Assessment of risk factors in corona virus 19 infected persons

Document Type : Original Article

Authors

1 Chest Diseases Faculty of Medicine, Al-Azhar University

2 Department of Chest Diseases, Faculty of Medicine - Al-Azhar University, Egypt

Abstract

ABSTRACT
Background: Mortality from Covid-19 is higher among patients with chronic diseases.
Aim of The study : evaluate predictors of severe illness in patients' coronavirus diseases.
Patients and Methods: retrospective study included 200 patients with COVID 19 pneumonia admitted in chest departments, Patients divided into 2 groups; Group1had 100 patients (30 male patients and 70 female) had pneumonia manifestations on radiology and had risk factors, group 2 was a control group 100 patients (53 male patients and 47 female) had pneumonia manifestations on radiology and hadn’t risk factors. Mild Covid 19cases with lymphopenia or leucopenia without radiological signs of pneumonia and Pediatric cases of Covid 19 excluded. Detailed history, clinical examination, Identifying coronavirus by transcription-polymerase chain reaction, arterial blood gases, complete blood picture, Liver functions testes, blood urea and serum creatinine, (CRP), D-dimer serum ferritin, CT chest.
Results: most common comorbid conditions were systemic hypertension (62%), diabetes mellitus (61%), and ischemic heart disease (48%)). older age and SpO2 <74% were significant predictors for invasive respiratory support or in-hospital death, patients with risk factors had myalgia (22%), chest pain and DCL symptoms 11%. D-dimer identified as an independent risk factor (P value 0.034), serum creatinine in Group 1 is higher than group2.
Conclusion: Advanced age was more liable for infection with COVID-19. Hypertension, diabetes mellitus and ischemic heart disease were independently associated with a higher risk of in-hospital death. COVID-19 infected patients with risk factors had poor outcome. Chronic patients had Lower blood oxygen saturation.

Keywords


 INTRODUCTION

The 2019 new coronavirus (COVID-19) has propagated unabatedly globally, infecting 42 million people and killing ˃ 2.5 million as of February 28, 2021. 1

COVID-19 is the coronavirus family's seventh member.2 Mortality caused by Covid-19 is in particular elevated in cases suffering including hypertension, DM, and IHD, and among those who reach the point of co-existing necessitating invasive mechanical ventilation. 3

COVID-19 has an increased reproduction number, and thus it's highly infectious in comparison with its precursors, leading to a tremendous burden on global health. COVID-19 infections have generally mild clinical symptoms, and the vast majority of cases have a favorable outcome. Nevertheless, in 10–20% of all cases, the situation may worsen, necessitating

 

transfer to an ICU and having an elevated fatality rate. 4

The goal of this study was to see if there is any predictor of severe illness in COVID-19 individuals.

PATIENTS AND METHODS

A retrospective study was performed on 200 COVID-19 pneumonia cases admitted to Damanhour chest diseases hospital.

The patients were divided into two groups:

The first group: included 100 patients (30 men & 70 women) (mean age ±SD62.45±11.96) who had manifestations of pneumonia on radiological examination and had COVID–19 risk factors for in-hospital fatalities.

The second group: was a control group of 100 patients (53 men & 47 women) (mean age ±SD57.39±11.92) with manifestations of pneumonia on radiological examination but no risk factors for COVID–19 in-hospital death.

The study included cases diagnosed with COVID-19 and having pneumonia manifestations on radiological examination. These included moderate cases (accompanied by symptoms and/or leucopenia or lymphopenia), severe cases (RR exceeded 30, SaO2 less than 92 at room air, PaO2/FiO2 ratio < 300, chest radiography showing ˃ 50% lesion or increasing lesion within 24-48 hrs.) and critically ill patients if SaO2 was less than 92 at room air, or RR exceeded 30, or PaO2/FiO2 ratio less than 200 in spite of O2 therapy and hemodynamic instability and required mechanical ventilation.

Mild Cases of Covid 19 which were characterized by symptomatic cases with lymphopenia or leucopenia without radiological signs of pneumonia and Pediatric cases of Covid 19 were ruled out.

Past history, general and chest examination, detection of the Covid19 by RT-PCR (Nasopharyngeal & oral swap for Covid19)

 Laboratory investigations: CBC, Liver function tests Blood urea and serum Cr, (CRP), D-dimer and serum ferritin. Chest imaging (CT):.Images were examined for signs of pneumonia if unilaterally or bilaterally involvement and included ≥ 1 lobe.

Statistical analysis

The IBM SPSS software package version 20.0 (Armonk, NY: IBM Corp) was used to analyze the data that was supplied to the computer. "Number and percent" have been employed to describe qualitative data. The Kolmogorov-Smirnov test has been employed to verify that the distribution is normal. Quantitative data has been expressed employing the following terms: range (min and max), mean, standard deviation, median, and interquartile range (IQR). The obtained findings have been determined to be significant at the 5% level.

 

RESULTS

               

Group I
(n = 100)

Group II
(n = 100)

Test of Sig.

p

 

No.

%

No.

%

Sex

 

 

 

 

 

 

Male

30

30.0

53

53.0

χ2=
10.895*

0.001*

Female

70

70.0

47

47.0

Age (years)

 

 

 

 

 

 

<65

52

52.0

100

100.0

χ2=
63.158*

<0.001*

≥65

48

48.0

0

0.0

Min. – Max.

24.0 – 90.0

23.0 – 64.0

t=
8.916*

<0.001*

Mean ± SD.

62.45 ± 11.96

47.39 ± 11.92

Median (IQR)

63.0(56.0 – 70.50)

48.0 (36.0 – 57.0)

IQR: Inter quartile range                         SD: Standard deviation                          

c2:  Chi square test                                 t: Student t-test

p: p-value for comparing the two groups under study

*: Statistically significant at p ≤ 0.05 

Group I:  Patient group with COVID-19-related mortality risk factors

Group II: Control group without COVID-19-related mortality risk factors

 

Table 1: Demographic characteristics of the studied patients

 

Risk factor

No.

%

Age ≥65 years

48

48.0

Cardiovascular diseases

48

48.0

Hypertension diseases

62

62.0

Diabetes diseases

61

61.0

Chronic chest diseases

18

18.0

Cancer

2

2.0

Hepatic diseases

5

5.0

Renal diseases

2

2.0

Others diseases

12

12.0

Table 2: Distribution of the examined cases in group 1 (n = 100) based on the risk factor

 

 

Group I
(n = 100)

Group II
(n = 100)

U

p

Serum creat

 

 

 

 

Min. – Max.

0.40 – 10.0

0.50 – 10.0

2075.0*

<0.001*

Mean ± SD.

2.38 ± 1.48

1.21 ± 0.99

Median (IQR)

2.40 (1.10 – 2.90)

1.0 (0.90 – 1.20)

Blood urea

 

 

 

 

Min. – Max.

17.0 – 141.0

15.0 – 237.0

3722.5*

0.002*

Mean ± SD.

50.96 ± 26.65

41.74 ± 28.55

Median (IQR)

41.0 (33.50 – 60.0)

36.0 (30.0 – 45.0)

ALT

 

 

 

 

Min. – Max.

11.0 – 259.0

13.0 – 212.0

3983.5*

0.013*

Mean ± SD.

85.56 ± 77.29

41.74 ± 31.68

Median (IQR)

41.50 (23.0 – 133.5)

33.0 (24.0 – 45.0)

AST

 

 

 

 

Min. – Max.

10.0 – 500.0

8.0 – 206.0

4704.0

0.469

Mean ± SD.

47.76 ± 69.93

40.24 ± 26.65

Median (IQR)

32.50(24.50 – 45.0)

35.0 (27.0 – 43.50)

IQR: Inter quartile range                         SD: Standard deviation                           U: Mann Whitney test

p: p-value for comparing the two groups under study

*: Statistically significant at p ≤ 0.05 

ALT:       Alanine aminotransferase

AST:       Aspartate aminotransferase

 

Table 3: Comparison of the two examined groups based on organ function tests

CT features

Group I
(n = 100)

Group II
(n = 100)

χ2

p

No.

%

No.

%

Ground glass opacity (GGO)

 

 

 

 

 

 

One lobes

14

14.0

28

28.0

8.363*

0.015*

Two lobes

30

30.0

34

34.0

More than two  lobes

56

56.0

38

38.0

Consolidations

30

30.0

15

15.0

6.452*

0.011*

Interstitial fibrosis

10

10.0

3

3.0

4.031*

0.045*

c2:  Chi square test

p: p-value for comparing the two groups under stud   *: Statistically significant at p ≤ 0.05

 

Table 4: Comparison of the two examined groups based on CT features

Treatment

Outcome

χ2

P

Improved
(n = 93)

Died
(n = 7)

No.

%

No.

%

Antiviral

 

 

 

 

 

 

Ivermectin (Iverzine)

37

39.8

0

0.0

4.421*

FEp=0.044*

Remdesivir

54

58.1

7

100.0

4.812*

FEp=0.041*

Hydroxychloroquine

10

10.8

0

0.0

0.836

FEp=1.0

Favipirvair

1

1.1

0

0.0

0.076

FEp=1.0

Anti-cytokines (tocilizumab)
(IL
-6 antagonist)

1

1.1

0

0.0

0.076

FEp=1.0

Corticosteroid

 

 

 

 

 

 

Dexamethone

62

66.7

0

0.0

12.281*

FEp=
0.001*

Methylpredensiolone

31

33.3

7

100.0

Anticoagulant

 

 

 

 

 

 

Prophylactic

35

37.6

0

0.0

4.053

FEp=
0.093

Therapeutic

58

62.4

7

100.0

Oxygen therapy

93

100.0

7

100.0

Noninvasive MV (CAPAP mode)

4

4.3

4

57.1

24.698*

FEp=
0.001*

Invasive MV
(SIMV VCV mode)

3

3.2

4

57.1

29.070*

FEp
<0.001*

c2:  Chi square test                                 FE: Fisher Exact

p: p value for comparing between improved and died

*: Statistically significant at p ≤ 0.05

 

Table 5: Relation between outcome and treatment in patients group with risk factors (n = 100)

Treatment

Group I
(n = 100)

Group II
(n = 100)

χ2

p

No.

%

No.

%

Antiviral

 

 

 

 

 

 

Ivermectin (Iverzine)

37

37.0

49

49.0

2.938

0.087

 (Remdesivir)

61

61.0

51

51.0

2.029

0.154

Hydroxychloroquine

10

10.0

32

32.0

14.587*

<0.001*

Favipirvair

1

1.0

0

0.0

1.005

FEp=1.0

Anti-cytokines (Tocilizumab)
(IL
-6 antagonist)

1

1.0

0

0.0

1.005

FEp=1.0

Corticosteroid

 

 

 

 

 

 

Dexamethone

62

62.0

82

82.0

9.921*

0.002*

Methylpredensiolone

38

38.0

18

18.0

Anticoagulant

 

 

 

 

 

 

Prophylactic

35

35.0

57

57.0

9.742*

0.002*

Therapeutic

65

65.0

43

43.0

Oxygen therapy

100

100.0

66

66.0

40.964*

<0.001*

Noninvasive MV (CAPAP mode)

8

8.0

1

1.0

5.701*

FEp=
0.035*

Invasive MV
(SIMV VCV mode)

7

7.0

0

0.0

7.254*

FEp=
0.014*

c2:  Chi square test                                             FE: Fisher Exact

p: p value for comparing between the two studied groups

*: Statistically significant at p ≤ 0.05 

 

Table 6:  Comparison between the two studied groups according to treatment

 

Group I
(n = 100)

Group II
(n = 100)

Test of Sig.

p

Hospital stay

 

 

 

 

Min. – Max.

4.0 – 20.0

2.0 – 13.0

U=
1411.5*

<0.001*

Mean ± SD.

8.53 ± 3.47

4.62 ± 2.54

Median (IQR)

8.0 (6.0 – 10.0)

4.0 (3.0 – 6.0)

Outcome

 

 

 

 

Improved

93 (93.0%)

100 (100.0%)

χ2=
7.254*

FEp=
0.014*

Died

7 (7.0%)

0 (0.0%)

IQR: Inter quartile range                         SD: Standard deviation                           U: Mann Whitney test

c2:  Chi square test                                 FE: Fisher Exact

p: p value for comparing between the two studied groups

*: Statistically significant at p ≤ 0.05

 

Table 7:  Comparison between the two studied groups according to hospital stay and outcome

 

 

DISCUSSION

In this study, the commonest comorbid conditions were systemic hypertension 62 patients (62%), DM 61 patients (61%), and IHD 48 patients (48%).

 As a result of the quick increase in crucially ill patients and restricted health resources during the pandemic of COVID 19, in particular ICU beds and ventilation systems, efficient triage techniques for detecting patients at the highest risk of the worst outcomes are required to ensure an efficient allocation of resources for both outpatients and inpatients .5

According to our findings, older age and a SpO2 of less than 74% were important indicators of invasive respiratory assistance and in-hospital mortality during the present outbreak. In two earlier identical investigations, older age was proven to be one of the independent diagnostic variables for mortality in verified COVID-19 pneumonia cases.6

In an initial study of 121 patients admitted to an intensive care unit in the United States, eighty percent of those who died were 65 or older. 7 Comorbidities, particularly CVS and chronic respiratory diseases, are more prevalent among the elderly. In elderly people, several comorbidities may play a role in the development of ARDS and severe pneumonia.8

 Our study results exhibited that advanced age was accompanied by higher risk of infection with COVID-19, as concluded by Zhou et al. (2020a). For infections like SARS and MERS, advanced age has been determined to be a crucial indicator of fatality. 9, 10

Myalgia (22%), chest pain, and DCL symptoms (atypical presentation,11 percent) were among the symptoms that were more frequent in the first group, according to our findings. It's possible that the start and persistence of symptoms indicate a poor prognosis. Non-survivors had a shorter time from disease beginning to admission and mortality, implying a faster disease progression.

In admitted cases, reduced blood O2 saturation has been used to determine the severity of COVID-19 pneumonia. SpO2 had previously been shown to be an important predictive method in community-acquired pneumonia, with a higher specificity for negative outcomes.11,12

Also, GGO observed in the CT chest was proved to be one of the frequent results in COVID-19 pneumonia, with 100% of diagnosed cases validated by RT-PCR having such a result in an Italian investigation of 58 patients. The authors demonstrated that none of the CT criteria (GGO, pneumonia distributed bilaterally, involvement in ˃ 2 lobes, consolidation, as well as lymphadenopathy) were considerably distinct between COVID-19 cases that needed hospitalization versus those who were discharged for home isolation .13

In this research, a statistically significant difference between both groups was observed. These outcomes were in harmony with the outcomes obtained in the previous studies.

Our study agrees with those of another study that comprised 73 COVID-19 patients, 25 of whom had severe or critical conditions, and employed a CT scoring system in which elevated scores were correlated with greater severity of the disease .14

Reduced lymphocyte count was postulated to be an independent risk factor for in-hospital mortality in the multivariable logistic regression assay, and additional investigation demonstrated that lymphocyte count was a crucial predictor in the prediction of COVID-19 pneumonia in-hospital mortality detected via the ROC assay. Prior research found that lymphopenia represented a risk factor for higher SARS and COVID-19 fatality rates.15 Adults with critically ill SARS have short-term outcomes as well as risk factors for death.16

In our study, we agree with the previous studies as statistically significant differences between the 2 examined groups were determined in regards to more lymphopenia in the 1st group.

Another study found that the percentage of lymphocytes (LYM) [percent] was an important predictor of the severity of COVID-19. 17

The decreased LYM (%) could be attributed to the fact that coronavirus can cause lymphocyte damage during the acute process. The reduced LYM% might indicate that the immune system is under-activated and/or over-exhausted, rendering it unable to contain COVID-19 infection. 18

According to prior research, ~90% of cases with severe pneumonia exhibited higher coagulation activity, as denoted by a high D-dimer concentration. 19

Increased D-dimer levels were shown to be associated with elevated fatality rates in emergency room cases of sepsis. 20

In addition, a prior study revealed that D-dimer ˃1 µg/ml was accompanied by deadly COVID-19 outcomes. 15

In this research, D-dimer has also been determined as an independent risk factor (P value 0.034) for in-hospital death.

Increased CRP on admission in cases with risk was accompanied by higher risk of mortality. CRP in the 1st group with Mean ± SD. 82.42 ± 71.69 was elevated when compared to the 2nd group with Mean ± SD 45.24 ± 23.05. We are in accordance with earlier studies that revealed a positive association of CRP level with the lung lesions as well as the severity of illness. Sahu et al demonestrated higher CRP concentration in cases died due to COVID-19 infections in comparison with survivors. Positive CRP was considered a predictor in Ruan   study 21.- 24

 In a cohort study, SARS-CoV-2 infections were associated with a higher morbidity rate, which was consistent with earlier research.25-30  Noteworthy, AKI was the commonest complication. Grasselli et al. revealed that AKI occurred in ~ 55% of 3988 consecutive severely sick patients with proven COVID-19 from an Italian Intensive Care Unit in Lombardy. The exhaustion of healthcare resources, combined with the relatively higher admission rate during the period of this research, might explain the relatively increased mortality and morbidity rates in comparison to following cohorts.27, 31

Previous studies have shown that increased serum Cr is associated with frequent COVID-19 cases. Increased serum Cr on admission might denote the initial stages of renal destruction and easy progression to AKI. Early intervention in cases having increased serum Cr on admission that might occur prior to the occurrence of clinical manifestations of renal failure may produce better outcomes when compared to treatment of only established AKI cases. 32

In this study in serum Cr in the first group is 2.38 ± 1.48, whereas in the second group is 1.21 ± 0.99 with while p value

CONCLUSION

Advanced age was more liable for COVID-19 infection.

Patients suffering COVID-19 infection in conjunction with risk factors had poor outcome when compared to others without.

 Systemic hypertension, DM, and IHD were independently accompanied by a greater risk of in-hospital mortality.

A statistically significant difference was detected in terms of more lymphopenia and increased inflammatory parameters in the first group.

The severity of COVID-19 pneumonia in hospitalized patients has been determined by reduced blood O2 saturation.

REFERENCES
1.     World Health Organization (WHO). WHO Coronavirus Disease (COVID-19) Dashboard. Geneva: Switzerland: WHO; 2021.
2.     Chan JF, Yuan S, Kok KH, To KK, Chu H, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet. 2020;395(10223):514–23.
3.     Brown AJ, Won JJ, Graham RL, Dinnon KH, Sims AC, et al. Broad spectrum antiviral remdesivir inhibits human endemic and zoonotic deltacoronaviruses with a highly divergent RNA dependent RNA polymerase. Antiviral Res. 2019;169:104541.
4.     Huang C, Wang Y, Li X, Ren L, Zhao J, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020; 395(10223):497–506.
5.     Emanuel EJ, Persad G, Upshur R, Thome B, Parker M, et al. Fair Allocation of Scarce Medical Resources in the Time of Covid-19. N Engl J Med. 2020;382(21):2049-55.
6.     Wu C, Chen X, Cai Y, Xia J, Zhou X, et al. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):934-43.
7.     CDC COVID-19 Response Team. Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) - United States, February 12-March 16, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(12):343-6.
8.     Villar J, Pérez-Méndez L, Basaldúa S, Blanco J, Aguilar G, et al. A risk tertiles model for predicting mortality in patients with acute respiratory distress syndrome: age, plateau pressure, and P(aO(2))/F(IO(2)) at ARDS onset can predict mortality. Respir Care. 2011;56(4):420-8.
9.     Liu M, Liang WN, Chen Q, Xie XQ, Wu J, et al. Risk factors for SARS-related deaths in 2003, Beijing. Biomed Environ Sci. 2006; 19(5):336-9.
10.   Ahmadzadeh J, Mobaraki K, Mousavi SJ, Aghazadeh-Attari J, Mirza-Aghazadeh-Attari M, et al. The risk factors associated with MERS-CoV patient fatality: A global survey. Diagn Microbiol Infect Dis. 2020;96(3):114876.
11.   Liu Y, Yan LM, Wan L, Xiang TX, Le A, et al. Viral dynamics in mild and severe cases of COVID-19. Lancet Infect Dis. 2020;20(6):656-7.
12.   Bewick T, Greenwood S, Lim WS. What is the role of pulse oximetry in the assessment of patients with community-acquired pneumonia in primary care? Prim Care Respir J. 2010; 19(4):378-82.
13.   Caruso D, Zerunian M, Polici M, Pucciarelli F, Polidori T, et al. Chest CT Features of COVID-19 in Rome, Italy. Radiology. 2020;296(2):E79-85.
14.   Li K, Wu J, Wu F, Guo D, Chen L, et al. The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia. Invest Radiol. 2020;55(6):327-31.
15.   Zhou F, Yu T, Du R, Fan G, Liu Y, 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(10229):1054-62.
16.   Hu X, Deng Y, Wang J, Li H, Li M, Lu Z. Short term outcome and risk factors for mortality in adults with critical severe acute respiratory syndrome (SARS). J Huazhong Univ Sci Technolog Med Sci. 2004;24(5):514-7.
17.   Liu Y, Yang Y, Zhang C, Huang F, Wang F, et al. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci. 2020;63(3):364-74.
18.   Terpos E, Ntanasis-Stathopoulos I, Elalamy I, Kastritis E, Sergentanis TN, et al. Hematological findings and complications of COVID-19. Am J Hematol. 2020;95(7):834-47.
19.   Milbrandt EB, Reade MC, Lee M, Shook SL, Angus DC, et al. Prevalence and significance of coagulation abnormalities in community-acquired pneumonia. Mol Med. 2009;15(11-12):438-45.
20.   Rodelo JR, De la Rosa G, Valencia ML, Ospina S, Arango CM, et al. D-dimer is a significant prognostic factor in patients with suspected infection and sepsis. Am J Emerg Med. 2012;30(9):1991-9.
21.   Bhargava A, Fukushima EA, Levine M, Zhao W, Tanveer F, et al. Predictors for Severe COVID-19 Infection. Clin Infect Dis. 2020; 71(8):1962-8.
22.   Wang L. C-reactive protein levels in the early stage of COVID-19. Med Mal Infect. 2020;50(4):332-4.
23.   Sahu BR, Kampa RK, Padhi A, Panda AK. C-reactive protein: A promising biomarker for poor prognosis in COVID-19 infection. Clin Chim Acta. 2020;509:91-4.
24.   Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020;46(5):846-8.
25.   Grasselli G, Greco M, Zanella A, Albano G, Antonelli M, et al. Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern Med. 2020;180(10):1345-55.
26.   Grimaldi D, Aissaoui N, Blonz G, Carbutti G, Courcelle R, et al. Characteristics and outcomes of acute respiratory distress syndrome related to COVID-19 in Belgian and French intensive care units according to antiviral strategies: the COVADIS multicentre observational study. Ann Intensive Care. 2020;10(1):131.
27.   Wendel Garcia PD, Fumeaux T, Guerci P, Heuberger DM, Montomoli J, et al. Prognostic factors associated with mortality risk and disease progression in 639 critically ill patients with COVID-19 in Europe: Initial report of the international RISC-19-ICU prospective observational cohort. EClinicalMedicine. 2020;25:100449.
28.   Zheng Y, Zhang Y, Chi H, Chen S, Peng M, et al. The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study. Clin Chem Lab Med. 2020; 58(7):1106-15.
29.   Zheng Y, Sun LJ, Xu M, Pan J, Zhang YT, et al. Clinical characteristics of 34 COVID-19 patients admitted to intensive care unit in Hangzhou, China. J Zhejiang Univ Sci B. 2020;21(5):378-87.
30.   Flythe JE, Assimon MM, Tugman MJ, Chang EH, Gupta S, et al. Characteristics and Outcomes of Individuals With Pre-existing Kidney Disease and COVID-19 Admitted to Intensive Care Units in the United States. Am J Kidney Dis. 2021;77(2):190-203.
31.   Armstrong RA, Kane AD, Cook TM. Outcomes from intensive care in patients with COVID-19: a systematic review and meta-analysis of observational studies. Anaesthesia. 2020;75(10):1340-9.
32.   Nardi G, Sanson G, Tassinari L, Guiotto G, Potalivo A, et al. Lactate Arterial-Central Venous Gradient among COVID-19 Patients in ICU: A Potential Tool in the Clinical Practice. Crit Care Res Pract. 2020; 2020:4743904.