يعرض 1 - 10 نتائج من 41 نتيجة بحث عن '"Time in range"', وقت الاستعلام: 1.02s تنقيح النتائج
  1. 1

    المصدر: Diabetes & Metabolic Syndrome: Clinical Research & Reviews
    Diabetes & Metabolic Syndrome

    الوصف: Background and aims In Colombia, the government established mandatory isolation after the first case of COVID-19 was reported. As a diabetes care center specialized in technology, we developed a virtual training program for patients with type 1 diabetes (T1D) who were upgrading to hybrid closed loop (HCL) system. The aim of this study is to describe the efficacy and safety outcomes of the virtual training program. Method ology: A prospective observational cohort study was performed, including patients with diagnosis of T1D previously treated with multiple doses of insulin (MDI) or sensor augmented pump therapy (SAP) who were updating to HCL system, from March to July 2020. Virtual training and follow-up were done through the Zoom video conferencing application and Medtronic Carelink System version 3.1 software. CGM data were analyzed to compare the time in range (TIR), time below range (TBR) and glycemic variability, during the first two weeks corresponding to manual mode with the final two weeks of follow-up in automatic mode. Results 91 patients were included. Mean TIR achieved with manual mode was 77.3 ± 11.3, increasing to 81.6% ± 7.6 (p
    Highlights • Hybrid closed-loop (HCL) systems allows type 1 diabetes (T1D) patients to improve Time in Range (TIR). • HCL training has been delivered in person. However, during COVID-19 pandemic we had to switch to a virtual modality. • Subjects with virtual training had an increase in TIR regardless of initial therapy after two weeks of automatic use. • This is the largest prospective non-sponsored study including adults treated with HCL trained through a virtual platform.

  2. 2

    المصدر: Diabetes Technol Ther
    Diabetes technology & therapeutics, vol 23, iss 10

    الوصف: Background: The impact of the coronavirus disease-2019 (COVID-19) pandemic on glycemic metrics in children is uncertain. This study evaluates the effect of the shelter-in-place (SIP) mandate on glycemic metrics in youth with type 1 diabetes (T1D) using continuous glucose monitoring (CGM) in Northern California, United States. Methods: CGM and insulin pump metrics in youth 3-21 years old with T1D at an academic pediatric diabetes center were analyzed retrospectively. Data 2-4 months before (distant pre-SIP), 1 month before (immediate pre-SIP), 1 month after (immediate post-SIP), and 2-4 months after (distant post-SIP) the SIP mandate were compared using paired t-tests, linear regression, and longitudinal analysis using a mixed effects model. Results: Participants (n = 85) had reduced mean glucose (-10.3 ± 4.4 mg/dL, P = 0.009), standard deviation (SD) (-5.0 ± 1.3 mg/dL, P = 0.003), glucose management indicator (-0.2% ± 0.03%, P = 0.004), time above range (TAR) >250 mg/dL (-3.5% ± 1.7%, P = 0.01), and increased time in range (TIR) (+4.7% ± 1.7%, P = 0.0025) between the distant pre-SIP and distant post-SIP periods. Relationships were maintained using a mixed effects model, when controlling for other demographic variables. There was improvement in SD, TAR 180-250 mg/dL, and TIR for participants with private insurance, but changes in the opposite direction for participants with public insurance. Conclusions: Improvement in CGM metrics in youth with T1D during the COVID-19 pandemic suggests that diabetes management can be maintained in the face of sudden changes to daily living. Youth with public insurance deserve more attention in research and clinical practice.

    وصف الملف: application/pdf

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    المؤلفون: Marc D. Breton, Boris Kovatchev

    المصدر: Diabetes Technology & Therapeutics

    الوصف: Background: The t:slim X2™ insulin pump with Control-IQ® technology from Tandem Diabetes Care is an advanced hybrid closed-loop system that was first commercialized in the United States in January 2020. Longitudinal glycemic outcomes associated with real-world use of this system have yet to be reported. Methods: A retrospective analysis of Control-IQ technology users who uploaded data to Tandem's t:connect® web application as of February 11, 2021 was performed. Users age ≥6 years, with >2 weeks of continuous glucose monitoring (CGM) data pre- and >12 months post-Control-IQ technology initiation were included in the analysis. Results: In total 9451 users met the inclusion criteria, 83% had type 1 diabetes, and the rest had type 2 or other forms of diabetes. The mean age was 42.6 ± 20.8 years, and 52% were female. Median percent time in automation was 94.2% [interquartile range, IQR: 90.1%–96.4%] for the entire 12-month duration of observation, with no significant changes over time. Of these users, 9010 (96.8%) had ≥75% of their CGM data available, that is, sufficient data for reliable computation of CGM-based glycemic outcomes. At baseline, median percent time in range (70–180 mg/dL) was 63.6 (IQR: 49.9%–75.6%) and increased to 73.6% (IQR: 64.4%–81.8%) for the 12 months of Control-IQ technology use with no significant changes over time. Median percent time

  4. 4

    المصدر: Journal of Diabetes Investigation, Vol 12, Iss 8, Pp 1417-1424 (2021)
    Journal of Diabetes Investigation

    الوصف: Aims/Introduction We recently reported the beneficial effect of the combination of sodium–glucose cotransporter 2 inhibitor and dipeptidyl peptidase‐4 inhibitor on daily glycemic variability in patients with type 2 diabetes mellitus. Additional favorable effects of combination therapy were explored in this secondary analysis. Materials and Methods The CALMER study was a multicenter, open‐label, prospective, randomized, parallel‐group comparison trial for type 2 diabetes mellitus involving continuous glucose monitoring under meal tolerance tests. Patients were randomly assigned to switch from teneligliptin to canagliflozin (SWITCH group) or to add canagliflozin to teneligliptin (COMB group). The continuous glucose monitoring metrics, including time in target range, were investigated. Results All 99 participants (mean age 62.3 years; mean glycated hemoglobin 7.4%) completed the trial. The time in target range was increased in the COMB group (71.2–82.7%, P

    وصف الملف: application/pdf

  5. 5

    المصدر: AACE Clinical Case Reports, Vol 7, Iss 3, Pp 177-179 (2021)
    AACE Clinical Case Reports

    الوصف: Objective: Hybrid closed-loop (HCL) devices can achieve tight glycemic control but are rarely used in pregnancy, which remains an off-label indication. We present a case of a pregnant patient with type 1 diabetes mellitus (T1DM) who used the Medtronic MiniMed 670G HCL system. Methods: MiniMed 670G includes an advanced automode option (HCL therapy), which our patient used from the first trimester to the end of the pregnancy. Results: An unplanned pregnancy was detected in the T1DM patient, with a glycated hemoglobin level of 8.7 mmol/L (7.1%). The patient started sensor-augmented pump therapy at week 13. Subsequently, she entered automode (HCL) at week 16. The time in range (3.7-7.8 mmol/mol, 63-140 mg/dL) increased from 46.8% to 51.3% after HCL initiation. The glycated hemoglobin level remained close to 48 mmol/mol (6.5%) until the end of the pregnancy. Furthermore, the time under range (

  6. 6

    المصدر: Diabetes, Obesity & Metabolism
    Diabetes, Obesity and Metabolism

    الوصف: Aims Time-in-ranges, such as time-in-range (TIR), and time-in-tight-range (TITR), time-below-range (TBR), time-above-range (TAR), are key metrics to evaluate glucose control. The duration of the CGM datastream used to compute them affects their reliability: short observation periods result in uncertain estimates, while long trials grant precise evaluation but are associated with higher costs and organizational difficulties. In this paper we propose a mathematical link between the precision/uncertainty of the estimates and the number of monitoring days, that could be useful for clinicians and researchers. Materials and methods Four formulas for the above-mentioned time-in-ranges were obtained by estimating the equation's parameters on a training set extracted from study A (226 subjects, ~180 days, 5-min Dexcom G4 Platinum sensor). The formulas were then validated on the remaining data. We also illustrate how to adjust the parameters for sensors with different sampling rates. Finally, we use Study B (45 subjects, ~365 days, 15-min Abbott Freestyle Libre sensor) to further validate our results. Results Our approach was effective in predicting the uncertainty when time-in-ranges are estimated using n days of CGM, matching the variability observed in the data. As an example, monitoring a population with TIR=70%, TITR=50%, TBR=5% and TAR=25% for n=30 days warrants a precision of ±3.50%, ±3.68%, ±1.33%, ±3.66%, respectively. Conclusions The presented approach can be used both to compute the uncertainty of time-in-ranges and to determine the minimal duration of a trial to achieve a desired precision. An online tool to facilitate its implementation is made freely available to the clinical investigator. This article is protected by copyright. All rights reserved.

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    المصدر: Сахарный диабет, Vol 24, Iss 2, Pp 185-192 (2021)

    الوصف: The Scientific Advisory Board chaired by Academician of the Russian Academy of Sciences, Peterkova V.A. was held 26 of November in Moscow to discuss the possibilities of continuous glucose monitoring technology (CGM) implementation into routine clinical practice in Russia in order to improve glycemic control in patients with diabetes mellitus (DM).The main aims for Advisory board were to determine the most significant indicators and parameters for CGM to be implemented in practice from a practical point of view of LMWH, necessary for implementation in clinical practice, for different patients groups with diabetes.The following questions and topics were raised within the discussion: the importance of additional indicators beyond glycated hemoglobin (HbA1c) for glycemic control assessment in diabetes patients, CGM positioning in International and Russian clinical guidelines, the accuracy of CGM devises and approaches to its assessment, the role of education programs for diabetic patients, including trainings in correct use and data interpretation and analysis of CGM data obtained, clinical evidence analysis for CGM in randomized trials and real world evidence.

  8. 8

    المصدر: International Journal of Diabetes in Developing Countries. 42:276-282

    الوصف: Introduction Predictive low-glucose suspend (PLGS) system helps prevent hypoglycemia. Aim To evaluate the effect of PLGS therapy on GV and percentage of time in range (TIR), time below range (TBR), and time above range (TAR) in pediatric type 1 diabetic patients. Method HbA(1c), coefficient of variation (CV). standard deviation (SD). and percentage of TIR, TBR, and TAR were evaluated in type 1 diabetic (T1D) pediatric patients followed up between Jan 2016 and Mar 2020 using PLGS system. Results Mean ages of diagnosis and duration of diabetes were 6.7 +/- 4.1 and 8.2 +/- 4.3 years, respectively. Nineteen of the patients were male (46.3%) and 22 were female (53.7%). Twenty-two (53.7%) of the patients were using low-glucose suspend system and 19 (46.3%) were on multiple daily injection therapy (MDI). On PLGS therapy, the 3rd, 6th, 9th, and 12th months' HbA(1c) of patients were not different from previous years' mean HbA(1c) in all participants. In the 3rd, 6th, 9th, and 12th months of PLGS therapy, % of TIR were 65.34 +/- 14.75%, 65.80 +/- 14.67%, 66.58 +/- 11.21%, and 70.04 +/- 10.16%, respectively (p = 0.01). Although statistically insignificant, CV decreased from 36.33 to 34.30% and SD decreased from 60.14 to 58.60 in the 1-year follow-up period (p = 0.062 and p = 0.246). Conclusion With PLGS therapy, TIR was > 70% and the time spent in hypoglycemia was very low.

  9. 9

    المصدر: Journal of Diabetes Investigation, Vol 12, Iss 6, Pp 940-949 (2021)
    Journal of Diabetes Investigation

    الوصف: Aims/Introduction Hemoglobin A1c (HbA1c), glycated albumin (GA) and 1,5‐anhydro‐d‐glucitol (1,5‐AG) are used as indicators of glycemic control, whereas continuous glucose monitoring (CGM) is used to assess daily glucose profiles. The aim of this study was to investigate the relationships between CGM metrics, such as time in range (TIR), and glycemic control indicators. Materials and Methods We carried out retrospective CGM and blood tests on 189 outpatients with impaired glucose tolerance (n = 22), type 1 diabetes mellitus (n = 67) or type 2 diabetes mellitus (n = 100). Results In type 1 diabetes mellitus and type 2 diabetes mellitus patients, HbA1c and GA were negatively correlated with TIR, whereas 1,5‐AG was positively correlated with TIR. In type 1 diabetes mellitus patients, a TIR of 70% corresponded to HbA1c, GA and 1,5‐AG of 6.9% (95% confidence interval [CI] 6.5–7.2%), 20.3% (95% CI 19.0–21.7%) and 6.0 µg/mL (95% CI 5.1–6.9 µg/mL), respectively. In type 2 diabetes mellitus patients, a TIR of 70% corresponded to HbA1c, GA and 1,5‐AG of 7.1% (95% CI 7.0–7.3%), 19.3% (95% CI 18.7–19.9%) and 10.0 µg/mL (95% CI 9.0–11.0 µg/mL), respectively. TIR values corresponding to HbA1c levels of 7.0% were 56.1% (95% CI 52.3–59.8%) and 74.2% (95% CI 71.3–77.2%) in type 1 diabetes mellitus and type 2 diabetes mellitus patients, respectively. Conclusions The results of this study showed that the estimated HbA1c corresponding to a TIR of 70% was approximately 7.0% for both type 1 diabetes mellitus and type 2 diabetes mellitus patients, and that the estimated 1,5‐AG calculated from the TIR of 70% might be different between type 1 diabetes mellitus and type 2 diabetes mellitus patients.
    Hemoglobin A1c was the most useful explanatory factor for mean sensor glucose levels. Glycated albumin was the most useful explanatory factor for glycemic variability and time in range.

  10. 10

    المساهمون: RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, Interne Geneeskunde, RS: Carim - B01 Blood proteins & engineering, MUMC+: DA CDL Algemeen (9), RS: CAPHRI - R2 - Creating Value-Based Health Care, RS: Carim - V02 Hypertension and target organ damage, MUMC+: HVC Pieken Maastricht Studie (9), Sociale Geneeskunde, RS: CAPHRI - R4 - Health Inequities and Societal Participation, Epidemiologie, Complexe Genetica, RS: NUTRIM - R3 - Respiratory & Age-related Health, Biomedische Technologie, RS: Carim - H07 Cardiovascular System Dynamics, MUMC+: MA Alg Interne Geneeskunde (9), MUMC+: MA Endocrinologie (9), MUMC+: Centrum voor Chronische Zieken (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), MUMC+: MA Interne Geneeskunde (3)

    المصدر: Diabetologia, 64(8), 1880-1892. Springer, Cham
    Diabetologia

    الوصف: Aims CVD is the main cause of morbidity and mortality in individuals with diabetes. It is currently unclear whether daily glucose variability contributes to CVD. Therefore, we investigated whether glucose variability is associated with arterial measures that are considered important in CVD pathogenesis. Methods We included participants of The Maastricht Study, an observational population-based cohort, who underwent at least 48 h of continuous glucose monitoring (CGM) (n = 853; age: 59.9 ± 8.6 years; 49% women, 23% type 2 diabetes). We studied the cross-sectional associations of two glucose variability indices (CGM-assessed SD [SDCGM] and CGM-assessed CV [CVCGM]) and time in range (TIRCGM) with carotid–femoral pulse wave velocity (cf-PWV), carotid distensibility coefficient, carotid intima–media thickness, ankle–brachial index and circumferential wall stress via multiple linear regression. Results Higher SDCGM was associated with higher cf-PWV after adjusting for demographics, cardiovascular risk factors and lifestyle factors (regression coefficient [B] per 1 mmol/l SDCGM [and corresponding 95% CI]: 0.413 m/s [0.147, 0.679], p = 0.002). In the model additionally adjusted for CGM-assessed mean sensor glucose (MSGCGM), SDCGM and MSGCGM contributed similarly to cf-PWV (respective standardised regression coefficients [st.βs] and 95% CIs of 0.065 [−0.018, 0.167], p = 0.160; and 0.059 [−0.043, 0.164], p = 0.272). In the fully adjusted models, both higher CVCGM (B [95% CI] per 10% CVCGM: 0.303 m/s [0.046, 0.559], p = 0.021) and lower TIRCGM (B [95% CI] per 10% TIRCGM: −0.145 m/s [−0.252, −0.038] p = 0.008) were statistically significantly associated with higher cf-PWV. Such consistent associations were not observed for the other arterial measures. Conclusions Our findings show that greater daily glucose variability and lower TIRCGM are associated with greater aortic stiffness (cf-PWV) but not with other arterial measures. If corroborated in prospective studies, these results support the development of therapeutic agents that target both daily glucose variability and TIRCGM to prevent CVD. Graphical abstract