Faculty Bibliography

OBJECTIVE:To evaluate the risks of incident dyslipidemia and abnormal body mass index (BMI) during the 28-179-day post-acute phase after documented SARS-CoV-2 infection in a large pediatric sample. STUDY DESIGN/METHODS:A retrospective cohort study using the RECOVER pediatric electronic health record (EHR) datasets from 25 US children's hospitals and health institutions, from March 2020 to September 2023. This study included 384,289 COVID-19-positive patients aged 0-21 years for dyslipidemia analyses and 285,559 aged 2-21 years for BMI analyses, each with at least 6 months of follow-up. COVID-19-negative controls included 1,080,413 and 817,315 patients, respectively. SARS-CoV-2 infection was defined by a positive polymerase-chain-reaction (PCR), antigen, or serologic test; a clinical diagnosis of COVID-19; or a documented diagnosis of post-acute sequelae of SARS-CoV-2 (PASC). Incident dyslipidemia and abnormal BMI were identified using age-specific laboratory or anthropometric thresholds. Adjusted relative risks (aRRs) were estimated using propensity-score-stratified modified Poisson regression with multiple sensitivity analyses. RESULTS:During the post-acute phase, the COVID-19-positive cohort had higher rates of new-onset composite dyslipidemia (aRR 1.24; 95% CI 1.18-1.29) and abnormal BMI (aRR 1.15; 95% CI, 1.12-1.18). Results were robust to sensitivity and stratified analyses. CONCLUSION/CONCLUSIONS:Children and adolescents with documented COVID-19 infection were associated with an increased risk of new-onset dyslipidemia and abnormal BMI during the post-acute phase, highlighting the need for metabolic monitoring after infection.

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.

BACKGROUND:The COVID-19 pandemic had a significant impact on healthcare delivery. Older adults with multimorbidities were at risk of healthcare disruptions for the management of their chronic conditions. OBJECTIVE:To characterize healthcare disruptions during the COVID-19 healthcare shutdown and recovery period (March 7, 2020-October 6, 2020) and their effects on disease management among older adults with multimorbidities who were patients of NYC Health + Hospitals (H + H), the largest municipal safety-net system in the United States. DESIGN/METHODS:Observational. PATIENTS/METHODS:Patients aged 50 + with hypertension or diabetes and at least one other comorbidity, at least one H + H ambulatory visit in the six months before COVID-19 pandemic onset (March 6, 2020), and at least one visit in the post-acute shutdown period (October 7, 2020 to December 31, 2023). MAIN MEASURES/METHODS:We characterized disruption in care (defined as no ambulatory or telehealth visits during the acute shutdown) and estimated the effect of disruption on blood pressure control, hemoglobin A1c (HbA1c), and low-density lipoprotein (LDL) cholesterol using difference-in-differences models. KEY RESULTS/RESULTS:Out of 73,889 individuals in the study population, 12.5% (n = 9,202) received no ambulatory or telehealth care at H + H during the acute shutdown. Low pre-pandemic healthcare utilization, Medicaid insurance, and self-pay were independent predictors of care disruption. In adjusted analyses, the disruption group had a 3.0-percentage point (95% CI: 1.2-4.8) greater decrease in blood pressure control compared to those who received care. Disruption did not have a significant impact on mean HbA1c or LDL. CONCLUSIONS:Care disruption was associated with declines in blood pressure control, which while clinically modest, could impact risk of cardiovascular outcomes if sustained. Disruption did not affect HbA1c or LDL. Telehealth mitigated impacts of the pandemic on care disruption and subsequent disease management. Targeted outreach to those at risk of care disruption is needed during future crises.

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.

BACKGROUND:Medication nonadherence is a common issue among patients with hypertension. Healthcare professionals often overlook medication nonadherence due to limited tools, time constraints, and competing demands. Integrating pharmacy medication fill data into electronic health records (EHRs) presents an opportunity to enhance medication adherence measurement and monitoring in real-time. This study identified facilitators and barriers to addressing adherence to antihypertensive medications by Medical Assistants (MAs), Registered Nurses (RNs), and Primary Care Providers (PCPs) in primary care settings. METHODS:We conducted a qualitative study with, 15 healthcare professionals (5 MAs, 5 RNs, and 5 PCPs) caring for patients with hypertension. Semi-structured interviews, guided by the Consolidated Framework for Implementation Research (CFIR), explored barriers and facilitators related to screening and addressing medication non-adherence during primary care clinical encounters. Thematic analysis and deductive coding were used to analyze the data. RESULTS:Four major themes emerged: motivation, work infrastructure, capability, and opportunity. MAs and PCPs were motivated to discuss medication adherence and build relationships. Capability varied; RNs were confident in their counseling skills based on their training and patient trust, and PCPs described adherence counseling as part of their role, particularly through motivational interviewing. Work infrastructure presented structural hurdles due to RN workflow limitations and MA role constraints. Opportunity to address non-adherence were constrained by tight schedules and competing clinical demands during brief visits. CONCLUSIONS:RNs and PCPs felt capable in their ability to address medication adherence but cited time and competing demands as significant barriers; conversely, MAs reported motivation but were limited by their role. These findings suggest opportunities for effective management of medication adherence in practice settings through a more coordinated strategy across multiple healthcare professionals. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov; NCT05349422; https://clinicaltrials.gov/ct2/show/NCT05349422.

BACKGROUND:Patients with heart failure (HF) often have difficulty obtaining life-saving medications due to coverage barriers, such as prior authorizations and high out-of-pocket costs. To promote better coverage for high value therapies and inform policymakers about cost effectiveness, the American Heart Association/American College of Cardiology/Heart Failure Society of America added Value Statements to HF guidelines. We assessed whether these guidelines influenced Medicare drug coverage policies for 2 life-saving, costly HF medications: angiotensin receptor neprilysin inhibitors (ARNI-guideline "high value") and sodium glucose cotransporter-2 inhibitors (SGLT2i-guideline "intermediate value"). METHODS:We performed an observational study using Medicare prescription drug plan formulary files from April 2020 to April 2023 to separately assess for changes in coverage barriers to ARNI and SGLT2i after Value Statement publication (April 2022), and subsequent Medicare plan online update (October 2022). The primary outcome was the percentage of plans each month with any barrier to drug coverage (prior authorizations, tier ≥3 out-of-pocket cost-sharing, step therapy, or no coverage). Analyses used interrupted time series and difference-in-differences approaches. Difference-in-differences analyses used direct oral anticoagulants as a control due to their comparable cost and use as ARNI and SGLT2i, but without a Value Statement. RESULTS:Among 7396 Medicare drug plans, monthly rates of any coverage barrier ranged from 94.3% to 97.4% for ARNI and 93.2% to 96.6% for SGLT2i. Most barriers were due to tier ≥3 out-of-pocket cost-sharing requirements (ARNI: 94.3%-95.8%; SGLT2i: 93.2%-95.6%). Coverage barriers remained stable in April 2022 and declined slightly in October 2022. In difference-in-differences analyses, the presence of a Value Statement was associated with a ~1 percentage point decline in coverage barriers for both ARNI (difference-in-differences estimate, -1.07% [95% CI, -1.44% to -0.70%]) and SGLT2i (-1.32% [95% CI, -1.63% to -1.00%]). CONCLUSIONS:Coverage barriers to ARNI and SGLT2i were common and changed only slightly after publication of Value Statements in HF guidelines. There is a critical need for robust strategies to improve access to life-saving HF medications.

Despite progress to define primary care practice transformation models, there remain gaps in translating evidence-based guidelines into routine clinical care. Primary care providers (MD, DO, NP, PA) and researchers need tools to assess modifiable factors that improve practice performance to inform practice transformation efforts. We aimed to develop a pragmatic tool for assessing practice-level primary care structures and processes that are associated with better care quality and clinical outcomes. We generated 314 candidate items for the Tool for Advancing Practice Performance (TAPP) using data from a comprehensive literature review, Delphi study, and qualitative interviews with high-performing practices. We used empirical criteria and expert review to eliminate redundancy and improve clarity via removing and retaining items. The retained items were formatted into a survey tool, and we further revised the tool based on feedback elicited from cognitive interviews and pilot testing with primary care providers and staff. The final candidate pool comprised 126 items after refinement and expert review. For the survey tool, we adapted and developed survey questions for each of the 126 items. Eight cognitive interview participants reviewed the tool and provided feedback on its content and language. Based on this feedback, we eliminated 13 items because they were poorly or incorrectly understood by participants, resulting in a 113-item tool. Fifteen participants pilot tested the tool and no additional items were eliminated. The TAPP is a novel, low-burden tool that researchers and primary care providers can use to identify areas for improvement at the practice-level. Practices and health systems could use the TAPP to assess their own performance and identify gaps in their structures and processes, and practice networks and health systems can use the tool to assess structures and processes at individual clinics, track this information over time, and evaluate its relationship to care quality and clinical outcomes.

The effectiveness of COVID-19 vaccination in children and adolescents with prior SARS-CoV-2 infection remains unclear, particularly for Omicron subvariants. We evaluated vaccine effectiveness against reinfection with Omicron BA.1/2, BA.4/5, XBB, and later subvariants among 5- to 17-year-olds using data from the RECOVER initiative, a national electronic health record database covering 37 U.S. pediatric institutions. We emulated target trials by age group and variant period, comparing previously infected participants between January 2022 and August 2023. During the BA.1/2 period, vaccination reduced the risk of reinfection, with effectiveness rates of 62% in children and 65% in adolescents. During the BA.4/5 period, protection effectiveness in children was 57%, whereas no statistically significant protection was observed in adolescents. During the XBB or later period, no significant protection was observed in either group. In summary, COVID-19 vaccination provided protection against reinfection during early and mid-Omicron periods in previously infected pediatric populations, but effectiveness declined for later variants.