Does comorbidity burden explain the higher COVID-19 mortality risk among men? A retrospective cross-sectional analysis of a well-defined cohort of patients in Bronx, New York.

OBJECTIVES: Men have a higher mortality rate and more severe COVID-19 infection than women. The mechanism for this is unclear. We hypothesise that innate sex differences, rather than comorbidity burden, drive higher male mortality. DESIGN: Retrospective cohort. SETTING: Montefiore Health System (MHS) in Bronx, New York, USA. PARTICIPANTS: A cohort population of 364 992 patients at MHS between 1 January 2018 and 1 January 2020 was defined, from which individuals hospitalised during the pre-COVID period (1 January 2020-15 February 2020) (n=5856) and individuals hospitalised during the COVID-19 surge (1 March 2020-15 April 2020) (n=4793) were examined for outcomes. A subcohort with confirmed COVID-19+ hospitalisation was also examined (n=1742). PRIMARY AND SECONDARY OUTCOME MEASURES: Hospitalisation and in-hospital mortality. RESULTS: Men were older, had more comorbidities, lower body mass index and were more likely to smoke. Unadjusted logistic regression showed a higher odds of death in hospitalised men than women during both the pre-COVID-19 and COVID-19 periods (pre-COVID-19, OR: 1.66 vs COVID-19 OR: 1.98). After adjustment for relevant clinical and demographic factors, the higher risk of male death attenuated towards the null in the pre-COVID-19 period (OR 1.36, 95% CI 1.05 to 1.76) but remained significantly higher in the COVID-19 period (OR 2.02; 95% CI 1.73 to 2.34).In the subcohort of COVID-19+ hospitalised patients, men had 1.37 higher odds of in-hospital death (95% CI 1.09 to 1.72), which was not altered by adjustment for comorbidity (OR remained at 1.38 (95% CI 1.08 to 1.76)) but was attenuated with addition of initial pulse oximetry on presentation (OR 1.26, 95% CI 0.99 to 1.62). CONCLUSIONS: Higher male mortality risk during the COVID-19 period despite adjustment for comorbidity supports the role of innate physiological susceptibility to COVID-19 death. Attenuation of higher male risk towards the null after adjustment for severity of lung disease in hospitalised COVID-19+ patients further supports the role of higher severity of COVID-19 pneumonia in men.

Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study.

BACKGROUND: During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model. OBJECTIVE: In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients' chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model. METHODS: Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients' data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables. RESULTS: Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791). CONCLUSIONS: We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning-based clinical decision support tools.

Private High School Reopened-COVID Mitigation and Clinical Surveillance Using an Internet Application.

CONTEXT: Recommendations for COVID-safe, in-person, high school education have included masks and distancing between students but do not describe a scalable surveillance solution to rapidly identify and mitigate disease prevalence or exposure. METHODS: Through an Internet application, all school participants reported symptoms, illness, or exposure daily. Physician-supervised follow-up interviews were reviewed and recorded in daily rounds. Students and faculty were allowed or prohibited to enter school based on the results. RESULTS: From August 30, 2020, until April 13, 2021, a high school in Bergen County, New Jersey (an epicenter of high COVID prevalence), with 889 students and 214 faculty members, staff, and volunteers, generated 1497 assessments. Reasons for initial evaluation included 48 (3%) participants with positive COVID tests, 520 (34%) COVID-exposed, 178 (12%) exposed to someone with symptoms and unknown COVID status, 208 (14%) subjects with symptoms themselves, 525 (35%) exposed to a high-risk geography or air travel, and 12 (1%) contacts of a contact. Of the 61 subjects ultimately diagnosed with COVID, the sources of infection were 36 (57%) home exposure, 16 (27%) confirmed nonschool sources, 8 (13%) unknown, 1 (2%) travel to a high-risk area, and only one potential case of in-school transmission. CONCLUSIONS: Masks, distance, and aggressive contact tracing supported by an Internet application with consistent application of quarantine protocols successfully permitted in-school education without COVID spread in a high prevalence environment. This finding remains important to guide safety measures should vaccine-resistant strains-or new pandemics-challenge us in the future.

Post-severe Acute Respiratory Syndrome Coronavirus 2 Monoclonal Antibody Treatment Hospitalizations as a Sentinel for Emergence of Viral Variants in New York City.

We partnered with the US Department of Health and Human Services to treat high-risk, nonadmitted coronavirus disease 2019 (COVID-19) patients with bamlanivimab in the Bronx, New York per Emergency Use Authorization criteria. Increasing posttreatment hospitalizations were observed monthly between December 2020 and March 2021 in parallel to the emergence of severe acute respiratory syndrome coronavirus 2 variants in New York City.

The association of race and COVID-19 mortality.

BACKGROUND: COVID-19 mortality disproportionately affects the Black population in the United States (US). To explore this association a cohort study was undertaken. METHODS: We assembled a cohort of 505,992 patients receiving ambulatory care at Bronx Montefiore Health System (BMHS) between 1/1/18 and 1/1/20 to evaluate the relative risk of hospitalization and death in two time-periods, the pre-COVID time-period (1/1/20-2/15/20) and COVID time-period (3/1/20-4/15/20). COVID testing, hospitalization and mortality were determined with the Black and Hispanic patient population compared separately to the White population using logistic modeling. Evaluation of the interaction of pre-COVID and COVID time periods and race, with respect to mortality was completed. FINDINGS: A total of 9,286/505,992 (1.8%) patients were hospitalized during either or both pre-COVID or COVID periods. Compared to Whites the relative risk of hospitalization of Black patients did not increase in the COVID period (p for interaction=0.12). In the pre- COVID period, compared to Whites, the odds of death for Blacks and Hispanics adjusted for comorbidity was statistically equivalent. In the COVID period compared to Whites the adjusted odds of death for Blacks was 1.6 (95% CI 1.2-2.0, p = 0.001). There was a significant increase in Black mortality risk from pre-COVID to COVID periods (p for interaction=0.02). Adjustment for relevant clinical and social indices attenuated but did not fully explain the observed difference in Black mortality. INTERPRETATION: The BMHS COVID experience demonstrates that Blacks do have a higher mortality with COVID incompletely explained by age, multiple reported comorbidities and available metrics of sociodemographic disparity. FUNDING: N/A.

Anticoagulation in COVID-19: Effect of Enoxaparin, Heparin, and Apixaban on Mortality.

BACKGROUND: Mortality in coronavirus disease of 2019 (COVID-19) is associated with increases in prothrombotic parameters, particularly D-dimer levels. Anticoagulation has been proposed as therapy to decrease mortality, often adjusted for illness severity. OBJECTIVE: We wanted to investigate whether anticoagulation improves survival in COVID-19 and if this improvement in survival is associated with disease severity. METHODS: This is a cohort study simulating an intention-to-treat clinical trial, by analyzing the effect on mortality of anticoagulation therapy chosen in the first 48 hours of hospitalization. We analyzed 3,625 COVID-19+ inpatients, controlling for age, gender, glomerular filtration rate, oxygen saturation, ventilation requirement, intensive care unit admission, and time period, all determined during the first 48 hours. RESULTS: Adjusted logistic regression analyses demonstrated a significant decrease in mortality with prophylactic use of apixaban (odds ratio [OR] 0.46, p = 0.001) and enoxaparin (OR = 0.49, p = 0.001). Therapeutic apixaban was also associated with decreased mortality (OR 0.57, p = 0.006) but was not more beneficial than prophylactic use when analyzed over the entire cohort or within D-dimer stratified categories. Higher D-dimer levels were associated with increased mortality (p < 0.0001). When adjusted for these same comorbidities within D-dimer strata, patients with D-dimer levels < 1 µg/mL did not appear to benefit from anticoagulation while patients with D-dimer levels > 10 µg/mL derived the most benefit. There was no increase in transfusion requirement with any of the anticoagulants used. CONCLUSION: We conclude that COVID-19+ patients with moderate or severe illness benefit from anticoagulation and that apixaban has similar efficacy to enoxaparin in decreasing mortality in this disease.

AKI in Hospitalized Patients with and without COVID-19: A Comparison Study.

BACKGROUND: Reports from centers treating patients with coronavirus disease 2019 (COVID-19) have noted that such patients frequently develop AKI. However, there have been no direct comparisons of AKI in hospitalized patients with and without COVID-19 that would reveal whether there are aspects of AKI risk, course, and outcomes unique to this infection. METHODS: In a retrospective observational study, we evaluated AKI incidence, risk factors, and outcomes for 3345 adults with COVID-19 and 1265 without COVID-19 who were hospitalized in a large New York City health system and compared them with a historical cohort of 9859 individuals hospitalized a year earlier in the same health system. We also developed a model to identify predictors of stage 2 or 3 AKI in our COVID-19. RESULTS: We found higher AKI incidence among patients with COVID-19 compared with the historical cohort (56.9% versus 25.1%, respectively). Patients with AKI and COVID-19 were more likely than those without COVID-19 to require RRT and were less likely to recover kidney function. Development of AKI was significantly associated with male sex, Black race, and older age (>50 years). Male sex and age >50 years associated with the composite outcome of RRT or mortality, regardless of COVID-19 status. Factors that were predictive of stage 2 or 3 AKI included initial respiratory rate, white blood cell count, neutrophil/lymphocyte ratio, and lactate dehydrogenase level. CONCLUSIONS: Patients hospitalized with COVID-19 had a higher incidence of severe AKI compared with controls. Vital signs at admission and laboratory data may be useful for risk stratification to predict severe AKI. Although male sex, Black race, and older age associated with development of AKI, these associations were not unique to COVID-19.

Older Adults and Covid-19: The Most Vulnerable, the Hardest Hit.

Older adults in the United States have been the age group hardest hit by the Covid pandemic. They have suffered a disproportionate number of deaths; Covid patients eighty years or older on ventilators had fatality rates higher than 90 percent. How could we have better protected older adults? Both the popular press and government entities blamed nursing homes, labeling them "snake pits" and imposing harsh fines and arduous new regulations. We argue that this approach is unlikely to improve protections for older adults. Rather than focusing exclusively on acute and critical resources, including ventilators, a plan that respected the best interests of older adults would have also supported nursing homes, a critical part of the health care system. Better access to protective equipment for staff members, early testing of staff members and patients, and enhanced means of communication with families were what was needed. These preventive measures would have offered greater benefit to the oldest members of our population than the exclusive focus on acute care.