Early treatment revisions by addition or switch for type 2 diabetes: impact on glycemic control, diabetic complications, and healthcare costs

Evaluating Therapeutic Inertia in Two Telehealth Interventions for Type 2 Diabetes: Secondary Analyses of a Randomized Trial

Introduction: Although therapeutic inertia is a known driver of suboptimal type 2 diabetes control, little is known about how to combat this phenomenon. We analyzed randomized trial data to determine whether a comprehensive telehealth intervention was more effective than a less structured telehealth approach (telemonitoring and care coordination) at promoting treatment intensification in poorly controlled diabetes. Methods: Patients with poorly controlled type 2 diabetes were randomized 1:1 to telemonitoring/care coordination or a comprehensive telehealth intervention, which included an active, study provider-guided medication management component. Prospectively collected medication lists were used to determine whether treatment intensification occurred for each patient during 3-month intervals throughout the study period. To examine between-arm differences in treatment intensification over time, we fit a generalized estimation equation model. In each arm, hemoglobin A1c levels at the beginning and end of each 3-month interval were used to distinguish between therapeutic inertia and potentially appropriate nonintensification of treatment. Results: The mean, model-estimated likelihood of treatment intensification during 3-month intervals was 61.3% in the comprehensive telehealth group versus 48.6% for telemonitoring/care coordination (odds ratio 1.7, 95% confidence interval 1.2-2.2; p = 0.0007), with no evidence that treatment effect varied over time (p = 0.54). Treatment intervals with observed therapeutic inertia were more common in the telemonitoring/care coordination arm than the comprehensive telehealth arm (116/300, 39% vs. 57/275, 21%). Conclusions: A comprehensive telehealth approach that integrated protocol-guided medication management increased treatment intensification and reduced therapeutic inertia compared with a less structured telehealth approach. The studied approaches may serve as examples of how systems might use telehealth to combat therapeutic inertia. Clinical Trial Registration: ClinicalTrials.gov NCT03520413.

The effect of close and intensive therapeutic monitoring of patients with poorly controlled type 2 diabetes with different glycemic background

Patients with type 2 diabetes who have HbA1c values ≥ 10% have different previous glycemic trends, including new diagnosis of diabetes. We aimed to assess the efficacy of 3 months of intensive and facilitated antihyperglycemic treatment in patients with different glycemic backgrounds. In this observational study, patients with type 2 diabetes and poor glycemic control (indicated by an HbA1c level of > = 10%) were divided into groups based on their previous HbA1c levels (group 1; newly diagnosed type 2 diabetics, group 2; patients with previously controlled but now deteriorated HbA1c levels, group 3; patients whose HbA1c was not previously in the target range but was now above 10%, and group 4; patients whose HbA1c was above 10% from the start). Patients received intensive diabetes management with close monitoring and facilitated hospital visits. For further analysis, patients who were known to have previously had good metabolic control (either did not have diabetes or had previously had an HbA1c value < =7) and patients who had prior poor metabolic control were analyzed separately. Of the 195 participants [female, n = 84 (43.1%)], the median age was 54 years (inter-quantile range [IQR] = 15, min = 29, max = 80) and the median baseline HbA1c was 11.8% (IQR = 2.6%, min = 10%, max = 18.3%). The median duration of diabetes was 10 years (IQR = 9, min = 1, max = 35) when newly diagnosed patients were excluded. The ≥ 20% reduction in HbA1c at month 3 was observed in groups 1 to 4 in 97%, 88.1%, 69.1%, and 55.4%, respectively. The percentage of patients who achieved an HbA1c level of 7% or less was 60.6%, 38.1%, 16.4%, and 6.2% in the groups, respectively. The rate of those who achieved an HbA1c of 7% or less was nearly 50% of patients with type 2 diabetes mellitus who had previously had good metabolic control, whereas successful control was achieved in only 1 in 10 patients with persistently high HbA1c levels. Patients' glycemic history played an important role in determining their HbA1c levels at 3 months, suggesting that previous glycemic management patterns may indicate future success in diabetes control.

Associations between second-line glucose-lowering combination therapies with metformin and HbA1c, body weight, quality of life, hypoglycaemic events and glucose-lowering treatment intensification: The DISCOVER study

AIM

To explore the effects of second-line combination therapies with metformin on body weight, HbA1c and health-related quality of life, as well as the risks of hypoglycaemia and further treatment intensification in the DISCOVER study, a 3-year, prospective, global observational study of patients with type 2 diabetes initiating second-line glucose-lowering therapy.

MATERIALS AND METHODS

Adjusted changes from baseline in weight, HbA1c and 36-item Short Form Health Survey version 2 (SF-36v2) summary scores at 6, 12, 24 and 36 months were assessed using linear mixed models. Risk of hypoglycaemia and further intensification were assessed using interval censored analyses.

RESULTS

At baseline, 7613 patients received metformin in combination with a sulphonylurea (SU; 40.9%), a dipeptidyl peptidase-4 (DPP-4) inhibitor (48.3%), a sodium-glucose co-transporter-2 (SGLT-2) inhibitor (8.3%) or a glucagon-like peptide-1 (GLP-1) receptor agonist (2.4%). After 36 months, all combinations showed similar reductions in HbA1c (0.8%-1.0%), however, metformin plus a DPP-4 inhibitor, an SGLT-2 inhibitor or a GLP-1 receptor agonist was associated with greater weight loss (1.9, 2.9 and 5.0 kg, respectively) than metformin plus an SU (1.3 kg, P < .0001). Proportions of further treatment intensification were similar across combinations (19.9%-26.2%). Patients prescribed metformin plus an SU more often reported one or more hypoglycaemic events (11.9%) than other combinations (3.9%-6.4%, P < .0001). SF-36v2 summary scores were typically lowest among patients prescribed metformin and an SU.

CONCLUSIONS

Combinations of metformin with an SU were associated with the lowest weight reduction, highest risk of hypoglycaemia and lower SF-36v2 scores.

Cost-Effectiveness of Point-of-Care A1C Tests in a Primary Care Setting

Objective: We evaluated the cost-effectiveness of the point-of-care A1c (POC-A1c) test device vs. the traditional laboratory dosage in a primary care setting for people living with type 2 diabetes. Materials and Methods: The Markov model with a 10-year time horizon was based on data from the HealthRise project, in which a group of interventions was implemented to improve diabetes and hypertension control in the primary care network of the urban area of a Brazilian municipality. A POC-A1c device was provided to be used directly in a primary care unit, and for a period of 18 months, 288 patients were included in the point-of-care group, and 1,102 were included in the comparison group. Sensitivity analysis was performed via Monte Carlo simulation and tornado diagram. Results: The results indicated that the POC-A1c device used in the primary care unit was a cost-effective alternative, which improved access to A1c tests and resulted in an increased rate of early control of blood glucose. In the 10-year period, POC-A1c group presented a mean cost of US$10,503.48 per patient and an effectiveness of 0.35 vs. US$9,992.35 and 0.09 for the traditional laboratory test, respectively. The incremental cost was US$511.13 and the incremental effectiveness was 0.26, resulting in an incremental cost-effectiveness ratio of 1,947.10. In Monte Carlo simulation, costs and effectiveness ranged between $9,663.20-$10,683.53 and 0.33-0.37 for POC-A1c test group, and $9,288.28-$10,413.99 and 0.08-0.10 for traditional laboratory test group, at 2.5 and 97.5 percentiles. The costs for nephropathy, retinopathy, and cardiovascular disease and the probability of being hospitalized due to diabetes presented the greatest impact on the model's result. Conclusion: This study showed that using POC-A1c devices in primary care settings is a cost-effective alternative for monitoring glycated hemoglobin A1c as a marker of blood glucose control in people living with type 2 diabetes. According to our model, the use of POC-A1c device in a healthcare unit increased the early control of type 2 diabetes and, consequently, reduced the costs of diabetes-related outcomes, in comparison with a centralized laboratory test.

Efficacy of a Combination of Metformin and Vildagliptin in Comparison to Metformin Alone in Type 2 Diabetes Mellitus: A Multicentre, Retrospective, Real-World Evidence Study

BACKGROUND

Early use of combination therapy in diabetes patients may lead to sustained glycemic control and thereby reduce the progression of diabetic complications. Given the limitation of the traditional stepwise intensification strategy, early combination therapy can be an effective approach. Therefore, this study aims to assess the real-world efficacy of a combination of metformin and vildagliptin in comparison to metformin alone in type 2 diabetes mellitus (T2DM) patients in India.

METHODS

This was an observational, retrospective, non-interventional study based on electronic medical records (EMRs) of 2740 T2DM patients, retrieved from 2010 onwards from 22 diabetes centres across India. Adult drug naïve patients with a 5-year history of T2DM treated with either metformin or a combination of metformin and vildagliptin for at least 3 months were considered for this study. Efficacy assessment was done to evaluate the post-treatment HbA1c levels and patients requiring additional oral antidiabetic drugs (OADs) at the time of follow-up visit. Patients were also analyzed for the occurrence of adverse events.

RESULTS

Out of the total, 2452 patients were in metformin only arm, and 288 patients were in metformin plus vildagliptin treatment arm. A more significant reduction in HbA1c level was observed in metformin plus vildagliptin arm than metformin only arm (median: -0.5% vs 0%, respectively; p<0.001). Patients requiring additional OAD at follow-up were significantly lesser in the metformin plus vildagliptin arm than the metformin only arm (15.6% vs 35.2%, respectively; p<0.001). The adverse events were comparable across the two arms, and commonly reported adverse events were giddiness, fatigue and gastric discomfort.

CONCLUSION

The findings of this EMR-based real-world study emphasizes the need for early initiation of combination therapy (metformin plus vildagliptin) over metformin monotherapy for achieving better glycemic control.

Effectiveness of a Diabetes Education Program based on Tailored interventions and Theory of Planned Behaviour: Cluster randomized controlled trial protocol

AIM

To measure the Effectiveness of a Diabetes Education Program for people with T2DM, based on Tailored interventions and the Theory of Planned Behaviour.

DESIGN

Cluster randomized controlled clinical trial.

METHODS

This multicentre study will be carried out at 30 primary healthcare centres, where 436 persons with Type 2 Diabetes Mellitus (T2DM), aged between 18-75 years, will be recruited. The experimental educational program to be applied is modelled using components obtained from a systematic review and prior qualitative analysis. In addition, a taxonomy of nursing practice is used to standardize the program, based on the Theory of Planned Behaviour as a conceptual model. The intervention will be carried out by community nurses, using ADAPP-Ti^® , an application developed with FileMaker Pro v.18. The control group will receive usual care and data will be collected at 6, 12, and 18 months, for both groups. The primary outcome considered will be glycosylated haemoglobin and cardiovascular factors, while the secondary ones will be tobacco consumption, body mass index, barriers to self-care, health-related quality of life, and lifestyle modification. The protocol was approved by the Ethics Committee of the Province of Malaga (Spain) in November 2014.

DISCUSSION

The degree of metabolic control in T2DM is not always associated with healthy lifestyles and significant levels of medication are often prescribed to achieve clinical objectives. An intervention focused on needs, based on the best available evidence and a solid conceptual framework, might successfully consolidate appropriate self-care behaviour in this population.

IMPACT

The study will result in the publication of an educational program featuring well-defined interventions and activities that will enable clinicians to tailor health care to the individual's needs and to combat treatment inertia in attending this population.

Does the Encounter Type Matter When Defining Diabetes Complications in Electronic Health Records?

BACKGROUND

Electronic health records (EHRs) and claims records are widely used in defining type 2 diabetes mellitus (T2DM) complications across different types of health care encounters.

OBJECTIVE

This study investigates whether using different EHR encounter types to define diabetes complications may lead to different results when examining associations between diabetes complications and their risk factors in patients with T2DM.

RESEARCH DESIGN

The study cohort of 64,855 adult patients with T2DM was created from EHR data from the Research Action for Health Network (REACHnet), using the Surveillance Prevention, and Management of Diabetes Mellitus (SUPREME-DM) definitions. Incidence of coronary heart disease (CHD) and stroke events were identified using International Classification of Diseases (ICD)-9/10 codes and grouped by encounter types: (1) inpatient (IP) or emergency department (ED) type, or (2) any health care encounter type. Cox proportional hazards regression was used to estimate associations between diabetes complications (ie, CHD and stroke) and risk factors (ie, low-density lipoprotein cholesterol and hemoglobin A1c).

RESULTS

The incidence rates of CHD and stroke in all health care settings were more than twice the incidence rates of CHD and stroke in IP/ED settings. The age-adjusted and multivariable-adjusted hazard ratios for incident CHD and stroke across different levels of low-density lipoprotein cholesterol and hemoglobin A1c were similar between IP/ED and all settings.

CONCLUSION

While there are large variations in incidence rates of CHD and stroke as absolute risks, the associations between both CHD and stroke and their respective risk factors measured by hazard ratios as relative risks are similar, regardless of alternative definitions.

Clinical Inertia in Poorly Controlled Type 2 Diabetes Mellitus Patients with Obesity: An Observational Retrospective Study

INTRODUCTION

To evaluate clinical inertia in patients with type 2 diabetes mellitus (T2DM), obesity and poor glycaemic control in routine clinical practice.

METHODS

This was a retrospective, observational study based on the analysis of medical records from the BIG-PAC® database. Subjects who required medical care in 2013 with the following characteristics were enrolled in the study: age ≥ 30 years, diagnosis of T2DM, glycosylated haemoglobin (HbA1c) ≥ 8%, obesity (body mass index [BMI] ≥ 30 kg/m²) and treatment with ≥ 2 oral antidiabetic drugs (OADs). Inertia was evaluated by time (days) to the first intensification during the period while HbA1c levels were ≥ 8% and percentage of patients whose treatment was not intensified at 6 months, 1, 2 and 3 years and the end of follow-up. The minimum length of follow-up was 4 years. Descriptive analyses and Kaplan-Meier survival curves were performed.

RESULTS

A total of 13,824 patients with T2DM receiving ≥ 2 OADs were identified; of these 2709 (19.6%) had HbA1c ≥ 8% and BMI ≥ 30 kg/m², thus fulfilling the inclusion criteria. Of these 2709 patients, the mean age was 65.5 (standard deviation [SD] 12.0) years; 54.9% were male, mean HbA1c level was 9.2% (SD 1.3%); mean BMI was 32.1 (SD 0.9) kg/m²; and mean time from diagnosis was 8.2 (SD 3.0) years. HbA1c remained ≥ 8% for a median of 440 (95% confidence interval [CI] 421-459) days. The median time to first intensification was 456 (95% CI 429-483) days. No intensification had occurred in 77.8, 59.5, 41.5, 28.1 and 22.4% of patients at 6 months, 1, 2, 3 years and the end of follow-up, respectively.

CONCLUSIONS

The patients with T2DM analysed in this study had a mean HbA1c of 9.2% at baseline, and this remained at ≥ 8% for > 1 year. The time to the first treatment intensification was longer than that recommended by guidelines. Treatment was not intensified in a large percentage of patients, with almost 60% of patients not receiving intensification at 1 year of follow-up.

Delays in Insulin Initiation among Patients with Type 2 Diabetes Mellitus in Southeast China: A Retrospective, Real-World Study

PURPOSE

To describe the extent of delays in insulin initiation, analyze its impact on glycemic control, and explore factors influencing delayed insulin initiation among Chinese type 2 diabetes mellitus (T2DM) patients.

METHODS

A real-world, retrospective cohort study with regional electronic health records from Fuzhou, southeast China was conducted among T2DM patients. Adult patients uncontrolled with oral antidiabetic drugs (OADs; HbA_1c ≥7%) and initiated on insulin treatment were included. Time to insulin initiation was described. After propensity-score matching, Wilcoxon rank-sum test and chi-square test were used to compare follow-up HbA_1c (first HbA_1c 3 months after insulin initiation) between timely (initiated insulin within 6 months after OAD failure) and delayed (initiated after 6 months) insulin-initiation groups. Sensitivity analysis was also performed by linear and logistic regression. Factors associated with delayed insulin initiation were explored using logistic regression.

RESULTS

A total of 940 patients were included, with mean±SD age 66.3±11.9 years. In sum, 328 had HbA_1c recorded 3 months after insulin initiation. After propensity-score matching (1:1 matching), 184 patients were included for further analysis. Median follow-up HbA_1c was lower in the timely-initiation group than the delayed-initiation group (7.25% vs 8.25%, P=0.009). Patients in the timely initiation group also had higher odds of achieving HbA_1c <7% (OR=3.15, P=0.001). Results were confirmed by logistic regression. Hypertension, coronary artery disease, baseline HbA_1c, and hospital level at insulin initiation were associated with delays in insulin initiation.

CONCLUSION

Timely insulin initiation after OAD failure is associated with better glycemic control.

Clinical Use of IDegLira: Initiation to Titration After Basal Insulin

Delayed treatment intensification is common in U.S. patients with type 2 diabetes uncontrolled on basal insulin. Concerns about weight gain, hypoglycemia, increased regimen complexity, and additional copayments may lead to reluctance to initiate prandial insulin. IDegLira is a titratable, fixed-ratio coformulation that combines the advantages of insulin degludec and the glucagon-like peptide 1 receptor agonist liraglutide in a single once-daily injection and mitigates the side effects associated with each component. Clinical trials have demonstrated that IDegLira improves glycemic control without the increased risk of hypoglycemia and weight gain observed with basal insulin up-titration and the addition of prandial insulin, and this is achieved using twice-weekly titration. Clinical trials and real-world studies have also shown that IDegLira has the potential to reduce therapeutic and titration inertia. However, better outcomes could be achieved with IDegLira initiation in suitable patients with timely titration and by providers sharing their experience with this combination product. This review describes considerations for initiation, titration, and intensification of IDegLira in patients previously receiving basal insulin.

Trends in comorbidity burden and treatment patterns in type 2 diabetes: Longitudinal data from a US cohort from 2006 to 2014

AIMS

To gather real-world data on treatment characteristics and comorbidity progression in patients with newly-diagnosed type 2 diabetes (T2D) and evaluate differences by patient age.

METHODS

Retrospective analysis of a US administrative claims database including 16,950 subjects with newly-diagnosed T2D in 2006 and a baseline Diabetes Complications Severity Index (DCSI) score of 0. Patients were categorized by DCSI score at year 8 (0, 1-2, or ≥3) and comparatively analyzed based on demographic variables, drug usage, and diabetes-related comorbidities.

RESULTS

Year 8 DCSI score distribution was 0 (29.9%), 1-2 (36.2%), and ≥3 (33.9%). The highest DCSI score subgroup (≥3) was characterized by a significantly greater percentage of males, older age at T2D diagnosis, and higher Medicare enrollment. DCSI progressed most rapidly in the oldest age group (≥65). Among all subjects at year 8, insulin use was significantly highest among subjects with DCSI ≥3 compared with those having a lower DCSI. However, for subjects with DCSI ≥3, insulin use was lower among those in the oldest age group (≥65) relative to younger age groups.

CONCLUSIONS

These real-world data suggest a relationship between age at T2D diagnosis and disease progression based on comorbidity burden and lower usage of injectable therapies in older patients.

Acceptability and Feasibility of Examining Physical Activity in Young Children with Type 1 Diabetes

Physical activity is important but may be difficult to evaluate in young children (YC) with Type 1 diabetes (T1D) because of parents' fears of hypoglycemia, difficulties engaging YC in physical activity, and use of assessment devices. This study aimed to explore the acceptability and feasibility of an in-lab exercise session for YC with T1D. Ten YC ages 3 through7 years with T1D participated in a 20-minute exercise session while wearing blinded continuous glucose monitors and accelerometers. High acceptability was found for participation in the exercise session; high feasibility and acceptability were reported for the assessments. Although most children completed the session, it did not produce moderate to vigorous physical activity. YC were found to spend most of their day sedentary, and they had frequent blood glucose excursions. Findings support the feasibility of conducting a more extensive examination of the relationship among blood glucose levels and physical activity in YC with T1D.

Therapeutic inertia in the treatment of hyperglycaemia in patients with type 2 diabetes: A systematic review

AIMS

Therapeutic inertia, defined as the failure to initiate or intensify therapy in a timely manner according to evidence-based clinical guidelines, is a key reason for uncontrolled hyperglycaemia in patients with type 2 diabetes. The aims of this systematic review were to identify how therapeutic inertia in the management of hyperglycaemia was measured and to assess its extent over the past decade.

MATERIALS AND METHODS

Systematic searches for articles published from January 1, 2004 to August 1, 2016 were conducted in MEDLINE and Embase. Two researchers independently screened all of the titles and abstracts, and the full texts of publications deemed relevant. Data were extracted by a single researcher using a standardized data extraction form.

RESULTS

The final selection for the review included 53 articles. Measurements used to assess therapeutic inertia varied across studies, making comparisons difficult. Data from low- to middle-income countries were scarce. In most studies, the median time to treatment intensification after a glycated haemoglobin (HbA1c) measurement above target was more than 1 year (range 0.3 to >7.2 years). Therapeutic inertia increased as the number of antidiabetic drugs rose and decreased with increasing HbA1c levels. Data were mainly available from Western countries. Diversity of inertia measures precluded meta-analysis.

CONCLUSIONS

Therapeutic inertia in the management of hyperglycaemia in patients with type 2 diabetes is a major concern. This is well documented in Western countries, but corresponding data are urgently needed in low- and middle-income countries, in view of their high prevalence of type 2 diabetes.

Therapeutic inertia in patients treated with two or more antidiabetics in primary care: Factors predicting intensification of treatment

AIMS

To determine the patterns and predictors of treatment intensification in patients with type 2 diabetes on ≥2 non-insulin antidiabetic drugs (NIADs) and inadequate glycaemic control in primary care in Catalonia, Spain.

MATERIAL AND METHODS

This was a retrospective analysis using electronic medical records from patients with HbA1c ≥ 7% and a first prescription for a new NIAD or insulin recorded from January 2010 to December 2014. Therapeutic inertia was defined as no intensification if HbA1c was ≥8% at baseline or during follow-up. Time to first intensification was evaluated by time-to-event analysis, and factors predicting intensification through a competing-risk regression model.

RESULTS

Among 23 678 patients with HbA1c ≥ 7%, 26.2% were censored without treatment intensification after a median follow up of 4.2 years. Among the 12 730 patients in the subgroup with HbA1c ≥ 8% at baseline or during follow-up, therapeutic inertia was present in 18.1% of cases. In the overall cohort, mean HbA1c at initiation of insulin and NIAD were 9.4% ± 1.5% and 8.7% ± 1.3%, respectively. Median time to first intensification was 17.1 months in patients with HbA1c 8.0% to 9.9%, and 10.1 months in those with HbA1c > 10%. Variables strongly associated with intensification were HbA1c values 8.0% to 9.9% (subhazard ratio [SHR], 1.7; 95% CI, 1.65-1.78) and >10% (SHR, 2.5; 95% CI, 2.37-2.68); diabetes duration ≥20 years (SHR, 1.25; 95% CI, 1.11-1.41) and, to a lesser extent, female gender, presence of comorbidities, chronic kidney disease and microvascular complications.

CONCLUSIONS

Intensification was not undertaken in 1 in 5 patients. Both HbA1c thresholds and time until therapy intensification exceeded current recommendations.

Slow Titration and Delayed Intensification of Basal Insulin Among Patients with Type 2 Diabetes

BACKGROUND

Clinical inertia in type 2 diabetes mellitus (T2DM) refers to the failure of clinicians to intensify therapy when indicated. Many T2DM patients remain suboptimally controlled after initiating basal insulin.

OBJECTIVE

To examine the prevalence of patients treated with basal insulin but in poor glycemic control (hemoglobin A1c [A1c] ≥ 7%) after initiation and subsequent treatment intensification patterns and glycemic outcomes in a real-world setting.

METHODS

Adults diagnosed with T2DM newly initiating a basal insulin analog (insulin glargine or detemir) from January 2010 to September 2014 were identified in the QuintilesIMS Real-World Data Adjudicated Claims linked to the QuintilesIMS Real-World Data Electronic Medical Records. Patients were previously naive to insulin and glucagon-like peptide-1 receptor agonists (GLP-1 RAs), were persistent on therapy for ≥ 6 months, and had ≥ 12 months of continuous health plan enrollment after initiation. First treatment intensification (increase in basal insulin dose [of ≥ 10%], addition of bolus insulin, GLP-1 RA, or a new oral antidiabetic drug [OAD]) was assessed among patients in poor glycemic control at 6 months after initiation over the available (minimum ≥ 12-month) follow-up. Subsequent glycemic outcomes and treatment intensification were assessed. Kaplan-Meier (KM) analysis evaluated time-to-treatment intensification and time to A1c goal.

RESULTS

Of 427 eligible patients with A1c available at 6 months, 59.3% were male; mean age was 53.9 years; mean follow-up was 29.4 months; and mean dose of the initiated prescription was 29.6 insulin units (U) (median 24U). Six months after initiating basal insulin, 81.0% of patients (n = 346) remained in poor glycemic control, and mean basal insulin dose was 31.0U (median 25U). Most (88.4%; n = 306) of these uncontrolled patients subsequently intensified treatment over the available follow-up. Using KM analysis, these patients intensified treatment in a median of 58 days (range: 17.5 days [GLP-1 RA addition] to 52 days [increase in basal insulin dose]) from the first elevated A1c measurement taken after 6 months, and 72.5% (GLP-1 RA addition) to 91.1% (OAD addition) of patients continued to remain in poor glycemic control at 12 months after intensification. Most patients (66.8%; n = 231/346) first intensified treatment by increasing their basal insulin dose, and mean dose increased to 61.7U (median 38U) at intensification. Six months following basal insulin increase, almost all patients remained on basal insulin therapy and among those with available A1c, 92.1% (140 of 152) were in poor glycemic control. In the subsequent 12 months, only a third (34%) of uncontrolled patients added another antihyperglycemic agent.

CONCLUSIONS

The vast majority of patients remained uncontrolled in the 6 months following basal insulin initiation. Basal insulin up-titration was slow and insufficient in the 6 months after initiation, indicating treatment inertia. Subsequently, most patients failed to achieve glycemic targets despite intensification with basal insulin. This finding suggests a substantial unmet need for effective treatment intensification among T2DM patients treated with basal insulin who remain uncontrolled. Improved provider education and guidelines on appropriate intensification are warranted.

DISCLOSURES

This study was funded by Novo Nordisk. Mocarski, Guerrero, Langer, and Thorsted are employees and shareholders of Novo Nordisk. Yeaw, Divino, and DeKoven are employed by QuintilesIMS, which received remuneration from Novo Nordisk for work on this study. Study concept and design were contributed by Mocarski, DeKoven, Langer, and Thorsted. Yeaw took the lead in data collection, along with Divino and DeKoven. Data interpretation was performed by Yeaw, Divino, DeKoven, and Guerrero. The manuscript was written by Mocarski and Divino and revised by Guerrero, Langer, and Thorsted, along with Yeaw and DeKoven. Some of the data from this study were presented via poster at the AMCP Annual Meeting in March 2017 and at the 53rd EASD Annual Meeting in September 2017.