Faculty Bibliography

BACKGROUND/UNASSIGNED:Despite the exponential growth in telemedicine visits in clinical practice due to the COVID-19 pandemic, it remains unknown if telemedicine visits achieved similar adherence to prescribed medications as in-person office visits for patients with heart failure. OBJECTIVE/UNASSIGNED:Our study examined the association between telemedicine visits (vs in-person visits) and medication adherence in patients with heart failure. METHODS/UNASSIGNED:This was a retrospective cross-sectional study of adult patients with a diagnosis of heart failure or an ejection fraction of ≤40% using data between April 1 and October 1, 2020. This period was used because New York University approved telemedicine visits for both established and new patients by April 1, 2020. The time zero window was between April 1 and October 1, 2020, then each identified patient was monitored for up to 180 days. Medication adherence was measured by the mean proportion of days covered (PDC) within 180 days, and categorized as adherent if the PDC was ≥0.8. Patients were included in the telemedicine exposure group or in-person group if all encounters were video visits or in-person office visits, respectively. Poisson regression and logistic regression models were used for the analyses. RESULTS/UNASSIGNED:A total of 9521 individuals were included in this analysis (telemedicine visits only: n=830 in-person office visits only: n=8691). Overall, the mean age was 76.7 (SD 12.4) years. Most of the patients were White (n=6996, 73.5%), followed by Black (n=1060, 11.1%) and Asian (n=290, 3%). Over half of the patients were male (n=5383, 56.5%) and over half were married or living with partners (n=4914, 51.6%). Most patients' health insurance was covered by Medicare (n=7163, 75.2%), followed by commercial insurance (n=1687, 17.7%) and Medicaid (n=639, 6.7%). Overall, the average PDC was 0.81 (SD 0.286) and 71.3% (6793/9521) of patients had a PDC≥0.8. There was no significant difference in mean PDC between the telemedicine and in-person office groups (mean 0.794, SD 0.294 vs mean 0.812, SD 0.285) with a rate ratio of 0.99 (95% CI 0.96-1.02; P=.09). Similarly, there was no significant difference in adherence rates between the telemedicine and in-person office groups (573/830, 69% vs 6220/8691, 71.6%), with an odds ratio of 0.94 (95% CI 0.81-1.11; P=.12). The conclusion remained the same after adjusting for covariates (eg, age, sex, race, marriage, language, and insurance). CONCLUSIONS/UNASSIGNED:We found similar rates of medication adherence among patients with heart failure who were being seen via telemedicine or in-person visits. Our findings are important for clinical practice because we provide real-world evidence that telemedicine can be an approach for outpatient visits for patients with heart failure. As telemedicine is more convenient and avoids transportation issues, it may be an alternative way to maintain the same medication adherence as in-person visits for patients with heart failure.

OBJECTIVE:To identify primary care structures and processes that have the highest and lowest impact on chronic disease management and screening and prevention outcomes as well as to assess the feasibility of implementing these structures and processes into practice. DESIGN/METHODS:A two-round Delphi study was conducted to establish consensus on the impact and feasibility of 258 primary care structures and processes. PARTICIPANTS/METHODS:29 primary care providers, health system leaders and health services researchers in the USA. OUTCOMES/RESULTS:Primary outcomes were (1) consensus on the impact of each structure and process on chronic disease management and screening and prevention outcomes, separately and (2) consensus on feasibility of implementation by primary care practices. RESULTS:Consensus on high impact and feasibility of implementation was reached on four items for chronic disease management: 'Providers use motivational interviewing to help patients set goals', 'Practice has designated staff to manage patient panel', 'Practice has onsite providers or staff that speak the most dominant, non-English language spoken by patients' and 'Practice includes mental health providers and/or behavioural health specialists in care team' and seven items for screening and prevention: 'Practice utilizes standing protocols and orders', 'Practice generates reports to alert clinicians to missed targets and to identify gaps in care, such as overdue visits, needed vaccinations, screenings or other preventive services', 'Practice has designated staff to manage patient panel', 'Practice sets performance goals and uses benchmarking to track quality of care', 'Practice uses performance feedback to identify practice-specific areas of improvement', 'Practice builds quality improvement activities into practice operations' and 'Pre-visit planning data are reviewed during daily huddles'. Only 'Practice has designated staff to manage patient panel' appeared on both lists. CONCLUSION/CONCLUSIONS:Findings suggest that practices need to focus on implementing mostly distinct, rather than common, structures and processes to optimise chronic disease and preventive care.

Electronic health record (EHR)-embedded tools are known to improve prescribing of guideline-directed medical therapy (GDMT) for patients with heart failure. However, physicians may perceive EHR tools to be unhelpful, and may be therefore hesitant to implement these in their practice. We surveyed cardiologists about two effective EHR-tools to improve heart failure care, and they perceived the EHR tools to be easy to use, helpful, and improve the overall management of their patients with heart failure.

IMPORTANCE/UNASSIGNED:Obesity is associated with increased severity of COVID-19. Whether obesity is associated with an increased risk of post-acute sequelae of SARS-CoV-2 infection (PASC) among pediatric populations, independent of its association with acute infection severity, is unclear. OBJECTIVE/UNASSIGNED:To quantify the association of body mass index (BMI) status before SARS-CoV-2 infection with pediatric PASC risk, controlling for acute infection severity. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This retrospective cohort study occurred at 26 US children's hospitals from March 2020 to May 2023 with a minimum follow-up of 179 days. Eligible participants included children and young adults aged 5 to 20 years with SARS-CoV-2 infection. Data analysis was conducted from October 2023 to January 2024. EXPOSURES/UNASSIGNED:BMI status assessed within 18 months before infection; the measure closest to the index date was selected. The BMI categories included healthy weight (≥5th to <85th percentile for those aged 5-19 years or ≥18.5 to <25 for those aged >19 years), overweight (≥85th to <95th percentile for those aged 5-19 years or ≥25 to <30 for for those aged >19 years), obesity (≥95th percentile to <120% of the 95th percentile for for those aged 5-19 years or ≥30 to <40 for those aged >19 years), and severe obesity (≥120% of the 95th percentile for those aged 5-19 years or ≥40 for those aged >19 years). MAIN OUTCOMES AND MEASURES/UNASSIGNED:To identify PASC, a diagnostic code specific for post-COVID-19 conditions was used and a second approach used clusters of symptoms and conditions that constitute the PASC phenotype. Relative risk (RR) for the association of BMI with PASC was quantified by Poisson regression models, adjusting for sociodemographic, acute COVID severity, and other clinical factors. RESULTS/UNASSIGNED:A total of 172 136 participants (mean [SD] age at BMI assessment 12.6 [4.4] years; mean [SD] age at cohort entry, 13.1 [4.4] years; 90 187 female [52.4%]) were included. Compared with participants with healthy weight, those with obesity had a 25.4% increased risk of PASC (RR, 1.25; 95% CI, 1.06-1.48) and those with severe obesity had a 42.1% increased risk of PASC (RR, 1.42; 95% CI, 1.25-1.61) when identified using the diagnostic code. Compared with those with healthy weight, there was an increased risk for any occurrences of PASC symptoms and conditions among those with obesity (RR, 1.11; 95% CI, 1.06-1.15) and severe obesity (RR, 1.17; 95% CI, 1.14-1.21), and the association held when assessing total incident occurrences among those with overweight (RR, 1.05; 95% CI, 1.00-1.11), obesity (RR, 1.13; 95% CI, 1.09-1.19), and severe obesity (RR, 1.18; 95% CI, 1.14-1.22). CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this cohort study, elevated BMI was associated with a significantly increased PASC risk in a dose-dependent manner, highlighting the need for targeted care to prevent chronic conditions in at-risk children and young adults.

Pediatric Long COVID has been associated with a wide variety of symptoms, conditions, and organ systems, but distinct clinical presentations, or subphenotypes, are still being elucidated. In this exploratory analysis, we identified a cohort of pediatric (age <21) patients with evidence of Long COVID and no pre-existing complex chronic conditions using electronic health record data from 38 institutions and used an unsupervised machine learning-based approach to identify subphenotypes. Our method, an extension of the Phe2Vec algorithm, uses tens of thousands of clinical concepts from multiple domains to represent patients' clinical histories to then identify groups of patients with similar presentations. The results indicate that cardiorespiratory presentations are most common (present in 54% of patients) followed by subphenotypes marked (in decreasing order of frequency) by musculoskeletal pain, neuropsychiatric conditions, gastrointestinal symptoms, headache, and fatigue.

RATIONALE/BACKGROUND:Cardiac monitoring has often been identified as an area of overutilization and remains a limited resource in many hospitals. With the aim of reducing telemetry overuse, we added clinical decision support to our health system's telemetry order with guidance on appropriate indications for monitoring. The new order requires selection of an appropriate clinical indication. AIMS AND OBJECTIVES/OBJECTIVE:In this study, we aimed to understand provider engagement with this tool by assessing concordance between selected indications within the order and the clinical presence of those conditions as documented within the patient chart. METHODS:We randomly selected 100 telemetry orders from July to October 2022 across four different hospitals at NYU Langone Health. Two independent, blinded reviewers used a structured protocol to identify documentation of actual indications for telemetry in each selected chart. We calculated the rate of concordance between indications selected in the order and indications that were determined to be clinically present on chart review. RESULTS:There were 30,839 telemetry orders placed during the study timeframe. Overall concordance between the selection within the order and the actual indication was 48% (95% confidence interval [CI], 38.21%-57.79%). We observed especially low concordance rates for vague indications, such as 'Other', and for 'Confirmed Stroke', which was the only indication allowing for indefinite telemetry. CONCLUSION/CONCLUSIONS:The overall low concordance suggests a disconnect between the support tool and clinical practice. Providers are more likely to select an indication that reduces downstream work regardless of a patient's true clinical indication.

BACKGROUND:This study aimed to examine the prevalence of inappropriate tight glycemic control in older adults with type 2 diabetes and other chronic conditions in New York City, and to identify factors associated with this practice. METHODS:We conducted a retrospective cohort study using the INSIGHT Clinical Research Network. The study population included 11,728 and 15,196 older adults in New York City (age ≥ 75 years) with a diagnosis of type 2 diabetes, and at least one other chronic medical condition, in 2017 and 2022, respectively. The main outcome of interest was inappropriate tight glycemic control, defined as HbA1c <7.0% (<53 mmol/mol) with prescription of at least one high-risk agent (insulin or insulin secretagogue). RESULTS:The proportion of older adults with inappropriate tight glycemic control decreased by nearly 19% over a five-year period (19.4% in 2017 to 15.8% in 2022). There was a significant decrease in insulin (27.8% in 2017; 24.3% in 2022) and sulfonylurea (29.4% in 2017; 21.7% in 2022) medication prescription, and increase in use of GLP-1 agonists (1.8% in 2017; 11.4% in 2022) and SGLT-2 inhibitors (5.8% in 2017; 25.1% in 2022), among the total population. Factors associated with inappropriate tight glycemic control in 2022 included history of heart failure (adjusted odds ratio [aOR] 1.38), chronic kidney disease ([aOR] 1.93), colorectal cancer ([aOR] 1.38), acute myocardial infarction ([aOR] 1.28), "other" ([aOR] 0.72) or "unknown" ([aOR] 0.72) race, and a point increase in BMI ([aOR] 0.98). CONCLUSIONS:We found an encouraging trend toward less use of high-risk medication strategies for older adults with type 2 diabetes and multiple chronic conditions. However, one in six patients in 2022 still had inappropriate tight glycemic control, indicating a need for continued efforts to optimize diabetes management in this population.

BACKGROUND:Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce heart failure (HF) hospitalizations, recurrent cardiovascular events, and chronic kidney disease (CKD) progression, and thus constitute a Class 1a recommendation in people with diabetes and atherosclerotic cardiovascular disease, HF, or CKD and in people with severe albuminuria or HF, regardless of diabetes status. OBJECTIVES/OBJECTIVE:The purpose of this study was to comprehensibly characterize the rate of SGLT2 inhibitor prescriptions among people with a Class 1a recommendation for SGLT2 inhibitor use. METHODS:Among 3,189,827 adults from 28 U.S. health systems within Optum Labs Data Warehouse between April 1, 2022, and March 31, 2023, we assessed SGLT2 inhibitor prescription rates, stratified by presence of diabetes and Class 1a recommendation. RESULTS:Among 716,387 adults with diabetes, 63.4% had a Class 1a recommendation for SGLT2 inhibitor therapy. There was little difference by Class 1a recommendation status (present: 11.9%; 95% CI: 11.9%-12.0% vs absent: 11.4%; 95% CI: 11.3%-11.6%; standardized mean difference: 1.3%). Among 2,473,440 adults without diabetes, 6.2% had a Class 1a recommendation for SGLT2 inhibitor therapy, and 3.1% (3.0%-3.2%) of those received a prescription. Internists/family practitioners initiated SGLT2 inhibitor prescriptions most commonly among people with diabetes, whereas specialists initiated SGLT2 inhibitor prescriptions most commonly among people without diabetes. No health system had >25% SGLT2 inhibitor prescription rate among people with a Class 1a recommendation. Health systems with higher proportions of patients with commercial insurance and lower proportions with Medicare had higher SGLT2 inhibitor prescription rates. CONCLUSIONS:In this analysis of U.S. data from 2022 to 2023, SGLT2 inhibitor prescription among people with a Class 1a recommendation is low. Interventions are needed to increase uptake of guideline-recommended SGLT2 inhibitor use.