameters were taken within 30 days of each other. GMI was taken in the last 30 days. We excluded patients with known anemia, chronic kidney disease, polycythemia, cirrhosis of the liver, or metabolic dysfunction associated with steatohepatitis (MASH) because disease states can affect the measured HbA1c. Also, pregnantand African American patients were excluded. We concluded the measured HbA1c was 0.34% (4 mmol/mol) higher than the CGM-derived GMI. The relationship between factors that affect glycemic control was discussed in the article, as well as the future utilization of them in improving diabetic control and management. As the use of CGM continues to grow, addressing differences between laboratory-measured HbA1c and CGM-derived GMI is critical.