Faculty Bibliography

BACKGROUND:Despite the central role of experiential learning in residency training, the actual clinical experiences residents participate in are not well characterized. A better understanding of the type, volume, and variation in residents' clinical experiences is essential to support precision medical education strategies. OBJECTIVE:We sought to characterize the entirety of the clinical experiences had by individual internal medicine residents throughout their time in training. METHOD/METHODS:We evaluated the clinical experiences of medicine residents (n = 51) who completed training at NYU Grossman School of Medicine's Brooklyn campus between 2020 and 2023. Residents' inpatient and outpatient experiences were identified using notes written, orders placed, and care team sign-ins; principal ICD-10 codes for each encounter were converted into medical content categories using a previously described crosswalk tool. RESULTS:Of 152,426 clinical encounters with available ICD-10 codes, 132,284 were mapped to medical content categories (94.5% capture). Residents' clinical experiences were particularly enriched in infectious and cardiovascular disease; most had very little exposure to allergy, dermatology, oncology, or rheumatology. Some trainees saw twice as many cases in a given content area as did others. There was little concordance between actual frequency of clinical experience and expected content frequency on the ABIM certification exam. CONCLUSIONS:Individual residents' clinical experiences in training vary widely, both in number and in type. Characterizing these experiences paves the way for exploration of the relationships between clinical exposure and educational outcomes, and for the implementation of precision education strategies that could fill residents' experiential gaps and complement strengths with targeted educational interventions.

BACKGROUND:We examined effects of single-nucleotide variants (SNVs) of IL1RN, the gene encoding the anti-inflammatory interleukin 1 receptor antagonist (IL-1Ra), on the cytokine release syndrome (CRS) and mortality in patients with acute severe respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS:IL1RN CTA haplotypes formed from 3 SNVs (rs419598, rs315952, rs9005) and the individual SNVs were assessed for association with laboratory markers of inflammation and mortality. We studied 2589 patients hospitalized with SARS-CoV-2 between March 2020 and March 2021. RESULTS:Mortality was 15.3% and lower in women than men (13.1% vs 17.3%, P = .0003). Carriers of the CTA-1/2 IL1RN haplotypes exhibited decreased inflammatory markers and increased plasma IL-1Ra. Evaluation of the individual SNVs of the IL1RN, carriers of the rs419598 C/C SNV exhibited significantly reduced inflammatory biomarker levels and numerically lower mortality compared to the C/T-T/T genotype (10.0% vs 17.8%, P = .052) in men, with the most pronounced association observed in male patients ≤74 years old, whose mortality was reduced by 80% (3.1% vs 14.0%, P = .030). CONCLUSIONS:The IL1RN haplotype CTA and C/C variant of rs419598 are associated with attenuation of the CRS and decreased mortality in men with acute SARS-CoV-2 infection. The data suggest that the IL1RN pathway modulates the severity of coronavirus disease 2019 (COVID-19) via endogenous anti-inflammatory mechanisms.

The recombinant SARS-CoV-2 Omicron XBB.1.5 variant was first detected in New York City (NYC) and rapidly became the predominant variant in the area by early 2023. The increased occurrence of circulating variants within the SARS-CoV-2 XBB-sublineage prompted the modification of COVID-19 mRNA vaccines by Moderna and Pfizer-BioNTech. This update, implemented in mid-September 2023, involved the incorporation of a monovalent XBB.1.5 component. Considering that NYC probably played a central role in the emergence of the XBB.1.5 variant, we conducted phylogeographic analysis to investigate the emergence and spread of this variant in the metropolitan area. Our analysis confirms that XBB.1.5 emerged within or near the NYC area and indicates that XBB.1.5 had a diffusion velocity similar to that of the variant Alpha in the same study area. Additionally, the analysis of 2,392 genomes collected in the context of the genomic surveillance program at NYU Langone Health system showed that there was no increased proportion of XBB.1.5, relative to all cocirculating variants, in the boosted compared to unvaccinated individuals. This study provides a comprehensive description of the emergence and dissemination of XBB.1.5.

The COVID-19 pandemic has boosted digital health utilization, raising concerns about increased physicians' after-hours clinical work ("work-outside-work"). The surge in patients' digital messages and additional time spent on work-outside-work by telemedicine providers underscores the need to evaluate the connection between digital health utilization and physicians' after-hours commitments. We examined the impact on physicians' workload from two types of digital demands - patients' messages requesting medical advice (PMARs) sent to physicians' inbox (inbasket), and telemedicine. Our study included 1716 ambulatory-care physicians in New York City regularly practicing between November 2022 and March 2023. Regression analyses assessed primary and interaction effects of (PMARs) and telemedicine on work-outside-work. The study revealed a significant effect of PMARs on physicians' work-outside-work and that this relationship is moderated by physicians' specialties. Non-primary care physicians or specialists experienced a more pronounced effect than their primary care peers. Analysis of their telemedicine load revealed that primary care physicians received fewer PMARs and spent less time in work-outside-work with more telemedicine. Specialists faced increased PMARs and did more work-outside-work as telemedicine visits increased which could be due to the difference in patient panels. Reducing PMAR volumes and efficient inbasket management strategies needed to reduce physicians' work-outside-work. Policymakers need to be cognizant of potential disruptions in physicians carefully balanced workload caused by the digital health services.

BACKGROUND:High rates of vaccination and natural infection drive immunity and redirect selective viral adaptation. Updated boosters are installed to cope with drifted viruses, yet data on adaptive evolution under increasing immune pressure in a real-world situation are lacking. METHODS:Cross-sectional study to characterise SARS-CoV-2 mutational dynamics and selective adaptation over >1 year in relation to vaccine status, viral phylogenetics, and associated clinical and demographic variables. FINDINGS/RESULTS:The study of >5400 SARS-CoV-2 infections between July 2021 and August 2022 in metropolitan New York portrayed the evolutionary transition from Delta to Omicron BA.1-BA.5 variants. Booster vaccinations were implemented during the Delta wave, yet booster breakthrough infections and SARS-CoV-2 re-infections were almost exclusive to Omicron. In adjusted logistic regression analyses, BA.1, BA.2, and BA.5 had a significant growth advantage over co-occurring lineages in the boosted population, unlike BA.2.12.1 or BA.4. Selection pressure by booster shots translated into diffuse adaptive evolution in Delta spike, contrasting with strong, receptor-binding motif-focused adaptive evolution in BA.2-BA.5 spike (Fisher Exact tests; non-synonymous/synonymous mutation rates per site). Convergent evolution has become common in Omicron, engaging spike positions crucial for immune escape, receptor binding, or cleavage. INTERPRETATION/CONCLUSIONS:Booster shots are required to cope with gaps in immunity. Their discriminative immune pressure contributes to their effectiveness but also requires monitoring of selective viral adaptation processes. Omicron BA.2 and BA.5 had a selective advantage under booster vaccination pressure, contributing to the evolution of BA.2 and BA.5 sublineages and recombinant forms that predominate in 2023. FUNDING/BACKGROUND:The study was supported by NYU institutional funds and partly by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center.

OBJECTIVE/UNASSIGNED:, the gene encoding the anti-inflammatory IL-1 receptor antagonist (IL-1Ra), on the cytokine release syndrome and mortality. METHODS/UNASSIGNED:gene were assessed for association with laboratory markers of the cytokine release syndrome (CRS) and mortality. RESULTS/UNASSIGNED:rs419598 CC SNV exhibited lower inflammatory biomarker levels, and was associated with reduced mortality compared to the CT/TT genotype in men (OR 0.49 (0.23 - 1.00); 0.052), with the most pronounced effect observed between the ages of 55-74 [5.5% vs. 18.4%, p<0.001]. CONCLUSION/UNASSIGNED:modulates the COVID-19 cytokine release syndrome via endogenous " anti-inflammatory" mechanisms. SIGNIFICANCE STATEMENT/UNASSIGNED:merits further evaluation in severe SARS-CoV-2 infection.

Competency-based medical education (CBME) is an outcomes-based approach to education and assessment that focuses on what competencies trainees need to learn in order to provide effective patient care. Despite this goal of providing quality patient care, trainees rarely receive measures of their clinical performance. This is problematic because defining a trainee's learning progression requires measuring their clinical performance. Traditional clinical performance measures (CPMs) are often met with skepticism from trainees given their poor individual-level attribution. Resident-sensitive quality measures (RSQMs) are attributable to individuals, but lack the expeditiousness needed to deliver timely feedback and can be difficult to automate at scale across programs. In this eye opener, the authors present a conceptual framework for a new type of measure - TRainee Attributable & Automatable Care Evaluations in Real-time (TRACERs) - attuned to both automation and trainee attribution as the next evolutionary step in linking education to patient care. TRACERs have five defining characteristics: meaningful (for patient care and trainees), attributable (sufficiently to the trainee of interest), automatable (minimal human input once fully implemented), scalable (across electronic health records [EHRs] and training environments), and real-time (amenable to formative educational feedback loops). Ideally, TRACERs optimize all five characteristics to the greatest degree possible. TRACERs are uniquely focused on measures of clinical performance that are captured in the EHR, whether routinely collected or generated using sophisticated analytics, and are intended to complement (not replace) other sources of assessment data. TRACERs have the potential to contribute to a national system of high-density, trainee-attributable, patient-centered outcome measures.