Faculty Bibliography

BACKGROUND: The transition of care from hospital to home is a vulnerable time. Prior studies indicate that the amount of discharge education and anticipatory guidance that patients may receive vary widely. Suboptimal understanding of the care plan can lead to posthospitalization morbidity and readmissions. Our aim was to identify the multiple patient-, provider-, and hospital system-level factors that contribute to the quality of discharge education a patient receives prior to discharge. Direct observation on the day of discharge can help inform contributors to inequities in the patient's discharge education experience.
METHOD(S): Purposeful sampling was used to select patients designated for discharge by noon. On the day of discharge, a trained medical student sat at the bedside of a single patient from 6am until time of discharge, and documented all communication between the patient and the healthcare team. Field notes were analyzed by three independent reviewers using a constant comparison method to identify the patient-, provider- and system-level themes that played a factor into the quality of patient education received in the discharge observations.
RESULT(S): We conducted over 150 hours of observation of 30 patients' discharge days across two academic hospitals. On the patient level, we found that patients with previous hospitalizations and thus, familiarity with the hospital discharge process engaged in more question-asking and in turn, received more in-depth education to prepare them for the next care setting. This "hospital literacy" was even more paramount when patients had a previous suboptimal care transition experience, and emerged as a consistent theme of self-advocacy, more so than one's race, perceived health literacy, or the presence of a caregiver. At the provider level, healthcare teams that were focused on pending tests or outstanding consult recommendations provided less comprehensive discharge education since conversations centered on the logistics of that pending factor. The system level factors contributing to less discharge education included patient discharge to a subacute rehabilitation center or discharge over a weekend.
CONCLUSION(S): Understanding the multi-level factors that contribute to the quality of patient discharge education can alert the healthcare team to patients at risk of suboptimal care transitions. Ensuring that our patients exit the hospital with an equitable quality of information, ready to manage their health in the next care setting, will require interventions at every level

Importance/UNASSIGNED:Black and Hispanic populations have higher rates of coronavirus disease 2019 (COVID-19) hospitalization and mortality than White populations but lower in-hospital case-fatality rates. The extent to which neighborhood characteristics and comorbidity explain these disparities is unclear. Outcomes in Asian American populations have not been explored. Objective/UNASSIGNED:To compare COVID-19 outcomes based on race and ethnicity and assess the association of any disparities with comorbidity and neighborhood characteristics. Design, Setting, and Participants/UNASSIGNED:This retrospective cohort study was conducted within the New York University Langone Health system, which includes over 260 outpatient practices and 4 acute care hospitals. All patients within the system's integrated health record who were tested for severe acute respiratory syndrome coronavirus 2 between March 1, 2020, and April 8, 2020, were identified and followed up through May 13, 2020. Data were analyzed in June 2020. Among 11 547 patients tested, outcomes were compared by race and ethnicity and examined against differences by age, sex, body mass index, comorbidity, insurance type, and neighborhood socioeconomic status. Exposures/UNASSIGNED:Race and ethnicity categorized using self-reported electronic health record data (ie, non-Hispanic White, non-Hispanic Black, Hispanic, Asian, and multiracial/other patients). Main Outcomes and Measures/UNASSIGNED:The likelihood of receiving a positive test, hospitalization, and critical illness (defined as a composite of care in the intensive care unit, use of mechanical ventilation, discharge to hospice, or death). Results/UNASSIGNED:Among 9722 patients (mean [SD] age, 50.7 [17.5] years; 58.8% women), 4843 (49.8%) were positive for COVID-19; 2623 (54.2%) of those were admitted for hospitalization (1047 [39.9%] White, 375 [14.3%] Black, 715 [27.3%] Hispanic, 180 [6.9%] Asian, 207 [7.9%] multiracial/other). In fully adjusted models, Black patients (odds ratio [OR], 1.3; 95% CI, 1.2-1.6) and Hispanic patients (OR, 1.5; 95% CI, 1.3-1.7) were more likely than White patients to test positive. Among those who tested positive, odds of hospitalization were similar among White, Hispanic, and Black patients, but higher among Asian (OR, 1.6, 95% CI, 1.1-2.3) and multiracial patients (OR, 1.4; 95% CI, 1.0-1.9) compared with White patients. Among those hospitalized, Black patients were less likely than White patients to have severe illness (OR, 0.6; 95% CI, 0.4-0.8) and to die or be discharged to hospice (hazard ratio, 0.7; 95% CI, 0.6-0.9). Conclusions and Relevance/UNASSIGNED:In this cohort study of patients in a large health system in New York City, Black and Hispanic patients were more likely, and Asian patients less likely, than White patients to test positive; once hospitalized, Black patients were less likely than White patients to have critical illness or die after adjustment for comorbidity and neighborhood characteristics. This supports the assertion that existing structural determinants pervasive in Black and Hispanic communities may explain the disproportionately higher out-of-hospital deaths due to COVID-19 infections in these populations.

BACKGROUND:The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain. OBJECTIVE:To estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-standardized hospital-wide readmission measure (HWR)-a quality performance measure in the U.S. RESEARCH DESIGN/METHODS:We assessed 71 community variables in 6 domains related to health outcomes: clinical care; health behaviors; social and economic factors; the physical environment; demographics; and social capital. SUBJECTS/METHODS:Medicare fee-for-service patients eligible for the HWR measure between July 2014-June 2015 (n = 6,790,723). Patients were linked to community variables using their 5-digit zip code of residence. METHODS:We used a random forest algorithm to rank variables for their importance in predicting HWR scores. Variables were entered into 6 domain-specific multivariable regression models in order of decreasing importance. Variables with P-values <0.10 were retained for a final model, after eliminating any that were collinear. RESULTS:Among 71 community variables, 19 were retained in the 6 domain models and in the final model. Domains which explained the most to least variance in HWR were: physical environment (R2 = 15%); clinical care (R2 = 12%); demographics (R2 = 11%); social and economic environment (R2 = 7%); health behaviors (R2 = 9%); and social capital (R2 = 8%). In the final model, the 19 variables explained more than a quarter of the variance in readmission rates (R2 = 27%). CONCLUSIONS:Readmissions for a wide range of clinical conditions are influenced by factors relating to the communities in which patients reside. These findings can be used to target efforts to keep patients out of the hospital.

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

OBJECTIVE:<0.001). Rates of adverse events increased with the magnitude of D-dimer elevation; individuals with presenting D-dimer >2000 ng/mL had the highest risk of critical illness (66%), thrombotic event (37.8%), acute kidney injury (58.3%), and death (47%). CONCLUSIONS:Abnormal D-dimer was frequently observed at admission with COVID-19 and was associated with higher incidence of critical illness, thrombotic events, acute kidney injury, and death. The optimal management of patients with elevated D-dimer in COVID-19 requires further study.

BACKGROUND:Little is known about long-term recovery from severe COVID-19 disease. Here, we characterize overall health, physical health and mental health of patients one month after discharge for severe COVID-19. METHODS:This was a prospective single health system observational cohort study of patients ≥18 years hospitalized with laboratory-confirmed COVID-19 disease who required at least 6 liters of oxygen during admission, had intact baseline cognitive and functional status and were discharged alive. Participants were enrolled between 30 and 40 days after discharge. Outcomes were elicited through validated survey instruments: the PROMIS Dyspnea Characteristics and PROMIS Global Health-10. RESULTS:A total of 161 patients (40.6% of eligible) were enrolled; 152 (38.3%) completed the survey. Median age was 62 years (interquartile range [IQR], 50-67); 57 (37%) were female. Overall, 113/152 (74%) participants reported shortness of breath within the prior week (median score 3 out of 10 [IQR 0-5]), vs. 47/152 (31%) pre-COVID-19 infection (0, IQR 0-1), p<0.001. Participants also rated their physical health and mental health as worse in their post-COVID state (43.8, standard deviation 9.3; mental health 47.3, SD 9.3) compared to their pre-COVID state, (54.3, SD 9.3; 54.3, SD 7.8, respectively), both p <0.001. A total of 52/148 (35.1%) patients without pre-COVID oxygen requirements needed home oxygen after hospital discharge; 20/148 (13.5%) reported still using oxygen at time of survey. CONCLUSIONS:Patients with severe COVID-19 disease typically experience sequelae affecting their respiratory status, physical health and mental health for at least several weeks after hospital discharge.

BACKGROUND AND PURPOSE/OBJECTIVE:Patients with the Coronavirus Disease of 2019 (COVID-19) are at increased risk for thrombotic events and mortality. Various anticoagulation regimens are now being considered for these patients. Anticoagulation is known to increase the risk for adverse bleeding events, of which intracranial hemorrhage (ICH) is one of the most feared. We present a retrospective study of 33 patients positive for COVID-19 with neuroimaging-documented ICH and examine anticoagulation use in this population. METHODS:Patients over the age of 18 with confirmed COVID-19 and radiographic evidence of ICH were included in this study. Evidence of hemorrhage was confirmed and categorized by a fellowship trained neuroradiologist. Electronic health records were analyzed for patient information including demographic data, medical history, hospital course, laboratory values, and medications. RESULTS:We identified 33 COVID-19 positive patients with ICH, mean age 61.6 years (range 37-83 years), 21.2% of whom were female. Parenchymal hemorrhages with mass effect and herniation occurred in 5 (15.2%) patients, with a 100% mortality rate. Of the remaining 28 patients with ICH, 7 (25%) had punctate hemorrhages, 17 (60.7%) had small- moderate size hemorrhages, and 4 (14.3%) had a large single site of hemorrhage without evidence of herniation. Almost all patients received either therapeutic dose anticoagulation (in 22 [66.7%] patients) or prophylactic dose (in 3 [9.1] patients) prior to ICH discovery. CONCLUSIONS:Anticoagulation therapy may be considered in patients with COVID-19 though the risk of ICH should be taken into account when developing a treatment regimen.

OBJECTIVE:To describe outcomes of people admitted to hospital with coronavirus disease 2019 (covid-19) in the United States, and the clinical and laboratory characteristics associated with severity of illness. DESIGN/METHODS:Prospective cohort study. SETTING/METHODS:Single academic medical center in New York City and Long Island. PARTICIPANTS/METHODS:5279 patients with laboratory confirmed severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection between 1 March 2020 and 8 April 2020. The final date of follow up was 5 May 2020. MAIN OUTCOME MEASURES/METHODS:Outcomes were admission to hospital, critical illness (intensive care, mechanical ventilation, discharge to hospice care, or death), and discharge to hospice care or death. Predictors included patient characteristics, medical history, vital signs, and laboratory results. Multivariable logistic regression was conducted to identify risk factors for adverse outcomes, and competing risk survival analysis for mortality. RESULTS:Of 11 544 people tested for SARS-Cov-2, 5566 (48.2%) were positive. After exclusions, 5279 were included. 2741 of these 5279 (51.9%) were admitted to hospital, of whom 1904 (69.5%) were discharged alive without hospice care and 665 (24.3%) were discharged to hospice care or died. Of 647 (23.6%) patients requiring mechanical ventilation, 391 (60.4%) died and 170 (26.2%) were extubated or discharged. The strongest risk for hospital admission was associated with age, with an odds ratio of >2 for all age groups older than 44 years and 37.9 (95% confidence interval 26.1 to 56.0) for ages 75 years and older. Other risks were heart failure (4.4, 2.6 to 8.0), male sex (2.8, 2.4 to 3.2), chronic kidney disease (2.6, 1.9 to 3.6), and any increase in body mass index (BMI) (eg, for BMI >40: 2.5, 1.8 to 3.4). The strongest risks for critical illness besides age were associated with heart failure (1.9, 1.4 to 2.5), BMI >40 (1.5, 1.0 to 2.2), and male sex (1.5, 1.3 to 1.8). Admission oxygen saturation of <88% (3.7, 2.8 to 4.8), troponin level >1 (4.8, 2.1 to 10.9), C reactive protein level >200 (5.1, 2.8 to 9.2), and D-dimer level >2500 (3.9, 2.6 to 6.0) were, however, more strongly associated with critical illness than age or comorbidities. Risk of critical illness decreased significantly over the study period. Similar associations were found for mortality alone. CONCLUSIONS:Age and comorbidities were found to be strong predictors of hospital admission and to a lesser extent of critical illness and mortality in people with covid-19; however, impairment of oxygen on admission and markers of inflammation were most strongly associated with critical illness and mortality. Outcomes seem to be improving over time, potentially suggesting improvements in care.

Background We sought to examine whether people with a diagnosis of cardiovascular disease (CVD) experienced a greater incidence of subsequent cognitive impairment (CI) compared to people without CVD, as suggested by prior studies, using a large longitudinal cohort. Methods We used biennial data collected on adults age >=50 from the Health and Retirement Study (HRS) to compare the incidence of CI over 8 years in 1,931 participants newly diagnosed with CVD vs. 3,862 age- and gender-matched controls. Diagnosis of CVD was adjudicated with an established HRS methodology. CI was defined as <=11 on the 27-point Telephone Interview for Cognitive Status, based on a previously accepted clinical cutpoint. To examine the incidence of CI, we used a cumulative incidence function accounting for competing risk of death. Results Mean age at study entry was 70 years, and 55% were female. CI developed in 1,335 participants over 8 years. Death was more common among participants with incident CVD (20.4% vs. 13.4%, p <.001). Cumulative incidence analysis for CI, after adjusting for death, showed no significant difference in incidence of cognitive impairment between the CVD and control groups at the end of the study period (Figure). Conclusion We found no increased risk of subsequent cognitive impairment among participants with CVD (compared with no CVD), despite previous research indicating that CVD accelerates cognitive decline. This finding may be due to appropriately accounting for the competing risk of death. [Figure presented]
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