Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients

Greater persistence and adherence to basal insulin therapy is associated with lower healthcare utilization and medical costs in patients with type 2 diabetes: a retrospective database analysis

INTRODUCTION

We aimed to assess persistence and adherence to basal insulin therapy, their association with all-cause healthcare resource utilization (HCRU) and direct medical costs, and predictors of persistence and adherence in adults with type 2 diabetes.

RESEARCH DESIGN AND METHODS

A retrospective cohort study was conducted with US adults with type 2 diabetes initiating basal insulin therapy between January 1, 2016, and December 31, 2018, using IQVIA PharMetrics Plus claims data. Persistence and adherence were assessed during 1 year post-initiation per previous definitions. Demographic/clinical characteristics were assessed during the 1 year pre-initiation. Inverse probability of treatment weighting (IPTW) was used to adjust for confounding variables. Post-IPTW, all-cause HCRU and direct medical costs were assessed during the first-year and second-year post-initiation by persistence and adherence status. Multivariable logistic regression was used to identify predictors of persistence and adherence.

RESULTS

The final sample comprised 64,953 patients; 56.8% demonstrated persistence and 41.9% demonstrated adherence. Patients demonstrating persistence and adherence were significantly less likely to have a hospitalization than patients demonstrating non-persistence or non-adherence, respectively. In the second-year post-initiation, total mean all-cause direct medical costs per patient were lower for patients demonstrating persistence and significantly lower for patients demonstrating adherence. Prior use of both oral and injectable antidiabetic medication predicted persistence and adherence compared with patients with only prior oral antidiabetic medication use (persistence OR, 1.50 (95% CI, 1.44 to 1.57); adherence OR, 1.48 (95% CI, 1.42 to 1.55)).

CONCLUSIONS

Persistence and adherence to basal insulin was associated with fewer hospitalizations and lower direct medical costs.

Sauver JL, Shah P, Vella A, Ryu E, Liu H, Marshall GD, Cerhan JR, Singh D, Summers RL. Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients

CONTEXT

Metformin is the first-line drug for treating diabetes but has a high failure rate.

OBJECTIVE

To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure.

METHODS

A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure.

RESULTS

In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P < .0001).

CONCLUSION

Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

Obesity, diabetes mellitus, and cardiometabolic risk: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023

Background

This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) is intended to provide clinicians an overview of type 2 diabetes mellitus (T2DM), an obesity-related cardiometabolic risk factor.

Methods

The scientific support for this CPS is based upon published citations and clinical perspectives of OMA authors.

Results

Topics include T2DM and obesity as cardiometabolic risk factors, definitions of obesity and adiposopathy, and mechanisms for how obesity causes insulin resistance and beta cell dysfunction. Adipose tissue is an active immune and endocrine organ, whose adiposopathic obesity-mediated dysfunction contributes to metabolic abnormalities often encountered in clinical practice, including hyperglycemia (e.g., pre-diabetes mellitus and T2DM). The determination as to whether adiposopathy ultimately leads to clinical metabolic disease depends on crosstalk interactions and biometabolic responses of non-adipose tissue organs such as liver, muscle, pancreas, kidney, and brain.

Conclusions

This review is intended to assist clinicians in the care of patients with the disease of obesity and T2DM. This CPS provides a simplified overview of how obesity may cause insulin resistance, pre-diabetes, and T2DM. It also provides an algorithmic approach towards treatment of a patient with obesity and T2DM, with "treat obesity first" as a priority. Finally, treatment of obesity and T2DM might best focus upon therapies that not only improve the weight of patients, but also improve the health outcomes of patients (e.g., cardiovascular disease and cancer).