Sociodemographic disparities in GLP-1RA and SGLT2i use among US adults with type 2 diabetes: NHANES 2005-March 2020.

OBJECTIVE: Type 2 Diabetes (T2D) is a major cause of morbidity and mortality. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT2i) are highly effective but underutilized. Our objective was to assess racial/ethnic and other sociodemographic disparities in GLP-1RA/SGLT2i use among US adults with T2D. METHODS: We conducted a retrospective analysis using the National Health and Nutrition Examination Survey from 2005-March 2020. Participants were adults with T2D taking ≥1 anti-diabetic medication, excluding pregnant women and adults with probable T1D. We performed univariate analyses to examine the characteristics of patients using GLP-1RA/SGLT2i and multivariable logistic regression to assess disparities in GLP-1RA/SGLT2i use after adjusting for other patient factors. RESULTS: Among 4777 people with T2D (representing >18 million US adults) taking ≥1 medication, GLP-1RA/SGLT2i usage increased from 1.4% in 2005-2006 to 13.3% in 2017-2020. In univariate analyses, patients using GLP-1RA/SGLT2i vs. other T2D drugs were more likely to be White than nonwhite (72% vs. 60%, p = .001), but in multivariable analysis there was no significant difference in GLP-1RA/SGLT2i use for nonwhite vs. White patients (aOR = 0.84, 95% CI [0.61, 1.16]). GLP-1RA/SGLT2i use was higher for patients who completed some college (aOR = 1.83, 95% CI [1.06, 3.15]) or above (aOR = 2.06, 95% CI [1.28, 3.32]) vs. high school or less, and for those with an income-poverty ratio ≥4 vs. <2 (aOR = 2.11, 95% CI [1.30, 3.42]). CONCLUSIONS: The use of GLP-1RA/SGLT2i drugs increased over time but remained low in March 2020. Higher education and income, but not race/ethnicity, were associated with GLP-1RA/SGLT2i use.

Development and Internal Validation of the Cleveland Clinic Bleeding Model to Predict Major Bleeding Risk at Admission in Medical Inpatients.

BACKGROUND: Guidelines recommend pharmacological VTE prophylaxis for acutely ill medical patients at acceptable bleeding risk, but only the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model has been validated for bleeding risk assessment. OBJECTIVE: We developed and internally validated a risk assessment model (RAM) to predict major in-hospital bleeding using risk factors at admission and compared our model to IMPROVE. METHODS: We selected patients admitted to medical services at 10 hospitals in the Cleveland Clinic Health System from 2017 to 2020. We identified major bleeding according to the International Society on Thrombosis and Hemostasis criteria, using a combination of diagnostic codes and laboratory values, and confirmed events with chart review. We fit a LASSO logistic regression model in the training set and compared the discrimination and calibration of our model and IMPROVE in the validation set. RESULTS: Among 46,314 admissions, 268 (0.58%) had a major bleed. The final RAM included 16 risk factors, of which prior bleeding (OR = 4.83), peptic ulcer (OR = 3.82), history of sepsis (OR = 3.26), and steroid use (OR = 2.59) were the strongest. The Cleveland Clinic Bleeding Model (CCBM) had better discrimination than IMPROVE (AUC = 0.85 vs. 0.70, p < .001) and, at equivalent sensitivity (52%), categorized fewer patients as high-risk (7.2% vs. 11.8%, p < .001). Calibration was adequate (Brier score = 0.0057). CONCLUSION: Using a large population of medical inpatients with verified major bleeding events, we developed and internally validated a RAM for major bleeding whose performance surpassed the IMPROVE model.

A Novel Risk Assessment Model Predicts Major Bleeding Risk at Admission in Medical Inpatients.

Background: Venous thromboembolism (VTE) is the leading cause of preventable hospital death in the US. Guidelines from the American College of Chest Physicians and American Society for Hematology recommend providing pharmacological VTE prophylaxis to acutely or critically ill medical patients at acceptable bleeding risk, but there is currently only one validated risk assessment model (RAM) for estimating bleeding risk. We developed a RAM using risk factors at admission and compared it with the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model. Methods: A total of 46,314 medical patients admitted to a Cleveland Clinic Health System hospital from 2017-2020 were included. Data were split into training (70%) and validation (30%) sets with equivalent bleeding event rates in each set. Potential risk factors for major bleeding were identified from the IMPROVE model and literature review. Penalized logistic regression using LASSO was performed on the training set to select and regularize important risk factors for the final model. The validation set was used to assess model calibration and discrimination and compare performance with IMPROVE. Bleeding events and risk factors were confirmed through chart review. Results: The incidence of major in-hospital bleeding was 0.58%. Active peptic ulcer (OR = 5.90), prior bleeding (OR = 4.24), and history of sepsis (OR = 3.29) were the strongest independent risk factors. Other risk factors included age, male sex, decreased platelet count, increased INR, increased PTT, decreased GFR, ICU admission, CVC or PICC placement, active cancer, coagulopathy, and in-hospital antiplatelet drug, steroid, or SSRI use. In the validation set, the Cleveland Clinic Bleeding Model (CCBM) had better discrimination than IMPROVE (0.86 vs. 0.72, p < .001) and, at equivalent sensitivity (54%), categorized fewer patients as high-risk (6.8% vs. 12.1%, p < .001). Conclusions: From a large population of medical inpatients, we developed and validated a RAM to accurately predict bleeding risk at admission. The CCBM may be used in conjunction with VTE risk calculators to decide between mechanical and pharmacological prophylaxis for at-risk patients.