Faculty Bibliography

OBJECTIVE/UNASSIGNED:Timely use of sodium-glucose cotransporter-2 inhibitors (SGLT2is) is vital for managing type 2 diabetes (T2DM) and preventing cardiovascular and renal complications. However, socioeconomic barriers and prescribing inertia may disproportionately affect disadvantaged populations. We examined racial and ethnic differences in the initiation of SGLT2i among Medicaid patients with T2DM. METHODS/UNASSIGNED:Adult participants 18-64 with T2DM and prescribed with metformin were drawn from MarketScan Multistate Medicaid Database (2015-2022) for this retrospective cohort study. We used a Cox proportional hazards model, adjusted for age, sex, type of Medicaid coverage, and comorbidities, to assess time to SGLT2i initiation. RESULTS/UNASSIGNED:Among 13 744 Medicaid patients, non-Hispanic Black patients had an 18% lower rate of SGLT2i initiation compared with non-Hispanic White patients (hazard ratio [HR] = 0.82; 95%CI: 0.75-0.90). This disparity was most pronounced among patients without pre-existing atherosclerotic cardiovascular disease, heart failure, or chronic kidney disease (HR = 0.79; 95%CI: 0.71-0.87). No significant racial/ethnic differences were observed among patients with these conditions. CONCLUSIONS/UNASSIGNED:Significant delays in SGLT2i initiation among Black Medicaid patients-particularly in early stage of diabetes-may increase their risk for long-term complications. Addressing structural barriers through targeted interventions is essential to promote equity in diabetes care.

OBJECTIVE/UNASSIGNED:Timely use of sodium-glucose cotransporter-2 inhibitors (SGLT2is) is vital for managing type 2 diabetes (T2DM) and preventing cardiovascular and renal complications. However, socioeconomic barriers and prescribing inertia may disproportionately affect disadvantaged populations. We examined racial and ethnic differences in the initiation of SGLT2i among Medicaid patients with T2DM. METHODS/UNASSIGNED:Adult participants 18-64 with T2DM and prescribed with metformin were drawn from MarketScan Multistate Medicaid Database (2015-2022) for this retrospective cohort study. We used a Cox proportional hazards model, adjusted for age, sex, type of Medicaid coverage, and comorbidities, to assess time to SGLT2i initiation. RESULTS/UNASSIGNED:Among 13 744 Medicaid patients, non-Hispanic Black patients had an 18% lower rate of SGLT2i initiation compared with non-Hispanic White patients (hazard ratio [HR] = 0.82; 95%CI: 0.75-0.90). This disparity was most pronounced among patients without pre-existing atherosclerotic cardiovascular disease, heart failure, or chronic kidney disease (HR = 0.79; 95%CI: 0.71-0.87). No significant racial/ethnic differences were observed among patients with these conditions. CONCLUSIONS/UNASSIGNED:Significant delays in SGLT2i initiation among Black Medicaid patients-particularly in early stage of diabetes-may increase their risk for long-term complications. Addressing structural barriers through targeted interventions is essential to promote equity in diabetes care.

OBJECTIVE:Using AI algorithms can exacerbate health disparities if care or resources are allocated away from underserved populations. We evaluated an algorithm for its potential to worsen health disparities across different clinical use cases. MATERIALS AND METHODS/METHODS:This was a retrospective study of patients with heart failure (HF) at an academic health system using an algorithm that predicts pharmacy fill nonadherence to evidence-based HF medications. We compared prediction performance metrics (accuracy, false positive rate, false negative rate), using rate-ratios (RRs), between subgroups with and without known HF care disparities: below vs above median neighborhood-level socioeconomic status (nSES) and Black vs White race. Results were then applied to 3 hypothetical clinical use cases. RESULTS:Among 34 697 patients (13% Black, 10% Hispanic, 65% White), algorithm accuracy was similar across nSES and racial subgroups. The algorithm assigned more false positives for medication nonadherence among low vs high nSES (RR [95%CI] 1.50 [1.44-1.56]) and Black vs White (2.05 [1.92-2.19]) subgroups. The algorithm also assigned fewer false negatives (0.63 [0.59-0.67]) to Black vs White subgroups. When applied to 3 hypothetical use cases, worsening of existing disparities was pertinent for clinical applications where false positives could be particularly harmful (e.g, if predictions of nonadherence prompted lower treatment priority). DISCUSSION/CONCLUSIONS:Although accuracy was similar across demographic groups, differences in false positive and false negative rates revealed that the same prediction may worsen disparities in some use cases, but not others. CONCLUSION/CONCLUSIONS:Evaluation of predictions in the context of clinical use is essential to avoid unintentionally worsening inequities.

OBJECTIVE:We compared health care spending and utilization associated with semaglutide relative to bariatric surgery in patients with obesity and type 2 diabetes (T2D). METHODS:Using MarketScan insurance claims of patients with BMI ≥ 35 and T2D from 2016 to 2021, we examined associations between choice of semaglutide, sleeve gastrectomy, or gastric bypass; 3-year health care spending (out-of-pocket [OOP] and total); and clinical outcomes (ED visits, hospital admissions, and major adverse cardiovascular events [MACE]). Analyses were adjusted using generalized linear models, inverse probability weighting, and instrumental variables. RESULTS:Among 6748 patients (2797 semaglutide, 2300 sleeve gastrectomy, 1651 gastric bypass), bariatric surgery patients had higher BMI and more comorbidities. In IPTW-adjusted analysis, semaglutide was associated with the highest 3-year OOP costs ($7752 vs. $5980 [sleeve gastrectomy] vs. $6591 [gastric bypass], p < 0.001), but total spending was not statistically different across the groups. Relative to semaglutide, the gastric bypass group showed higher observed ED visits (hazard ratio relative to semaglutide [95% CI]: 1.36 [1.28-1.45]) and inpatient admissions (1.25 [1.13-1.37]) and fewer MACE (0.71 [0.59-0.88]). Sleeve gastrectomy was associated with fewer long-term admissions (0.79 [0.72-0.86]) and MACE (0.79 [0.66-0.93]). CONCLUSIONS:For patients with T2D and obesity, compared with semaglutide, bariatric surgery is associated with lower OOP spending and similar total spending at 3 years, as well as lower long-term MACE rates.

BACKGROUND:Prior authorizations could hinder the filling of life-saving heart failure (HF) medications, such as angiotensin receptor neprilysin inhibitors (ARNIs) and sodium glucose cotransporter 2 inhibitors (SGLT2is). OBJECTIVES/OBJECTIVE:The aim of the study was to determine whether prior authorizations were associated with delayed or decreased filling for ARNI and SGLT2i. METHODS:This was a retrospective cohort study using electronic health record, pharmacy fill, and neighborhood-level data from a large, academic health system. We included patients with HF and a new prescription for ARNI or SGLT2i between April 1, 2021, and April 30, 2023, and assessed for presence of prior authorization requirement. Outcomes included days to first fill and never filling the prescription. Analyses were conducted using inverse probability weighting methods. RESULTS:Among 2,183 patients, 12.2% (152/1,243) and 14.3% (165/1,150) had a prior authorization requirement for ARNI or SGLT2i, respectively. Patients requiring prior authorization tended to be younger, identify as non-Hispanic Black or Hispanic, have non-Medicare insurance, and have fewer comorbidities. In weighted models, patients requiring prior authorization took 3.03 (95% CI: 2.16-4.25) times longer to fill ARNI, 6.75 (95% CI: 4.44-10.3) times longer to fill SGLT2i, and were 2.23 (95% CI: 1.37-3.65) times more likely to never fill SGLT2i prescriptions (all P < 0.001). CONCLUSIONS:Prior authorization requirements were more common for patients identifying as Black or Hispanic and were associated with decreased and delayed filling of ARNI and SGLT2i. Our findings highlight an important barrier to mortality-reducing, guideline-recommended medications for HF.

IMPORTANCE/OBJECTIVE:Patients with poor glycaemic control have a high risk for major cardiovascular events. Improving glycaemic monitoring in patients with diabetes can improve morbidity and mortality. OBJECTIVE:To assess the effectiveness of a patient portal message in prompting patients with poorly controlled diabetes without a recent glycated haemoglobin (HbA1c) result to have their HbA1c repeated. DESIGN/METHODS:A pragmatic, randomised clinical trial. SETTING/METHODS:A large academic health system consisting of over 350 ambulatory practices. PARTICIPANTS/METHODS:Patients who had an HbA1c greater than 10% who had not had a repeat HbA1c in the prior 6 months. EXPOSURES/METHODS:A single electronic health record (EHR)-based patient portal message to prompt patients to have a repeat HbA1c test versus usual care. MAIN OUTCOMES/RESULTS:The primary outcome was a follow-up HbA1c test result within 90 days of randomisation. RESULTS:The study included 2573 patients with a mean (SD) HbA1c of 11.2%. Among 1317 patients in the intervention group, 24.2% had follow-up HbA1c tests completed within 90 days, versus 21.1% of 1256 patients in the control group (p=0.07). Patients in the intervention group were more likely to log into the patient portal within 60 days as compared with the control group (61.2% vs 52.3%, p<0.001). CONCLUSIONS:Among patients with poorly controlled diabetes and no recent HbA1c result, a brief patient portal message did not significantly increase follow-up testing but did increase patient engagement with the patient portal. Automated patient messages could be considered as a part of multipronged efforts to involve patients in their diabetes care.

OBJECTIVE:While timely interventions can improve medication adherence, it is challenging to identify which patients are at risk of nonadherence at point-of-care. We aim to develop and validate flexible machine learning (ML) models to predict a continuous measure of adherence to guideline-directed medication therapies (GDMTs) for heart failure (HF). MATERIALS AND METHODS/METHODS:We utilized a large electronic health record (EHR) cohort of 34,697 HF patients seen at NYU Langone Health with an active prescription for ≥1 GDMT between April 01, 2021 and October 31, 2022. The outcome was adherence to GDMT measured as proportion of days covered (PDC) at 6 months following a clinical encounter. Over 120 predictors included patient-, therapy-, healthcare-, and neighborhood-level factors guided by the World Health Organization's model of barriers to adherence. We compared performance of several ML models and their ensemble (superlearner) for predicting PDC with traditional regression model (OLS) using mean absolute error (MAE) averaged across 10-fold cross-validation, % increase in MAE relative to superlearner, and predictive-difference across deciles of predicted PDC. RESULTS:Superlearner, a flexible nonparametric prediction approach, demonstrated superior prediction performance. Superlearner and quantile random forest had the lowest MAE (mean [95% CI] = 18.9% [18.7%-19.1%] for both), followed by MAEs for quantile neural network (19.5% [19.3%-19.7%]) and kernel support vector regression (19.8% [19.6%-20.0%]). Gradient boosted trees and OLS were the 2 worst performing models with 17% and 14% higher MAEs, respectively, relative to superlearner. Superlearner demonstrated improved predictive difference. CONCLUSION/CONCLUSIONS:This development phase study suggests potential of linked EHR-pharmacy data and ML to identify HF patients who will benefit from medication adherence interventions. DISCUSSION/CONCLUSIONS:Fairness evaluation and external validation are needed prior to clinical integration.