key: cord-0784995-dj1xqu6j
authors: Kirkup, C.; Pawlowski, C.; Puranik, A.; Conrad, I.; O'Horo, J. C.; Gomaa, D.; Banner-Goodspeed, V. M.; Mosier, J. M.; Zabolotskikh, I. B.; Daugherty, S. K.; Bernstein, M. A.; Zaren, H. A.; Bansal, V.; Pickering, B.; Badley, A. D.; Kashyap, R.; Venkatakrishnan, A.; Soundararajan, V.
title: Healthcare disparities among anticoagulation therapies for severe COVID-19 patients in the multi-site VIRUS registry
date: 2020-11-10
journal: nan
DOI: 10.1101/2020.11.06.20226035
sha: 5ef097af6cf7ee1447dfe61564ab88c5e945476d
doc_id: 784995
cord_uid: dj1xqu6j

COVID-19 patients are at an increased risk of thrombosis and various anticoagulants are being used in patient management without an established standard-of-care. Here, we analyze hospitalized and ICU patient outcomes from the Viral Infection and Respiratory illness Universal Study (VIRUS) registry. We find that severe COVID patients administered unfractionated heparin but not enoxaparin have a higher mortality-rate (311 deceased patients out of 760 total patients = 41%) compared to patients administered enoxaparin but not unfractionated heparin (214 deceased patients out of 1,432 total patients = 15%), presenting a risk ratio of 2.74 (95% C.I.: [2.35, 3.18]; p-value: 1.4e-41). This difference persists even after balancing on a number of covariates including: demographics, comorbidities, admission diagnoses, and method of oxygenation, with an amplified mortality rate of 39% (215 of 555) for unfractionated heparin vs. 23% (119 of 522) for enoxaparin, presenting a risk ratio of 1.70 (95% C.I.: [1.40, 2.05]; p-value: 2.5e-7). In these balanced cohorts, a number of complications occurred at an elevated rate for patients administered unfractionated heparin compared to those administered enoxaparin, including acute kidney injury (227 of 642 [35%] vs. 156 of 608 [26%] respectively, adjusted p-value 0.0019), acute cardiac injury (40 of 642 [6.2%] vs. 15 of 608 [2.5%] respectively, adjusted p-value 0.01), septic shock (118 of 642 [18%] vs. 73 of 608 [12%] respectively, adjusted p-value 0.01), and anemia (81 of 642 [13%] vs. 46 of 608 [7.6%] respectively, adjusted p-value 0.02). Furthermore, a higher percentage of Black/African American COVID patients (375 of 1,203 [31%]) were noted to receive unfractionated heparin compared to White/Caucasian COVID patients (595 of 2,488 [24%]), for a risk ratio of 1.3 (95% C.I.: [1.17, 1.45], adjusted p-value: 1.6e-5). After balancing upon available clinical covariates, this difference in anticoagulant use remained statistically significant (272 of 959 [28%] for Black/African American vs. 213 of 959 [22%] for White/Caucasian, adjusted p-value: 0.01, relative risk: 1.28, 95% C.I.: [1.09, 1.49]). While retrospective studies cannot suggest any causality, these findings motivate the need for follow-up prospective research in order to elucidate potential socioeconomic, racial, or other disparities underlying the use of anticoagulants to treat severe COVID patients.

Major complications of severe COVID-19 include coagulopathy and cardiovascular events [1] [2] [3] . Through the National Institutes of Health (NIH) ACTIV initiative, there are multiple ongoing research studies to evaluate the safety and effectiveness of various types and doses of anticoagulants 4 . According to NIH Director Francis S. Collins, M.D., Ph.D., "There is currently no standard of care for anticoagulation in hospitalized COVID-19 patients, and there is a desperate need for clinical evidence to guide practice." 4 Due to the current knowledge gap in evidencebased anticoagulant treatments for severe COVID-19, there are many open questions on topics including: types of anticoagulant medications to prescribe, dosing for anticoagulants, indications for anticoagulant prescriptions, and prophylactic vs. therapeutic use.

In this paper, we focus on which types of anticoagulant medications to prescribe for patients with severe COVID-19. We conduct this analysis on the Society for Critical Care Medicine's VIRUS registry 5 , a large-scale, international, multi-site study of hospitalized COVID-19 patients. We consider three categories of anticoagulant medications: (1) Unfractionated Heparin, (2) Enoxaparin, and (3) Other types of Low Molecular Weight Heparin (LMWH). First, we consider head-to-head comparisons of enoxaparin vs. unfractionated heparin and enoxaparin vs. other types of LMWH by constructing cohorts of hospitalized COVID patients who received one anticoagulant medication but not the other during their hospital stay for COVID-19. For each cohort comparison, we evaluate patient outcomes including: mortality at hospital discharge, 28day mortality status, average hospital length of stay in days, average ICU length of stay in days, and complications during the 28-day follow-up period. In addition, for each comparison we repeat the analysis using propensity score matching to control for potential confounding variables including: demographics, comorbidities, evidence of infiltrates, ICU admission status, initial oxygenation method, admission diagnoses, and time in days to anticoagulant administration. Finally, we analyzed the rates of anticoagulant medication administration by race, focusing on cohorts of Black/African American and White/Caucasian patients. Similar, we used propensity score matching to construct race-based cohorts balanced on the clinical covariates listed previously, and we report patient outcomes for both the original and the propensity-matched racestratified cohorts.

The Society of Critical Care Medicine (SCCM)'s Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS): Registry is composed of data collected from patients hospitalized for COVID-19. The registry was granted exempt status for human subjects research by the institutional review board at Mayo Clinic (IRB:20-002610). The ClinicalTrials.gov number is NCT04323787. Each study site submitted a proposal to their local review boards for approval and signed a data use agreement before being granted permission to extract and enter deidentified data into the registry.

As of September 26, 2020, the total size of the study population is 28,964 patients reported by 244 hospitals across 21 countries. While a portion of sites report data for each day in the hospital for each patient, emphasis is placed on capturing data at key events in the treatment process. These include the day of admission to the hospital, the first three days in the hospital, and first day of admission to the ICU (if admitted) as well as outcomes measures like the duration of stay in the hospital and the ICU (if admitted) and the 28-day survival status. Data completeness of the features is variable depending on the frequency of updates from the sites. Data for the registry is collected via REDCap and can be automatically filled from a site's EHR data.

Features reported include comorbidities listed in the VIRUS questionnaire (obesity, diabetes, hypertension, etc.), complications (acute kidney injury, deep vein thrombosis, coagulopathy, etc.), medications prescribed in hospital (antibacterials, anticoagulants, statins, etc.) as well as more refined medication features within a category (Antivirals: Remdesivir, Ritonavir, Lopinavir, etc.). Other features collected for each patient include Hospital Length of Stay, ICU Length of Stay, Height, Weight, etc.

Prior studies suggest that enoxaparin may be more efficacious than unfractionated heparin in the treatment of conditions like acute coronary syndromes 6 and these are two most frequently administered anticoagulants (Supplementary Table S1 ). Thus, we compare the outcomes of patients taking enoxaparin and heparin by constructing two cohorts: (i) patients who were administered enoxaparin but not unfractionated heparin and (ii) patients who were administered unfractionated heparin but not enoxaparin. The cohort sizes were 1814 and 887 respectively. Statistical tests were applied to 21 outcomes (with Benjamini-Hochberg procedure applied to account for the problem of multiple comparisons; details below). Mortality at hospital discharge was the primary outcome of interest. Outcomes that were compared include (1) mortality at hospital discharge, (2) mortality at 28 days, (3) admission to ICU (within 28 days of hospitalization), (4) length of stay in ICU (among alive patients), (5) length of stay in hospital (among alive patients), and the following 16 complications: (6) acute cardiac injury, (7) acute kidney injury, (8) anemia, (9) bacteremia, (10) bacterial pneumonia, (11) cardiac arrest, (12) cardiac arrhythmia, (13) co-or secondary infection, (14) congestive heart failure, (15) To account for potentially confounding variables, we performed propensity score matching to balance covariates between the two cohorts. The statistical tests for differences in outcomes were repeated on the matched cohorts. The covariates which were balanced include demographics, comorbidities and various features on admission. Further detail on the procedure, including a listing of covariates used, is below. The code to process the raw data files was written in R v3.6.1. The code to perform the statistical analyses was written in Python v3.7.7, using the scikit-learn package v0.23.2 to train the logistic regression models for the propensity score matching step. In Supplementary Table S3 , we show the data completeness for the clinical covariates that we used for matching. Most covariates have close to full completeness (over 90%), with the exception of the "evidence of infiltrates" covariate, which has roughly 80% data completeness. For this field, missing values were imputed to be the mean of other values of the field within the treatment group.

For each of the cohort comparisons, we ran a series of statistical significance tests in order to compare across each of the patient outcome variables of interest. For categorical outcome variables (e.g. mortality status, complications), we report the proportion of patients in each cohort that have the outcome variable, the relative risk (ratio of proportions for each cohort), 95% confidence interval for the relative risk, and Chi-squared p-value. The function stats.chi2_contingency from the SciPy package in Python was used to compute the Chisquared p-values. For continuous outcome variables (e.g. hospital/ICU length of stay), we report the mean and standard deviation of the variable in each cohort, along with the p-value from a twosided Mann-Whitney test (stats.mannwhitneyu from SciPy) comparing the two cohorts. Finally, we apply the Benjamini-Hochberg correction to adjust p-values for multiple comparisons.

In order to control for potential confounding factors which may explain differences in patient outcomes between the Enoxaparin and Unfractionated Heparin cohorts, we used Propensity Score Matching to balance the cohorts 7 . First, propensity scores for each of the patients in the two cohorts were computed by fitting a logistic regression model as a function of the clinical covariates (listed below). Next, patients from the Enoxaparin and Unfractionated Heparin cohorts were matched using a 1:1 matching ratio and a heuristic caliper of 0.1 x pooled standard deviation 8 , allowing for drops. Prior to matching, there were 1,814 patients in the enoxaparin cohort (administered enoxaparin but not unfractionated heparin), and there were 887 patients in the unfractionated heparin cohort (administered unfractionated heparin but not enoxaparin). From these two cohorts, 659 matched pairs were found, and statistical analyses were run on the final matched cohorts. Here is the full list of covariates that were considered for the propensity score matching step:

• Demographics: Age, gender, race, ethnicity.

• Comorbidities: Pre-existing conditions, including: (1) asthma, (2) blood loss anemia, (3) cardiac arrhythmias, (4) chronic kidney disease, (5) chronic dialysis, (6) chronic pulmonary disease, (7) coagulopathy, (8) congestive heart failure, (9) coronary artery disease, (10) dementia, (11) depression, (12) diabetes, (13) dyslipidemia/hyperlipidemia, • In ICU on admission to hospital . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

The copyright holder for this this version posted November 10, 2020. • Evidence of infiltrates via X-ray or CT scan The same propensity score matching procedure was done with enoxaparin vs. other low molecular weight heparin in place of enoxaparin vs. unfractionated heparin. Propensity score matching was also applied to balance covariates between the Black/African American and White/Caucasian patient cohorts; the "outcome" compared in this case was the rate of administration of each anticoagulant. All of the same covariates (except race and day of anticoagulant administration) listed above were used in this balancing.

In Figure 1 , we present the mortality rate and ICU admission rate for patients in the SCCM VIRUS registry 5 with outcomes data available. Among the 28,964 patients in the VIRUS registry at the time of the study, hospital discharge status was available for 8,623 patients, of which 6,208 (72%) were alive at discharge. For patients that were administered enoxaparin but not unfractionated heparin, hospital discharge status was available for 1,432 patients, of which 1,217 (85%) were alive at discharge. For patients that were administered unfractionated heparin but not enoxaparin, hospital discharge status was available for 760 patients, and 448 (59%) were alive at discharge. Comparing the mortality outcomes (unadjusted), patients in the heparin cohort have a higher mortality rate compared to those in the enoxaparin cohort (risk ratio: 2.74; 95% C.I.: [2.35, 3 .18]; p-value: 1.4e-41) (Figure 1a) . For patients that were administered enoxaparin but not unfractionated heparin, ICU admission status was available for 1,690 patients, of which 794 (47%) were admitted to the ICU. Similarly, for patients that were administered unfractionated heparin but not enoxaparin, ICU admission status was available for 863 patients, of which 570 (66%) were admitted to the ICU. Comparing the ICU admission status (unadjusted), patients administered unfractionated heparin had a higher rate of admission to the ICU compared to . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

The copyright holder for this this version posted November 10, 2020. ; https://doi.org/10.1101/2020.11.06.20226035 doi: medRxiv preprint patients administered enoxaparin (risk ratio: 1.41; 95% C.I.: [1.32, 1.51]; p-value: 3.4e-20) ( Figure  1b) .

Next, we compared the average lengths of stay in the ICU and hospital for the two cohorts. Here, we restricted the analysis to only patients that were alive at discharge. The length of stay in the ICU and in the hospital were shorter for the enoxaparin patients (mean ICU duration: 10.9 days, mean hospital duration: 11.9 days) compared to the unfractionated heparin patients (mean ICU duration: 13.9 days, mean hospital duration: 16.7 days) (Figure 1c-d) . While the difference in average hospital length of stay is statistically significant (Mann-Whitney p-value: 5.2e-5), the difference in average ICU length of stay is not statistically significant (Mann-Whitney p-value: 0.16).

In Figure 2 , we present the mortality rate and ICU admission rate for patients with different comorbidities: diabetes, hypertension, chronic kidney disease, and congestive heart failure. We observe that for the subgroups of patients with diabetes, hypertension, and congestive heart failure, patients administered enoxaparin have significantly lower rates of ICU admission and death compared to patients administered unfractionated heparin. For patients with chronic kidney disease, the difference in ICU admission rates between the enoxaparin and unfractionated heparin cohorts is statistically significant (risk ratio: 1.32, 95% C.I.: [1.06, 1.64], p-value: 0.01), however, the difference in mortality status is not statistically significant (risk ratio: 1.31, 95% C.I.:

[0.94, 1.82], p-value: 0.12).

Next, we perform propensity score matching to control for a wide array of confounding factors simultaneously. The clinical characteristics of the matched and original unfractionated heparin and enoxaparin cohorts are shown in Table 1 . Most covariates (including demographics, comorbidities, and admission diagnoses) appear well-matched. Of the 659 patients in the matched enoxaparin cohort, mortality status at discharge was available for 522, of which 119 (23%) were deceased on discharge; in the matched heparin cohort, information was available for 555 patients of which 215 (39%) were deceased on discharge ( Table 2) . This difference in mortality rates upon discharge was statistically significant (risk ratio: 1.70, 95% CI: [1.40, 2.05], adjusted p-value: 2.5e-7). The mortality rates reported at 28-days for both cohorts were consistent with the mortality rates reported upon hospital discharge, and differences in rates between the two cohorts were similarly statistically significant. Differences between the two cohorts in the average hospital and ICU length of stays were not statistically significant after matching.

Information on complications that occurred after hospitalization was available for 608 of 659 patients in the matched enoxaparin cohort, and for 642 of 659 patients in the matched heparin cohort. Complications that occurred at a significantly higher rate in the matched heparin cohort compared to the matched enoxaparin cohort include: acute kidney injury ( We also conducted an equivalent analysis comparing Enoxaparin vs other types of low molecular weight heparins (LMWH). The matching table is shown in Table 3 . Of the 717 patients . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

The copyright holder for this this version posted November 10, 2020. ; https://doi.org/10.1101/2020.11.06.20226035 doi: medRxiv preprint in this matched enoxaparin cohort, mortality status at discharge was available for 569 patients, of which 122 (21%) were deceased on discharge. In the 717 patients in the matched other LMWH cohort, information was available for 648 patients, of which 217 (33%) were deceased on discharge (risk ratio: We also examined whether there were any race-based differences in the administration of enoxaparin and unfractionated heparin. The cohorts of interest were Black/African American patients with anticoagulant information available (n = 1,203) and White/Caucasian patients with anticoagulant information available (n = 2,488). Propensity score matching was performed, and the clinical characteristics of the matched and original cohorts are shown in Table 5 for White/Caucasian patients, adjusted p-value: 0.01) ( Table 7) . On the other hand, enoxaparin and other low molecular weight heparins are administered at similar rates in the unmatched and matched cohorts ( Tables 6-7) . Finally, the proportion of patients which took exclusively either enoxaparin or unfractionated heparin are similar for the Black/African American and White/Caucasian cohorts.

Prior work has shown that anticoagulant treatments and prophylaxis are associated with improved outcomes for COVID-19 patients 9,10, . In particular, there is evidence to suggest that low molecular weight heparin can be used to effectively treat COVID-19 patients with coagulopathy 11 . This retrospective analysis suggests that enoxaparin, a particular form of low molecular weight heparin, shows promise as an anticoagulant therapy for severe COVID-19, compared to both unfractionated heparin and other low molecular weight heparin therapies. These findings are consistent with a retrospective study on electronic health records from the Mayo Clinic which has found that enoxaparin is associated with lower rates of thrombotic events, kidney injury, and mortality in comparison with unfractionated heparin 12 . However, this study goes beyond the previous analysis by leveraging the massive SCCM VIRUS data registry of hospitalized COVID-19 patients from multiple sites around the world. As a result, we find that there are additional complications which are enriched at a statistically significant level in the unfractionated heparin cohort compared to the enoxaparin cohort, including septic shock and anemia.

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There are several limitations of this study. While we have longitudinal data from the registry on daily anticoagulant use, we do not have access to the detailed physician notes for these patients. Therefore, in this dataset we cannot distinguish between prophylactic and therapeutic anticoagulant use. Since we include only patients who received an anticoagulant medication, there is potential for immortal time bias because there may be some patients who died prior to anticoagulant administration in the hospital. Another limitation of this study is the lack of follow-up data for all patients. For many sites, we do not have access to follow-up data for patients to determine 28-day mortality status, so the mortality rates may be skewed towards the sites of the study where this outcome data is most available. Finally, there are differences in the FDA drug labels for unfractionated heparin, enoxaparin, and other forms of low molecular weight heparin, which can lead to differences in real-world patterns of prescription 13,14 . For example, patients with active kidney disease are contraindicated for higher doses of enoxaparin. However, unfractionated heparin does not require any dose modifications for patients with active kidney disease, so there may be a preference for this medication among this cohort of patients. Although these biases in prescription patterns are partially controlled for by the propensity score matching algorithm, there may be some additional unobserved confounding factors which are not taken into consideration.

There are numerous follow-up analyses which may be inspired from this study. As more data becomes available, we may investigate differential patient outcomes for other variants of low molecular weight heparin beyond enoxaparin. Similar comparative analyses may be undertaken for other COVID-19 treatment options beyond anticoagulants, such as supplemental oxygenation methods. This study demonstrates the utility of the SCCM VIRUS data registry for analyzing diverse research questions related to therapeutics for severe COVID-19 patients 5 . Finally, a number of studies have been analyzing the association between race/ethnicity and clinical outcomes in COVID-19 15, 16 . From this study, the race-associated differences in the administration of the anticoagulants enoxaparin and unfractionated heparin warrants further analyses into the associations between patients' race/ethnicity, comorbidities, and administration of medications in managing COVID-19.

We thank the SCCM Discovery VIRUS data registry and the Collaborative Co-authors listed in Supplementary Table S4 for providing and maintaining the database of hospitalized COVID-19 patients which made this study possible. We are grateful to Vishakha Kumar for all the timely help and inputs, and to the SCCM colleagues for their internal review and feedback on this study. We are particularly thankful to Ognjen Gajic of the Mayo Clinic and Allan Walkey of Boston University for their helpful contributions to this study. Finally, we would like to thank Murali Aravamudan and Patrick Lenehan of nference for their reviews and feedback this manuscript.

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Bar charts show a comparison of mortality status at discharge from the hospital between patient cohorts receiving enoxaparin but not heparin (blue) or unfractionated heparin but not enoxaparin (orange) during hospitalization (b) Bar charts show a comparison of mortality status at discharge from the hospital between patient cohorts receiving enoxaparin but not unfractionated heparin (blue) or unfractionated heparin but not enoxaparin (orange) during hospitalization (c) Histograms show ICU Length of Stay in days for cohorts of alive patients who received enoxaparin but not unfractionated heparin (blue) and reported a length of stay in the ICU and alive patients who received unfractionated heparin but not enoxaparin (orange) and reported a length of stay in the ICU (d) Histograms show hospital Length of Stay in days for cohorts of alive patients who received enoxaparin but not unfractionated heparin (blue) and reported a length of stay in the ICU and alive patients who received unfractionated heparin but not enoxaparin (orange) and reported a length of stay in the hospital. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

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The copyright holder for this this version posted November 10, 2020. ; https://doi.org/10.1101/2020.11.06.20226035 doi: medRxiv preprint Table 2 : Comparison of patient outcomes for Enoxaparin and Unfractionated Heparin cohorts. Summary of clinical outcomes for matched cohorts of hospitalized COVID-19 patients who have taken either unfractionated heparin or enoxaparin (but not both). For categorical variables such as mortality status and complications, patient counts are shown with the percentage of each cohort in parentheses. Only patients with reported outcomes in each cohort are used to determine the percentages. For numeric variables such as hospital and ICU length of stay, the mean value for each cohort is shown with standard deviation in parentheses. In addition, Benjamini-Hochberg adjusted p-values are shown for the statistical tests comparing the outcome variables for the matched enoxaparin and Heparin cohorts; relative risk of outcomes (heparin relative to enoxaparin) are shown as well, along with 95% confidence interval. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this this version posted November 10, 2020. ; https://doi.org/10.1101/2020.11.06.20226035 doi: medRxiv preprint Table 3 . Covariate balancing results for Enoxaparin and other LMWH cohorts. Summary of patient characteristics for matched and original cohorts of hospitalized COVID-19 patients who have taken either enoxaparin or some other low molecular weight Heparin (LMWH). For numeric variables such as age and first date of anticoagulant administration, the mean value for each cohort is shown with standard deviation in parentheses. For categorical variables such as race and ethnicity, patient counts are shown with the percentage of each cohort in parentheses. Denominators are shown for the covariates which have some missing data. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

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Supplementary 

Must meet stage 1 AKI criteria or higher as defined by 2012 Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for Acute Kidney Injury (AKI) Stage 1 Criteria: 1) serum Cr increases by 1.5-1.9 above baseline or >= 0.3mg/dl increase in serum Cr.

2) Urine output < 0.5 ml/Kg/hour for 6-12 hours

Congestive Heart Failure Heart Failure is a complex clinical syndrome resulting from any structural or functional cardiac disorder that impairs the ability of the ventricle to fill with or eject blood. It is characterized by dyspnea and fatigue in the medical history and edema, rales on physical examination.

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The copyright holder for this this version posted November 10, 2020. ; https://doi.org/10.1101/2020.11.06.20226035 doi: medRxiv preprint New York Heart Association (NYHA) Classification -The Stages of Heart Failure: Class I -No symptoms and no limitation in ordinary physical activity, e.g. shortness of breath when walking, climbing stairs etc. Class II -Mild symptoms (mild shortness of breath and/or angina) and slight limitation during ordinary activity. Class III -Marked limitation in activity due to symptoms, even during less-than-ordinary activity, e.g. walking short distances (20-100 m). Comfortable only at rest. Class IV -Severe limitations. Experiences symptoms even while at rest. Mostly bed bound patients. Evidence of infiltrates (X-ray or CT scan) 4058 (80%)

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Cardiovascular complications of COVID-19

COVID-19 Pandemic: Cardiovascular Complications and Future Implications

Cardiovascular Complications in Patients with COVID-19: Consequences of Viral Toxicities and Host Immune Response

NIH ACTIV initiative launches adaptive clinical trials of blood-clotting treatments for COVID-19

The Viral Infection and Respiratory Illness Universal Study (VIRUS): An International Registry of Coronavirus 2019-Related Critical Illness

Efficacy and safety of the low-molecular weight heparin enoxaparin compared with unfractionated heparin across the acute coronary syndrome spectrum: a meta-analysis

SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHING

Optimal caliper width for propensity score matching of three treatment groups: a Monte Carlo study

Association of Treatment Dose Anticoagulation With In-Hospital Survival Among Hospitalized Patients With COVID-19

Impact of anticoagulation prior to COVID-19 infection: a propensity score-matched cohort study

Anticoagulant treatment is associated with decreased mortality in severe coronavirus disease 2019 patients with coagulopathy

Enoxaparin is associated with lower rates of thrombosis, kidney injury, and mortality than Unfractionated Heparin in hospitalized COVID patients

The impact of ethnicity on clinical outcomes in COVID-19: A systematic review

Assessing racial and ethnic disparities using a COVID-19 outcomes continuum for New York State

Maisonneuve Ridgecrest Regional Hospital