Faculty Bibliography

BACKGROUND:It is not yet known whether socioeconomic factors (ie, social determinants of health) are associated with readmission following hospitalization for coronavirus disease 2019 (COVID-19). METHODS:We conducted a retrospective cohort study of 6191 adult patients hospitalized with COVID-19 in a large New York City safety-net hospital system between March 1 and June 1, 2020. Associations between 30-day readmission and selected demographic characteristics, socioeconomic factors, prior health care utilization, and relevant features of the index hospitalization were analyzed using a multivariable generalized estimating equation model. RESULTS:The readmission rate was 7.3%, with a median of 7 days between discharge and readmission. The following were risk factors for readmission: age 65 and older [adjusted odds ratio (aOR): 1.32; 95% confidence interval (CI): 1.13-1.55], history of homelessness, (aOR: 2.03 95% CI: 1.49-2.77), baseline coronary artery disease (aOR: 1.68; 95% CI: 1.34-2.10), congestive heart failure (aOR: 1.34; 95% CI: 1.20-1.49), cancer (aOR: 1.68; 95% CI: 1.26-2.24), chronic kidney disease (aOR: 1.74; 95% CI: 1.46-2.07). Patients' sex, race/ethnicity, insurance, and presence of obesity were not associated with increased odds of readmission. A longer length of stay (aOR: 0.98; 95% CI: 0.97-1.00) and use of noninvasive supplemental oxygen (aOR: 0.68; 95% CI: 0.56-0.83) was associated with lower odds of readmission. Upon readmission, 18.4% of patients required intensive care, and 13.7% expired. CONCLUSION:We have found some factors associated with increased odds of readmission among patients hospitalized with COVID-19. Awareness of these risk factors, including patients' social determinants of health, may ultimately help to reduce readmission rates.

Once medical students attain a certain level of medical knowledge, success in residency often depends on noncognitive attributes, such as conscientiousness, empathy, and grit. These traits are significantly more difficult to assess than cognitive performance, creating a potential gap in measurement. Despite its promise, competency-based medical education (CBME) has yet to bridge this gap, partly due to a lack of well-defined noncognitive observable behaviors that assessors and educators can use in formative and summative assessment. As a result, typical undergraduate to graduate medical education handovers stress standardized test scores, and program directors trust little of the remaining information they receive, sometimes turning to third-party companies to better describe potential residency candidates. The authors have created a list of noncognitive attributes, with associated definitions and noncognitive skills-called observable practice activities (OPAs)-written for learners across the continuum to help educators collect assessment data that can be turned into valuable information. OPAs are discrete work-based assessment elements collected over time and mapped to larger structures, such as milestones, entrustable professional activities, or competencies, to create learning trajectories for formative and summative decisions. Medical schools and graduate medical education programs could adapt these OPAs or determine ways to create new ones specific to their own contexts. Once OPAs are created, programs will have to find effective ways to assess them, interpret the data, determine consequence validity, and communicate information to learners and institutions. The authors discuss the need for culture change surrounding assessment-even for the adoption of behavior-based tools such as OPAs-including grounding the work in a growth mindset and the broad underpinnings of CBME. Ultimately, improving assessment of noncognitive capacity should benefit learners, schools, programs, and most importantly, patients.

Rationale: Allergic and non-allergic adverse reactions (ARs) to Covid-19 vaccine (Cov19V) have been reported. Understanding the characteristics of Cov19V ARs, particularly those that are allergic in nature, may help us to better counsel patients who are at risk of developing a vaccine AR.
Method(s): We performed a retrospective chart review of ARs voluntarily reported to our Occupational Health Services following Cov19V at a multi-site academic medical center between December 2020-June 2021.
Result(s): 464 Cov19V ARs among 71,281 vaccine doses given (0.65%) were reported. 57 ARs (12.3%) were determined to be allergic (10 after the second dose), 356 were nonallergic, and 51 (11.0%) were undetermined. Of the 47 first-dose allergic ARs, 30 (63.8%) received a second dose, 16 did not complete the vaccine series, and 1 had no data. 3 employees received an alternative Cov19V. Of the 356 nonallergic ARs, 110 were following second dose, 2 were following Janssen, and 4 had no data. 228 of first dose reactions (95.0%, 228/240) completed the vaccine series. 22/57 (38.6%) allergic ARs versus 38/356 (10.7%) nonallergic ARs required ER transfer. More allergic ARs were categorized as moderate/severe (80.7%, 46/57) than nonallergic ARs (66.3%, 236/356).
Conclusion(s): Cov19V ARs are extremely uncommon with nonallergic AR more common than allergic. A vast majority of ARs, allergic or nonallergic, are able to receive subsequent Cov19V. Employees with allergic ARs were less likely to receive a second Cov19V and more frequently required emergent medical evaluation compared to those with nonallergic ARs.
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Patients with diabetes mellitus are at increased risk of cardiovascular events. We aimed to analyze the impact of serum HbA1c levels on coronary plaque characteristics in stable coronary disease. Two hundred sixty-one patients who underwent optical coherence tomography (OCT) examination before elective percutaneous coronary intervention for a de novo obstructive lesions were included in this single-center retrospective analysis. Patients were divided into tertiles according to HbA1c level (tertile 1: HbA1c < 6.3%, tertile 2: 6.3 ≤ HbA1c < 7.8%, tertile 3: HbA1c ≥ 7.8%) and OCT findings were compared. Fibrous cap thickness (FCT) was significantly thinner in tertile 3 compared to tertile 1 and tertile 2 (103.9 ± 48.2 µm [tertile 1] vs. 107.5 ± 60.6 µm [tertile 2] vs. 86.2 ± 35.8 µm [tertile 3], p = 0.03). Higher prevalence of thin-cap fibroatheroma (TCFA) was observed in tertile 3 vs tertile 1 and tertile 2 (19.5% [tertile 1] vs. 19.5% [tertile 2] vs. 33.3% [tertile 3], p = 0.04). HbA1c inversely correlated with FCT (beta coefficient - 4.89, 95% confidence interval - 8.40 to - 1.39, p < 0.01). The logistic regression model revealed that the probability of having TCFA was positively associated with HbA1c with a small change in the range of low and medium HbA1c and a big change in the range of high HbA1c. Furthermore, minimal lumen area and reference lumen area were smaller in tertile 3. In patients with stable coronary disease, high serum HbA1c levels are associated with higher plaque burden and thinner FCT on OCT, while low and medium HbA1c levels result in similar plaque vulnerability.

PURPOSE/UNASSIGNED:Earlier literature has reported on the utility of diagnostic codes and demographic information for identifying transgender patients. We aim to assess which method identifies the most transgender patients utilizing readily available tools from within the electronic health record (EHR). METHODS/UNASSIGNED:(ICD-10) diagnostic codes and demographic data specific to transgender patients from January 2011 to April 2019. RESULTS/UNASSIGNED:Demographic data and ICD-10 codes yielded 1494 individual EHRs with transgender-specific data domains. ICD-10 diagnostic codes alone identified 942 (63.05%) unique EHRs. Demographics alone identified 218 (14.59%) unique EHRs. A total of 334 (22.36%) unique EHRs had both ICD-10 and demographic identifiers. Of those identified by transgender-specific demographic data (552), 294 (53.26%) were trans masculine, 215 (38.95%) were trans feminine, and 43 (7.79%) were nonbinary. Of the 552 demographic-identified transgender patients, 141 (25.86%) were identified by a two-part gender identity demographic question. CONCLUSIONS/UNASSIGNED:ICD-10 diagnostic codes, not demographic data, identified the most transgender patient records, but neither diagnostic codes alone nor demographic data captured the full population. Only 26.36% of the charts identified as transgender patients had both ICD-10 codes and demographic data. We recommend that when identifying transgender populations through EHR domains, a combination of diagnostic codes and demographic data be used. Furthermore, research is needed to optimize disclosure and collection of demographic information for gender minority populations.

Background/UNASSIGNED:Little outcome data exist on 3-year MD (3YMD) programs to guide residency program directors (PDs) in deciding whether to select these graduates for their programs. Objective/UNASSIGNED:To compare performance outcomes of 3YMD and 4-year MD (4YMD) students at New York University Grossman School of Medicine. Methods/UNASSIGNED:In 2020, using the Kirkpatrick 4-level evaluation model, outcomes from 3 graduating cohorts of 3YMD students (2016-2018) were compared with the 4YMD counterparts. Results/UNASSIGNED:=.03), other metrics and overall intern ratings did not differ by pathway. Conclusions/UNASSIGNED:Exploratory findings from a single institution suggest that 3YMD students performed similarly to 4YMD students in medical school and the first year of residency.

PURPOSE/OBJECTIVE:Emergency department (ED) visits by patients with cancer frequently end in hospitalization. As concerns about ED and hospital crowding increase, observation unit care may be an important strategy to deliver safe and efficient treatment for eligible patients. In this investigation, we compared the prevalence and clinical characteristics of cancer patients who received observation unit care with those who were admitted to the hospital from the ED. METHODS:We performed a multicenter prospective cohort study of patients with cancer presenting to an ED affiliated with one of 18 hospitals of the Comprehensive Oncologic Emergency Research Network (CONCERN) between March 1, 2016 and January 30, 2017. We compared patient characteristics with the prevalence of observation unit care usage, hospital admission, and length of stay. RESULTS:Of 1,051 enrolled patients, 596 (56.7%) were admitted as inpatients, and 72 (6.9%) were placed in an observation unit. For patients admitted as inpatients, 23.7% had a length of stay ≤2 days. The conversion rate from observation to inpatient was 17.1% (95% CI 14.6-19.4) among those receiving care in an observation unit. The average observation unit length of stay was 14.7 hours. Patient factors associated ED disposition to observation unit care were female gender and low Charlson Comorbidity Index. CONCLUSION/CONCLUSIONS:In this multicenter prospective cohort study, the discrepancy between observation unit care use and short inpatient hospitalization may represent underutilization of this resource and a target for process change.