STRUCTURED ABSTRACT (250 words, current: 250)Background: The degree of myocardial injury, reflected by troponin elevation, and associated outcomes among hospitalized patients with Coronavirus Disease in the US are unknown.Objectives: To describe the degree of myocardial injury and associated outcomes in a large hospitalized cohort with laboratory-confirmed COVID-19. Methods:Patients with COVID-19 admitted to one of five Mount Sinai Health System hospitals in New York City between February 27th and April 12th, 2020 with troponin-I (normal value <0.03ng/mL) measured within 24 hours of admission were included (n=2,736). Demographics, medical history, admission labs, and outcomes were captured from the hospitals' EHR. Results:The median age was 66.4 years, with 59.6% men. Cardiovascular disease (CVD) including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes. A total of 506 (18.5%) patients died during hospitalization. Even small amounts of myocardial injury (e.g. troponin I 0.03-0.09ng/mL, n=455, 16.6%) were associated with death (adjusted HR: 1.77, 95% CI 1.39-2.26; P<0.001) while greater amounts (e.g. troponin I>0.09 ng/dL, n=530, 19.4%) were associated with more pronounced risk (adjusted HR 3.23, 95% CI 2.59-4.02). Conclusions:Myocardial injury is prevalent among patients hospitalized with COVID-19, and is associated with higher risk of mortality. Patients with CVD are more likely to have myocardial injury than patients without CVD. Troponin elevation likely reflects non-ischemic or secondary myocardial injury. Unstructured Abstract (100/100 words): Myocardial injury reflected as elevated troponin in Coronavirus Disease (COVID-19) is not well characterized among patients in the United States. We describe the prevalence and impact of myocardial injury among hospitalized patients with confirmed COVID-19 and troponin-I measurements within 24 hours of admission (N=2,736). Elevated troponin concentrations (normal <0.03ng/mL) were commonly observed in patients hospitalized with COVID-19, most often present at low levels, and associated with increased risk of death. Patients with cardiovascular disease (CVD) or risk factors for CVD were more likely to have myocardial injury. Troponin elevation likely reflects non-ischemic or secondary myocardial injury.
Coronavirus disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had devastating effects worldwide. Patients with kidney failure on dialysis may have a higher risk of worse outcomes. Reports from China found that these patients with SARS-CoV-2 had fewer symptoms and required less intensive care than expected (1). A recent observational study of hospitalized patients with kidney failure and COVID-19 reported 31% mortality (2). However, this study lacked a comparator group, and thus, it is unclear if this high mortality would be found in patients without kidney failure with a similarly high comorbidity burden. Therefore, we conducted this retrospective cohort study of patients with kidney failure hospitalized with COVID-19 in the Mount Sinai Health Care System (MSHS) and compared it with a propensity-matched cohort without kidney failure.Only patients age $18 years admitted between March 15 and June 7, 2020, with laboratoryconfirmed SARS-CoV-2 within 48 hours of admission were included. Patients with kidney failure were identified by a combination of kidney failure diagnosis and dialysis procedure International Classificaton of Diseases codes. Patients with previous kidney transplants were not excluded if they had kidney failure at the time of study. The Mount Sinai Institutional Review Board approved this research.We propensity matched patients with kidney failure to those without kidney failure (1:5) without use of a caliper by age, sex, race/ethnicity, comorbidities (atrial fibrillation, coronary artery disease, cancer, congestive heart failure, diabetes, hypertension, chronic obstructive pulmonary disease, asthma, peripheral vascular disease, stroke, and liver disease), body mass index (kilograms per meter 2 ), admission facility, and admission week using nearest neighbor matching. Despite propensity matching, significant differences in patient characteristics remained between kidney failure and non-kidney failure cohorts. Therefore, we performed logistic regression analysis after controlling for age, diabetes, hypertension, stroke, coronary artery disease, and congestive heart failure to determine the association between kidney failure and mechanical ventilation, intensive care unit (ICU) admission, and
ObjectiveMultiple clinical trials fail to identify clinically measurable health benefits of daily multivitamin and multimineral (MVM) consumption in the general adult population. Understanding the determinants of widespread use of MVMs may guide efforts to better educate the public about effective nutritional practices. The objective of this study was to compare self-reported and clinically measurable health outcomes among MVM users and non-users in a large, nationally representative adult civilian non-institutionalised population in the USA surveyed on the use of complementary health practices.DesignCross-sectional analysis of the effect of MVM consumption on self-reported overall health and clinically measurable health outcomes.ParticipantsAdult MVM users and non-users from the 2012 National Health Interview Survey (n=21 603).Primary and secondary outcome measuresFive psychological, physical, and functional health outcomes: (1) self-rated health status, (2) needing help with routine needs, (3) history of 10 chronic diseases, (4) presence of 19 health conditions in the past 12 months, and (5) Kessler 6-Item (K6) Psychological Distress Scale to measure non-specific psychological distress in the past month.ResultsAmong 4933 adult MVM users and 16 670 adult non-users, MVM users self-reported 30% better overall health than non-users (adjusted OR 1.31; 95% CI 1.17 to 1.46; false discovery rate adjusted p<0.001). There were no differences between MVM users and non-users in history of 10 chronic diseases, number of present health conditions, severity of current psychological distress on the K6 Scale and rates of needing help with daily activities. No effect modification was observed after stratification by sex, education, and race.ConclusionsMVM users self-reported better overall health despite no apparent differences in clinically measurable health outcomes. These results suggest that widespread use of multivitamins in adults may be a result of individuals’ positive expectation that multivitamin use leads to better health outcomes or a self-selection bias in which MVM users intrinsically harbour more positive views regarding their health.
BACKGROUND Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS The LASSO<sub>federated</sub> model outperformed the LASSO<sub>local</sub> model at 3 hospitals, and the MLP<sub>federated</sub> model performed better than the MLP<sub>local</sub> model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSO<sub>pooled</sub> model outperformed the LASSO<sub>federated</sub> model at all hospitals, and the MLP<sub>federated</sub> model outperformed the MLP<sub>pooled</sub> model at 2 hospitals. CONCLUSIONS The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.
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