Search

Aims: Globally, many patients with insulin-treated type-2 diabetes are suboptimally controlled. The PDM-ProValue study program evaluated whether integrated personalized diabetes management (iPDM) has the potential to improve clinical outcomes., Methods: 101 practices with 907 patients participated in the 12-month, prospective, controlled, cluster-randomized study program. HbA1c levels, therapy changes, frequency of hypoglycemic episodes, patient reported outcomes, and physician satisfaction were assessed., Results: iPDM led to a greater reduction in HbA1c after 12 months vs. usual care (-0.5%, p < 0.0001 vs. -0.3%, p < 0.0001), (Diff. 0.2%, p = 0.0324). Most of the HbA1c reduction occurred after 3 months and remained stable thereafter. The percentage of patients with therapy adjustments was higher in the iPDM group at all visits (p < 0.01 at week 3, month 3, month 6). Patient adherence at month 12 was higher in the iPDM group compared to baseline (Odds ratio = 2.39; p = 0.0003); also, patient treatment satisfaction (DTSQc: 12.2 vs. 10.4, δ = 1.78, p = 0.004; DTSQs: 31.0 vs. 30.0, δ = 0.924, p = 0.02), and physician satisfaction was higher in the intervention group., Conclusions: iPDM improved the use of diagnostic data leading to better glycemic control, more timely treatment adjustments (indicating reduced clinical inertia), and increased patient adherence and treatment satisfaction among patients and physicians., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

The DIAMOND study demonstrated that the addition of real-time continuous glucose monitoring (rtCGM) effectively lowers glycated hemoglobin (HbA 1c ) in patients with type 1 (T1D) and type 2 diabetes (T2D), treated with multiple daily injections (MDI). This post hoc analysis investigated whether DIAMOND study participants at progressively higher baseline HbA 1c levels benefit from using rtCGM. We examined outcomes data from a large, randomized, controlled trial of MDI-treated participants with T1D (N = 158) and T2D (N = 158), comparing monitoring by rtCGM versus self-monitoring of blood glucose (SMBG). The primary outcome was the magnitude of HbA 1c reductions among study participants within elevated baseline HbA 1c levels (≥8.0%-10.0%, ≥8.5%-10.0%, and ≥9.0%-10.0%). Analyses were performed on three subgroups: T1D, T2D, and combined T1D/T2D. The full T1D analysis population had a mean baseline HbA 1c value of 8.6 ± 0.6% (range 7.5%-9.9%), randomized to rtCGM (n = 105) or control (n = 53). The full T2D analysis population had a mean baseline HbA 1c value of 8.5 ± 0.6% (range 7.5%-9.9%), randomized to rtCGM (n = 79) or control (n = 79). Participants had improvements in glycemic status regardless of monitoring method. In the three subgroups, the change in HbA 1c was significantly greater in rtCGM participants compared to SMBG at all predefined baseline HbA 1c levels at 12 and 24 weeks. Among the rtCGM participants, the change in HbA 1c was numerically greatest at the highest baseline HbA 1c subgroup (≥9.0%). Participants with elevated baseline HbA 1c had improvements in glycemic status regardless of monitoring method. However, the magnitudes of improvements appeared greater among participants using rtCGM.

Background: Real-time continuous glucose monitoring (rtCGM) improves glycemic control in type 1 diabetes (T1D) patients treated with continuous subcutaneous insulin infusion (CSII). However, the benefits of rtCGM in T1D patients treated with multiple daily insulin injection (MDI) therapy has not been well studied. We explored the effects of rtCGM versus self-monitoring of blood glucose (SMBG) on clinical outcomes within a large T1D population treated with either CSII or MDI therapy., Methods: This retrospective, longitudinal analysis utilized datasets from T1D patients enrolled in a commercial health plan to assess changes in HbA1c in 187 naïve to rtCGM users and 6260 SMBG users. Propensity score modeling was used to assess inpatient admissions, emergency room (ER) visits in 1130 patients (565 rtCGM, 565 SMBG). Differences in HbA1c reduction (rtCGM+MDI vs rtCGM+CSII) were evaluated., Results: Larger, clinically meaningful HbA1c reductions were seen among rtCGM versus SMBG users: -0.5% ( P = .004) versus -0.2% ( P < .0001); 0.3% diff in diff, P = .03. All-cause inpatient admissions were lower for rtCGM users: -42%, P = .013. Emergency room visits coded for diabetic ketoacidosis (DKA) were four times higher for SMBG patients than rtCGM patients: 17 versus 4, P = .0318. HbA1c reductions were most notable with rtCGM+MDI versus rtCGM+CSII treatment: -0.6% ( P = .01) versus -0.3% ( P = .16)., Conclusions: Use of rtCGM in T1D patients facilitates greater HbA1c improvements and reduced health care system utilization compared with traditional SMBG use regardless of insulin administration method. Treatment with rtCGM in conjunction with MDI confers similar or greater glycemic benefits without the additional costs associated with CSII therapy.

The modeling approach described here is designed to support the development of spreadsheet-based simple predictive models. It is based on 3 pillars: association of the complications with HbA1c changes, incidence of the complications, and average cost per event of the complication. For each pillar, the goal of the analysis was (1) to find results for a large diversity of populations with a focus on countries/regions, diabetes type, age, diabetes duration, baseline HbA1c value, and gender; (2) to assess the range of incidences and associations previously reported. Unlike simple predictive models, which mostly are based on only 1 source of information for each of the pillars, we conducted a comprehensive, systematic literature review. Each source found was thoroughly reviewed and only sources meeting quality expectations were considered. The approach allows avoidance of unintended use of extreme data. The user can utilize (1) one of the found sources, (2) the found range as validation for the found figures, or (3) the average of all found publications for an expedited estimate. The modeling approach is intended for use in average insulin-treated diabetes populations in which the baseline HbA1c values are within an average range (6.5% to 11.5%); it is not intended for use in individuals or unique diabetes populations (eg, gestational diabetes). Because the modeling approach only considers diabetes-related complications that are positively associated with HbA1c decreases, the costs of negatively associated complications (eg, severe hypoglycemic events) must be calculated separately.

Objective: The effect of therapeutic strategies on cardiovascular (CV) disease can be evaluated by monitoring changes in CV risk biomarkers. This study investigated the effect of a structured self-monitoring of blood glucose (SMBG) protocol and the resulting improvements in glycemic control on changes in high-sensitivity C-reactive protein (hs-CRP) in insulin-naïve patients with type 2 diabetes., Research Design and Methods: The Structured Testing Program (STeP) study was a prospective, cluster-randomized, multicenter trial in which 483 poorly controlled, insulin-naïve patients with type 2 diabetes were randomized to active control (ACG) or structured testing (STG) that included quarterly structured SMBG. Changes in A1C, hs-CRP, and glycemic variability (STG subjects only) were measured at baseline and quarterly., Results: Reductions in geometric mean hs-CRP values were significantly greater in the STG group at months 3 (P = 0.005), 6 (P = 0.0003), and 12 (P = 0.04) than in the ACG group. STG patients at high CV risk (>3 mg/L) showed significantly greater reductions in hs-CRP levels than ACG patients at high CV risk: -3.64 mg/dL (95% CI -4.21 to -3.06) versus -2.18 mg/dL (-2.93 to -1.43), respectively (P = 0.002). There was a strong correlation between reductions in hs-CRP and A1C in both groups: standardized coefficient (β) was 0.25 for the entire cohort (P < 0.0001), 0.31 for STG (P < 0.0001), and 0.16 for ACG (P = 0.02)., Conclusions: Reductions in hs-CRP level are associated with reductions in A1C but not reductions in lipids or glycemic variability. Comprehensive structured SMBG-based interventions that lower A1C may translate into improvements in CV risk, as evidenced by levels of the biomarker hs-CRP.

Background: People with T1DM and insulin-treated T2DM often do not follow and/or adjust their insulin regimens as needed. Key contributors to treatment non-adherence are fear of hypoglycaemia, difficulty and lack of self-efficacy associated with insulin dose determination. Because manual calculation of insulin boluses is both complex and time consuming, people may rely on empirical estimates, which can result in persistent hypoglycaemia and/or hyperglycaemia. Use of automated bolus advisors (BA) has been shown to help insulin pump users to more accurately meet prandial insulin dosage requirements, improve postprandial glycaemic excursions, and achieve optimal glycaemic control with an increased time within optimal range. Use of a BA containing an early algorithm based on sliding scales for insulin dosing has also been shown to improve HbA1c levels in people treated with multiple daily insulin injections (MDI). We designed a study to determine if use of an automated BA can improve clinical and psychosocial outcomes in people treated with MDI., Methods/design: The Automated Bolus Advisor Control and Usability Study (ABACUS) is a 6-month, prospective, randomised, multi-centre, multi-national trial to determine if automated BA use improves glycaemic control as measured by a change in HbA1c in people using MDI with elevated HbA1c levels (#62;7.5%). A total of 226 T1DM and T2DM participants will be recruited. Anticipated attrition of 20% will yield a sample size of 90 participants, which will provide #62;80% power to detect a mean difference of 0.5%, with SD of 0.9%, using a one-sided 5% t-test, with 5% significance level. Other measures of glycaemic control, self-care behaviours and psychosocial issues will also be assessed., Discussion: It is critical that healthcare providers utilise available technologies that both facilitate effective glucose management and address concerns about safety and lifestyle. Automated BAs may help people using MDI to manage their diabetes more effectively and minimise the risk of long-term diabetes related complications. Findings from a recent study suggest that BA use positively addresses both safety and lifestyle concerns; however, randomised trials are needed to confirm these perceptions and determine whether bolus advisor use improves clinical outcomes. Our study is designed to make these assessments., Trial Registration: NCT01460446.

Self-monitoring of blood glucose (SMBG) is one component of diabetes management. SMBG presents information about current glycemic status and provides the ability to obtain immediate feedback regarding the impact of behavioral and pharmacological interventions on glucose levels. However, SMBG is useful only when the glucose information is understood correctly, data are accurately interpreted, and results prompt appropriate therapeutic actions. The International Diabetes Federation (IDF) recently published guidelines for SMBG use in non-insulin treated people with diabetes, recommending that SMBG should be used only when patients and/or their clinicians possess the ability and willingness to incorporate SMBG monitoring and therapy adjustment into their diabetes care plan. If SMBG is used, the IDF also recommends that structured SMBG be performed utilizing defined regimens to meet individual needs. Structured SMBG can be performed as daily glucose profiles that are representative of daily glucose excursions. Measuring preprandial/postprandial blood glucose (bG) levels on consecutive or alternating days ("testing in pairs") also provides impactful glucose information for daily diabetes management. This article reviews recent studies that appropriately utilized structured SMBG as an integral component of comprehensive diabetes management and discusses how their findings support the IDF recommendations. Our goal is to help clinicians make more informed decisions about the value and utility of SMBG in diabetes management., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Objective: To assess the effectiveness of structured blood glucose testing in poorly controlled, noninsulin-treated type 2 diabetes., Research Design and Methods: This 12-month, prospective, cluster-randomized, multicenter study recruited 483 poorly controlled (A1C ≥ 7.5%), insulin-naïve type 2 diabetic subjects from 34 primary care practices in the U.S. Practices were randomized to an active control group (ACG) with enhanced usual care or a structured testing group (STG) with enhanced usual care and at least quarterly use of structured self-monitoring of blood glucose (SMBG). STG patients and physicians were trained to use a paper tool to collect/interpret 7-point glucose profiles over 3 consecutive days. The primary end point was A1C level measured at 12 months., Results: The 12-month intent-to-treat analysis (ACG, n = 227; STG, n = 256) showed significantly greater reductions in mean (SE) A1C in the STG compared with the ACG: -1.2% (0.09) vs. -0.9% (0.10); Δ = -0.3%; P = 0.04. Per protocol analysis (ACG, n = 161; STG, n = 130) showed even greater mean (SE) A1C reductions in the STG compared with the ACG: -1.3% (0.11) vs. -0.8% (0.11); Δ = -0.5%; P < 0.003. Significantly more STG patients received a treatment change recommendation at the month 1 visit compared with ACG patients, regardless of the patient's initial baseline A1C level: 179 (75.5%) vs. 61 (28.0%); <0.0001. Both STG and ACG patients displayed significant (P < 0.0001) improvements in general well-being (GWB)., Conclusions: Appropriate use of structured SMBG significantly improves glycemic control and facilitates more timely/aggressive treatment changes in noninsulin-treated type 2 diabetes without decreasing GWB.