Faculty Bibliography

Background/UNASSIGNED:SARS-CoV-2 antigen-based tests are well-calibrated to infectiousness and have a critical role to play in the COVID-19 public health response. We report the development and performance of a unique lateral flow immunoassay (LFA). Methods/UNASSIGNED:Combinations of several monoclonal antibodies targeting multiple antigenic sites on the SARS-CoV-2 nucleocapsid protein (NP) were isolated, evaluated, and chosen for the development of a LFA termed CoV-SCAN (BioMedomics, Inc.). Clinical point-of-care studies in symptomatic and asymptomatic individuals were conducted to evaluate positive predictive agreement (PPA) and negative predictive agreement (NPA) with RT-PCR as comparator. Results/UNASSIGNED:In laboratory testing, CoV-SCAN detected 14 recombinant N-proteins of SARS-CoV-2 variants with sensitivity in the range of 0.2-3.2 ng/mL, and 10 authentic SARS-CoV-2 variants with sensitivity in the range of 1.6-12.5 TCID50/swab. No cross reactivity was observed with other human coronaviruses or other respiratory pathogens. In clinical point-of-care testing on 148 individuals over age 2 with symptoms of ≤5 days, PPA was 87.2% (CI 95: 78.3-94.8%) and NPA was 100% (CI 95: 94.2-100%). In another 884 asymptomatic individuals, PPA was 85.7% (CI 95: 42.1-99.6%) and 99.7% (99.0-99.9%). Overall, CoV-SCAN detected over 97.2% of specimens with CT values <30 and 93.8% of nasal swab specimens with the Omicron variant, even within the first 2 days after symptom onset. Conclusions/UNASSIGNED:The unique construction of CoV-SCAN using two pairs of monoclonal antibodies has resulted in a test with high performance that remains durable across multiple variants in both laboratory and clinical evaluations. CoV-SCAN should identify almost all individuals harboring infectious SARS-CoV-2. Summary/UNASSIGNED:Unique construction of a point-of-care rapid antigen test using two pairs of monoclonal antibodies has led to good performance that remained durable across multiple variants in laboratory and clinical evaluations. Test should identify almost all individuals harboring infectious SARS-CoV-2.

BACKGROUND:Telemedicine as a mode of health care work has grown dramatically during the COVID-19 pandemic; the impact of this transition on clinicians' after-hours electronic health record (EHR)-based clinical and administrative work is unclear. OBJECTIVE:This study assesses the impact of the transition to telemedicine during the COVID-19 pandemic on physicians' EHR-based after-hours workload (ie, "work outside work") at a large academic medical center in New York City. METHODS:We conducted an EHR-based retrospective cohort study of ambulatory care physicians providing telemedicine services before the pandemic, during the acute pandemic, and after the acute pandemic, relating EHR-based after-hours work to telemedicine intensity (ie, percentage of care provided via telemedicine) and clinical load (ie, patient load per provider). RESULTS:A total of 2129 physicians were included in this study. During the acute pandemic, the volume of care provided via telemedicine significantly increased for all physicians, whereas patient volume decreased. When normalized by clinical load (ie, average appointments per day by average clinical days per week), telemedicine intensity was positively associated with work outside work across time periods. This association was strongest after the acute pandemic. CONCLUSIONS:Taking physicians' clinical load into account, physicians who devoted a higher proportion of their clinical time to telemedicine throughout various stages of the pandemic engaged in higher levels of EHR-based after-hours work compared to those who used telemedicine less intensively. This suggests that telemedicine, as currently delivered, may be less efficient than in-person-based care and may increase the after-hours work burden of physicians.

BACKGROUND:The Revised Illness Perception Questionnaire (IPQ-R) measures individuals' unique perceptions of their illness. While psychometric properties of the IPQ-R have been demonstrated in many disease populations, its content validity has not been extensively studied in non-dialysis chronic kidney disease (CKD). Unique features of CKD (e.g., few symptoms in early stages) may impact the measurement of illness perceptions. The purpose of this study was to explore the IPQ-R content validity in a sample of CKD patients. METHODS:Thirty-one participants completed the IPQ-R and were interviewed regarding their subscale scores (timeline, consequences, personal control, treatment control, coherence, cyclical, and emotions). Participants' agreement with their scores was tallied and assessed qualitatively for themes related to the content validity of the measure. RESULTS:Individual participant agreement with their subscale scores averaged 79% (range: 29-100%). Subscale agreement varied: timeline (100%), consequences, coherence, and emotion (83% each), cyclical (75%), personal control (65%), and treatment control (64%). A qualitative exploration of disagreement responses revealed concerns with the relevance and comprehensibility of personal control and treatment control. CONCLUSIONS:Some IPQ-R subscales may pose content validity concerns in the non-dialysis CKD population. Item modification for comprehensibility (personal control) and relevance (treatment control) should be considered. Future studies should explore the impact of a patient's symptom experience on IPQ-R validity, especially in populations like CKD with a higher proportion of asymptomatic patients.

BACKGROUND:In 2021, Delta became the predominant SARS-CoV-2 variant worldwide. While vaccines have effectively prevented COVID-19 hospitalization and death, vaccine breakthrough infections increasingly occurred. The precise role of clinical and genomic determinants in Delta infections is not known, and whether they contributed to increased rates of breakthrough infections compared to unvaccinated controls. METHODS:We studied SARS-CoV-2 variant distribution, dynamics, and adaptive selection over time in relation to vaccine status, phylogenetic relatedness of viruses, full genome mutation profiles, and associated clinical and demographic parameters. FINDINGS/RESULTS:We show a steep and near-complete replacement of circulating variants with Delta between May and August 2021 in metropolitan New York. We observed an increase of the Delta sublineage AY.25 (14% in vaccinated, 7% in unvaccinated), its spike mutation S112L, and AY.44 (8% in vaccinated, 2% in unvaccinated) with its nsp12 mutation F192V in breakthroughs. Delta infections were associated with younger age and lower hospitalization rates than Alpha. Delta breakthrough infections increased significantly with time since vaccination, and, after adjusting for confounders, they rose at similar rates as in unvaccinated individuals. INTERPRETATION/CONCLUSIONS:We observed a modest adaptation of Delta genomes in breakthrough infections in New York, suggesting an improved genomic framework to support Delta's epidemic growth in times of waning vaccine protection despite limited impact on vaccine escape. FUNDING/BACKGROUND:The study was supported by NYU institutional funds. The NYULH Genome Technology Center is partially supported by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center.

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

BACKGROUND:Point-of-care ultrasound (POCUS) is rapidly becoming ubiquitous across healthcare specialties. This is due to several factors including its portability, immediacy of results to guide clinical decision-making, and lack of radiation exposure to patients. The recent growth of handheld ultrasound devices has improved access to ultrasound for many clinicians. Few studies have directly compared different handheld ultrasound devices among themselves or to cart-based ultrasound machines. We conducted a prospective observational study comparing four common handheld ultrasound devices for ease of use, image quality, and overall satisfaction. Twenty-four POCUS experts utilized four handheld devices (Butterfly iQ+â„¢ by Butterfly Network Inc., Kosmosâ„¢ by EchoNous, Vscan Airâ„¢ by General Electric, and Lumifyâ„¢ by Philips Healthcare) to obtain three ultrasound views on the same standardized patients using high- and low-frequency probes. RESULTS:Data were collected from 24 POCUS experts using all 4 handheld devices. No single ultrasound device was superior in all categories. For overall ease of use, the Vscan Airâ„¢ was rated highest, followed by the Lumifyâ„¢. For overall image quality, Lumifyâ„¢ was rated highest, followed by Kosmosâ„¢. The Lumifyâ„¢ device was rated highest for overall satisfaction, while the Vscan Airâ„¢ was rated as the most likely to be purchased personally and carried in one's coat pocket. The top 5 characteristics of handheld ultrasound devices rated as being "very important" were image quality, ease of use, portability, total costs, and availability of different probes. CONCLUSIONS:In a comparison of four common handheld ultrasound devices in the United States, no single handheld ultrasound device was perceived to have all desired characteristics. POCUS experts rated the Lumifyâ„¢ highest for image quality and Vscan Airâ„¢ highest for ease of use. Overall satisfaction was highest with the Lumifyâ„¢ device, while the most likely to be purchased as a pocket device was the Vscan Airâ„¢. Image quality was felt to be the most important characteristic in evaluating handheld ultrasound devices.

Introduction: Obesity has long been associated with all-cause mortality and cardiovascular disease (CVD). Visceral abdominal tissue (VAT) has been proposed as an important CVD risk stratification metric given its independent correlation with myocardial infarction and stroke. This study aims to clarify the relationship between the presence and severity of VAT with the presence and severity of coronary artery plaque as defined by total plaque volume, calcified plaque volume, non-calcified plaque volume, and high risk low-density non-calcified plaque using quantitative coronary computed tomography atherosclerosis imaging (AI-QCT).
Method(s): CCTA was performed using single or dual source CT scanners of >64-detector rows. Coronary atherosclerosis evaluation by CCTA (AI-QCT) was performed. The AI-based approach to CCTA interpretation in this study was performed using an FDA-cleared software service (Cleerly Lab, Cleerly, New York, NY) that performs automated analysis of CCTA using a series of validated convolutional neural network models (including VGG19 network, 3D U-Net, and VGG Network Variant) for image quality assessment, coronary segmentation and labeling, lumen wall evaluation and vessel contour determination and plaque characterization. Coronary segments with a diameter >1.5 mm were included in the analysis using the modified 18-segment SCCT model. Plaque volume was categorized using Hounsfield unit (HU) ranges, with LD-NCP defined as plaques <30 HU, NCP defined as HU between -30 and +350, and CP defined as >350 HU12,13.
Result(s): The study cohort was comprised of 145 patients, 56.1 +/- 8.5 years, 84.0% male. Overall, 3.5% had a history of diabetes, 19% hypertension, 38% dyslipidemia, 8% current smokers, and 34% had a family history of CAD. At the time of the examination, 37% were taking statins, 21% anti-hypertension medications, and 11% non-stating lipid lowering medication. There was a stepwise progression of the median coronary plaque volume for each quartile of visceral fat including TPV (Q1: 19.8, Q2: 48.1, Q3: 86.4, and Q4: 136.6 mm³ (P=0.0098)), NCP (Q1: 15.7, Q2: 35.4, Q3: 86.4, and Q4: 136.6 mm³ (P=0.0032)) and LD-NCP (Q1: 0.6, Q2: 0.81, Q3: 2.0, and Q4: 5.0 mm³ (P<0.0001)). There was also a stepwise progression of the median maximum diameter stenosis for each quartile of visceral fat (Q1: 18.0, Q2: 27.0, Q3: 27.5, and Q4: 29.5% (P=0.0414)) as well as medial coronary plaque length (Q1: 12.8, Q2: 19.5, Q3: 26.4, and Q4: 27.5mm (P=0.0077))
Conclusion(s): Our findings represent the first demonstration of a stepwise progression with regards to visceral abdominal tissue and total plaque volume, non-calcified plaque volume, and low density non-calcified plaque volume. This relationship is particularly striking with regards to the progression and severity of VAT and the presence and amount of low density non-calcified plaque, which is known to be high risk for cardiovascular events.
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BACKGROUND:Variation in clinicians' diagnostic test utilization is incompletely explained by demographics and likely relates to cognitive characteristics. We explored clinician factors associated with diagnostic test utilization METHODS: We used a self-administered survey of attitudes, cognitive characteristics, and reported likelihood of test ordering in common scenarios; frequency of lipid and liver testing in patients on statin therapy. Participants were 552 primary care physicians, nurse practitioners, and physician assistants from practices in 8 US states across 3 regions, from June 1, 2018 to November 26, 2019. We measured Testing Likelihood Score: the mean of 4 responses to testing frequency and self-reported testing frequency in patients on statins. RESULTS:Respondents were 52.4% residents, 36.6% attendings, and 11.0% nurse practitioners/physician assistants; most were white (53.6%) or Asian (25.5%). Median age was 32 years; 53.1% were female. Participants reported ordering tests for a median of 20% (stress tests) to 90% (mammograms) of patients; Testing Likelihood Scores varied widely (median 54%, interquartile range 43%-69%). Higher scores were associated with geography, training type, low numeracy, high malpractice fear, high medical maximizer score, high stress from uncertainty, high concern about bad outcomes, and low acknowledgment of medical uncertainty. More frequent testing of lipids and liver tests was associated with low numeracy, high medical maximizer score, high malpractice fear, and low acknowledgment of uncertainty. CONCLUSIONS:Clinician variation in testing was common, with more aggressive testing consistently associated with low numeracy, being a medical maximizer, and low acknowledgment of uncertainty. Efforts to reduce undue variations in testing should consider clinician cognitive drivers.

BACKGROUND:Residents receive infrequent feedback on their clinical reasoning (CR) documentation. While machine learning (ML) and natural language processing (NLP) have been used to assess CR documentation in standardized cases, no studies have described similar use in the clinical environment. OBJECTIVE:The authors developed and validated using Kane's framework a ML model for automated assessment of CR documentation quality in residents' admission notes. DESIGN, PARTICIPANTS, MAIN MEASURES/UNASSIGNED:Internal medicine residents' and subspecialty fellows' admission notes at one medical center from July 2014 to March 2020 were extracted from the electronic health record. Using a validated CR documentation rubric, the authors rated 414 notes for the ML development dataset. Notes were truncated to isolate the relevant portion; an NLP software (cTAKES) extracted disease/disorder named entities and human review generated CR terms. The final model had three input variables and classified notes as demonstrating low- or high-quality CR documentation. The ML model was applied to a retrospective dataset (9591 notes) for human validation and data analysis. Reliability between human and ML ratings was assessed on 205 of these notes with Cohen's kappa. CR documentation quality by post-graduate year (PGY) was evaluated by the Mantel-Haenszel test of trend. KEY RESULTS/RESULTS:The top-performing logistic regression model had an area under the receiver operating characteristic curve of 0.88, a positive predictive value of 0.68, and an accuracy of 0.79. Cohen's kappa was 0.67. Of the 9591 notes, 31.1% demonstrated high-quality CR documentation; quality increased from 27.0% (PGY1) to 31.0% (PGY2) to 39.0% (PGY3) (p < .001 for trend). Validity evidence was collected in each domain of Kane's framework (scoring, generalization, extrapolation, and implications). CONCLUSIONS:The authors developed and validated a high-performing ML model that classifies CR documentation quality in resident admission notes in the clinical environment-a novel application of ML and NLP with many potential use cases.