Intensification patterns and the probability of HbA1c goal attainment in Type 2 diabetes mellitus: real-world evidence for the concept of 'intensification inertia'

Diabetes-related complications, glycemic levels, and healthcare utilization outcomes after therapeutic inertia in type 2 diabetes mellitus

AIMS

To assess diabetes-related complications, glycemic levels, and healthcare utilization 12 months after exposure to therapeutic inertia among patients with type 2 diabetes mellitus (T2D).

METHODS

This retrospective cohort study analyzed data from the OneFlorida Clinical Research Consortium (electronic health records from Florida practices/clinics). The cohort included adult patients (≥18 years old) with T2D who had an HbA1c≥7.0% (53 mmol/mol) recorded from January 1, 2014-September 30, 2019. Therapeutic inertia (exposed vs. not exposed) was evaluated during the six months following HbA1c≥7.0% (53 mmol/mol). The outcomes assessed during the 12-month follow-up period included diabetes-related complications (continuous Diabetes Complications and Severity Index (DCSI)), glycemic levels (continuous follow-up HbA1c lab), and healthcare utilization counts. We analyzed data using multivariable regression models, adjusting for covariates.

RESULTS

The cohort included 26,881 patients with T2D (58.94% White race, 49.72% female, and mean age of 58.82 (SD=13.09)). After adjusting for covariates, therapeutic inertia exposure was associated with lower DCSI (estimate=-0.14 (SE=0.03), p < 0.001), higher follow-up HbA1c (estimate=0.14 (SE=0.04), p < 0.001), and lower rates of ambulatory visits (rate ratio=0.79, 95% CI=0.75-0.82).

CONCLUSIONS

Findings communicate the clinical practice implications and public health implications for combating therapeutic inertia in diabetes care.

Therapeutic Inertia in the Management of Type 2 Diabetes: A Narrative Review

Adequate glycemic control is key to prevent morbi-mortality from type 2 diabetes (T2D). Despite the increasing availability of novel, effective, and safe medications for the treatment of T2D, and periodically updated guidelines on its management, the overall rate of glycemic goal attainment remains low (around 50%) and has not improved in the past decade. Therapeutic inertia (TI), defined as the failure to advance or de-intensify medical therapy when appropriate to do so, has been identified as a central contributor to the lack of progress in the rates of HbA1c goal attainment. The time to treatment intensification in patients not meeting glycemic goals has been estimated to be between 1 and 7 years from the time HbA1c exceeded 7%, and often, even when an intervention is carried out, it proves insufficient to achieve glycemic goals, which led to the concept of intensification inertia. Therefore, finding strategies to overcome all forms of TI in the management of T2D is a fundamental initiative, likely to have an enormous impact in health outcomes for people with T2D. There are several factors that have been described in the literature leading to TI, including clinician-related, patient-related, and healthcare system-related factors, which are discussed in this review. Likewise, several interventions addressing TI had been tested, most of them proving limited efficacy. Within the most effective interventions, there appear to be two common factors. First, they involve a team-based effort, including nurses, pharmacists, and diabetes educators. Second, they were built upon a framework based on results of qualitative studies conducted in the same context where they were later implemented, as will be discussed in this article. Given the complex nature of TI, it is crucial to use a research method that allows for an in-depth understanding of the phenomenon. Most of the literature on TI is focused on quantitatively describing its consequences; unfortunately, however, not many study groups have undertaken qualitative studies to deeply investigate the drivers of TI in their diverse contexts. This is particularly true in the United States, where there is an abundance of publications exploring the effects of different strategies to overcome TI in type 2 diabetes, but a severe shortage of qualitative studies aiming to truly understand the phenomenon.

A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data

OBJECTIVE

To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of health (SDOH).

METHODS

This prognostic study with a retrospective cohort design used OneFlorida data (linked electronic health records (EHRs) from 1240 practices/clinics in Florida). The study cohort included adults (aged ≥ 18) with type 2 diabetes, HbA1C ≥ 7% (53 mmol/mol), ≥one ambulatory visit, and ≥one antihyperglycemic medication prescribed (excluded patients prescribed insulin before HbA1C). The outcome was therapeutic inertia, defined as absence of treatment intensification within six months after HbA1C ≥ 7% (53 mmol/mol). The predictors were patient, provider, and healthcare system factors. Machine learning methods included gradient boosting machines (GBM), random forests (RF), elastic net (EN), and least absolute shrinkage and selection operator (LASSO). The DeLong test compared the discriminative ability (represented by C-statistics) between models.

RESULTS

The cohort included 31,087 patients with type 2 diabetes (mean age = 58.89 (SD = 13.27) years, 50.50% male, 58.89% White). The therapeutic inertia prevalence was 39.80% among the 68,445 records. GBM outperformed (C-statistic from testing sample = 0.84, 95% CI = 0.83-0.84) RF (C-statistic = 0.80, 95% CI = 0.79-0.80), EN (C-statistic = 0.80, 95% CI = 0.80-0.81), and LASSO (C-statistic = 0.80, 95% CI = 0.80-0.81), p < 0.05. Area-level SDOH significantly increased the discriminative ability versus models without SDOH (C-statistic for GBM = 0.84, 95% CI = 0.84-0.85 vs. 0.84, 95% CI = 0.83-0.84), p < 0.05.

CONCLUSIONS

Using EHRs of patients with type 2 diabetes from a large state, machine learning predicted therapeutic inertia (prevalence = 40%). The model's ability to predict patients at high risk of therapeutic inertia is clinically applicable to diabetes care.

Medication use and contextual factors associated with meeting guideline-based glycemic levels in diabetes among a nationally representative sample

Introduction

Based on the long-lasting diabetes management challenges in the United States, the objective was to examine glycemic levels among a nationally representative sample of people with diabetes stratified by prescribed antihyperglycemic treatment regimens and contextual factors.

Methods

This serial cross-sectional study used United States population-based data from the 2015 to March 2020 National Health and Nutrition Examination Surveys (NHANES). The study included non-pregnant adults (≥20 years old) with non-missing A1C and self-reported diabetes diagnosis from NHANES. Using A1C lab values, we dichotomized the outcome of glycemic levels into <7% versus ≥7% (meeting vs. not meeting guideline-based glycemic levels, respectively). We stratified the outcome by antihyperglycemic medication use and contextual factors (e.g., race/ethnicity, gender, chronic conditions, diet, healthcare utilization, insurance, etc.) and performed multivariable logistic regression analyses.

Results

The 2042 adults with diabetes had a mean age of 60.63 (SE = 0.50), 55.26% (95% CI = 51.39-59.09) were male, and 51.82% (95% CI = 47.11-56.51) met guideline-based glycemic levels. Contextual factors associated with meeting guideline-based glycemic levels included reporting an "excellent" versus "poor" diet (aOR = 4.21, 95% CI = 1.92-9.25) and having no family history of diabetes (aOR = 1.43, 95% CI = 1.03-1.98). Contextual factors associated with lower odds of meeting guideline-based glycemic levels included taking insulin (aOR = 0.16, 95% CI = 0.10-0.26), taking metformin (aOR = 0.66, 95% CI = 0.46-0.96), less frequent healthcare utilization [e.g., none vs. ≥4 times/year (aOR = 0.51, 95% CI = 0.27-0.96)], being uninsured (aOR = 0.51, 95% CI = 0.33-0.79), etc.

Discussion

Meeting guideline-based glycemic levels was associated with medication use (taking vs. not taking respective antihyperglycemic medication classes) and contextual factors. The timely, population-based estimates can inform national efforts to optimize diabetes management.

Episodic Real-Time CGM Use in Adults with Type 2 Diabetes: Results of a Pilot Randomized Controlled Trial

INTRODUCTION

Resistance to initiating insulin therapy is common for people with type 2 diabetes (T2D) using multiple oral agents, resulting in sustained poor glycemic control. We explored a non-pharmacologic option and examined whether adults with T2D and elevated hemoglobin A1c (HbA1c) who were using multiple, non-insulin antihyperglycemics could obtain glycemic benefit from limited, episodic use of real-time continuous glucose monitoring (rtCGM).

METHODS

A randomized, pilot trial enrolled patients with T2D who were using two or more non-insulin therapies and had HbA1c values of 7.8-10.5%. Following a baseline, 10-day, blinded CGM session, participants were randomized 2:1, rtCGM or self-monitoring of blood glucose (SMBG). Medication changes were not made during the 12-week study unless required for safety; benefits would result from lifestyle changes. The rtCGM group used unblinded rtCGM for three sessions at weeks 0, 4, and 8, and the control group managed diabetes with SMBG and wore blinded rtCGM at week 8. Glycemic endpoints were assessed.

RESULTS

Seventy participants were enrolled from eight North American sites and data were available from 68 (n = 45 rtCGM; n = 23 SMBG). Median (IQR) baseline HbA1c was 8.4 (0.8)% and 8.3 (1.2)% and median (IQR) change in HbA1c at week 12 was - 0.5 (1.3)% and - 0.2 (1.1)% for the rtCGM and SMBG groups, respectively (between-group difference p = 0.74). More than one-third (34.1%) of the rtCGM group vs 17.4% of the SMBG group reached the HbA1c goal of less than 7.5% at week 12 (between-group difference p = 0.12). Compared to run-in, mean (SD) time in range (TIR 70-180 mg/dL) at week 8 increased for the rtCGM group (56.3 [24.5]% vs 63.1 [25.5]%) while it decreased for the SMBG group (68.4 [21.5]% vs 55.1 [30.3]%). HbA1c reductions were not sustained at month 9.

CONCLUSION

In this pilot study, limited episodic rtCGM use in people failing multiple non-insulin therapies resulted in modest, short-term glycemic benefits.

Real-world clinical outcomes following treatment intensification with GLP-1 RA, OADs or insulin in patients with type 2 diabetes on two oral agents (PATHWAY 2-OADs)

INTRODUCTION

Most patients with type 2 diabetes require sequential addition of glucose-lowering agents to maintain long-term glycemic control. In this retrospective, observational study, we compared intensification with a glucagon-like peptide-1 receptor agonist (GLP-1 RA), oral antidiabetic drugs (OADs), and insulin in patients receiving two OADs, using US electronic health records and claims data.

RESEARCH DESIGN AND METHODS

For inclusion, patients in the IBM MarketScan Explorys database were required to have claims for two different OADs in the 180-day baseline period and ≥1 claim for a different OAD/GLP-1 RA/insulin at index date (treatment intensification). Changes in glycated hemoglobin (HbA_1c) and weight from baseline were assessed at 180 days postindex. Patients were propensity score-matched by baseline characteristics and exact-matched by HbA_1c category (HbA_1c cohort and weight/composite outcomes cohort) and body mass index (BMI) category (weight/composite outcomes cohort only) to obtain balanced treatment arms. The primary endpoint was the percentage of patients reaching target HbA_1c <7% (53 mmol/mol).

RESULTS

Significantly more patients intensifying with a GLP-1 RA achieved HbA_1c <7% than those receiving OAD(s) (OR: 1.35; 95% CI 1.03 to 1.77; p=0.032) or insulin (OR: 1.77; 95% CI 1.27 to 2.47; p<0.001). GLP-1 RAs were also associated with a significantly greater chance of not gaining weight; significantly greater HbA_1c and weight decreases from baseline; and a significantly greater chance of HbA_1c <7%, no weight gain and discontinuation of ≥1 baseline OAD (composite outcome), compared with OAD(s) or insulin.

CONCLUSIONS

In propensity score-matched cohorts, GLP-1 RAs demonstrated significant benefits for both glycemic control and weight management over additional OAD(s) or insulin, respectively, indicating that they may represent the optimal choice at these points in the treatment pathway.

The present and future scope of real-world evidence research in diabetes: What questions can and cannot be answered and what might be possible in the future?

The last decade has witnessed an exponential growth in the opportunities to collect and link health-related data from multiple resources, including primary care, administrative, and device data. The availability of these "real-world," "big data" has fuelled also an intense methodological research into methods to handle them and extract actionable information. In medicine, the evidence generated from "real-world data" (RWD), which are not purposely collected to answer biomedical questions, is commonly termed "real-world evidence" (RWE). In this review, we focus on RWD and RWE in the area of diabetes research, highlighting their contributions in the last decade; and give some suggestions for future RWE diabetes research, by applying well-established and less-known tools to direct RWE diabetes research towards better personalized approaches to diabetes care. We underline the essential aspects to consider when using RWD and the key features limiting the translational potential of RWD in generating high-quality and applicable RWE. Only if viewed in the context of other study designs and statistical methods, with its pros and cons carefully considered, RWE will exploit its full potential as a complementary or even, in some cases, substitutive source of evidence compared to the expensive evidence obtained from randomized controlled trials.