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Article

Kidney and Liver Predictors of Adults Hospitalized with COVID-19 Infection

by
Mariana Boulos
1,2,
Moeen Sbeit
1,
Maamoun Basheer
1,2,
Guy Ben Arie
1,
Yuval Mirkin
1 and
Nimer Assy
1,2,*
1
Internal Medicine Department A, Galilee Medical Center, Nahariya 221001, Israel
2
The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
*
Author to whom correspondence should be addressed.
COVID 2024, 4(4), 452-465; https://doi.org/10.3390/covid4040030
Submission received: 25 February 2024 / Revised: 2 April 2024 / Accepted: 4 April 2024 / Published: 7 April 2024
(This article belongs to the Special Issue COVID-19 in Nephrology)
Review Reports Versions Notes

Abstract

:
SARS-CoV-2 damages not only the lungs, but also the liver and kidney. Most critically ill COVID-19 patients have liver and kidney dysfunctions. The early identification of patients with COVID-19 who will develop severe or critical disease symptoms is important for delivering proper and early treatment. This research studies the correlation of liver and kidney function indexes and COVID-19 outcomes. Electronic medical record data from 391 patients diagnosed with COVID-19 in the COVID-19 Department, Galilee Medical Center, Nahariya, Israel were collected. Epidemiological, clinical, laboratory, and imaging variables were analyzed. The liver and kidney enzyme indexes were measured upon admission and discharge. A correlation between laboratory levels and severity and mortality of COVID-19 patients was undertaken. This study included 391 COVID-19 patients, 258 mild patients and 133 severe patients. Multivariate stepwise regression analyses and discriminant analyses were used to identify and validate powerful predictors. The main outcome was death or invasive ventilation. Three factors, namely higher urea nitrogen (BUN) and IL-6, and lower albumin levels, were the most powerful predictors of mortality, and classified the results (survival vs. death) correctly in 85% of cases (diagnostic accuracy) with a sensitivity of 88% and a specificity 55%. Compared with mild patients, severe patients had lower albumin (ALB), higher alanine aminotransferase (ALT), aspartate aminotransferase (AST) and BUN (all p < 0.001). COVID-19 patients, especially severe patients, have damage to liver and kidney function. BUN, IL-6 and albumin are factors predicting mortality while fibrinogen and AST could be independent factors for predicting the severity of COVID-19.

1. Introduction

The coronavirus disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) is the most recent pandemic and has greatly impacted social and human health issues [1]. Previous studies revealed that SARS-CoV-2 uses the angiotensin-converting enzyme 2 (ACE2) receptor to enter the lungs [2,3] which can cause severe lung fibrosis and consolidation, resulting in high mortality rates in severe cases [4,5]. Further studies have demonstrated that this virus can also damage the liver [6,7,8,9,10] and kidneys [11,12,13,14], since ACE2 is also expressed in both organs [15]. Other possible associated mechanisms of liver and kidney damage include the inflammatory cytokine storm [1,5,16,17,18], drug-induced injury [14,19,20,21,22,23], chronic liver and kidney disease [24,25,26,27] and other factors such as hemodynamic changes [28,29].
A recent study conducted in our department reported an increase risk of morbidity and/or mortality of COVID-19 with the association of age, neutrophil to lymphocyte ratio, BUN and the use of high-flow oxygen therapy.
Studies show the liver injury incidence of COVID-19 patients range from 14.8% to 53%, mainly presented by abnormal laboratory indexes including alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), lactatedehydrogenase (LDH) and gamma-glutamyltransferase (GGT) [9,22,30,31]. Moreover, the severe cases are more likely to have severe liver injury than mild cases [7,32]. In addition, kidney involvement is common in COVID-19 patients who usually present with proteinuria. Severe patients are prone to present with sepsis-related acute kidney injury (AKI) and hypo perfusion-related AKI [24]. COVID-19 combined with kidney damage is an independent risk factor for poor prognosis and is associated with high mortality rates in the ICU [24,33]. So far, there has been little discussion about early monitoring of liver and kidney function to predict the patient’s condition changes, thereby reducing mortality.
We aimed to investigate whether liver and kidney indices can predict severity and mortality in COVID-19 patients.

2. Methods

This is an analytical study. The relevant data regarding the patients were received and recorded in the medical files prospectively, but their use and analysis was performed retrospectively.

2.1. Study Population

A total of 391 COVID-19 patients were analyzed using the Electronic Medical Records (EMRs) between November 2020 and June 2021, in the COVID-19 department in the Galilee Medical Center.

2.2. Inclusion Criteria

(1)
Age > 18 years.
(2)
COVID-19 positive in real-time PCR assay.
(3)
Hospitalized in COVID-19 internal medicine department.

2.3. Exclusion Criteria

(1)
Pregnant woman.

2.4. Data Collection

We performed a retrospective analysis using the EMR data. The analyzed parameters were demographic features, prior and present medical status, clinical features, laboratory tests and treatments upon hospitalization. The diagnosis of COVID-19 was confirmed by positive real-time PCR assays from nasal and nasopharyngeal swab specimens.

2.5. Laboratory Measurements

Liver enzyme levels (AST, ALT) were measured by the International Federation of Clinical Chemistry (IFCC) method, while synthetic function (Albumin) was measured by the colorimetric (with bromocresol green) method. Ferritin was quantitatively measured using the chemiluminescent microparticle immunoassay (CMIA). Fibrinogen was automatically calculated using the HemosIL reagents. IL-6 was quantitatively determined by the electrochemiluminescence immunoassay (ECLIA). BUN was calculated using urease with an enzymatic procedure.

2.6. Definitions

We used the definition of cytokine storm reported by Carucchio et al. [19]. We categorized the severity of the disease as follows; mild disease, with mild clinical symptoms and no pneumonia manifestations in imaging; moderate disease, a patient has symptoms such as fever and respiratory tract symptoms and pneumonia manifestations can be seen in imaging. Severe patients were those who met any of the following criteria: oxygen saturation ≤ 93% at a resting state, respiratory rate ≥ 30 breaths/min, arterial partial pressure of oxygen (PaO2)/oxygen concentration (FiO2) ≤ 300 mm Hg, patients requiring mechanical ventilation, shock presence, other organ failure requiring monitoring and treatment in the intensive care unit [34]. Liver injury patients were defined as follows: liver function examination with one or more of the following abnormalities during treatment: ALT > 40 u/l, AST > 40 u/l, ALB < 2.6 g/dL and INR > 1.8. The kidney injury patients were defined as follows; BUN > 7.1 mmol/L or creatinine (Cr) > 106 mol/L.

2.7. Ethics

Our medical center’s local ethics committee approved this study (N 231-20). Retrospective analysis of data from our EMR database was performed under the oversight of the ICH guidelines for good clinical practice.

2.8. Outcomes

We defined severity using the 4-C score [35,36]. Critical COVID-19 was defined if one of the following criteria were met: admissions to ICU, the need of mechanical ventilation, ARDS or death.

2.9. Statistical Analysis

WinSTAT statistical analysis was performed. It is the statistical add-on program for Microsoft Excel. Univariate direct regression analysis and multivariate stepwise regression analysis were performed for individual variables, including clinical and biochemical variables as independent variables, and severity or death as dependent variables. For categorical variables, the frequency and corresponding diagnosis percentage are provided. Diagnostic analysis used to check the percentage of correct classification of the powerful predictors. Results are presented as mean + SD for continuous variables. Tests of significance were two-tailed, with a significance level set at p < 0.05.

3. Results

3.1. Patients’ Characteristics

EMR data of 391 patients were collected (summarized in Table 1). The average age was 61, 149 (47%) male patients were in the survivor group and 60% were non-survivors. Ethnically Arab patients were twice as likely to be in the survivor group than patients of Jewish ethnicity. The opposite ratio was true in the non-survivors group. Comorbidities were much higher in the non-survivor group, with the most frequent ones being diabetes (57%), hypertension (84%), and hemodialysis (15%). Fever and dyspnea had the same percentages in both groups (57% and 59%, respectively). Diarrhea was threefold higher in the survivor group than in the non-survivors (6%, 2%, respectively). In lab results, statistical significance was seen in lymphocyte counts with lower values in the non-survivor group, 1.4 ± 4 versus 2.06 ± 11 in the survivor group. BUN values (40 ± 27) in the non-survivors were double than in in survivor group (19 ± 14). In addition, there were increased CRP values in the non-survivors (109) compared to the survivors (68). D-DIMER levels were also higher in the non-survivors (2539) in comparison with the survivors (1664). IL-6 was more in the non-survivors than in the survivors (264 ±111, 34 ± 52, respectively). Higher ALT values were seen in non-survivors in comparison to survivors (37 ± 51, 23 ±21) and albumin levels were lower in the non-survivors with an average of 3.4 (Table 1).

3.2. Correlation between Risk Factors and Mortality in COVID-19 Patients

There was a strong correlation between age, BMI and hemodialysis with mortality in COVID-19 patients in univariate and multivariate analyses, while gender, ethnicity and comorbidities (diabetes, hypertension, lung disease) were not correlated with mortality rates in these patients (Table 2).

3.3. Correlation between LFTS and Mortality in COVID-19 Patients

Higher mortality rates were associated with higher fibrinogen (T 3.7) and lower AST in univariate analysis. Albumin levels were the only strong predictor of mortality in the multivariate analysis (Table 3).

3.4. Correlation between Liver Function Tests and Severity in COVID-19 Patients according to 4c Score

Higher rates of AST and fibrinogen were associated in univariate analysis with severe COVID-19, with risk ratios of 2.74 and 1.97, respectively. The same results were seen in a multivariate analysis with a risk ratio of 2.37 for fibrinogen and a risk ratio of 2.97 in severe disease with higher AST levels (Table 4).

3.5. Correlation between Kidney Function Tests and Severity in COVID-19 Patients according to the 4c Score

More severe disease was correlated with higher BUN levels in univariate analysis with a risk ratio of 8. Moreover, the same was observed in a multivariate analysis with a risk ratio of 9.56 (Table 5).

3.6. Multivariate Stepwise Regression Analysis including Liver and Kidney Functions Tests

The three strong parameters that predict mortality were BUN followed by albumin and IL-6 levels (Table 6).

3.7. Diagnostic Accuracy of the Predictors in COVID-19 Pneumonia

The predictive value of BUN, IL-6 and albumin on mortality in COVID-19 patients the number of samples was 175 survivals and 27 non-survivors, with 85% of calculation accuracy. There is good correlation between BUN, IL-6 and albumin and the disease severity and mortality in COVID-19 patients. Sensitivity, specificity, positive predictive value and negative predictive value are 88%, 55%, 84% and 64%, respectively (Table 7).

4. Discussion

This study indicates that liver and kidney function tests predict severity and mortality of COVID-19 patients. It shows that higher levels of BUN, IL-6 and lower levels of albumin are correlated with higher risks of mortality.
Multiple studies have reported higher BUN levels being associated with higher mortality rates [34,35,36]. However, the mechanism for this is yet to be fully elucidated. The primary cellular receptor of SARS-CoV-2 is angiotensin-converting enzyme 2 (ACE-2) [37]. It is highly expressed in renal endothelial cells among other sites, and might cause an interaction between the receptor and the virus, causing lower expressions of the ACE2 protein, which can result in dysregulation of the renin-angiotensin axis. This abnormal activation might lead to increased urea absorption through the tubules, which might lead to increased BUN levels [38,39]. An increase in BUN is not only due to renal dysfunction, it might also increase due to changes in the nitrogen equilibrium, catabolic state, inflammation and renal hypoperfusion due to reduced cardiac output, hypovolemia or sepsis, which were all reported to be closely associated complications in COVID-19 patients [27,40,41].
Our analysis demonstrates that albumin levels are inversely related to mortality rates. This result is consistent with multiple studies of negative prognostic factors in COVID-19 patients [42,43,44]. The mechanism of hypoalbuminemia is not clear. Albumin is synthesized by the liver, and its half-life is three weeks [45]. Albumin plays important roles in maintaining oncotic pressure, lipid metabolism, transporting molecules, inflammation and thrombosis and low albumin levels are considered a biomarker of poor health status and malnutrition [46,47]. Low albumin levels in COVID-19 patients are less likely to be due to lower synthesis since there is a gap between clinical symptoms and albumin’s half-life [48]. It is important to emphasize that albumin levels decrease with age [49]. This in our opinion partially explains the higher rates of disease severity and worsening outcomes related to lower albumin levels. In addition, the inflammatory state of COVID-19 patients described in several studies [50,51,52] is responsible for the lower albumin levels, since inflammation extravagates albumin into interstitial space due to capillary permeability [48].
Higher IL-6 values were associated with mortality of COVID-19 patients which is similar to multiple reports indicating an association between IL-6 and mortality [53,54,55,56]. IL-6 is a multifunctional cytokine mainly produced by macrophages and T lymphocytes. It regulates immune cells and plays an important role in inflammation, hematological and tumoral diseases [57]. While hemostatic values contribute the treatment of infection, exacerbated production contributes to creating a cytokine storm [58,59,60]. This storm can be triggered by multiple factors such as infection, toxins or responses to drugs, characterized by elevated levels of pro-inflammatory cytokines, especially tumor necrosis factor (TNF), IL-6 and IL-1 [61,62]. In addition, recent studies show that the release of IL-6 and other cytokines, due to the activation of innate and adaptive immune responses, cause respiratory failure and increased vascular permeability [63]. For its important role in the pathogenesis of the severe COVID-19 disease, IL-6 was targeted as a potential treatment. In critically ill patients with COVID-19, interleukin 6 antagonists were associated with improved outcomes including survival [64].
COVID-19 has a significant impact on the coagulation system that may manifest in coagulopathy and thrombotic complications [65]. The role of fibrinogen in predicting outcomes in COVID-19 patients has yet to be decided [66]. Levels were found to be elevated in the beginning of the disease and later decreased when disseminated intravascular coagulation happened. It was reported that levels were higher in both survivors and non-survivors, but with no statistical significance, while other studies reported an association between fibrinogen elevation and poor outcome [67,68,69]. One study reported that higher fibrinogen levels are associated with severe disease [70]. In our study, we found that elevated fibrinogen levels upon admission were associated with disease severity but not mortality. Fibrinogen is a glycoprotein complex that is converted to fibrin by thrombin when the tissue is injured causing bleeding to stop and blood clotting [71]. One study showed higher gene expression in vitro of the procoagulant fibrinogen factor in cells infected by the virus [72]. In addition, IL-6 release promotes the activation of the coagulation cascade that also causes higher fibrinogen levels [73].
Liver dysfunction was reported in COVID-19 patients with a wide range of liver and cholestatic enzyme abnormalities [31,69,74,75]. Abnormal liver parameters, especially ALT and AST, are also correlated with increased mortality and morbidity in COVID-19 patients [76,77,78,79,80]. The potential causes of liver involvement in COVID-19 are through different mechanisms. Angiotensin converting enzyme 2 receptors are found in the liver and in bile duct epithelial cells, thus providing access to the cholangiocytes and creating liver injury [81,82,83]. Studies have reported disruption in AST levels, which indicates a direct impairment by the virus [84]. Another potential cause of liver injury is the systemic inflammatory response because of the overproduction of inflammatory cytokines triggering the formation of the cytokine storm [58]. Moreover, a variety of medications used to treat the virus, including antipyretics, antiviral, antibiotics and steroids, are known to cause liver damage. One study noted that drug-induced liver injury (DILI) could be a cause of liver abnormalities, with others showing antivirals, such as oseltamivir ritonavir, hydroxychlroquine and remdesivir, causing hepatotoxic effects resulting in increased liver enzymes [32,85,86].
When interpreting the result of this study, several potential limitations have to be considered. As we were confined to a single center, the sample size is not large enough to show statistical power for some of the results. Importantly, there could be several confounding parameters related to our patients characteristics, which might be the underlying cause of difference in IL-6, albumin, and BUN between the survivors and non- survivors groups.
Firstly, there is a 20-year difference in the mean age of the survivors group and the non-survivors group. Therefore, there could be an age-related cause of death, or a background diagnosis explanation for the lab value differences. In this scene, age might be a confounding parameter, particularly in regards to mortality.
For example, an age-related increase in fibrinogen is a common phenomenon that is well-documented in the literature [87]. It is possible that the increased mean fibrinogen levels in the non-survivors group is at least partially related to the aging process, and not a direct indicator of COVID-19 disease progression. Similarly, and as mentioned in the discussion, serum albumin levels are known to decrease with age. This might be at least partially related to an increase in severity of disease and increased mortality, thereby obscuring the actual relation between decreased albumin and increased COVID-19 mortality. Secondly, hypertension was 35% more prevalent in the non-survivors group compared to the survivors group. Long standing hypertension is a major cause of renal damage, as demonstrated multiple times [88]. As renal damage progresses, BUN levels increase chronically, and it is possible that the increased BUN levels are partially due to previous kidney damage, caused by long standing hypertension, although creatinine levels were not different between the two groups, on admission. Another major difference in patient characteristics between the two groups was the prevalence of diabetes mellitus, which was almost double in the non-survivors compared to the survivors. Similar to hypertension as discussed, DM is also a leading cause for renal damage and decreased GFR, and increased BUN levels are part of the course of diabetic nephropathy [89,90]. Moreover, the risk of developing diabetic nephropathy among DM patients increases with age, and as mentioned, there is a remarkable difference in the mean age of the survivors and non-survivors group. Therefore, it is possible that diabetes mellitus served as another confounder in our attempt to predict disease severity based on BUN.
BMI can also serve as a confounder as it is also linked with metabolic changes that cause alterations in kidney function. There is a close connection between BMI and the ability of the kidneys to perform its physiological role. In fact, both DM and obesity have been demonstrated as risk factors for the deterioration of kidney function. Non-survivors had a higher BMI average as their baseline state. This means that the elevation in BUN seen in this group might be affected by elevated BMI. It is important to emphasize that in our study, patient’s laboratory values were documented for research analysis on admission. Therefore, there was no ability to see the progression of these values as the hospitalization continued. In our opinion, this is an important limitation of the study, as changes in the discussed markers are possibly more indicative of disease progression, and could be more useful for the clinical management of COVID-19 patients in real life settings. Focusing on marker dynamic changes as opposed to absolute values could also address the limitation of preexisting comorbidities as confounders, as discussed above [89,90,91].
Because plenty of time has passed since the beginning of our research, in addition to the changing of COVID-19 variant, we cannot incorporate older control adults older than 60. Instead, we tried to decrease the age effect by filtering our database and compared survival results from patients older than 60 years old and non-survivors over 60, then compared our kidney and liver enzymes again. In this age subgroup, the age difference is much lower with 71.50 ± 9.727 and 79.97 ± 9.593 in survivals and non- survivals, respectively. IL-6, BUN, fibrinogen are statistically significant between both group.
In order to demonstrate stronger evidence for the predictive value of kidney and liver markers for COVID-19 severity, further research must be carried out. Larger sample sizes will allow for better stratification of the cohort, and will enable us to better address the question of whether or not IL-6, BUN and albumin are true markers of COVID-19 severity, independent of pre-existing comorbidities and medication use, and particularly independent of age. Perhaps more importantly, future research will need to examine the dynamic change in lab values, and the trend of change compared to each patient’s baseline value on admission. For these reasons, we suggest carrying out a multi-center prospective cohort study, in a scale large enough to allow stratification of the results to address the limiting confounders. In addition, in such an initiative, we suggest that liver and kidney biomarkers will be measured throughout the hospitalization period in constant intervals (e.g., on a daily basis) to enable the analysis of dynamic changes in these values, as true predictors of COVID-19 severity.

5. Conclusions

In conclusion, when estimating kidney and liver function tests in COVID-19 patients with of Jewish and Arab ethnicity, those with higher BUN, IL6 and lower albumin levels correlated with higher mortality rates. While higher fibrinogen and AST levels were in relation with the severity of the disease. Nonetheless, larger multi-center, prospective research should be performed to assess the issue comprehensively.

Author Contributions

Conceptualization, M.B. (Mariana Boulos) and N.A.; methodology, M.B. (Mariana Boulos); software, M.B. (Mariana Boulos), M.S. and N.A.; validation, M.B. (Mariana Boulos), M.B. (Maamoun Basheer), G.B.A., Y.M. and N.A.; formal analysis, M.B. (Mariana Boulos), Y.M., G.B.A. and N.A.; investigation M.B.(Mariana Boulos), M.B. (Maamoun Basheer) and N.A.; resources, M.B. (Mariana Boulos) and N.A.; data curation, M.B. (Mariana Boulos), M.B. (Maamoun Basheer) and N.A.; writing—original draft preparation, M.B. (Mariana Boulos) and N.A.; writing—review and editing, M.B. (Mariana Boulos), M.B. (Maamoun Basheer) and N.A.; visualization, M.B. (Mariana Boulos), M.B. (Maamoun Basheer) and N.A.; supervision, N.A.; project administration, M.B. and N.A.; funding acquisition, N.A. All authors have read and agreed to the published version of the manuscript.

Funding

This study did not receive any specific grants from funding agencies in the public, commercial or not-for-profit sectors.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by medical center’s local ethics committee (N 231-20).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy of the patient.

Acknowledgments

We would like to thank the directors and medical teams of the Internal Medicine Departments in Galilee Medical Center, Nahariya, Israel for their help and support.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

BUN: Blood urea nitrogen; NLR: Neutrophil to lymphocyte ratio.

References

  1. Wei, M.; Yang, N.; Wang, F.; Zhao, G.; Gao, H.; Li, Y. Epidemiology of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2. Disaster Med. Public Health Prep. 2020, 14, 796–804. [Google Scholar] [CrossRef] [PubMed]
  2. Rico-Mesa, J.S.; White, A.; Anderson, A.S. Outcomes in Patients with COVID-19 Infection Taking ACEI/ARB. Curr. Cardiol. Rep. 2020, 22, 1–4. [Google Scholar] [CrossRef] [PubMed]
  3. Velavan, T.P.; Meyer, C.G. The COVID-19 epidemic. Trop. Med. Int. Health 2020, 25, 278–280. [Google Scholar] [CrossRef] [PubMed]
  4. Xu, X.; Chen, P.; Wang, J.; Feng, J.; Zhou, H.; Li, X.; Zhong, W.; Hao, P. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Sci. China Life Sci. 2020, 63, 457–460. [Google Scholar] [CrossRef] [PubMed]
  5. Ge, H.; Wang, X.; Yuan, X.; Xiao, G.; Wang, C.; Deng, T.; Yuan, Q.; Xiao, X. The epidemiology and clinical information about COVID-19. Eur. J. Clin. Microbiol. Infect. Dis. 2020, 39, 1011–1019. [Google Scholar] [CrossRef] [PubMed]
  6. Wang, Y.; Liu, S.; Liu, H.; Li, W.; Lin, F.; Jiang, L.; Li, X.; Xu, P.; Zhang, L.; Zhao, L.; et al. SARS-CoV-2 infection of the liver directly contributes to hepatic impairment in patients with COVID-19. J. Hepatol. 2020, 73, 807–816. [Google Scholar] [CrossRef]
  7. Cardoso, F.S.; Pereira, R.; Germano, N. Liver injury in critically ill patients with COVID-19: A case series. Crit. Care 2020, 24, 1–2. [Google Scholar] [CrossRef] [PubMed]
  8. Chen, P.; Zhou, B. Clinical Characteristics of COVID-19 in Patients with Liver Injury. Clin. Gastroenterol. Hepatol. 2020, 18, 2846–2847. [Google Scholar] [CrossRef]
  9. Fan, Z.; Chen, L.; Li, J.; Cheng, X.; Yang, J.; Tian, C.; Zhang, Y.; Huang, S.; Liu, Z.; Cheng, J. Clinical Features of COVID-19-Related Liver Functional Abnormality. Clin. Gastroenterol. Hepatol. 2020, 18, 1561–1566. [Google Scholar] [CrossRef]
  10. Xu, L.; Liu, J.; Lu, M.; Yang, D.; Zheng, X. Liver injury during highly pathogenic human coronavirus infections. Liver Int. 2020, 40, 998–1004. [Google Scholar] [CrossRef]
  11. Liu, Y.; Qi, F.Y.; Wei, L.; Cheng, Q.L. Clinical analysis of kidney injury in patients with COVID-19. Zhonghua Yi Xue Za Zhi 2020, 100, E022. [Google Scholar] [CrossRef]
  12. Fanelli, V.; Fiorentino, M.; Cantaluppi, V.; Gesualdo, L.; Stallone, G.; Ronco, C.; Castellano, G. Acute kidney injury in SARS-CoV-2 infected patients. Crit Care 2020, 24, 155. [Google Scholar] [CrossRef] [PubMed]
  13. Post, A.; den Deurwaarder, E.S.G.; Bakker, S.J.L.; de Haas, R.J.; van Meurs, M.; Gansevoort, R.T.; Berger, S.P. Kidney Infarction in Patients With COVID-19. Am. J. Kidney Dis. 2020, 76, 431–435. [Google Scholar] [CrossRef] [PubMed]
  14. Su, H.; Yang, M.; Wan, C.; Yi, L.-X.; Tang, F.; Zhu, H.-Y.; Yi, F.; Yang, H.-C.; Fogo, A.B.; Nie, X.; et al. Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China. Kidney Int. 2020, 98, 219–227. [Google Scholar] [CrossRef] [PubMed]
  15. Donoghue, M.; Hsieh, F.; Baronas, E.; Godbout, K.; Gosselin, M.; Stagliano, N.; Donovan, M.; Woolf, B.; Robison, K.; Jeyaseelan, R.; et al. A Novel Angiotensin-Converting Enzyme–Related Carboxypeptidase (ACE2) Converts Angiotensin I to Angiotensin 1-9. Circ. Res. 2000, 87, e1–e9. [Google Scholar] [CrossRef] [PubMed]
  16. Zhang, Y.; Gao, Y.; Qiao, L.; Wang, W.; Chen, D. Inflammatory Response Cells During Acute Respiratory Distress Syndrome in Patients with Coronavirus Disease 2019 (COVID-19). Ann. Intern. Med. 2020, 173, 402–404. [Google Scholar] [CrossRef] [PubMed]
  17. Taghizadeh-Hesary, F.; Akbari, H. The powerful immune system against powerful COVID-19: A hypothesis. Med. Hypotheses 2020, 140, 109762. [Google Scholar] [CrossRef]
  18. Zhou, Y.; Fu, B.; Zheng, X.; Wang, D.; Zhao, C.; Qi, Y.; Sun, R.; Tian, Z.; Xu, X.; Wei, H. Pathogenic T cells and inflammatory monocytes incite inflammatory storm in severe COVID-19 patients. Natl. Sci. Rev. 2020, 7, 998–1002. [Google Scholar] [CrossRef]
  19. Olry, A.; Meunier, L.; Délire, B.; Larrey, D.; Horsmans, Y.; Le Louët, H. Drug-Induced Liver Injury and COVID-19 Infection: The Rules Remain the Same. Drug Saf. 2020, 43, 615–617. [Google Scholar] [CrossRef] [PubMed]
  20. Muhović, D.; Bojović, J.; Bulatović, A.; Vukčević, B.; Ratković, M.; Lazović, R.; Smolović, B. First case of drug-induced liver injury associated with the use of tocilizumab in a patient with COVID-19. Liver Int. 2020, 40, 1901–1905. [Google Scholar] [CrossRef]
  21. Sise, M.E.; Baggett, M.V.; Shepard, J.-A.O.; Stevens, J.S.; Rhee, E.P. Case 17-2020: A 68-Year-Old Man with COVID-19 and Acute Kidney Injury. N. Engl. J. Med. 2020, 382, 2147–2156. [Google Scholar] [CrossRef]
  22. Chen, T.; Wu, D.; Chen, H.; Yan, W.; Yang, D.; Chen, G.; Ma, K.; Xu, D.; Yu, H.; Wang, H.; et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. BMJ 2020, 368, m1091. [Google Scholar] [CrossRef]
  23. Rismanbaf, A.; Zarei, S. Liver and Kidney Injuries in COVID-19 and Their Effects on Drug Therapy; a Letter to Editor. Arch. Acad. Emerg. Med. 2020, 8, e17. [Google Scholar]
  24. Hassanein, M.; Thomas, G.; Taliercio, J. Management of acute kidney injury in COVID-19. Clevel. Clin. J. Med. 2020. Online ahead of print. [Google Scholar] [CrossRef]
  25. Cheng, Y.; Luo, R.; Wang, K.; Zhang, M.; Wang, Z.; Dong, L.; Li, J.; Yao, Y.; Ge, S.; Xu, G. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int. 2020, 97, 829–838. [Google Scholar] [CrossRef]
  26. Rabb, H. Kidney diseases in the time of COVID-19: Major challenges to patient care. J. Clin. Investig. 2020, 130, 2749–2751. [Google Scholar] [CrossRef] [PubMed]
  27. Ronco, C.; Reis, T.; Husain-Syed, F. Management of acute kidney injury in patients with COVID-19. Lancet Respir. Med. 2020, 8, 738–742. [Google Scholar] [CrossRef] [PubMed]
  28. Su, T.-H.; Kao, J.-H. The clinical manifestations and management of COVID-19-related liver injury. J. Formos. Med Assoc. 2020, 119, 1016–1018. [Google Scholar] [CrossRef]
  29. Barros Camargo, L.; Quintero Marzola, I.D.; Cárdenas Gómez, J.C.; Mendoza Daza, L.T.; Quintana Pájaro, L. Acute kidney injury associated with COVID-19: Another extrapulmonary manifestation. Int. Urol. Nephrol. 2020, 52, 1403–1404. [Google Scholar] [CrossRef] [PubMed]
  30. Basheer, M.; Saad, E.; Hagai, R.; Assy, N. Clinical Predictors of Mortality and Critical Illness in Patients with COVID-19 Pneumonia. Metabolites 2021, 11, 679. [Google Scholar] [CrossRef]
  31. Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506. [Google Scholar] [CrossRef] [PubMed]
  32. Li, J.; Fan, J.-G. Characteristics and Mechanism of Liver Injury in 2019 Coronavirus Disease. J. Clin. Transl. Hepatol. 2020, 8, 1–5. [Google Scholar] [CrossRef] [PubMed]
  33. Staico, M.F.; Zaffanello, M.; Di Pietro, G.; Fanos, V.; Marcialis, M.A. The kidney in COVID-19: Protagonist or figurant? Panminerva Med. 2020, 65, 65–75. [Google Scholar] [CrossRef] [PubMed]
  34. Singh, K.; Singh, S. Blood Urea Nitrogen/Albumin Ratio and Mortality Risk in Patients with COVID-19. Indian J. Crit. Care Med. 2022, 26, 626–631. [Google Scholar] [CrossRef] [PubMed]
  35. Knight, S.R.; Ho, A.; Pius, R.; Buchan, I.; Carson, G.; Drake, T.M.; Dunning, J.; Fairfield, C.J.; Gamble, C.; Green, C.A.; et al. Risk stratification of patients admitted to hospital with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol: Development and validation of the 4C Mortality Score. BMJ 2020, 370, m3339. [Google Scholar] [CrossRef] [PubMed]
  36. Gordon, A.J.; Govindarajan, P.; Bennett, C.L.; Matheson, L.; A Kohn, M.; Camargo, C.; Kline, J. External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network. BMJ Open 2022, 12, e054700. [Google Scholar] [CrossRef]
  37. Tai, W.; He, L.; Zhang, X.; Pu, J.; Voronin, D.; Jiang, S.; Zhou, Y.; Du, L. Characterization of the receptor-binding domain (RBD) of 2019 novel coronavirus: Implication for development of RBD protein as a viral attachment inhibitor and vaccine. Cell. Mol. Immunol. 2020, 17, 613–620. [Google Scholar] [CrossRef] [PubMed]
  38. Soleimani, M. Acute Kidney Injury in SARS-CoV-2 Infection: Direct Effect of Virus on Kidney Proximal Tubule Cells. Int. J. Mol. Sci. 2020, 21, 3275. [Google Scholar] [CrossRef]
  39. Wang, Q.; Zhang, Y.; Wu, L.; Niu, S.; Song, C.; Zhang, Z.; Lu, G.; Qiao, C.; Hu, Y.; Yuen, K.Y.; et al. Structural and Functional Basis of SARS-CoV-2 Entry by Using Human ACE2. Cell 2020, 181, 894–904.e9. [Google Scholar] [CrossRef]
  40. Zhang, P.; Zhu, L.; Cai, J.; Lei, F.; Qin, J.-J.; Xie, J.; Liu, Y.-M.; Zhao, Y.-C.; Huang, X.; Lin, L.; et al. Association of Inpatient Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Mortality Among Patients With Hypertension Hospitalized With COVID-19. Circ. Res. 2020, 126, 1671–1681. [Google Scholar] [CrossRef]
  41. Thum, T. SARS-CoV-2 receptor ACE2 expression in the human heart: Cause of a post-pandemic wave of heart failure? Eur. Heart J. 2020, 41, 1807–1809. [Google Scholar] [CrossRef] [PubMed]
  42. Guan, W.-J.; Liang, W.-H.; Zhao, Y.; Liang, H.-R.; Chen, Z.-S.; Li, Y.-M.; Liu, X.-Q.; Chen, R.-C.; Tang, C.-L.; Wang, T.; et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: A nationwide analysis. Eur. Respir. J. 2020, 55, 2000547. [Google Scholar] [CrossRef]
  43. Xie, H.; Zhao, J.; Lian, N.; Lin, S.; Xie, Q.; Zhuo, H. Clinical characteristics of non-ICU hospitalized patients with coronavirus disease 2019 and liver injury: A retrospective study. Liver Int. 2020, 40, 1321–1326. [Google Scholar] [CrossRef] [PubMed]
  44. Zhang, Y.; Zheng, L.; Liu, L.; Zhao, M.; Xiao, J.; Zhao, Q. Liver impairment in COVID-19 patients: A retrospective analysis of 115 cases from a single centre in Wuhan city, China. Liver Int. 2020, 40, 2095–2103. [Google Scholar] [CrossRef] [PubMed]
  45. Rothschild, M.A.; Oratz, M.; Schreiber, S.S. Serum albumin. J. Hepatol. 1988, 8, 385–401. [Google Scholar] [CrossRef]
  46. Nazha, B.; Moussaly, E.; Zaarour, M.; Weerasinghe, C.; Azab, B. Hypoalbuminemia in colorectal cancer prognosis: Nutritional marker or inflammatory surrogate? World J. Gastrointest. Surg. 2015, 7, 370–377. [Google Scholar] [CrossRef]
  47. Anderson, C.F.; Wochos, D.N. The utility of serum albumin values in the nutritional assessment of hospitalized patients. Mayo Clin. Proc. 1982, 57, 181–184. [Google Scholar] [CrossRef]
  48. Huang, J.; Cheng, A.; Kumar, R.; Fang, Y.; Chen, G.; Zhu, Y.; Lin, S. Hypoalbuminemia predicts the outcome of COVID-19 independent of age and co-morbidity. J. Med. Virol. 2020, 92, 2152–2158. [Google Scholar] [CrossRef]
  49. Veering, B.; Burm, A.; Souverijn, J.; Serree, J.; Spierdijk, J. The effect of age on serum concentrations of albumin and alpha 1-acid glycoprotein. Br. J. Clin. Pharmacol. 1990, 29, 201–206. [Google Scholar] [CrossRef]
  50. Paces, J.; Strizova, Z.; Smrz, D.; Cerny, J. COVID-19 and the Immune System. Physiol. Res. 2020, 69, 379–388. [Google Scholar] [CrossRef]
  51. Anka, A.U.; Tahir, M.I.; Abubakar, S.D.; Alsabbagh, M.; Zian, Z.; Hamedifar, H.; Sabzevari, A.; Azizi, G. Coronavirus disease 2019 (COVID-19): An overview of the immunopathology, serological diagnosis and management. Scand. J. Immunol. 2021, 93, e12998. [Google Scholar] [CrossRef] [PubMed]
  52. Parasher, A. COVID-19: Current understanding of its Pathophysiology, Clinical presentation and Treatment. Postgrad. Med. J. 2020, 97, 312–320. [Google Scholar] [CrossRef] [PubMed]
  53. Merad, M.; Blish, C.A.; Sallusto, F.; Iwasaki, A. The immunology and immunopathology of COVID-19. Science 2022, 375, 1122–1127. [Google Scholar] [CrossRef] [PubMed]
  54. Zhou, J.; He, W.; Liang, J.; Wang, L.; Yu, X.; Bao, M.; Liu, H. Association of Interleukin-6 Levels with Morbidity and Mortality in Patients with Coronavirus Disease 2019 (COVID-19). Jpn. J. Infect. Dis. 2021, 74, 293–298. [Google Scholar] [CrossRef] [PubMed]
  55. Avila-Nava, A.; Cortes-Telles, A.; Torres-Erazo, D.; López-Romero, S.; Chim, A.k.é.R.; Gutiérrez Solis, A.L. Serum IL-6: A potential biomarker of mortality among SARS-CoV-2 infected patients in Mexico. Cytokine 2021, 143, 155543. [Google Scholar] [CrossRef] [PubMed]
  56. Chen, G.; Wu, D.; Guo, W.; Cao, Y.; Huang, D.; Wang, H.; Wang, T.; Zhang, X.; Chen, H.; Yu, H.; et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J. Clin. Investig. 2020, 130, 2620–2629. [Google Scholar] [CrossRef] [PubMed]
  57. Guirao, J.J.; Cabrera, C.M.; Jiménez, N.; Rincón, L.; Urra, J.M. High serum IL-6 values increase the risk of mortality and the severity of pneumonia in patients diagnosed with COVID-19. Mol. Immunol. 2020, 128, 64–68. [Google Scholar] [CrossRef] [PubMed]
  58. Velazquez-Salinas, L.; Verdugo-Rodriguez, A.; Rodriguez, L.L.; Borca, M.V. The Role of Interleukin 6 During Viral Infections. Front. Microbiol. 2019, 10, 1057. [Google Scholar] [CrossRef] [PubMed]
  59. Tanaka, T.; Narazaki, M.; Kishimoto, T. Immunotherapeutic implications of IL-6 blockade for cytokine storm. Immunotherapy 2016, 8, 959–970. [Google Scholar] [CrossRef]
  60. Dienz, O.; Rud, J.G.; Eaton, S.M.; A Lanthier, P.; Burg, E.; Drew, A.; Bunn, J.; Suratt, B.T.; Haynes, L.; Rincon, M. Essential role of IL-6 in protection against H1N1 influenza virus by promoting neutrophil survival in the lung. Mucosal Immunol. 2012, 5, 258–266. [Google Scholar] [CrossRef]
  61. Wang, Z.; Han, W. Biomarkers of cytokine release syndrome and neurotoxicity related to CAR-T cell therapy. Biomark. Res. 2018, 6, 4. [Google Scholar] [CrossRef] [PubMed]
  62. Norelli, M.; Camisa, B.; Barbiera, G.; Falcone, L.; Purevdorj, A.; Genua, M.; Sanvito, F.; Ponzoni, M.; Doglioni, C.; Cristofori, P.; et al. Monocyte-derived IL-1 and IL-6 are differentially required for cytokine-release syndrome and neurotoxicity due to CAR T cells. Nat. Med. 2018, 24, 739–748. [Google Scholar] [CrossRef]
  63. Hadjadj, J.; Yatim, N.; Barnabei, L.; Corneau, A.; Boussier, J.; Smith, N.; Péré, H.; Charbit, B.; Bondet, V.; Chenevier-Gobeaux, C.; et al. Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science 2020, 369, 718–724. [Google Scholar] [CrossRef] [PubMed]
  64. REMAP-CAP Investigators; Gordon, A.C.; Mouncey, P.R.; Al-Beidh, F.; Rowan, K.M.; Nichol, A.D.; Arabi, Y.M.; Annane, D.; Beane, A.; van Bentum-Puijk, W.; et al. Interleukin-6 Receptor Antagonists in Critically Ill Patients with COVID-19. N. Engl. J. Med. 2021, 384, 1491–1502. [Google Scholar] [CrossRef] [PubMed]
  65. Levi, M.; Thachil, J.; Iba, T.; Levy, J.H. Coagulation abnormalities and thrombosis in patients with COVID-19. Lancet Haematol. 2020, 7, e438–e440. [Google Scholar] [CrossRef] [PubMed]
  66. Gao, Y.; Li, T.; Han, M.; Li, X.; Wu, D.; Xu, Y.; Zhu, Y.; Liu, Y.; Wang, X.; Wang, L. Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19. J. Med. Virol. 2020, 92, 791–796. [Google Scholar] [CrossRef] [PubMed]
  67. Toh, C.H.; Hoots, W.K. The scoring system of the Scientific and Standardisation Committee on Disseminated Intravascular Coagulation of the International Society on Thrombosis and Haemostasis: A 5-year overview. J. Thromb. Haemost. 2007, 5, 604–606. [Google Scholar] [CrossRef] [PubMed]
  68. Han, H.; Yang, L.; Liu, R.; Liu, F.; Wu, K.L.; Li, J.; Liu, X.H.; Zhu, C.L. Prominent Changes in Blood Coagulation of Patients with SARS-CoV-2 Infection. Clin. Chem. Lab. Med. 2020, 58, 1116–1120. [Google Scholar] [CrossRef] [PubMed]
  69. Tang, N.; Li, D.; Wang, X.; Sun, Z. Abnormal Coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J. Thromb. Haemost. 2020, 18, 844–847. [Google Scholar] [CrossRef]
  70. Sui, J.; Noubouossie, D.F.; Gandotra, S.; Cao, L. Elevated Plasma Fibrinogen Is Associated with Excessive Inflammation and Disease Severity in COVID-19 Patients. Front. Cell. Infect. Microbiol. 2021, 11, 734005. [Google Scholar] [CrossRef]
  71. Demelo-Rodríguez, P.; Cervilla-Muñoz, E.; Ordieres-Ortega, L.; Parra-Virto, A.; Toledano-Macías, M.; Toledo-Samaniego, N.; García-García, A.; García-Fernández-Bravo, I.; Ji, Z.; De-Miguel-Diez, J.; et al. Incidence of Asymptomatic Deep Vein Thrombosis in Patients With COVID-19 Pneumonia and Elevated D-dimer Levels. Thromb. Res. 2020, 192, 23–26. [Google Scholar] [CrossRef] [PubMed]
  72. Giannis, D.; Ziogas, I.A.; Gianni, P. Coagulation disorders in coronavirus infected patients: COVID-19, SARS-CoV-1, MERS-CoV and lessons from the past. J. Clin. Virol. 2020, 127, 104362. [Google Scholar] [CrossRef] [PubMed]
  73. Stouthard, J.M.L.; Levi, M.; Hack, C.E.; Veenhof, C.H.N.; A Romijn, H.; Sauerwein, H.P.; van der Poll, T. Interleukin-6 Stimulates Coagulation, not Fibrinolysis, in Humans. Arthritis Res. Ther. 1996, 76, 738–742. [Google Scholar] [CrossRef]
  74. Chen, N.; Zhou, M.; Dong, X.; Qu, J.; Gong, F.; Han, Y.; Qiu, Y.; Wang, J.; Liu, Y.; Wei, Y.; et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 2020, 395, 507–513. [Google Scholar] [CrossRef] [PubMed]
  75. Guan, W.J.; Ni, Z.Y.; Hu, Y.; Liang, W.H.; Ou, C.Q.; He, J.X.; Liu, L.; Shan, H.; Lei, C.L.; Hui, D.S.C.; et al. China Medical Treatment Expert Group for COVID19. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720. [Google Scholar] [CrossRef] [PubMed]
  76. Cai, Q.; Huang, D.; Yu, H.; Zhu, Z.; Xia, Z.; Su, Y.; Li, Z.; Zhou, G.; Gou, J.; Qu, J.; et al. COVID-19: Abnormal liver function tests. J. Hepatol. 2020, 73, 566–574. [Google Scholar] [CrossRef] [PubMed]
  77. Ye, L.; Chen, B.; Wang, Y.; Yang, Y.; Zeng, J.; Deng, G.; Deng, Y.; Zeng, F. Prognostic value of liver biochemical parameters for COVID-19 mortality. Ann. Hepatol. 2020, 21, 100279. [Google Scholar] [CrossRef]
  78. Phipps, M.M.; Barraza, L.H.; Lasota, E.D.; Sobieszczyk, M.E.; Pereira, M.R.; Zheng, E.X.; Fox, A.N.; Zucker, J.; Verna, E.C. Acute Liver Injury in COVID-19: Prevalence and Association with Clinical Outcomes in a Large U.S. Cohort. Hepatology 2020, 72, 807–817. [Google Scholar] [CrossRef] [PubMed]
  79. Mendizabal, M.; Piñero, F.; Ridruejo, E.; Anders, M.; Silveyra, M.D.; Torre, A.; Montes, P.; Urzúa, A.; Pages, J.; Toro, L.G.; et al. Prospective Latin American cohort evaluating outcomes of patients with COVID-19 and abnormal liver tests on admission. Ann. Hepatol. 2021, 21, 100298. [Google Scholar] [CrossRef]
  80. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X.; et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020, 28, 1054–1062. [Google Scholar] [CrossRef]
  81. Yuki, K.; Fujiogi, M.; Koutsogiannaki, S. COVID-19 pathophysiology: A review. Clin. Immunol. 2020, 215, 108427. [Google Scholar] [CrossRef] [PubMed]
  82. Zaim, S.; Chong, J.H.; Sankaranarayanan, V.; Harky, A. COVID-19 and Multiorgan Response. Curr. Probl. Cardiol. 2020, 45, 100618. [Google Scholar] [CrossRef] [PubMed]
  83. Zhang, C.; Shi, L.; Wang, F.-S. Liver injury in COVID-19: Management and challenges. Lancet Gastroenterol. Hepatol. 2020, 5, 428–430. [Google Scholar] [CrossRef] [PubMed]
  84. Li, Y.; Hu, Y.; Yu, J.; Ma, T. Retrospective analysis of laboratory testing in 54 patients with severe- or critical-type 2019 novel coronavirus pneumonia. Mod. Pathol. 2020, 100, 794–800. [Google Scholar] [CrossRef]
  85. U.S. Food & Drug Administration. Remdesivir EUA Letter of Authorization. 2020. Available online: www.fda.gov/media/137564/download (accessed on 22 October 2021).
  86. U.S. Food and Drug Administration. FDA Approves First Treatment for COVID-19. 2020. Available online: https://www.fda.gov/news-events/press-announcements/fda-approves-first-treatment-covid-19 (accessed on 22 October 2021).
  87. Fu, A.; Nair, K.S. Age effect on fibrinogen and albumin synthesis in humans. Am. J. Physiol. -Endocrinol. Metab. 1998, 275, E1023–E1030. [Google Scholar] [CrossRef] [PubMed]
  88. Young, J.H.; Klag, M.J.; Muntner, P.; Whyte, J.L.; Pahor, M.; Coresh, J. Blood pressure and decline in kidney function: Findings from the Systolic Hypertension in the Elderly Program (SHEP). J. Am. Soc. Nephrol. 2002, 13, 2776–2782. [Google Scholar] [CrossRef]
  89. Kussman, M.J.; Goldstein, H.H.; Gleason, R.E. The Clinical Course of Diabetic Nephropathy. JAMA 1976, 236, 1861–1863. [Google Scholar] [CrossRef] [PubMed]
  90. Kjaergaard, A.D.; Teumer, A.; Witte, D.R.; Stanzick, K.-J.; Winkler, T.W.; Burgess, S.; Ellervik, C. Obesity and Kidney Function: A Two-Sample Mendelian Randomization Study. Clin. Chem. 2021, 68, 461–472. [Google Scholar] [CrossRef]
  91. Zhong, J.-B.; Yao, Y.-F.; Zeng, G.-Q.; Zhang, Y.; Ye, B.-K.; Dou, X.-Y.; Cai, L. A closer association between blood urea nitrogen and the probability of diabetic retinopathy in patients with shorter type 2 diabetes duration. Sci. Rep. 2023, 13, 9881. [Google Scholar] [CrossRef]
Table 1. Clinical characteristics of the surviving and non-surviving patients with COVID-19 infection.
Table 1. Clinical characteristics of the surviving and non-surviving patients with COVID-19 infection.
VariableSurvivorsNon-Survivorsp-Value
TotalN = 318N = 74
Age58 ± 1878 ± 120.001
Male (%)0.470.6600
Ethnicity % (Arab/Jewish)(65/35)(40/60)0.004
BMI29 ± 632 ± 110.008
Comorbidities %
          Diabetes (%)30570.001
Hypertension (%)48840.001
          Lung disease (%)9180.03
          Hemodialysis (%)5150.003
Aspirin use (%)30610.001
Symptom’s duration before admission to hospitals6 ± 59 ± 120.19
Fever %57560.77
Diarrhea %620.26
Dyspnea %59590.74
Clinical severity on admission %30540.001
Lab Findings upon admission
Hemoglobin (mg/dl)13 ± 312 ± 1.20.06
Absolute neutrophil count (×103/µL)13 ± 913 ± 60.9
Absolute lymphocyte count (×103/µL)2.06 ± 111.4 ± 40.001
Neutrophil to lymphocyte ratio (NLR)7.01 ± 39.1 ± 0.80.001
Platelet (×103/µL)220± 86220 ± 900.79
BUN (mg/dl)19 ± 1440 ± 270.001
Creatinine (mg/dl)2 ± 82.2 ± 20.7
Triglycerides (mg/dl)145 ± 148157 ± 480.08
HDL (mg/dl)33 ± 1230 ± 130.09
Insulin Resistance (TG/HDL)5.4 ± 5.86.1 ± 5.00.02
C-reactive protein (CRP) (mg/dl)68 ± 78109 ± 970.001
Ferritin632 ± 9871041 ± 35940.57
D-dimer1664 ± 35132539 ± 33740.001
Fibrinogen641 ± 183660 ± 1800.33
IL-634 ± 52264 ± 1110.001
ALT23 ± 2137 ± 510.001
AST40.6 ± 34.3237.9 ± 23.1???
Albumin3.8 ± 0.63.4 ± 0.4???
Cytokine storm (% of total)25510.001
4-C score8 ± 2012 ± 30.001
SOFA score1.3 ± 1.42.7 ± 2.20.001
O2 supplement on admission %551000.001
High flow use (% of total)14820.001
Mechanical ventilation (% of total)2460.001
ARDS (% of total)17340.006
O2 supply at day 7 (% of total)18990.001
Hospitalization length9.5 ± 1017.2 ± 130.001
Time to negative PCR17 ± 820 ± 80.16
Treatment in hospitalization
Steroid therapy (% of total)93970.26
Methylprednisolone86900.75
Dexamethasone 14100.75
LMWH (% of total)96990.34
Remdesivir (% of total)12190.21
Vitamin D (% of total)97970.9
BMI: Body Mass Index, IL-6: Interleukin 6, ALT: Alanine transaminase AST: Aspartate Aminotransferase, ARDS: acute respiratory distress syndrome, LMWH: low molecular weight heparin.
Table 2. A: Univariate analysis of risk factor strengths in mortality prediction. B: Multi-regression analysis of risk factors.
Table 2. A: Univariate analysis of risk factor strengths in mortality prediction. B: Multi-regression analysis of risk factors.
A—ConstantCoefficientConf. (±)Std. ErrorTp-Value
Age0.0060.00020.0015.431.07 × 10−7
Gender−0.0490.0710.036−1.360.17
Ethnicity−0.0470.0710.03−1.230.21
BMI0.0060.0050.0022.230.02
DM0.0470.0880.0441.50.29
HTN−0.0240.0940.048−0.50.69
Hemodialysis0.160.140.0712.240.025
Lung Diseaase0.050.1160.0520.860.38
B—ConstantCoefficientConf. (±)Std. ErrorTp
Age0.0070.00190.0017.71.45 × 10−13
BMI0.0060.050.00252.340.0195
HEmodialysis0.1760.1370.06962.530.01166
BMI: Body Mass Index, DM: diabetes mellitus, HTN: hypertension. Conf. (±): confidence interval, Std. Error: Standard.
Table 3. A: Univariate analysis of the correlation between liver function tests and mortality in COVID-19 patients. B: Multivariate analysis of the correlation between liver function tests and mortality in COVID-19 patients.
Table 3. A: Univariate analysis of the correlation between liver function tests and mortality in COVID-19 patients. B: Multivariate analysis of the correlation between liver function tests and mortality in COVID-19 patients.
A—ConstantCoefficientConf. (±)Std. ErrorTp-Value
Fibrinogen0.9960.5190.2633.7820.0002
INR−0.000010.00030.0001−0.07520.94
Ferritin−0.0070.1090.055−0.1280.898
IL-60.0000170.000020.000011.2490.212
ALB0.000030.000050.000021.1580.248
AST−0.19330.1140.0578−3.3380.001
ALT−0.000320.00340.0017−0.1880.85
B—ConstantCoefficientConf. (±)Std. ErrorTp
ALB−0.21360.10970.0556−3.840.00016
INR: International Normalized Ratio, IL-6: Interleukin 6, ALT: Alanine transaminase AST: Aspartate Aminotransferase, ALB: Albumin. Conf. (±): confidence interval, Std. Error: Standard.
Table 4. A: Univariate analysis of the correlation between liver function tests and severity in COVID-19 patients according to the 4c score B: Multivariate analysis of the correlation between liver function tests and severity in COVID-19 patients according to the 4c score.
Table 4. A: Univariate analysis of the correlation between liver function tests and severity in COVID-19 patients according to the 4c score B: Multivariate analysis of the correlation between liver function tests and severity in COVID-19 patients according to the 4c score.
A—ConstantCoefficientConf. (±)Std. ErrorTp
Fibrinogen0.00030.000350.000181.97780.0493
INR−0.00630.12560.0637−0.09970.9206
Ferritin0.0000070.000030.000010.47910.6323
IL-6−0.000030.000060.00003−1.01350.312
ALB−0.04170.13130.0666−0.62590.532
AST0.00550.0040.0022.74370.0066
ALT−0.0020.00270.0014−1.45860.1462
B—ConstantCoefficientConf. (±)Std. ErrorTp
Fibrinogen0.00040.00030.000172.37520.01845
AST0.00350.002360.001192.97810.00324
INR: International Normalized Ratio, IL-6 Interleukin 6, ALT: Alanine transaminase AST: Aspartate Aminotransferase, ALB: Albumin. Conf. (±): confidence interval, Std. Error Standard.
Table 5. A: Univariate analysis of the correlation between kidney function tests and severity in COVID-19 patients according to the 4c score. B: Multivariate analysis of the correlation between kidney function tests and severity in COVID-19 patients according to the 4c score.
Table 5. A: Univariate analysis of the correlation between kidney function tests and severity in COVID-19 patients according to the 4c score. B: Multivariate analysis of the correlation between kidney function tests and severity in COVID-19 patients according to the 4c score.
A—ConstantCoefficientConf. (±)Std. ErrorTp
BUN0.00850.0020.0018.03521.1822 × 10−14
Creatinine−0.00340.00490.0025−1.37070.1712
GFR−0.000590.000730.00037−1.59420.1117
B—ConstantCoefficientConf. (±)Std. ErrorTp
BUN0.0090890.001860.000959.56541.4013 × 10−19
BUN: blood urine nitrogen GFR glomerular filtration rate. Conf. (±) confidence interval, Std. Error Standard.
Table 6. A: Univariate analysis of the between kidney function tests and severity in COVID-19 patients according to 4c score. B: Multivariate analysis of between kidney function tests and severity in COVID-19 patients according to 4c score.
Table 6. A: Univariate analysis of the between kidney function tests and severity in COVID-19 patients according to 4c score. B: Multivariate analysis of between kidney function tests and severity in COVID-19 patients according to 4c score.
ConstantCoefficientConf. (±)Std. ErrorTp
IL-60.00010.000090.000042.14740.03312
ALB−0.12970.10740.05442−2.38380.01819
BUN0.00850.00330.001675.07389.83908 × 10−7
IL-6 Interleukin 6, ALB: Albumin. BUN Blood Urine Nitrogen. Conf. (±) confidence interval, Std. Error Standard.
Table 7. A. Discriminant analysis: The validity (predictive power) of BUN, IL-6 and albumin on mortality in COVID-19 patients. The accuracy of the calculations is 85%. The number of samples: Predicted condition—175 survival, 53 death, and true condition 0 for survivors and 1 for non-survivors B. The sensitivity, specificity, positive predictive value and negative predictive value.
Table 7. A. Discriminant analysis: The validity (predictive power) of BUN, IL-6 and albumin on mortality in COVID-19 patients. The accuracy of the calculations is 85%. The number of samples: Predicted condition—175 survival, 53 death, and true condition 0 for survivors and 1 for non-survivors B. The sensitivity, specificity, positive predictive value and negative predictive value.
AActual Count0—Survivors1—Non-Survivors
Survivor (0)17514827
Non-survivor(1) 531934
B
Sensitivity 88%
Specificity 55%
Positive Predictive Value 84%
Negative Predictive Value 64%
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Boulos, M.; Sbeit, M.; Basheer, M.; Ben Arie, G.; Mirkin, Y.; Assy, N. Kidney and Liver Predictors of Adults Hospitalized with COVID-19 Infection. COVID 2024, 4, 452-465. https://doi.org/10.3390/covid4040030

AMA Style

Boulos M, Sbeit M, Basheer M, Ben Arie G, Mirkin Y, Assy N. Kidney and Liver Predictors of Adults Hospitalized with COVID-19 Infection. COVID. 2024; 4(4):452-465. https://doi.org/10.3390/covid4040030

Chicago/Turabian Style

Boulos, Mariana, Moeen Sbeit, Maamoun Basheer, Guy Ben Arie, Yuval Mirkin, and Nimer Assy. 2024. "Kidney and Liver Predictors of Adults Hospitalized with COVID-19 Infection" COVID 4, no. 4: 452-465. https://doi.org/10.3390/covid4040030

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