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Predictors of severity and mortality among patients hospitalized with COVID-19 in Rhode Island

  • Aakriti Pandita ,

    Roles Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing

    aakriti.pandita@cuanschutz.edu (AP); CBeckwith@lifespan.org (CGB)

    Affiliation Department of Medicine, University of Colorado School of Medicine, Denver, Colorado, United States of America

  • Fizza S. Gillani,

    Roles Formal analysis, Methodology, Writing – review & editing

    Affiliation Division of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America

  • Yiyun Shi,

    Roles Data curation, Investigation, Writing – review & editing

    Affiliation Department of Internal Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America

  • Anna Hardesty,

    Roles Data curation, Writing – review & editing

    Affiliation Department of Internal Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America

  • Meghan McCarthy,

    Roles Methodology, Software, Writing – review & editing

    Affiliation Division of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America

  • Jad Aridi,

    Roles Methodology, Validation, Writing – review & editing

    Affiliation Division of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America

  • Dimitrios Farmakiotis,

    Roles Methodology, Validation, Writing – review & editing

    Affiliation Division of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America

  • Silvia S. Chiang ,

    Contributed equally to this work with: Silvia S. Chiang, Curt G. Beckwith

    Roles Methodology, Supervision, Writing – review & editing

    Affiliation Department of Pediatrics, Division of Pediatric Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America

  • Curt G. Beckwith

    Contributed equally to this work with: Silvia S. Chiang, Curt G. Beckwith

    Roles Funding acquisition, Investigation, Methodology, Supervision, Validation, Writing – review & editing

    aakriti.pandita@cuanschutz.edu (AP); CBeckwith@lifespan.org (CGB)

    Affiliation Division of Infectious Diseases, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America

Predictors of severity and mortality among patients hospitalized with COVID-19 in Rhode Island

  • Aakriti Pandita, 
  • Fizza S. Gillani, 
  • Yiyun Shi, 
  • Anna Hardesty, 
  • Meghan McCarthy, 
  • Jad Aridi, 
  • Dimitrios Farmakiotis, 
  • Silvia S. Chiang, 
  • Curt G. Beckwith
PLOS
x

Abstract

Background

In order for healthcare systems to prepare for future waves of COVID-19, an in-depth understanding of clinical predictors is essential for efficient triage of hospitalized patients.

Methods

We performed a retrospective cohort study of 259 patients admitted to our hospitals in Rhode Island to examine differences in baseline characteristics (demographics and comorbidities) as well as presenting symptoms, signs, labs, and imaging findings that predicted disease progression and in-hospital mortality.

Results

Patients with severe COVID-19 were more likely to be older (p = 0.02), Black (47.2% vs. 32.0%, p = 0.04), admitted from a nursing facility (33.0% vs. 17.9%, p = 0.006), have diabetes (53.9% vs. 30.4%, p<0.001), or have COPD (15.4% vs. 6.6%, p = 0.02). In multivariate regression, Black race (adjusted odds ratio [aOR] 2.0, 95% confidence interval [CI]: 1.1–3.9) and diabetes (aOR 2.2, 95%CI: 1.3–3.9) were independent predictors of severe disease, while older age (aOR 1.04, 95% CI: 1.01–1.07), admission from a nursing facility (aOR 2.7, 95% CI 1.1–6.7), and hematological co-morbidities predicted mortality (aOR 3.4, 95% CI 1.1–10.0). In the first 24 hours, respiratory symptoms (aOR 7.0, 95% CI: 1.4–34.1), hypoxia (aOR 19.9, 95% CI: 2.6–152.5), and hypotension (aOR 2.7, 95% CI) predicted progression to severe disease, while tachypnea (aOR 8.7, 95% CI: 1.1–71.7) and hypotension (aOR 9.0, 95% CI: 3.1–26.1) were associated with increased in-hospital mortality.

Conclusions

Certain patient characteristics and clinical features can help clinicians with early identification and triage of high-risk patients during subsequent waves of COVID-19.

Introduction

The pandemic due to SARS-CoV-2, a newly described human coronavirus causing the disease known as COVID-19, continues to challenge the U.S. healthcare system. To date, there have been over 30,357,579 cases in the United States (US) and 138,255 cases in Rhode Island (RI), which has one of the highest infection rate per capita (12,910 cases per 100,000) in the country [1, 2]. In the early phase of the pandemic, many studies noted factors associated with severe outcomes [35]. Comorbidities like diabetes, obesity, and hypertension were observed to be prevalent in those with severe disease [6, 7]. We aimed to do an in-depth study of such predictors in the state of Rhode Island (RI). Such an understanding is crucial so that healthcare systems can manage the continued influx of patients with COVID-19 [8, 9] by appropriately triaging patients who present to the hospital.

We conducted a retrospective cohort study of persons with COVID-19 who were hospitalized in RI to identify (1) patient demographics and comorbidities associated with severe disease and death, and (2) presenting symptoms and vital signs that predicted progression to severe disease and death.

Methods

Study design and patient selection

We performed a retrospective cohort study of patients hospitalized with COVID-19 at the Lifespan academic hospitals affiliated with Brown University in Providence, RI. Patients of all ages who presented to the hospital with symptoms of COVID-19 and had a positive real time polymerase chain reaction (RT-PCR) result for SARS-CoV-2 were eligible for the study. Patients with asymptomatic infection or those who developed symptoms of COVID-19 after the first 48 hours of hospitalization were excluded.

A list of medical record numbers (MRNs) was extracted from the integrated electronic medical record (EMR) for all patients with COVID-19 positive test results hospitalized between February 1, 2020 and May 18, 2020. There were 822 patients admitted during this time period. We included all 106 eligible patients who were hospitalized between February 17 to April 3 and a subset of patients hospitalized between April 4 and May 18, the peak of the pandemic surge in RI. For the latter group, we selected a random sample of 153 from the master patient list (Fig 1). In total, 259 patients, 31.5% of all patients admitted with COVID-19 during the time period, were included in this study. To assess representativeness of our cohort, we compared age, gender, and race among patients selected and not selected for the study. To ensure that we had a representative sample, we compared the weekly case fatality rates between the study sample and all COVID-19 patients admitted to the hospitals during the study period. We did not observe a statistical significance (Wilcoxon test P>0.1) [10].

The Lifespan Academic Medical Center institutional review board approved the study and waived informed consent for participation.

Data collection

Demographic and laboratory data were extracted from the EMR. The following clinical data were collected by manual chart review and entered into a REDCap (Research Electronic Data Capture) database: baseline comorbidities, presenting symptoms and vital signs, microbiology results, imaging results, antimicrobial and other medical treatments (e.g. vasopressors), supplemental oxygen (O2), non-invasive and invasive forms of ventilation, intensive care unit (ICU) admission, renal replacement therapy, prior hospital visits, 15-day follow-up data (including telephone encounters and hospital re-admissions), co-infections, complications, and hospitalization outcome (death or discharge). Study team members were trained to perform chart reviews, and random charts were re-abstracted by the lead author to evaluate interrater agreement and calibrate data collection methods.

Study outcomes

We defined severe COVID-19 as requiring high flow O2 (flow rate of more than 8L/min or use of high flow oxygen cannula), non-invasive ventilation (e.g. BIPAP), or invasive mechanical ventilation at any time point during the hospitalization. We evaluated four combinations of predictors and outcomes. Among all 259 patients, we examined differences in patient demographics and comorbidities in relation to (1) severe disease or (2) death at any time during hospitalization. To examine early clinical predictors of progression to severe disease and death, we excluded patients who met criteria for severe disease or died on the day of admission, and identified presenting symptoms, signs, laboratory results, and imaging findings during the first 24 hours that were associated with (3) developing severe disease or (4) death.

Statistical analysis

Chi-square or Fisher’s exact tests for categorical variables and the Student’s t-test for continuous variables were used to compare demographics, comorbidities, and clinical data between groups. Variables that differed at a significance level of <0.30 were included in stepwise multivariable logistic regression analyses. Variables with a significance level of <0.35 were maintained in the model during stepwise selection. Estimated correlation matrix was used to check multicollinearity between clinically related variables. In multivariate regression, we excluded cases with ≤5 missing values for a given covariate and used the missing indicator method for variables with >5 missing observations. SAS 9.4 (SAS Institute, Cary, U.S.A.) and R 3.6.3 (R Computing, Vienna, Austria) were used for statistical analyses.

Results

The 259 patients included in our cohort did not differ from the 563 patients not selected for the study with respect to age, gender, and race [Supplementary Materials]. Among the participants, median age was 62 years [interquartile range (IQR), 51–73]; 138 (53%) were male; 75 (29%) were Hispanic; and 53 (20.5%) Black [Table 1]. Sixty (23%) participants were admitted from a nursing facility; 52/259 (20%) had additional emergency room (ER) visits in the 15 days before hospitalization. The median length of stay for all patients was 8 days [IQR, 5–15]. ICU admission was required in 74/259 (28%); 42/259 (16%) patients required mechanical ventilation with a median time of 7 days [IQR, 3–12] on a ventilator.

Of the 259 participants, 91 (35%) had severe COVID-19, and 38 (15%) died at any time during hospitalization. Two hundred twenty-three participants did not have severe disease or die during the first 24 hours of admission. Among this group, 55 (24.7%) progressed to severe disease, and 24 (10.8%) died. Compared to the 168 participants who did not progress to severe disease, the 55 participants who progressed to severe disease had a higher incidence of arrhythmias (35% versus 5%; p<0.001) and death (40% vs. 1.2%, p<0.001). The differences in incidence of thromboembolic events (11% vs. 4%, p = 0.06) did not reach statistical significance and bleeding was rare (7% vs. 5%; p = 0.6).

Follow-up telehealth visits were completed in 124/221 discharged patients within a median time of 2 days [IQR, 2–4] post-discharge. At the time of follow-up visit, 84/124 (67%) still reported symptoms and 15/124 (12%) still required oxygen. Among all patients who survived, 16/221(7.2%) patients who were discharged required re-admission within 15 days.

We did not find any significant difference in distribution of black patients among remdesivir versus non remdesivir treatment groups (15.9 vs 17.4%, p-value 0.78) in our study.

Baseline characteristics associated with severe COVID-19 at any time during hospitalization

Compared to patients with non-severe COVID-19, those with severe COVID-19 were older (65 [IQR 57–74] vs. 60 [IQR 49–72] years, p = 0.02) and more likely to be Black (47.2% vs. 32.0%, p = 0.04), admitted from a nursing facility (33.0% vs. 17.9%, p = 0.006), have diabetes (53.9% vs. 30.4%, p<0.001), or COPD (15.4% vs. 6.6%, p = 0.02) [Table 2]. No significant differences were noted with respect to gender, blood type, or current active immunosuppressive medication. In multivariate regression, only Black race (adjusted odds ratio [aOR] 2.0, 95% confidence interval [CI]: 1.1–3.9) and diabetes (aOR 2.2, 95% CI: 1.3–3.9) were found to be independent predictors of severe disease [Table 3].

Patient characteristics associated with in-hospital mortality

Each one-year increase in patient age (aOR 1.04, 95% CI: 1.01–1.07) and admission from a skilled nursing facility (aOR 2.7, 95% CI 1.1–6.7) were associated with death during hospitalization. While many comorbidities were more common in the deceased group [Supplementary Materials], underlying hematological disorders (chronic anemia, coagulation disorders, hematological malignancies, and sickle cell disease) were the only comorbidity that predicted mortality in multivariate regression (aOR 3.4, 95% CI 1.1–10.0) [Table 4].

Progression to severe disease after the first 24 hours of hospitalization

Among the 223 participants in this analysis, respiratory symptoms (94.6% vs. 83.3%, p = 0.04), tachypnea (85.5% vs. 64.9%, p = 0.003), and hypoxia (96.4% vs. 67.9%, p<0.0001) in the first 24 hours were associated with progressing to severe COVID-19 [Table 5]. For laboratory values, only estimated glomerular filtration rate (eGFR) <60 mL/min (48% versus 31%, p = 0.02), hypoalbuminemia (<3.5g/dl) (38% versus 17%, p = 0.01), and elevated D-dimer (>300ng/ml) (71% versus 44%, p = 0.02) were found to be significantly more common during the first 24 hours of admission among those who progressed to severe disease compared to those who did not [Table 6]. A higher proportion of patients who progressed to severe disease had abnormal findings on chest imaging, including bilateral disease on chest x-ray (44% versus 29%, p = 0.04) or chest CT (27% versus 15%, p = 0.03), and ground glass opacities on chest CT (35% versus 14%; p<0.001); a higher proportion of patients with non-severe disease had normal chest x-rays (23% versus 9%; p = 0.02) [Table 7]. In multivariate logistic regression, progression to severe disease was associated with respiratory symptoms (aOR 7.0, 95% CI: 1.4–34.1), hypoxia (aOR 19.9, 95% CI: 2.6–152.5), and hypotension (aOR 2.7, 95% CI) during first 24 hours [Table 8].

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Table 5. Presenting symptoms and signs during the first 24 hours of admission.

https://doi.org/10.1371/journal.pone.0252411.t005

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Table 7. Radiology imaging results during the first 24 hours of admission.

https://doi.org/10.1371/journal.pone.0252411.t007

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Table 8. Symptoms, signs, and findings associated with progression to severe disease.

Multivariate stepwise regression for patients who did not have severe disease or die in the first day of hospitalization (n=223).a

https://doi.org/10.1371/journal.pone.0252411.t008

Progression to death after the first 24 hours of hospitalization

On admission, chest pain, fever, elevated troponin, elevated BUN, elevated creatinine, and eGFR<60 were more common in patients who died during hospitalization, compared to those who were discharged [Supplementary Materials]. In the adjusted model, only the presence of tachypnea (aOR 8.7, 95% CI: 1.1–71.7) or hypotension (aOR 9.0, 95% CI: 3.1–26.1) during the first 24 hours were independently associated with in-hospital mortality [Table 9].

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Table 9. Symptoms, signs, and findings associated with in-hospital mortality.

Multivariate stepwise regression for patients who did not have severe disease or die in the first day of hospitalization (n=223).a

https://doi.org/10.1371/journal.pone.0252411.t009

Discussion

In this study, we identified baseline patient characteristics that were associated with severe COVID-19 or death; and presenting signs and symptoms that were associated with progressing to severe COVID-19 or death after the first 24 hours of admission. These findings will help providers on the front lines of the pandemic triage patients and prioritize hospital resources.

Patients who developed severe disease were older, and age was an independent predictor of mortality, similar to findings from other studies [1114]. We did not observe any significant effects of gender on outcomes. Multivariate regression revealed Black race to be an independent predictor of severe disease. Our findings are aligned with emerging literature on racial and socioeconomic disparities affecting COVID-19 outcomes [1518]. Almost half of Black persons in our study developed severe disease, but Black race was not independently associated with increased mortality. Rather, advanced age or admission from a nursing home/rehabilitation center was associated with mortality, reflecting that the number and severity of comorbidities is an important driver behind risk of death during hospitalization. This finding underscores the importance of adjusting for age as well as comorbidities when interpreting the impact of race on mortality. Other studies from the U.S. also found that when adjusted for other covariates including age and comorbidities, Black race was not an independent predictor of death [15, 19]. While data from the United Kingdom (UK) [20] has also suggested increased severity of COVID-19 among those with Black race, provisional nationwide analysis from the UK points towards increased mortality even after adjusting for age and comorbidities [21]. Additional research is needed to determine the extent of racial disparities among persons who die from COVID-19.

Comorbidities like diabetes, hypertension, obesity, etc. have been associated with poor COVID-19 outcomes [13, 2224]. In our study, comorbidities were also found to be associated with severe disease. Notably, while the prevalence of diabetes, obesity, and COPD was higher in those with severe disease, on adjusted regression analysis only diabetes was found to be an independent predictor of severe disease. Diabetes creates a hyperinflammatory state and impairs innate and cell-mediated immunity, which may predispose patients to the cytokine storm known to occur in severe COVID-19 [25, 26]. Furthermore, increased release of cytokines like interleukin-6 in patients with diabetes and COVID-19, in the face of possible blunted antiviral interferon responses and the delayed activation of Th1/Th17, may contribute to worse outcomes [2729]. However, causality remains to be proven and severe manifestations could be reflective of other factors such as high viral burden, therefore preemptive use of immunosuppressive agents or IL-6 inhibitors remains controversial [30].

In addition to diabetes, the presence of hematological disorders was independently associated with mortality. Chronic anemia was the most common in this subgroup, followed by coagulation disorders, hematological malignancies, and sickle cell disease. This finding may be indicative of underlying chronic inflammation or baseline dysregulation of the coagulation/endothelial dysfunction interplay, which is another driver of severity in COVID-19 [26, 31, 32].

In our cohort, hypoxia in the first 24 hours was an independent predictor of progression to severe disease. Likewise, presence of tachypnea was also an early indicator of subsequent worsening. Hypotension predicted mortality in addition to clinical worsening. These findings underscore the importance of early frequent monitoring of vital signs, which could provide early clues of impending decompensation or death. Patients with these vital sign abnormalities merit close monitoring.

Higher prevalence of hypoalbuminemia in patients with severe COVID-19 likely reflects a catabolic state and critical illness [33]. We also noted eGFR<60 to be common in the severe group. The kidney damage could be due to direct cytopathic effects of the virus from ACE receptor mediated entry [34] or from hypotension. Elevations in D-dimer reflect thrombosis and abnormal coagulation cascade that is common in COVID-19 [35]. Chest x-rays were normal in many patients with non-severe disease. This could be due to lower sensitivity of chest x-ray earlier in the disease [36, 37]. Presence of bilateral infiltrates and ground glass opacities were also associated with disease progression. However, on multivariate regression analysis none of these lab makers or imaging findings independently predicted outcomes.

This study had limitations. First, we analyzed a subset of all patients admitted to our hospitals during the time period of interest, and thus may have introduced selection bias. However, selected patients did not differ from unselected patients with respect to demographics. Second, due to missing D-dimer, ferritin, LDH, and CRP in a subset of patients, we may not have captured associations between these laboratory values and outcomes. Third, imaging findings were compiled from radiology reports. Therefore, subjectivity in reporting style may affect whether or not our descriptive variables were used by the reporting radiologist. Finally, the evaluation of treatment was beyond the scope of this study, and the statistical models did not adjust for treatment. However, remdesivir and steroids, the treatments that have been shown to improve outcomes in patients with COVID-19 [3843], were administered to a small percentage of patients with non-severe disease [Supplementary materials]. Therefore, treatment would not be expected to significantly alter our findings of factors associated with severe COVID-19, but we cannot confirm this hypothesis. Although racial disparities could affect treatment outcomes [4446] we did not find any significant difference in distribution of black patients among remdesivir versus non remdesivir treatment groups in our study.

Conclusions

In this cohort of hospitalized patients with COVID-19 in RI, Black race and diabetes were found to be independent predictors of severe disease. Older age, admission from nursing home or rehabilitation facilities, and presence of hematological disorders predicted mortality. Tachypnea, hypoxia, and hypotension in the first 24 hours predicted progression to severe disease or death later during the hospital stay. These findings can help clinicians with early identification and triage of high-risk patients in order to optimize the allocation of hospital resources.

Supporting information

S1 Table. Comparison of patients included and not included in the study.

https://doi.org/10.1371/journal.pone.0252411.s001

(DOCX)

S2 Table. Demographics characteristics of patients who died vs. patients who were discharged.

https://doi.org/10.1371/journal.pone.0252411.s002

(DOCX)

S3 Table. Medical comorbidities in patients who died vs. patients who were discharged.

https://doi.org/10.1371/journal.pone.0252411.s003

(DOCX)

S4 Table. Presenting symptoms and signs during the first 24 hours of admission.

https://doi.org/10.1371/journal.pone.0252411.s004

(DOCX)

S5 Table. Laboratory values during the first 24 hours of admission.

https://doi.org/10.1371/journal.pone.0252411.s005

(DOCX)

S6 Table. Radiology imaging results during the first 24 hours of admission.

https://doi.org/10.1371/journal.pone.0252411.s006

(DOCX)

S7 Table. Treatment received during hospitalization.

https://doi.org/10.1371/journal.pone.0252411.s007

(DOCX)

Acknowledgments

We would like to acknowledge Dr. Erika D’ Agata for her help in preparation of this manuscript.

References

  1. 1. CDC. Cases in the U.S. 2020 [cited 28 Jul 2020]. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html
  2. 2. CDC COVID Data Tracker. [cited 28 Jul 2020]. https://www.cdc.gov/covid-data-tracker/index.html#cases
  3. 3. Myers LC, Parodi SM, Escobar GJ, Liu VX. Characteristics of Hospitalized Adults With COVID-19 in an Integrated Health Care System in California. JAMA. 2020. pmid:32329797
  4. 4. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020. pmid:32320003
  5. 5. Goyal P, Choi JJ, Pinheiro LC, Schenck EJ, Chen R, Jabri A, et al. Clinical Characteristics of Covid-19 in New York City. N Engl J Med. 2020;382: 2372–2374. pmid:32302078
  6. 6. Petrilli CM, Jones SA, Yang J, Rajagopalan H, O’Donnell L, Chernyak Y, et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ. 2020;369: m1966. pmid:32444366
  7. 7. Stokes EK, Zambrano LD, Anderson KN, Marder EP, Raz KM, El Burai Felix S, et al. Coronavirus Disease 2019 Case Surveillance—United States, January 22–May 30, 2020. MMWR. Morbidity and Mortality Weekly Report. 2020. pp. 759–765. pmid:32555134
  8. 8. Allen D. Roadmap to pandemic resilience. 2020. https://ethics.harvard.edu/files/center-for-ethics/files/roadmaptopandemicresilience_updated_4.20.20_1.pdf
  9. 9. Madhuripan N, Cheung HMC, Alicia Cheong LH, Jawahar A, Willis MH, Larson DB. Variables Influencing Radiology Volume Recovery During the Next Phase of the Coronavirus Disease 2019 (COVID-19) Pandemic. J Am Coll Radiol. 2020;17: 855–864. pmid:32505562
  10. 10. Shi Y, Pandita A, Hardesty A, McCarthy M, Aridi J, Weiss ZF, et al. Validation of pneumonia prognostic scores in a statewide cohort of hospitalised patients with COVID-19. International Journal of Clinical Practice. 2021. pmid:33296132
  11. 11. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395: 1054–1062. pmid:32171076
  12. 12. Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Internal Medicine. 2020. p. 934. pmid:32167524
  13. 13. Hu L, Chen S, Fu Y, Gao Z, Long H, Wang J-M, et al. Risk Factors Associated with Clinical Outcomes in 323 COVID-19 Hospitalized Patients in Wuhan, China. Clinical Infectious Diseases. 2020. pmid:32361738
  14. 14. Imam Z, Odish F, Armstrong J, Elassar H, Dokter J, Langnas E, et al. Independent Correlates of Hospitalization in 2040 Patients with COVID-19 at a Large Hospital System in Michigan, United States. Journal of General Internal Medicine. 2020. pmid:32519326
  15. 15. Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and Mortality among Black Patients and White Patients with Covid-19. N Engl J Med. 2020;382: 2534–2543. pmid:32459916
  16. 16. Pan D, Sze S, Minhas JS, Bangash MN, Pareek N, Divall P, et al. The impact of ethnicity on clinical outcomes in COVID-19: A systematic review. EClinicalMedicine. 2020. p. 100404. pmid:32632416
  17. 17. Website. [cited 30 Jul 2020]. Available: The COVID Racial Data Tracker. https://covidtracking.com/race. Accessed 28 July 2020.
  18. 18. Holmes L, Enwere M, Williams J, Ogundele B, Chavan P, Piccoli T, et al. Black–White Risk Differentials in COVID-19 (SARS-COV2) Transmission, Mortality and Case Fatality in the United States: Translational Epidemiologic Perspective and Challenges. International Journal of Environmental Research and Public Health. 2020. p. 4322. pmid:32560363
  19. 19. Gold JAW, Wong KK, Szablewski CM, Patel PR, Rossow J, da Silva J, et al. Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19—Georgia, March 2020. MMWR. Morbidity and Mortality Weekly Report. 2020. pp. 545–550. pmid:32379729
  20. 20. Lassale C, Gaye B, Hamer M, Gale CR, Batty GD. Ethnic Disparities in Hospitalization for COVID-19: a Community-Based Cohort Study in the UK. medRxiv. 2020. pmid:32511503
  21. 21. White C, Nafilyan V. Coronavirus (COVID-19) related deaths by ethnic group, England and Wales—Office for National Statistics. In: Office for National Statistics [Internet]. 6 May 2020 [cited 28 Jul 2020]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/coronavirusrelateddeathsbyethnicgroupenglandandwales/2march2020to10april2020?hootPostID=b229db5cd884a4f73d5bd4fadcd8959b
  22. 22. Zhang Y, Li H, Zhang J, Cao Y, Zhao X, Yu N, et al. The clinical characteristics and outcomes of patients with diabetes and secondary hyperglycaemia with coronavirus disease 2019: A single-centre, retrospective, observational study in Wuhan. Diabetes, Obesity and Metabolism. 2020. pp. 1443–1454. pmid:32406594
  23. 23. Zhu L, She Z-G, Cheng X, Qin J-J, Zhang X-J, Cai J, et al. Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes. Cell Metab. 2020;31: 1068–1077.e3. pmid:32369736
  24. 24. Ji D, Zhang D, Xu J, Chen Z, Yang T, Zhao P, et al. Prediction for Progression Risk in Patients with COVID-19 Pneumonia: the CALL Score. Clin Infect Dis. 2020. pmid:32271369
  25. 25. Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ. COVID-19: consider cytokine storm syndromes and immunosuppression. The Lancet. 2020. pp. 1033–1034.
  26. 26. Guo W, Li M, Dong Y, Zhou H, Zhang Z, Tian C, et al. Diabetes is a risk factor for the progression and prognosis of COVID-19. Diabetes Metab Res Rev. 2020; e3319. pmid:32233013
  27. 27. Yan Y, Yang Y, Wang F, Ren H, Zhang S, Shi X, et al. Clinical characteristics and outcomes of patients with severe covid-19 with diabetes. BMJ Open Diabetes Res Care. 2020;8. pmid:32345579
  28. 28. Hodgson K, Morris J, Bridson T, Govan B, Rush C, Ketheesan N. Immunological mechanisms contributing to the double burden of diabetes and intracellular bacterial infections. Immunology. 2015. pp. 171–185. pmid:25262977
  29. 29. Muniyappa R, Gubbi S. COVID-19 pandemic, coronaviruses, and diabetes mellitus. Am J Physiol Endocrinol Metab. 2020;318: E736–E741. pmid:32228322
  30. 30. Ritchie AI, Singanayagam A. Immunosuppression for hyperinflammation in COVID-19: a double-edged sword? The Lancet. 2020. p. 1111.
  31. 31. Wang J, Jiang M, Chen X, Montaner LJ. Cytokine storm and leukocyte changes in mild versus severe SARS-CoV-2 infection: Review of 3939 COVID-19 patients in China and emerging pathogenesis and therapy concepts. Journal of Leukocyte Biology. 2020. pp. 17–41. pmid:32534467
  32. 32. Cao W, Li T. COVID-19: towards understanding of pathogenesis. Cell Research. 2020. pp. 367–369. pmid:32346073
  33. 33. Aziz M, Fatima R, Lee-Smith W, Assaly R. The association of low serum albumin level with severe COVID-19: a systematic review and meta-analysis. Crit Care. 2020;24: 255. pmid:32456658
  34. 34. Cheng Y, Luo R, Wang K, Zhang M, Wang Z, Dong L, et al. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney International. 2020. pp. 829–838. pmid:32247631
  35. 35. Cummings MJ, Baldwin MR, Abrams D, Jacobson SD, Meyer BJ, Balough EM, et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study. Lancet. 2020;395: 1763–1770. pmid:32442528
  36. 36. Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, et al. Coronavirus Disease 2019 (COVID-19): A Perspective from China. Radiology. 2020;296: E15–E25. pmid:32083985
  37. 37. Wong HYF, Lam HYS, Fong AH-T, Leung ST, Chin TW-Y, Lo CSY, et al. Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19. Radiology. 2020;296: E72–E78. pmid:32216717
  38. 38. Beigel JH, Tomashek KM, Dodd LE, Mehta AK, Zingman BS, Kalil AC, et al. Remdesivir for the Treatment of Covid-19—Final Report. N Engl J Med. 2020;383: 1813–1826. pmid:32445440
  39. 39. Horby P, Lim WS, Emberson J, Mafham M, Bell J, Linsell L, et al. Effect of Dexamethasone in Hospitalized Patients with COVID-19 –Preliminary Report. pmid:32690491
  40. 40. Wang Y, Zhang D, Du G, Du R, Zhao J, Jin Y, et al. Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial. Lancet. 2020;395: 1569–1578. pmid:32423584
  41. 41. Spinner CD, Gottlieb RL, Criner GJ, Arribas López JR, Cattelan AM, Soriano Viladomiu A, et al. Effect of Remdesivir vs Standard Care on Clinical Status at 11 Days in Patients With Moderate COVID-19: A Randomized Clinical Trial. JAMA. 2020;324: 1048–1057. pmid:32821939
  42. 42. Goldman JD, Lye DCB, Hui DS, Marks KM, Bruno R, Montejano R, et al. Remdesivir for 5 or 10 Days in Patients with Severe Covid-19. N Engl J Med. 2020;383: 1827–1837. pmid:32459919
  43. 43. Kalligeros M, Tashima KT, Mylona EK, Rybak N, Flanigan TP, Farmakiotis D, et al. Remdesivir Use Compared With Supportive Care in Hospitalized Patients With Severe COVID-19: A Single-Center Experience. Open Forum Infect Dis. 2020;7: ofaa319. pmid:33117850
  44. 44. Pandita A, Gil RM, Farmakiotis D. Call for Action: Racial Disparities in Clinical Research. Clin Infect Dis. 2020. pmid:32894751
  45. 45. Olender SA, Perez KK, Go AS, Balani B, Price-Haywood EG, Shah NS, et al. Remdesivir for Severe COVID-19 versus a Cohort Receiving Standard of Care. Clin Infect Dis. 2020. pmid:32706859
  46. 46. Rivera DR, Peters S, Panagiotou OA, Shah DP, Kuderer NM, Hsu C-Y, et al. Utilization of COVID-19 Treatments and Clinical Outcomes among Patients with Cancer: A COVID-19 and Cancer Consortium (CCC19) Cohort Study. Cancer Discovery. 2020. pp. 1514–1527. pmid:32699031