Glycemic Goals Research Articles

MON-LB117 Differences in Metabolic Control, Treatment and Associated Complications in Patients Living With Type 1 Diabetes Receiving Private or Public Health Care in Mexico

Background. Type 1 Diabetes (T1D) is the most common chronic endocrinological disease diagnosed during childhood. It requires continuous education, monitoring and treatment. An analysis was developed to learn if there were discrepancies in the level of metabolic control and complications between private and public healthcare in Mexico, based on the data obtained in the National Registry of patients with Type 1 Diabetes (RENACED DT1). Objective: Describe the sociodemographic characteristics, metabolic control, treatment and complications in patients receiving private or public health care registered in RENACED DT1.Methodology: Sociodemographic and anthropometric variables, metabolic control, diabetes education, type of insulin and delivery method used for treatment, glucose monitoring and acute and chronic complications were compared between patients receiving private vs public health care. Results Of the 1458 patients registered, significant differences between HbA1c levels were seen (7.8% private health care vs 8.7% public healthcare, p<0.001), achievement of glycemic goal HbA1c <7% (30.1% private healthcare vs 19.6% public healthcare, p<0.001). In private institutions, 47.1% of patients use an insulin pump and 44.7% a basal bolus regimen with insulin analogues (MDI) (p<0.001). Meanwhile in public institutions, 2.6% use an insulin pump and 84.8% use MDI (p<0.001). 30.4% of patients in private institutions check their blood glucose levels 6 to 10 times a day vs 14.3% of patients from public institutions (p<0.001). Continuous glucose monitoring is used in 46.4% of patients from private healthcare vs 5% from public healthcare (p<0.001). A larger number of patients from private healthcare use insulin to carbohydrate ratios to calculate meal insulin compared with patients from public healthcare (89.1% vs31.3%, p<0.001). No significant differences were found related to the incidence of mild to moderate hypoglycemia, but there was a higher incidence of severe hypoglycemia in the public sector compared to the private one (57 vs 43%, p<0.001). Also, a higher prevalence of diabetic nephropathy was found in patients from public vs private healthcare (82.9 vs 17.1%, p=0.034). Conclusions: Significant differences were found in glucose monitoring, carbohydrate counting and insulin delivery methods, between patients from public and private healthcare, which could explain the differences observed in metabolic control and diabetes associated complications. It is imperative that better public policies are implemented in public health, to reduce this health disparity.

MON-621 Prescription Analysis Shows High Metformin Use and Acceptance in a Diabetes Specialty Centre in Eastern India

Background and aims: Achieving glycemic goals is crucial in the overall management of diabetes. Selecting the right medication for the individual patient is of paramount importance in the present day’s patient centric glucose control. Metformin is the first line and gold standard antihyperglycemic agent that can be offered to type 2 diabetics. Addition of a second or third agent or insulin should be considered in those whose HbA1c remains high despite the up-regulated metformin dose or those who do not tolerate metformin. We aimed to find the pattern of metformin use in type 2 diabetic subjects in a diabetes specialty centre in coastal Odisha. Materials and methods: This observational study was conducted in a diabetes setup in coastal Odisha in June 2018. After obtaining consent from patients, authors looked into the prescriptions of all type 2 diabetic adults. Subjects who were prescribed metformin (in any dose) were enrolled in the study. Those with established nephropathy, coronary artery disease, stroke or cancers were excluded. Results:There were 802 footfalls noted during the study period, of which 723 metformin taking participants (298 females, 41.2%) were considered for analysis (79 persons were excluded: not meeting inclusion criteria/ not willing to participate/ history of nephropathy/ CAD/ stroke). Mean age, diabetes duration, FPG, HbA1c, serum creatinine, eGFR of the study population were 51.6±10.6 years, 11.9±11.2 years, 138.7±51.7 mg/dl, 7.8±2.1%, 0.93±0.29 mg/dl and 96.5±11.1ml/min respectively. Patients were prescribed metformin in various doses, i.e., 500mg (42 patients, 5.8%), 850mg (47 patients, 6.5%), 1000mg (396 patients, 54.8%), 1500mg (13 patients, 1.8%), 1700mg (86 patients, 11.9%) and 2000mg (130 patients, 18.0%), and 2500mg (9 patients, 1.2%). Metformin was prescribed as monotherapy (n=34, 4.7%) or along with other OADs (n=589, 81.5%) or in combination with insulin (n=178, 24.6%). Retrospective analysis of the medical records and further questioning revealed that gastric intolerance was the commonest reason for withdrawal of metformin in otherwise eligible subjects. Conclusion: Metformin was the most commonly prescribed antidiabetic drug and the daily dose of more than 85% of the metformin administered individuals was 1000mg or above.

MON-LB301 Application of Lean Six-Sigma DMAIC Tool for Improvement and Sustainability of Diabetes Management in Outpatient Clinics

DMAIC: Define, Measure, Analyze, Improve, Control. LSL: Lower specified limit. USL: Upper specified limit.Lean Six Sigma DMAIC is quality improvement methodology used for strategic business management. Frequent applications of this methodology in healthcare include improvement of patient satisfaction, reduction of emergency department waiting times, prescription error reduction and monetary recovery by reducing waste.Patients with a HgbA1c testing frequency of >6 months have poorer glycemic control. The American Diabetes Association recommends HgbA1c test quarterly in patients whose therapy has changed or who are not meeting glycemic goals and at least two times a year in patients who are meeting treatment goals.We hypothesized that the Lean Six-Sigma DMAIC tools can be used in the outpatient clinic setting to improve frequency of HgbA1c testing in patients with diabetes mellitus.At baseline, 19% of our patients with diabetes mellitus had HgbA1c tested infrequently, defined as more than 6 months. This high percentage is a concern as it could lead to poor diabetes control. The aim was to increase percentage of patients having an HgbA1c tested between 3 to 6 months before an appointment in our clinic, to a goal of 90%. Target population included all patients with diabetes mellitus seen in outpatient endocrinology clinic.A baseline analysis of existing processes was done through brainstorming with the clinic staff using a fish bone diagram. Lack of follow up and HgbA1c testing orders were some of the modifiable factors identified. The new processes implemented include nurse driven standing medical orders for HgbA1c testing and pre-visit planning. Control phase included regular audits to sustain the improvements.The percentage of patients with a HgbA1c testing within 3-6 months of appointment improved from a baseline of 76.7% (LSL:70%, USL:94%) to 92.2% (LSL:88%, USL:93.7%). The improvement was noticeable within 1 month of new process implementation and continues to sustain. The mean had an absolute improvement of 15.5%. The variation from the mean decreased from 25% at baseline to 6% at the end of the control phase. The reduction in variation made our future results more predictable.The use of Lean Six-Sigma DMAIC quality improvement tools are an effective method to improve quality of care in the outpatient setting. These strategies can be replicated for other clinical quality outcomes.

Successful Treatment with Bedtime Basal Insulin Added to Metformin without Weight Gain or Hypoglycaemia over Three Years.

The aim of this observational study was to follow-up patients with bedtime basal insulin (NPH insulin) added to metformin. In 285 patients with type 2 diabetes, a therapy with bedtime basal insulin added to metformin was started due to failure to achieve a glycaemic goal. Up until July 2019, 272 patients (95.4%) were followed-up (59.5 y, 92.6 kg, diabetes duration 6.6 y, HbA1c 8.4%/68.6 mmol/mol). HbA1c decreased by −1.2% and bodyweight by −1.7 kg after a duration of 31.7 ± 29.1 (range 2–133) months. Severe hypoglycaemia did not occur. In 144/272 patients (52.9%), the therapeutic goal for HbA1c was achieved over 32.7 months. In 69/272 patients (25.4%), the HbA1c target was achieved over 25.0 months (afterwards, therapy with basal insulin was discontinued because HbA1c was under target). In 36/272 patients (13.2%), the HbA1c goal was achieved until the submission of this manuscript (mean duration of treatment 57.4 ± 28.2 (range 13–121) months). Over 90% of patients with type 2 diabetes and failure of metformin reached their HbA1c goal with additional basal insulin at bedtime over several years in association with a reduction of bodyweight and without any event of severe hypoglycaemia.

Clinical inertia among health care providers in a public health safety-net clinic in the management of type 2 diabetes

BackgroundClinical inertia is defined as the lack of treatment intensification in patients who are not at evidence-based goals of therapy; it may be related to provider, patient, and health system–wide factors. Patient factors, including nonadherence and tablet burden, are further compounded by health and social disparities present in safety-net clinics. Clinical pharmacist–based interventions may impact provider or health system factors to reduce inertia in patients with poorly controlled diabetes. ObjectivesTo evaluate the rate of clinical inertia between health care providers in a safety-net clinic. MethodsA single-center, cross-sectional, retrospective study compared 2 cohorts of adult patients with type 2 diabetes and glycosylated hemoglobin (A1C) greater than 8% in 2016. Diabetes care was provided by clinical pharmacists in the intervention group and by primary care providers in the control group. The primary outcome was the difference in clinical inertia, measured by pharmacologic treatment intensification between groups within 4 months following the first A1C greater than 8% of the study period. ResultsOf 276 eligible patients, 72 were in the intervention group and 204 in the control group. There was no statistical difference between baseline A1C between groups, with an average A1C of 10.01% for the study population. In the pharmacist group versus provider group, the rate of overall treatment, noninsulin, and insulin intensification was 79% versus 49% (P < 0.001), 40% versus 32% (P = 0.19), and 54% versus 19% (P < 0.001), respectively. Patients were contacted an average of 4 times during the follow-up period in the pharmacist group as compared to 1 time in the provider group (P < 0.001). ConclusionIn this safety-net clinic, pharmacist-based interventions reduced clinical inertia in patients with poorly controlled diabetes. Future studies evaluating inertia long term and the impact on glycemic goals are needed.

Effect of short-term intensive insulin therapy on α-cell function in patients with newly diagnosed type 2 diabetes.

The effect of intensive insulin therapy on hyperglucagonemia in newly diagnosed type 2 diabetes (T2DM), and its associations with β-cell function, has not been elucidated. This study assessed the effect of 12 weeks of intensive insulin therapy on hyperglucagonemia in newly diagnosed T2DM and its associations with β-cell function, with reference to the effects of 12 weeks of oral hypoglycemic agents (OHAs).One hundred eight patients with newly diagnosed T2DM were enrolled from January 2015 to December 2015. The patients were randomly divided to receive, for 12 weeks, either intensive insulin therapy or OHAs. Meal tolerance tests were conducted at baseline before treatment (0 week), at 12 weeks (end of treatment), and 12 months after the initiation of treatment. The levels of glucagon, proinsulin, C-peptide (CP), and blood glucose were measured at timepoints 0, 30, and 120 minutes during the meal tolerance test.Intensive insulin treatment was associated with a decrease in glucagon levels (at 0, 30, and 120 minutes) and proinsulin/CP, and an increase in the insulin-secretion index ΔCP30/ΔG30 and ΔCP120/ΔG120, at 12 weeks and 12 months during the follow-up, compared with the corresponding effects of OHAs. Intensive insulin therapy could reduce but failed to normalize glucagon levels at 12 weeks. There were no correlations between the change of percentages in total area under the curve of glucagon and other glycemic parameters (proinsulin/CP; ΔCP30/ΔG30; or ΔCP120/ΔG120). Patients who received intensive insulin therapy were more likely to achieve their target glycemic goal and remission, compared with those who received OHAs.Short-term intensive insulin therapy facilitates the improvement of both β-cell and α-cell function in newly diagnosed T2DM mellitus. Decline of β-cell secretion and concomitant α-cell dysfunction may both be involved in the pathogenesis of T2DM.

Accuracy assessment of the blood glucose meter «Satellite Express» for glycemic control in children with diabetes

Monitoring glycemic control in diabetes includes daily measurements of glucose levels at home, along with periodic monitoring of glycemia indicators on the whole. The main purpose of monitoring glycemic control is to accurately and authentically assess the glycemic level reached by each patient in order to ensure the glycemic goals achievement. Glycemic control assessment at a certain point in time is best achieved by self-monitoring the level of glucose in the blood, in that allows you to assess the presence of hyperglycemia and hypoglycemia to optimize the treatment strategy. Accuracy assessment of a medical device &laquo;Portable blood glucose concentration meter with electrochemical single-use strip PKG-03 &laquo;Satellite Express&raquo; for glycemic control of children with diabetes. In a group of children with diabetes (n=105, age from 1 month to 17 years), simultaneously was taken capillary blood from one drop to assess the accuracy of glycemia levels on the &laquo;Satellite Express&raquo; glucose meter and on the laboratory glucose and lactate analyzer SUPER GL. In a comparative analysis of 660 blood samples on the Clark error scale, was found that all deviations of glycemia values obtained with the &laquo;Satellite Express&raquo; glucose meter were in zones A (99.7% &mdash; clinically correct values) and B (0.3% &mdash; safe deviations). The limits of permissible systematic error of measurements that meet the requirements of the National Standard of Russian Federation GOST R ISO 15197-2015 in terms of clause 6.3 System Accuracy amounted to 96.8% of deviations of glucose meter indications from reference values, which matches to areas of clinically correct and safe deviations. Using the &laquo;Satellite Express&raquo; glucose meter in clinical practice makes it possible to control glycemia qualitatively and it is safe for children aged from 1 month to 17 years.

513 Simplified Approach to Incorporating Glycemic Response Within a Continuous Insulin Infusion Algorithm to Improve Incidence of Hypoglycemia in a Single Burn Center

Abstract Introduction Attaining adequate glycemic control in burn patients can reduce infection-related mortality. Previous internal evaluation of four insulin infusion protocols showed a hypoglycemia (&amp;lt; 60 mg/dL) rate of 0.6% and an average time within goal glycemic range (70-149 mg/dL) of 13.8 hours per day. A new algorithm underwent six iterations over two years before the final version was implemented. The purpose of our study was to assess the post-implementation performance of the newly-developed continuous insulin infusion (CII) protocol designed to adjust dosage based on glycemic fluctuation. Methods This study was a two year retrospective, IRB approved, review of adult patients admitted to a single burn center, who received a CII. The pre-implementation study served as a historical control comparator. The current study was powered to detect a 50% reduction in hypoglycemic events, requiring 4,245 point-of-care samples. Based on average usage, at least 20 patients were required for adequate power of 80 % and alpha of 0.05. Demographics, insulin infusion data, infection, and mortality outcomes were collected. Nominal data were analyzed by Fisher’s exact test. Continuous variables were compared using either Mann-Whitney U test or student’s t-test, depending on normality. Results Of the 27 records screened for inclusion, seven were excluded. The average patient was a 59-year-old Caucasian male with diabetes and a total body surface area burn of 17%. There were no differences in demographics between groups. Among the 20 records reviewed, 5,239 point-of-care glucoses were assessed. Post implementation, hypoglycemia rates were significantly lower (0.6% vs 0.2%; p &amp;lt; 0.001). There was no difference in median blood glucose between groups (149.9 mg / dL vs. 146.5 mg / dL; p = 0.56) or time to achieve the goal glycemic range (6 vs 6.5 hours; p = 0.28). Time spent within goal glycemic range was not significantly different (13.8 vs. 14.7 hours / day; p = 0.23). The overall median time until glucoses were monitored after holding CII for down trending glucoses was 65.7 (53.5–111.3) minutes. Of the times CII were held, 53 of the follow up glucoses (25%) surpassed 200 mg / dL before subsequently settling. There was no differences in infection, length of stay, or survival. Conclusions The consolidation, education, and implementation of a single, dynamic CII algorithm reduced the incidence of hypoglycemia. The authors expect, education and diligence with follow up glucose monitoring will further improve time within goal glycemic range by preventing rebound hyperglycemia. Applicability of Research to Practice Improving glycemic control can be obtained by simple means. Implementing a two step algorithm that incorporates glycemic response and goal with vigorous education will reduce hypoglycemia and improve glycemic control.

Decision Support Systems for Insulin Treatment Adjustment in People with Type 1 Diabetes.

For people with type 1 diabetes, achieving optimal glycemic control requires use of intensive insulin therapy. To achieve this goal individuals are required to become proficient in accurately determining the appropriate amount of insulin needed to address a variety of situations throughout the day while considering numerous influencing factors. They also need to perform multiple tasks a day such as counting carbohydrates to accurately determine the required premeal bolus. There is also a need to periodically adjust insulin dosing as insulin sensitivity varies considerably over time. Sophisticated advanced technologies are being used by growing numbers of individuals to manage diabetes. However, despite innovations in glucose monitoring technologies and new insulin formulations, many of these individuals are not achieving their glycemic goals. The new technologies provide ample amount of valuable diabetes related data that may complicate even further the insulin dosing decision making for people with diabetes as well as for the health care providers. Mobile health, digital tools and decision support systems can help to increase accurate, timely insulin dosing and insulin titration and holds the potential to improve glycemic control to a wide range of individuals with diabetes. The emerging mobile toolbox for insulin dosing adjustments that will be reviewed in this paper includes insulin management Apps; diverse range of Apps to guide insulin adjustments such as in times of physical activity and eating; data management systems which enable visualization and analysis of insulin and glucose data of various devices; real-time alarms and glucose prediction Apps that help to prevent hypoglycemia and hyperglycemia events; various bolus calculators for meal time dosing; sophisticated decision support algorithms for people using insulin pump, multiple insulin injections and closed loop systems for real-time and retrospective insulin dosing adjustments.

Influence of gender on the distribution of type 2 diabetic complications at the obafemi awolowo teaching hospital, Ile-Ife, Nigeria.

Sex specific differences appear particularly relevant in the management of type 2 DM. We determined gender specific differences in cardio-metabolic risk, microvascular and macrovascular complications in patients with type 2 diabetes. Four hundred type 2 diabetes patients, males and females, matched for age and disease duration were recruited from the diabetes clinic. Relevant clinical and laboratory information were obtained or performed. 190(47.5%) were male and 210 (52.5%) were female respectively. The mean age of the study population was 60.6 + 9.93 years. Women had higher prevalence of hypertension (and obesity. Mean total cholesterol was significantly higher in women but men were more likely to achieve LDL treatment goals than women (69.5% vs 59.0%, p<0.05). More women (47.1% & 31.4%) reached glycaemic goals of <10mmol/l for 2HPP and HBA1c of <7.0%.There were no gender differences in the distribution of microvascular and macrovascular complications (p>0.05) but women were more likely to develop moderate and severe diabetic retinopathy (p= 0.027). Women with T2DM had worse cardiometabolic risk profile with regards to hypertension, obesity and lipid goals. Men achieved therapeutic goals less frequently than did women in terms of glycaemia. Microvascular and macrovascular complications occurred commonly in both sexes.

Central obesity among an African type 2 diabetic population: Prevalence and correlates

BackgroundThe rising prevalence of non-communicable diseases especially obesity and type 2 diabetes among African populace is becoming a public health epidemic with socio-cultural and economic implications. We assayed for the prevalence and correlates of central obesity among an African type 2 diabetic population. MethodA multi-centered, cross-sectional and descriptive study among 399 type 2 diabetic participants aged 18–70 years from three study sites (urban, semi-urban and rural) was conducted. Prevalence was compared among age-groups, sex, location, BMI category, blood pressure category, glycemic control status and dyslipidemic status. ResultsAn overall prevalence of 55.4% was obtained while age-based prevalence rose with age before declining among the 61–70 years group of persons. The females had the higher sex-based prevalence of 70.7%–23.3% for males and semi-urban participants gave the highest location-based prevalence of 67.7% in contrast to 55.8% and 40.9% for urban and rural residents respectively. General obesity, hypertension, good glycemic control, and non-dyslipidemia gave the highest prevalence in their various categories. After adjusting for co-founders, age 51–60, age 61–70, female sex, semi-rural residence, rural residence, been over-weight, general obesity, and hypertension were independently associated with CO. ConclusionFindings from our study revealed that CO is highly prevalent among Africans with type 2 diabetes. Elderly persons, female sex, BMI above 25 kg/m2, hypertension and residence in semi-urban and rural settings were highlighted as independent predisposing factors. Regular exercise and consumption of low-calorie diets (weight loss strategies) are recommended for type 2 diabetics within the middle age class and those in the elderly category in addition to their glycemic and lipidemic control goals. Government at all levels should ensure the provision of basic social amenities to meet the needs of rural and semi-urban areas experiencing rapid urbanization.

Hypoglycemia during Short-Term Intensive Insulin Therapy and Its Association with Long-Term Glycemic Remission in Patients with Newly Diagnosed Type 2 Diabetes.

Background Short-term intensive insulin therapy induces long-term glycemic remission in half of patients with newly diagnosed type 2 diabetes. The concomitant hypoglycemia needs further analysis. Methods We collected data from three randomized trials conducted with the same inclusion and exclusion criteria at our institution from 2002 to 2015. Continuous subcutaneous insulin infusion (CSII) was provided to achieve the glycemic goals within a week and then maintained for 14 days. Hypoglycemia episodes during short-term treatment and the one-year drug-free glycemic remission were observed. Results A total of 244 patients were included. The per day episode of mild hypoglycemia (3.0-3.9 mmol/L) was higher in the remission group than in the nonremission group (0.26 ± 0.20 vs. 0.18 ± 0.21, P = 0.005). However, a moderate hypoglycemia episode (<3.0 mmol/L) per day was insignificantly lower in the remission group (0.02 ± 0.04 vs. 0.03 ± 0.04, P = 0.005). However, a moderate hypoglycemia episode (<3.0 mmol/L) per day was insignificantly lower in the remission group (0.02 ± 0.04 vs. 0.03 ± 0.04, P = 0.005). However, a moderate hypoglycemia episode (<3.0 mmol/L) per day was insignificantly lower in the remission group (0.02 ± 0.04 vs. 0.03 ± 0.04, P = 0.005). However, a moderate hypoglycemia episode (<3.0 mmol/L) per day was insignificantly lower in the remission group (0.02 ± 0.04 vs. 0.03 ± 0.04, Conclusions Mild hypoglycemic episodes during the continuing insulin dose reduction period indicate a long-term drug-free euglycemic remission in patients with newly diagnosed type 2 diabetes. However, the insulin dosage should be reduced even more quickly in the future treatment to decrease the potential harms.

Diabetes Management According to Health Status in Older Adults with Type 2 Diabetes Staying in Geriatric Care Facilities

About 25% of adults >70 years suffer from type 2 diabetes. Due to the heterogeneity of the geriatric population, guidelines emphasize the need to individualize glycemic goals and simplify treatment strategies with the main focus of avoiding hypoglycemia. The aim of this study was to assess glycemic control in patients with type 2 diabetes in geriatric care facilities based on their individual health status. 170 medical records of older adults with type 2 diabetes in geriatric care facilities were retrospectively assessed (64.7% female, age 80 ± 9 years; glycated hemoglobin 6.8% ± 3.6% [51 ± 16 mmol/mol]; body mass index 27.9 ± 5.8 kg/m2). Based on the individual health status, patients were allocated to three groups (healthy n = 27, complex n = 86, and poor n = 57). The overall blood glucose (BG) value was highest in the poor health group with 188 ± 47 mg/dL (poor) vs 167 ± 42 mg/dL (complex) vs 150 ± 34 mg/dL (healthy). BG values of 1.6% (poor) vs 2.8% (complex) vs 1.5% (healthy) of patients were below 90 mg/dL. 36.8% (poor) vs 23.4% (complex) vs 18.5% (healthy) of patients received insulin as the main diabetes therapy, but of these only 14.3% (poor) vs 20% (complex) vs 40% (healthy) were treated with basal insulin. Overall, BG values were higher in the poor and complex health group. There were a few low BG values in all groups. Although recommended by international guidelines, basal insulin therapy with its low complexity and low hypoglycemic risk is still underused, especially in the poor health group. Therefore, simplification of diabetes therapy should be considered further.

Diabetes control in Asian Americans — Disparities and the role of acculturation

Asian Americans (AA) are disproportionately affected by diabetes (DM) and its complications than non-Hispanic whites (whites). We examined white-AA disparities in glycemic, cholesterol and blood pressure control, known as ‘ABCs of DM’, and evaluated if acculturation plays a role in DM control in AA with DM. Using data from NHANES 2011–2016, we found AA patients were significantly less likely to meet glycemic, cholesterol and the collective ‘ABCs’ goals than their white counterparts. Acculturation was positively associated with glycemic goal achievement in AA patients. This study identified disparities and pointed to strategies related to acculturation to improve DM control for AA.

Optimizing Diabetes Care With the Standardized Continuous Glucose Monitoring Report.

Continuous glucose monitoring (CGM) technology has brought about a paradigm shift in defining glycemic control in diabetes management and research. A1C and blood glucose monitoring have been widely accepted measurements in diabetes care, yet each has limitations in its clinical utility. A1C is established as an indicator of population health and long-term risk for microvascular complications but is less useful in personalizing glycemic goals, guiding therapy changes, or understanding patterns of glycemic excursions (1). Furthermore, A1C has limitations in accuracy and reliability in the context of hemoglobinopathy, anemia, iron deficiency (2), pregnancy (3), and racial differences (4). With increasing evidence regarding the relationship of glycemic variability with micro- and macrovascular risks, the definition of diabetes “control” is changing, and that change is bringing opportunity to tailor therapy decisions that truly improve outcomes (5). Self-monitoring of blood glucose (SMBG) has been an important tool for calculating insulin doses and gaining an understanding of daily glucose patterns. SMBG provides the glucose level at a single point in time without the context of past or future directionality and carries a burden of pain and inconvenience for patients, further limiting the amount of data available to analyze (1). The accuracy of an SMBG reading is dependent on the user’s testing technique and on the accuracy of the glucose meter itself; many glucose meters do not meet accuracy standards (6). Although CGM provides a wealth of information that A1C testing and SMBG lack, adoption of and persistence with this technology have been limited (7). However, with the arrival of systems for personal use (real-time use for patients) and professional use (blinded for patients with retrospective analysis by clinicians) that are more affordable and user-friendly (i.e., that do not require SMBG calibration and are indicated as an alternative to SMBG for making treatment …

Distance to glycemic goal at the time of treatment intensification in patients with type 2 diabetes mellitus failing metformin monotherapy in the United States

Background: A substantial proportion of patients with type 2 diabetes mellitus (T2DM) do not reach their target HbA1c level on metformin. The objective of this retrospective observational cohort study is to better characterize the distance between HbA1c target and patient’s actual HbA1c level (the distance to goal), using a target HbA1c of 7.0% (53 mmol/mol), in patients with T2DM who have started metformin monotherapy.Methods: We used data from the GE Centricity Electronic Medical Record database by IQVIA in 2016 in the United States (US) to identify adults with T2DM who started metformin monotherapy (MM) and received at least 90 days of treatment. Patients were categorized into three groups: those who achieved the goal of HbA1c <7.0%, those who did not achieve the goal of HbA1c <7.0% (i.e. failed MM) and received intensified treatment, and those who failed MM and did not receive intensified treatment. Distance to goal was computed for patients in each group.Results: We identified 20,704 patients in the US database who started MM; 1741 (8.4%) failed MM and received intensified treatment, while 4977 (24.0%) failed MM and did not receive intensified treatment. The mean post-MM HbA1c for those who failed MM and received intensified treatment was 8.7% (72 mmol/mol) (median 8.2%, 66 mmol/mol) and the mean distance to goal was 1.7% (median 1.2%). The mean post-MM HbA1c for those who failed MM and did not receive intensified treatment was 8.0% (64 mmol/mol) (median 7.5%, 58 mmol/mol) and the mean distance to goal was 1.0% (median 0.5%).Conclusion: A proportion of US T2DM patients do not achieve glycemic control (target HbA1c < 7.0%) despite 90 days of MM. Patients who failed MM and eventually received intensified treatment did so when their HbA1c distance to goal exceeded the level at which one add-on therapy alone might be sufficient to bring them to goal.

Annals for Educators - 4 February 2020.

Annals for Educators - 4 February 2020.

Real-world Evaluation of glycemic control and hypoglycemic Events among type 2 Diabetes mellitus study (REEDS): a multicentre, cross-sectional study in Thailand

ObjectivePatients with type 2 diabetes mellitus (T2DM) often experience hypoglycaemia and weight gain due to treatment side effects. Sulfonylureas (SU) and the combination of SU and metformin (SU+MET) were the...

Improving Outcomes of People With Diabetes Through Overcoming Therapeutic InertiaPreface.

Clinical and therapeutic inertia continue to be major issues in the management of chronic diseases such as diabetes. Only about 50% of patients with diabetes are reaching evidence-based glycemic goals (1), and fewer than 20% of patients have A1C, blood pressure, and LDL cholesterol levels all at goal (2). In addition, the rates of preventive practices such as checks for retinopathy, neuropathy, and nephropathy have not improved in the past decade (3). The Oxford English Dictionary defines inertia as “a tendency to do nothing or to remain unchanged” (4). The term “clinical inertia” has been used to refer to the underuse of effective therapies and interventions despite a wealth of evidence regarding their benefits in preventing serious negative outcomes (5,6). However, the term “therapeutic inertia” may be more appropriate specifically to describe a lack of timely adjustment to the treatment regimen when a patient’s treatment goals are not met, whereas the wider concept of “clinical inertia” also encompasses other deficits in care provision, such as lack of appropriate screening or referrals (7). Because this Diabetes Spectrum From Research to Practice section focuses on the prescribing decisions of health care providers (and related patient education interventions), we will use the more specific term “therapeutic inertia” throughout. …

Profile and factors associated with glycaemic control of patients with type 2 diabetes in Greece: results from the diabetes registry

BackgroundStrict glycaemic control early in the treatment process has been shown to reduce the occurrence of micro- and macro- vascular complications of diabetes in the long-term. Thus, treatment guidelines advise early intensification of treatment to achieve glycaemic control goals. However, evidence in Greece suggests that, despite guideline recommendations, glycaemic control among patients with T2DM remains challenging. This study presents the demographic and clinical characteristics of patients with T2DM in Greece using data from an electronic registry designed specifically for this treatment category and investigates the factors that are independently associated with glycaemic control.MethodsThis is a multi-center, observational, cross-sectional study to investigate epidemiological and clinical factors affecting glycaemic control among patients with T2DM in Greece. Data was collected via a web-based disease registry, the Diabetes Registry, which operated from January 1st to December 31st, 2017. Five large specialized diabetes centers operating in Greek hospitals participated in the study.ResultsData for 1141 patients were retrieved (aged 63.02 ± 12.65 years, 56.9% male). Glycaemic control (Hb1Ac < 7%) was not achieved in 57.1% of patients. Factors independently associated with poor glycaemic control were: family history of diabetes [OR: 1.53, 95% CI: 1.06–2.23], BMI score between 25 to 30 [OR: 2.08, 95% CI: 1.05–4.13] or over 30 [OR: 2.12, 95% CI 1.12–4.07], elevated LDL levels [OR: 1.53, 95% 1.06–2.21] and low HDL levels [OR: 2.12, 95% CI: 1.44–3.12]. Lastly, use of injectable antidiabetic agents (in monotherapy or in combination) was less likely to be associated with poor glycaemic control versus treatment with combination of oral and injectable agents [OR: 0.50, 95% CI: 0.24–1.01]. This association was found to be marginally statistically significant.ConclusionInadequate lipid control, family history of diabetes and presence of obesity (ΒΜΙ ≥ 30 kg/m2) were associated with poor glycaemic control among study sample, whereas use of injectable antidiabetic agents was less likely to be associated with poor glycaemic control. These findings indicate how complex optimal glycaemic control is, highlighting the need for tailored interventions in high-risk subpopulations with T2DM.