Faculty Bibliography

Early reports showed high mortality from coronavirus disease 2019 (COVID-19). Mortality rates have recently been lower, raising hope that treatments have improved. However, patients are also now younger, with fewer comorbidities. We explored whether hospital mortality was associated with changing demographics at a 3-hospital academic health system in New York. We examined in-hospital mortality or discharge to hospice from March through August 2020, adjusted for demographic and clinical factors, including comorbidities, admission vital signs, and laboratory results. Among 5,121 hospitalizations, adjusted mortality dropped from 25.6% (95% CI, 23.2-28.1) in March to 7.6% (95% CI, 2.5-17.8) in August. The standardized mortality ratio dropped from 1.26 (95% CI, 1.15-1.39) in March to 0.38 (95% CI, 0.12-0.88) in August, at which time the average probability of death (average marginal effect) was 18.2 percentage points lower than in March. Data from one health system suggest that mortality from COVID-19 is decreasing even after accounting for patient characteristics.

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.

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.

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.

BACKGROUND/OBJECTIVE/OBJECTIVE:The connection between gout and various cancers remains unclear. We assessed the relationship between gout and colorectal cancer in a population of veterans. METHODS:We reviewed the Computerized Patient Record System of the VA New York Harbor Health Care System to assess the 10-year occurrence of colorectal cancer in patients with gout undergoing colonoscopy, versus patients with osteoarthritis but no gout. RESULTS:Gout and osteoarthritis subjects were similar in age, ethnicity, body mass index, and smoking history. Among 581 gout and 598 osteoarthritis subjects with documented colonoscopies, the 10-year prevalence of colorectal cancer was significantly lower in gout (0.8%) versus osteoarthritis (3.7%) (p = 0.0008) patients. Differences in colorectal cancer rates remained significant after stratifying for nonsteroidal anti-inflammatory drug use. Among gout subjects, use of colchicine and/or allopurinol, as well as the presence/absence of concomitant osteoarthritis, did not influence colorectal cancer occurrence. On subanalysis, differences in colorectal cancer occurrence between gout and osteoarthritis subjects persisted among those who underwent diagnostic (0.5% in gout vs 4.6% in osteoarthritis subjects, p < 0.001) but not screening (0.9% in gout subjects vs 1% in osteoarthritis subjects, p = 1.0) colonoscopy. There was no significant difference in nonmalignant colorectal polyp occurrence between gout and osteoarthritis subjects. CONCLUSIONS:Subjects with gout had decreased colonoscopy-documented occurrence of colorectal cancer compared with osteoarthritis subjects, suggesting a possible protective effect.

BACKGROUND:Reducing costs while increasing or maintaining quality is crucial to delivering high value care. OBJECTIVE:To assess the impact of a hospital value-based management programme on cost and quality. DESIGN/METHODS:Time series analysis of non-psychiatric, non-rehabilitation, non-newborn patients discharged between 1 September 2011 and 31 December 2017 from a US urban, academic medical centre. INTERVENTION/METHODS:NYU Langone Health instituted an institution-wide programme in April 2014 to increase value of healthcare, defined as health outcomes achieved per dollar spent. Key features included joint clinical and operational leadership; granular and transparent cost accounting; dedicated project support staff; information technology support; and a departmental shared savings programme. MEASUREMENTS/METHODS:Change in variable direct costs; secondary outcomes included changes in length of stay, readmission and in-hospital mortality. RESULTS:The programme chartered 74 projects targeting opportunities in supply chain management (eg, surgical trays), operational efficiency (eg, discharge optimisation), care of outlier patients (eg, those at end of life) and resource utilisation (eg, blood management). The study cohort included 160 434 hospitalisations. Adjusted variable costs decreased 7.7% over the study period. Admissions with medical diagnosis related groups (DRG) declined an average 0.20% per month relative to baseline. Admissions with surgical DRGs had an early increase in costs of 2.7% followed by 0.37% decrease in costs per month. Mean expense per hospitalisation improved from 13% above median for teaching hospitals to 2% above median. Length of stay decreased by 0.25% per month relative to prior trends (95% CI -0.34 to 0.17): approximately half a day by the end of the study period. There were no significant changes in 30-day same-hospital readmission or in-hospital mortality. Estimated institutional savings after intervention costs were approximately $53.9 million. LIMITATIONS/CONCLUSIONS:Observational analysis. CONCLUSION/CONCLUSIONS:A systematic programme to increase healthcare value by lowering the cost of care without compromising quality is achievable and sustainable over several years.

Background/Purpose: The relationship between gout and cancer remains unclear. Whereas some studies have reported possible anti-cancer benefits of uric acid and monosodium urate crystals, others have found an increased risk of cancer in gout patients. Our study aimed to clarify the relationship between gout and colon metaplasia, including cancer and polyps. Methods: We conducted a retrospective study of patients in a VA hospital system using two distinct approaches. To obtain a historical, cross-sectional view of colon cancer prevalence, we assessed the presence of physiciancoded diagnoses of colon cancer and/or polyps in gout patients, versus patients with osteoarthritis (OA) but no gout, with active records in our computerized patient record system (CPRS) between 2007 and 2008. Lung and prostate cancer prevalence were recorded for comparison. In the second approach, we included only patients with documented colonoscopy reports in CPRS, and performed a retrospective cohort study of colon cancer and polyp incidences in gout versus OA patients over a ten-year period (2001-2010). In addition, colon cancer and polyp incidences were compared between patients who had undergone screening versus diagnostic colonoscopy, those who used aspirin or NSAIDs and those who did not, and between gout patients who used allopurinol and/or colchicine and those who did not. Results: 1287 gout patients and 1287 OA patients were included. Gout and OA patients were similar in age, ethnicity, BMI and smoking history. Gout patients had a lower physician-coded prevalence of all colonic lesions (cancer or polyp: 1.8 versus 9.6%, p<0.001), and a lower prevalence of colon cancer (1.0 versus 1.9%, p<0.001), than OA patients (Figure A). Lung and prostate cancer were similar between the two groups. Among 581 gout patients and 598 OA subjects with documented colonoscopies, the ten-year incidence of colon cancer was lower in gout patients than in patients with OA (0.8 versus 3.7%, p=0.0008) (Figure B). This difference in colon cancer incidence remained significant after accounting for NSAID and/or aspirin use. Among gout patients, the use of colchicine and/or allopurinol, as well as the presence or absence of concomitant of OA, did not appear to influence colon cancer prevalence. Differences in colon cancer incidence were significant between gout and OA patients undergoing diagnostic colonoscopy (0.5% in gout patients versus 4.6% in OA patients, p<0.001) but not those undergoing screening colonoscopy (0.9% in gout patients versus 1% in OA patients, p=1.0). No protective effect of gout was observed for prostate or lung cancer. Conclusion: Patients with gout had decreased physician-reported prevalence, and colonoscopy-documented incidence of colon cancer compared to patients with OA, suggesting a possible protective effect of gout or a goutassociated clinical, epidemiological or genetic factor. (Figure Presented)