Radiology

The prognostic value of biomarker levels and chest imaging in patients with COVID-19 presenting to the emergency department

a b s t r a c t

Introduction: We aimed to compare the prognostic value of a quantitative CT severity score with several labora- tory parameters, particularly C-reactive protein, Procalcitonin, Neutrophil to lymphocyte ratio, D-dimer, ferritin, lactate dehydrogenase, lactate, troponin and B-type Natriuretic Peptide in predicting in-hospital mortality.

Methods: This was a retrospective chart review study of COVID-19 patients who presented to the Emergency De- partment of a tertiary care center between February and December 2020. All patients >=18 years old who tested positive for the COVID-19 real-time reverse transcriptase polymerase chain reaction and underwent CT imaging at presentation were included. The primary outcome was the Prognostic ability of CT severity score versus bio- markers in predicting in-hospital mortality.

Results: The AUCs were: D-dimer (AUC: 0.67 95% CI = 0.57-0.77), CT severity score (0.66, 95% CI = 0.55-0.77), LDH (0.66, 95% CI = 0.55-0.77), Pro-BNP (0.65, 95% CI = 0.55-0.76), NLR (0.64, 95% CI = 0.53-0.75) and tropo-

nin (0.64, 95% CI = 0.52-0.75). In the stepwise logistic regression, age (OR = 1.07 95% CI = 1.05-1.09), obesity (OR = 2.02 95% CI = 1.25-3.26), neutrophil/lymphocyte ratio (OR = 1.02 95% CI = 1.01-1.04), CRP (OR = 1.01

95% CI = 1.004-1.01), lactate dehydrogenase (OR = 1.003 95% CI = 1.001-1.004) and CT severity score (OR = 1.17 95% CI = 1.12-1.23) were significantly associated with in-hospital mortality.

Conclusion: In summary, CT severity score outperformed several biomarkers as a Prognostic tool for covid related mortality. In COVID-19 patients requiring lung imaging, such as patients requiring ICU admission, patients with Abnormal vital signs and those requiring mechanical ventilation, the results suggest obtaining and calculating the CT severity score to use it as a prognostic tool. If a CT was not performed, the results suggest using LDH, CRP or NLR if already done as Prognostic tools in COVID-19 as these biomarkers were also found to be prognostic in COVID-19 patients.

(C) 2022 Published by Elsevier Inc.

  1. Introduction
    1. Background

Coronavirus disease 2019 (COVID-19) emerged in Wuhan City, Hubei Province, China in December 2019. Since then, it has spread

* Corresponding author at: clinical emergency Medicine, Department of Emergency Medicine, American University of Beirut Medical Center, P.O. Box 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon.

E-mail addresses: [email protected] (G.A. Dagher), [email protected] (A.A. Ghanem), [email protected] (S. Haidar), [email protected] (N. Kattouf), [email protected] (M. Assaf), [email protected] (M. Khdhir), [email protected]

(R. Chahine), [email protected] (J. Rizk), [email protected] (M. Makki), [email protected] (H. Tamim), [email protected] (R.B. Chebl).

across every continent, becoming a global health problem, and causing tremendous burden on the healthcare system. As of October 2021, it is estimated that 241 million people have been infected and 4.9 million have died from the novel coronavirus (1). It mainly manifests with nonspecific symptoms such as fever, cough, shortness of breath, and fatigue (2). However, in more severe cases it can lead to acute respira- tory distress syndrome (ARDS), increased risk of venous thromboembo- lism (VTE), intensive care (ICU) admissions, superimposed bacterial infections, cardiac injury and death (3,4). The current medical literature suggests that a variety of patient factors could prove useful in pre- dicting disease severity and patient outcomes of COVID-19. Marin et al. highlighted several demographic, biochemical, radiographic, clinical, hematologic, and immunologic factors that could be used to as- sess the severity of COVID-19 and predict mortality among Covid-19

https://doi.org/10.1016/j.ajem.2022.06.043 0735-6757/(C) 2022 Published by Elsevier Inc.

patients (5). Laboratory parameters such as Neutrophil to lymphocyte ratio , procalcitonin , C-reactive protein (CRP), D-dimer, fer- ritin, Lactate dehydrogenase , lactate, troponin and B-type Natri- uretic Peptide (Pro-BNP) have been studied in COVID-19 patients (6- 14). They have been shown to be associated with disease severity, mor- tality and hospital complications (ARDS, need for invasive and noninva- sive mechanical ventilation, venous thromboembolism and acute kidney injury) (6-14). Similarly, the prognostic utility of chest com- puted tomography (CT) has been studied in COVID-19 patients. In April 2020, a multinational committee advised that chest CT is a more sensitive imaging modality than Chest x-ray for early detection of and assessment of COVID-19 disease severity (15). Ground glass opacities or bilateral consolidations in the lower lung field are the most common CT features among COVID-19 patients (16,17). Studies have reported scoring systems, findings and quantitative analyses of CT images that correlate with disease severity and predict outcomes of COVID-19 (16-21).

    1. Importance

This study may help identify tools that can be used to risk stratify pa- tients presenting to the emergency department with Covid-19 and guide patient management.

    1. Goal of the investigation

In this study, we aimed to compare the prognostic value of a quanti- tative CT severity score with several laboratory parameters, particularly C-reactive protein (CRP), procalcitonin , Neutrophil to lymphocyte ratio , D-dimer, ferritin, Lactate dehydrogenase , lactate, tro- ponin and B-type Natriuretic Peptide (Pro-BNP) in predicting in- hospital mortality.

  1. Methods
    1. Study design and setting

This was a retrospective chart review study of patients who pre- sented to the Emergency Department (ED) of a tertiary care center be- tween February 2020 and December 22, 2020 and were diagnosed with COVID-19.

    1. Selection of participants

All patients >=18 years old who tested positive for the COVID-19 real- time reverse transcriptase polymerase chain reaction (RT-PCR) and un- derwent CT imaging at presentation were included. Patients below 18 years old, pregnant women, trauma patients and cardiac arrest patients were excluded. Patients who did not undergo computer tomography (CT) Chest imaging were also excluded. The included patients were di- vided into 2 groups: survivors and non-survivors. This study was con- ducted during a time where the COVID-19 vaccine was not available. It was approved by the hospital’s Institutional Review Board (IRB): BIO-2020-0548.

    1. Data abstraction

Three research fellows were responsible for data abstraction. They completed training for data abstraction before the start of the study using practice medical records as well as having several meeting with the principal investigator for quality control. A list of patients with both a positive COVID-19 PCR and a chest CT was obtained from the hos- pital electronic health record system (EPIC(C)). The research fellows screened the obtained list based on the inclusion and exclusion criteria by reviewing their medical charts. Relevant patient characteristics, vital signs, and lab parameters at baseline, in addition to diagnostic and

therapeutic interventions and outcomes of the included patients were extracted from the hospital electronic health record system (EPIC(C)). Standardized abstraction forms were used during data collection (Research Electronic Data Capture- Redcap). The performance of the data abstractors was monitored frequently by the principal investiga- tors of the study and the department’s research coordinator. The data abstractors were not blinded to the hypothesis and objectives of the study because they were involved in the writing of the manuscript. In addition, each medical chart was reviewed by a single research fellow for data abstraction.

    1. Variables and definitions

The variables of interest that were extracted were: age, gender, height, weight, body mass index (BMI), medical comorbidities, smoking status, vital signs, patients’ presenting symptoms, complete blood count, electro- lytes, biomarkers (lactate, albumin, C-reactive protein, procalcitonin, ferritin, lactate dehydrogenase, D-dimer, Bilirubin, pro-BNP, cardiac troponin), Coagulation studies(PT, PTT, INR), Liver enzymes(AST, ALT, Alkaline phosphatase, Gamma-glutamyl transferase). CT severity score was calculated for all included patients. Other variables of interest were: ED disposition (discharged, regular hospital floor admission or ICU admis- sion), therapeutic interventions in the ED and during hospital admission (the need for noninvasive or invasive ventilation, hospital and ICU lengths of stay, the presence of pulmonary bacterial co-infection, development of complications such as sepsis, Acute respiratory distress syndrome , Cardiovascular complications, Thromboembolic events and death). The Berlin definition of ARDS was used to diagnose patients with ARDS (22). Sepsis was defined based on the Sepsis-3 criteria (23). A patient was con- sidered to have a pulmonary bacterial co-infection if the infectious disease team treated them with antibiotics based on clinical, radiographic and laboratory findings.

    1. CT severity score

The CT images of COVID-19 patients were reviewed at presentation by board certified radiologists at our institution. The radiologists re- sponsible for reviewing the CT images were blinded to the clinical and laboratory data of the patients. The findings were reported according to the “Radiological Society of North America Expert Consensus Docu- ment on Reporting Chest CT Findings Related to COVID-19” (24).

The semi-quantitative CT scoring system used was the same as the one used by Pan et al., whereby the degree of each lung lobe involve- ment translates to a raw score of 0-5. 0 = no involvement; 1 < 5% involvement; 2 = 25% involvement; 3 = 26%-49% involvement;

4 = 50%-75% involvement; 5 >= 75% involvement (25). Thus, the total CT score was the sum of the individual lobar scores i.e. a total score of 0 means no Lung involvement, and a 25 indicates maximum lung in- volvement (25). Examples of CT chest images with their corresponding COVID-19 lung involvement and CT severity scores are provided in Fig. 1.

    1. Outcome of interest

The primary outcome was the prognostic ability of CT severity score versus biomarkers in predicting in-hospital mortality.

    1. Statistical analysis

In the univariate analysis, continuous variables were presented in the form of mean +- standard deviation, and categorical variables were presented as frequency with percentage. In the bivariate analysis, Student’s t-test and Pearson’s Chi-square test were used to assess the significance of the statistical association between the independent var- iables in the different groups.

In the multivariate analysis, logistic regression was used to assess the association between biomarkers and CT severity score with

Image of Fig. 1

Fig. 1. Axial CT images showing lobar lung involvement and the assigned score.

mortality. The following variables were included in the logistic regres- sion: Age, gender, BMI, smoking, obesity, Hypertension, Dyslipidemia, Atrial fibrillation, Coronary Artery Disease, Congestive Heart Failure, Malignancy, Diabetes Mellitus, Chronic Obstructive Pulmonary Disease, Neutrophil/lymphocyte ratio, Troponin, CRP, Procalcitonin, Lactate de- hydrogenase, Ferritin, D-dimer, IL-6, Pro-BNP, CT severity score.

receiver operating characteristic curves were plotted (with regards to mortality) to calculate the area under the curve (AUC) for the relevant biomarkers and CT severity score. Multiple imputations for miss- ing data were not performed because the percentage of missing values for all included variables was <5%. All tests were assessed for significance using 95% CI (Confidence Intervals) and alpha of 0.05. All statistical analy- ses were performed using SPSS 24 (Statistical Package for Social Sciences.)

  1. Results
    1. Baseline characteristics of presenting patients

During the study period a total of 1043 COVID-19 patients were identified, of whom 761 met the inclusion criteria and 282 were

1043 patients with COVID-19

282 were excluded (220 with no CT scan of the chest; 30 were pregnant; 32 were below the age of 18)

excluded (220 with no CT scan of the chest; 30 were pregnant; 32 were below the age of 18) (Fig. 2)0.119 patients (15.6%) died during their hospital stay (Table 1).

The mean age of the study population was 60.81 +- 16.93 years, of whom 68.1% (N = 518) were male. The average BMI of the sample pop- ulation was 28.90 +- 5.21. 21.7% of the total patient population were current smokers. The four most prevalent comorbidities were hyperten- sion (47.7%), dyslipidemia (37.2%), obesity (30.7%) and diabetes

mellitus (27.3%). (Table 1). Non-survivors were older (72.94 +- 12.07 years vs. 58.56 +- 16.76 years, p < 0.0005) and had a higher percentage of hypertension (67.2% vs. 44.1%, p < 0.005), atrial fibrillation (15.1% vs 6.1%, p = 0.001), coronary artery disease (30.3% vs. 15.7%, p < 0.005),

malignancy (24.4% vs 12.8%, p = 0.001) and diabetes mellitus (38.7%

vs. 25.2%, p = 0.003), (Table 1).

    1. Initial vital signs, presenting symptoms and lab parameters

Non-survivors had higher respiratory rates (22 breaths/min +- 5.79 vs 20 +- 3.29 breaths/min, p < 0.0005) and lower oxygen saturation (95% +- 6.71 vs 86% +- 12.03, p < 0.0005) than survivors at presentation.

761 met the inclusion criteria

(Positive COVID-19 PCR, CT scan available and >= 18 years)

Survivors (642 patients)

Non-survivors (119 patients)

Fig. 2. Flow diagram.

Table 1

Baseline Characteristics of the COVID-19 patients presenting to the Emergency Department of a tertiary care center.

Total

Survivors

Non survivors

P value

N = 761

Mean +- SD N (%)

N = 642

Mean +- SD N (%)

N = 119

Mean +- SD N (%)

Age (years)

60.81 +- 16.93

58.56 +- 16.76

72.94 +- 12.07

<0.0005

Male

518 (68.1)

439 (68.4)

79 (66.4)

0.67

Height (m)

1.69 +- 0.10

1.69 +- 0.10

1.68 +- 0.10

0.26

Weight (kg)

82.35 +- 17.71

82.27 +- 17.60

83.02 +- 18.15

0.67

BMI

28.90 +- 5.21

28.81 +- 5.06

29.33 +- 5.80

0.34

Obesity

Smoking

234 (30.7)

188 (29.3%)

46 (38.7)

Current

165 (21.7)

150 (23.4)

15 (12.6)

<0.0005

Previous

122 (16)

88 (13.7)

34 (28.6)

None

474 (62.3)

404 (62.9)

70 (58.8)

Chronic kidney disease

56 (7.4)

41 (6.4)

15 (12.6)

0.017

end stage renal disease

5 (0.7)

4 (0.6)

1 (0.8)

0.57

Hypertension

363 (47.7)

283 (44.1)

80 (67.2)

<0.0005

Dyslipidemia

283 (37.2)

227 (35.4)

56 (47.1)

0.015

Atrial fibrillation

57 (7.5)

39 (6.1)

18 (15.1)

0.001

Coronary Artery Disease

137 (18.0)

101 (15.7)

36 (30.3)

<0.0005

Congestive Heart Failure

38 (5.0)

26 (4.0)

12 (10.1)

0.006

Malignancy

111 (14.6)

82 (12.8)

29 (24.4)

0.001

History of thrombo-embolic disease

60 (7.9)

45 (7.0)

15 (12.6)

0.037

Diabetes Mellitus

208 (27.3)

162 (25.2)

46 (38.7)

0.003

Chronic Obstructive Pulmonary Disease

48 (6.3)

35 (5.5)

13 (10.9)

0.024

With regards to laboratory parameters, non-survivors had higher WBC counts (9968cu.mm +- 8491 vs. 7157cu.mm +- 4758, p = 0.001), neutrophil to lymphocyte ratio (18.92 +- 22.65 vs. 8.5 +- 10.05, p <

0.0005), creatinine (1.3 mg/dL +- 1.24 vs. 1.06 mg/dL +- 0.73, p =

0.04), lactate (2.3 mmol/L +- 1.41 vs. 1.89 mmol/L +- 1.56, p = 0.03),

CRP (143 mg/L +- 96 vs. 80 mg/L +- 77.75, p < 0.0005), procalcitonin

(1.18 ng/mL +- 3.02 vs. 0.48 ng/mL +- 2.54, p = 0.02), D-dimer (1363

ng/mL +- 2856.94 vs. 730 ng/mL +- 1122.68, p = 0.03) and Pro-BNP

(5822 ng/L +- 6836.06 vs. 1470 ng/L +- 3398.87, p = 0.02). Finally

non-survivors had a higher semi-quantitative CT severity score (16.02

+- 5.89 vs 10.84 +- 6.11, p < 0.0005) than survivors (Table 2).

    1. Oxygen therapy requirements

The percentages of patients who received non-invasive ventilation during the first 24 and 48 h of hospitalization were significantly higher in the non-survivor group (79% vs. 33.2% within the first 24 h and 73.1% vs. 32.4% within 24-48 h p < 0.0005). Moreover, the percentages of pa- tients requiring intubation within the first 24 or 24-48 h of hospitaliza- tion were significantly higher in the non-survivor group (10.9% vs 1.7% within 24 h and 16.8% vs. 0.9% within 24-48 h, both p < 0.0005)0.13.9% received mechanical ventilation during their hospital stay (Table 3).

    1. Outcomes of COVID-19 patients

During their hospital stay, 14.5% of patients were directly admitted to the ICU from the ED, 13.5% developed septic shockand 12.2% devel- oped ARDS (Table 4).

Non-survivors had a significantly higher rate of ICU admission (52.9% vs 7.3%), septic shock (69.7% vs 3.1%) and ARDS (62.2% vs 3%) than survivors (Table 4). Non-survivors also had a significantly higher hospital length of stay and ICU length of stay: (130.39 +- 123.21 days vs 26.64 +- 51.23 days and 20.25 +- 4.57 days vs 10.45 +- 9.25 days re-

spectively) (Table 4).

    1. ROC curves and AUCs with regards to mortality

The variables that had the highest AUCs were: D-dimer (AUC: 0.67 95% CI = 0.57-0.77, p = 0.003), CT severity score (0.66, 95% CI =

0.55-0.77 p = 0.005), LDH (0.66, 95% CI = 0.54-0.77, p = 0.007),

Pro-BNP (0.65, 95% CI = 0.55-0.76, p = 0.009), NLR (0.64, 95%

CI = 0.53-0.75 p = 0.02) and troponin (0.64, 95% CI = 0.53-0.75,

p = 0.02) (Fig. 3).

    1. Multivariate logistic regression analysis for in-hospital mortality

In the stepwise logistic regression, age (OR = 1.07 95% CI = 1.05-1.09, p < 0.0001), obesity (OR = 2.02 95% CI = 1.25-3.26, p =

0.004), neutrophil/lymphocyte ratio (OR = 1.02 95% CI = 1.01-1.04,

p = 0.003), CRP (OR = 1.01 95% CI = 1.004-1.009, p < 0.0001), lactate

dehydrogenase (OR = 1.003 95% CI = 1.001-1.004, p < 0.0001) and CT severity score (OR = 1.17 95% CI = 1.12-1.23, p < 0.0001) were significantly associated with in-hospital mortality (Table 5).

  1. Discussion

In this retrospective chart review, our main aim was to identify and compare the prognostic utility of a semi-quantitative CT severity score with several biomarkers. Out of the total patient population, 119 patients (15.6%) died during their hospital stay. The AUCs for predicting mortality from highest to lowest were CT severity score (0.66), LDH (0.66) and NLR (0.64). The variables that were found to be associated with in-hospital mortality were NLR, CRP, LDH and CT severity score, with the CT severity score outperforming all other biomarkers as a prognostic tool. Our results are in line with the existing literature, where several studies have demonstrated that certain baseline patient characteristics, laboratory parameters and imaging findings are associated with adverse complications in COVID-19.

Age and obesity have been reported to play a major role in predicting mortality and assessing severity of COVID-19 (26-29). Gao et al. and Marin et al. reported that age was an independent predictor of in-hospital mortality (5,9). Moreover, COVID-19 patients above 59 years were 5.1 times more likely to die after developing symptoms than those aged between 30 and 59 years (9). According to the Center for Disease Control (CDC), a BMI >30 (obesity) increases the risk of se- vere COVID-19 disease (30). Obesity was also shown to be an indepen- dent risk factor associated with hospitalization and death, particularly

Initial vital signs, presenting symptoms and lab parameters of the COVID-19 patients presenting to the Emergency Department of a tertiary care center.

Total

Survivors

Non survivors

P value

N = 761

Mean +- SD N (%)

N = 642

Mean +- SD N (%)

N = 119

Mean +- SD N (%)

Systolic blood pressure (mmHg)

129.12 +- 18.62

129.03 +- 18.53

129.60 +- 19.17

0.8

Diastolic blood pressure (mmHg)

73.28 +- 11.8

73.29 +- 11.66

73.29 +- 12.59

0.9

Heart rate (beats/min)

92.03 +- 17.4

91.29 +- 16.93

96.95 +- 19.37

0.015

O2 saturation (%)

93 +- 8.34

95 +- 6.71

86 +- 12.03

<0.0005

Temperature (C)

37.19 +- 0.84

37.18 +- 0.83

37.25 +- 0.92

0.415

Respiratory rate (Breaths/min)

Presenting symptoms:

20 +- 3.89

20 +- 3.29

22 +- 5.79

<0.0005

Fever

483 (63.5)

405 (63.1)

78 (65.5)

0.61

Cough

432 (56.8)

366 (57.0)

66 (55.5)

0.75

Shortness of Breath

472 (62.0)

378 (58.9)

94 (79.0)

<0.0005

Fatigue

372 (48.9)

312 (48.6)

60 (50.4)

0.76

Myalgias

179 (23.5)

164 (25.5)

15 (12.6)

0.002

Diarrhea

139 (18.3)

123 (19.2)

16 (13.4)

0.138

Vomiting

59 (7.8)

52 (8.1)

7 (5.9)

0.41

Headaches

54 (7.1)

53 (8.3)

1 (0.8)

0.004

Altered mental status

42 (5.5)

28 (4.4)

14 (11.8)

0.001

Loss of taste

18 (2.4)

18 (2.8)

0 (0.0)

0.093

Loss of smell

18 (2.4)

18 (2.8)

0(0.0)

0.093

Congestion

23 (3.0)

22 (3.4)

1 (0.8)

0.24

Chest pain

139 (18.3)

127 (19.8)

12 (10.1)

0.012

Abdominal pain

71 (9.3)

65 (10.1)

6 (5.0)

0.08

Sore throat

53 (7.0)

49 (7.6)

4 (3.4)

0.093

Rhinorrhea

43 (5.7)

38 (5.9)

5 (4.2)

0.46

White blood cell count (cu.mm)

7600 +- 5603

7157 +- 4758

9968 +- 8491

0.001

Neutrophil count (cu.mm)

5886 +- 3829

5470 +- 3274

8135 +- 5490

<0.0005

lymphocyte count (cu.mm)

1194 +- 3952

1167 +- 3212

1338 +- 6666

0.7

Neutrophil/lymphocyte ratio

10.14 +- 10.13

8.50 +- 10.05

18.92 +- 22.65

<0.0005

Hematocrit (%)

12.92 +- 1.83

13.04 +- 1.72

12.29 +- 2.25

0.004

Hemoglobin (g/dL)

38.72 +- 5.28

39.00 +- 4.97

37.18 +- 6.49

0.001

Platelets (cu.mm)

214,346 +- 87,886

211,988 +- 82,086

226,966 +- 113,635

0.2

Glucose (mg/dL)

136.77 +- 62.38

133.54 +- 60.08

154.18 +- 71.33

0.006

BUN (mg/dL)

20.79 +- 15.27

18.93 +- 13.07

30.78 +- 21.28

<0.0005

Creatinine (mg/dL)

1.10 +- 0.84

1.06 +- 0.73

1.30 +- 1.24

0.04

Bicarbonate (mmol/L)

23.68 +- 3.32

23.88 +- 3.06

22.62 +- 4.33

0.003

AST

60.59 +- 68.05

54.75 +- 63.30

84.37 +- 100.61

0.06

ALT

48.49 +- 47.29

45.63 +- 51.82

60.18 +- 66.84

0.3

Alkaline phosphatase

78.17 +- 74.74

78.00 +- 67.98

78.85 +- 49.89

0.9

GGT

83.58 +- 51.84

85.69 +- 63.79

75.04 +- 89.35

0.7

Troponin ng/mL

0.024 +- 0.0556

0.019 +- 0.039

0.051 +- 0.099

0.002

PH

7.43 +- 0.07

7.43 +- 0.06

7.42 +- 0.09

0.4

pCO2

32.41 +- 7.20

33.21 +- 7.13

31.28 +- 7.19

0.07

pO2

81.7397 +- 33.70

82.39 +- 35.60

80.84 +- 30.43

0.8

INR

1.27 +- 0.54

1.25 +- 0.52

1.35 +- 0.60

0.09

Lactate (mmol/L)

2.01 +- 1.53

1.89 +- 1.56

2.30 +- 1.41

0.03

Albumin (g/L)

36.08 +- 5.22

36.95 +- 4.85

33.02 +- 5.39

<0.0005

Lactate/albumin ratio

0.065 +- 0.055

0.061 +- 0.054

0.073 +- 0.057

0.1

CRP (mg/L)

91 +- 84.66

80 +- 77.75

143 +- 96.00

<0.0005

Procalcitonin (ng/mL)

0.605 +- 2.64

0.48 +- 2.54

1.18 +- 3.02

0.02

Lactate dehydrogenase (IU/mL)

375 +- 395

356 +- 178

527 +- 381

<0.0005

Ferritin (ng/mL)

941 +- 2009

755 +- 844

1604 +- 3932

0.07

D-dimer (ng/mL)

841 +- 1587

730 +- 1122

1362 +- 2856

0.03

IL-6 (pg/mL)

152 +- 424

137 +- 468

182 +- 317

0.4

Pro-BNP (ng/L)

3610 +- 1956

1470 +- 3398

5822 +- 6836

0.02

CT severity score

11.67 +- 6.35

10.84 +- 6.11

16.02 +- 5.89

<0.0005

Table 3

Oxygen therapy required by the COVID-19 patients during their hospital stay.

Total

Survivors

Non survivors

P value

N = 761

N = 642

N = 119

Mean +- SD

Mean +- SD

Mean +- SD

N (%)

N (%)

N (%)

noninvasive ventilation within the first 24 h

307 (40.3)

213 (33.2)

94 (79.0)

<0.0005

Noninvasive ventilation within 24-48 h

295 (38.8)

208 (32.4)

87 (73.1)

<0.0005

Intubation within the first 24 h

24 (3.2)

11 (1.7)

13 (10.9)

<0.0005

Intubation within 24-48 h

26 (3.4)

6 (0.9)

20 (16.8)

<0.0005

Mechanical ventilation anytime throughout the hospital stay

106 (13.9)

21 (3.3)

85 (71.4)

<0.0005

Outcomes of patients with COVID-19.

Total

Survivors

Non survivors

P value

N = 761

N = 642

N = 119

Mean +- SD

Mean +- SD

Mean +- SD

N (%)

N (%)

N (%)

ED discharge disposition Home

290 (38.1)

290 (45.2)

0 (0.0)

Covid regular floor

361 (47.4)

305 (47.5)

56 (47.1)

<0.0005

Covid ICU

110 (14.5)

47 (7.3)

63 (52.9)

Length of hospital stay (days)

42.42 +- 76.85

26.64 +- 51.23

130.39 +- 123.21

<0.0005

ICU length of stay (days)

11.27 +- 9.33

10.45 +- 9.25

20.25 +- 4.57

0.01

Presence of bacterial pulmonary superimposed infection

279(36.7)

169 (26.3)

110 (92.4)

<0.0005

Did the patient develop septic shock?

103 (13.5)

20 (3.1)

83 (69.7)

<0.0005

Acute respiratory distress syndrome

93 (12.2)

19 (3.0)

74 (62.2)

<0.0005

Pulmonary Embolism

35 (4.6)

21 (3.3)

14 (11.8)

<0.0005

Stroke

5 (0.7)

4 (0.6)

1 (0.8)

0.6

Myocardial infarction

9 (1.2)

4 (0.6)

5 (4.2)

0.006

Deep vein thrombosis

12 (1.6)

7 (1.1)

5 (4.2)

0.03

among adults younger than 65 years (31). This is most likely multifacto- rial and could be due to the impairment of immune function in obese patients (32-35). The above studies are in line with the results of this study where age and obesity were found to be independent risk factors of mortality in COVID-19.

Liu et al. reported an 8% increase in in-hospital mortality for each unit increase in NLR (OR = 1.08; 95% CI = [1.01 to 1.14], p = 0.0147)

(36). A meta-analysis by Li et al. demonstrated an association between NLR and mortality with a pooled sensitivity of 0.83 (95% CI [0.75-0.89]), a pooled specificity of 0.83 (95% CI [0.74-0.89]) and a

Image of Fig. 3

Test Result Variable(s)

AUC

Asymptotic 95% Confidence Interval

P-value

Lower Bound

Upper Bound

Lactate

0.53

0.42

0.65

0.56

Neutrophil_lymphocyte_ratio

0.64

0.53

0.75

0.02

Procalcitonin

0.60

0.48

0.71

0.10

D-Dimer

0.67

0.57

0.77

0.003

Pro-BNP

0.65

0.55

0.76

0.009

Ferritin

0.59

0.47

0.70

0.13

Troponin

0.64

0.53

0.75

0.02

CT severity score

0.66

0.55

0.77

0.005

Lactate dehydrogenase

0.66

0.55

0.77

0.007

CRP

0.60

0.49

0.71

0.08

Fig. 3. ROC curves for the different biomarkers with the primary outcome being in-hospital mortality among COVID-19 patients (with their respective AUCs).

Table 5

Stepwise logistic regression with the primary outcome being in-hospital mortality.

OR

95% C.I.

P-value

Lower

Upper

Age

1.07

1.05

1.09

<0.0001

Obesity

2.02

1.25

3.26

0.004

Neutrophil/lymphocyte ratio

1.02

1.01

1.04

0.003

CRP

1.01

1.004

1.01

<0.0001

Lactate dehydrogenase

1.003

1.001

1.004

<0.0001

CT severity score

1.17

1.12

1.23

<0.0001

Variables included in the model: Age, gender (reference: female); BMI; smoking (refer- ence: none); obesity (reference: no); Hypertension; Dyslipidemia; Atrial fibrillation; Cor- onary Artery Disease; Congestive Heart Failure; Malignancy; Diabetes Mellitus; Chronic Obstructive Pulmonary Disease; Neutrophil/lymphocyte ratio; Troponin; CRP; Procalcitonin (ng/mL); Lactate dehydrogenase; Ferritin; D-dimer; IL-6; Pro-BNP; CT sever- ity score.

pooled AUC of 0.90 (95% CI [0.87-0.92]) (37). This is also in line with the results of our study where NLR was slightly associated with mortality in COVID-19 and had an OR of 1.02 with an AUC of 0.64, p = 0.02.

Another extensively studied biomarker was CRP. It is a widely used inflammatory biomarker that was shown to correlate with disease se- verity (38-40) and adverse outcomes and in-hospital mortality in COVID-19 patients (40-44). Among hospitalized COVID-19 patients, Smilowitz et al. reported that elevated CRP concentrations above the median value predicted in-hospital mortality (OR 2.59, 95% CI 2.11-3.18) (8). Izcovich et al. showed than an elevated CRP increases the mortality risk by 7.9% (10). In our study, CRP was found to be mar- ginally associated with mortality in patients with COVID-19 with an OR of 1.01. This was similar to what was found in 2 other studies where CRP had an OR of 1.007 (95% CI = 1.004-1.010, p = 0.0001 and 95% CI =

1.005-1.009, p < 0.001) (45,46).

LDH is a cytoplasmic enzyme involved in glycolysis, and has been linked to tissue damage and inflammation (47-49). Izcovich et al. re- ported that an LDH (>240-250 U/L) correlates to a 10.4% increased mortality risk (10). Furthermore, Dong et al. showed that a cutoff value of 353.5 U/L had a high AUC of 0.949 (sensitivity and specificity of 94.4% and 89.2%, respectively) for predicting in-hospital mortality, and a Pooled analysis confirmed that elevated LDH was an independent risk factor associated with 16-fold increased odds of mortality (50). Although it did not perform as well as in the other studies, LDH was found to be slightly associated with mortality (OR = 1.003 with an AUC = 0.66, p = 0.007).

Chest CT is a well-established imaging modality for the assess- ment of COVID-19, and its prognostic utility has been investigated

(15). Colombi et al. reported that a < 73% degree of lung aeration correlates with ICU admission and death (OR 5.4, p < 0.001) (51). Yu et al. demonstrated that upper lobe consolidations are associated with ICU admission, acute respiratory failure occurrence and shock during COVID-19 hospitalization (52). Zhang et al. have also reported that the number of affected lobes on chest CT are associated with death (OR, 1.71; 95% CI, 1.06-2.78) (53). In addition, Lieveld et al. reported that a CT severity score >= 17 had a >= 90% specificity for predicting 30-day mortality, and a score <= 5 excluded 30-day mortal- ity with a sensitivity of >=90% (54). This is similar to our study where chest CT findings were associated with mortality in COVID-19 (AUC = 0.66, p = 0.005 and an OR of 1.17).

In summary, in this retrospective chart review, CT severity score outperformed several biomarkers as a prognostic tool for covid related mortality. The authors suggest obtaining a CT chest in COVID-19 pa- tients requiring lung imaging, such as patients requiring ICU admission, patients with abnormal vital signs and those requiring mechanical ven- tilation and to calculate the CT severity score and use it as a prognostic tool. If a CT was not performed, we suggest using LDH, CRP or NLR if al- ready done as prognostic tools in COVID-19 as these biomarkers were also found to be prognostic in COVID-19 patients.

    1. Limitations

Our study has several limitations. It was a retrospective single- center study which can limit the generalizability of the results and lead to selection bias. The study only included COVID-19 patients who underwent a CT chest. This would lead to a selection bias of sicker COVID-19 patients since a CT chest would me more likely or- dered for this patient population. A CT chest would not be ordered for COVID-19 patients with a milder presentation. The individuals in- volved in data abstraction were not blinded to the hypothesis and outcomes of the study since they were involved in the writing of the manuscript (however they were not involved in the analysis of the data). In addition, interrater reliability was not tested since each medical chart was reviewed by a single data abstractor and each CT image and CT severity score were reviewed by a single radi- ologist. All the included patients were not vaccinated as the vaccine was not available at the time. Vaccine status might have an influence on biomarker levels, CT findings and hospital outcomes. Also, the study was conducted before the emergence and testing of the differ- ent COVID-19 variants. Despite the previous limitations, a large pro- portion of the world population remains unvaccinated. In addition, in the setting of the emergence of new COVID-19 variants break- through cases still occur in fully vaccinated patients. Thus, our re- sults remain relevant to date. Moreover, we only included patients with a positive RT-PCR. We are well aware of the false negative rate of the PCR and that some patients would have had the clinical and ra- diographic presentations COVID during the study period and were not included. However, we are not sure of the number of those patients.

We did not trend biomarker levels and did not obtain a second semi- quantitative CT score. This could have provided further information on their prognostic utility. The next step would be to conduct a prospective multi-center study to validate our results.

Ethics approval and consent to participate

The design of this study ensured that it strongly abided by all ethical considerations according to the hospital’s Institutional Review Board (IRB), and consent for participation was waived as this was a retrospec- tive chart review. Reference number: BIO-2020-0548.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Funding

None.

CRediT authorship contribution statement Gilbert Abou Dagher: Writing – review & editing, Writing – original

draft, Supervision, Data curation, Conceptualization. Alain Abi Ghanem: Writing – review & editing, Writing – original draft, Supervision, Data curation, Conceptualization. Saadeddine Haidar: Writing – review & editing, Writing – original draft, Data curation, Conceptualization. Nadim Kattouf: Writing – review & editing, Writing – original draft, Data curation, Conceptualization. Mohamad Assaf: Writing – review & editing, Writing – original draft, Data curation, Conceptualization. Mihran Khdhir: Writing – review & editing, Writing – original draft, Data curation, Conceptualization. Reve Chahine: Writing – review &

editing, Writing – original draft, Data curation, Conceptualization. Jennifer Rizk: Writing – review & editing, Writing – original draft, Data curation, Conceptualization. Maha Makki: Formal analysis. Hani Tamim: Formal analysis. Ralph Bou Chebl: Writing – review & editing, Writing – original draft, Supervision, Data curation, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influ- ence the work reported in this paper.

Acknowledgements

None.

References

  1. World Health Organization (WHO). Coronavirus disease (COVID-19) pandemic. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed Oc- tober 20, 2020.
  2. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical char- acteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a de- scriptive study. Lancet. 2020;395:507-13.
  3. Wu C, Chen X, Cai Y, et al. Risk factors associated with acute respiratory distress syn- drome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):1-11.
  4. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. https://doi.org/ 10.1016/S0140-6736(20)30183-5. PubMed: 31986264.
  5. Gallo Marin B, Aghagoli G, Lavine K, Yang L, Siff EJ, Chiang SS, et al. Predictors of COVID-19 severity: A literature review. Rev Med Virol. 2021 Jan;31(1):1-10. https://doi.org/10.1002/rmv.2146. Epub 2020 Jul 30. PMID: 32845042; PMCID: PMC7855377.
  6. Liu J, Liu Y, Xiang P, Pu L, Xiong H, Li C, et al. neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J Transl Med. 2020 May 20;18(1):206. https://doi.org/10.1186/s12967-020-02374-0. PMID: 32434518; PMCID: PMC7237880.
  7. Zeng F, Huang Y, Guo Y, Yin M, Chen X, Xiao L, et al. Association of inflammatory markers with the severity of COVID-19: A meta-analysis. Int J Infect Dis. 2020 Jul; 96:467-74. https://doi.org/10.1016/j.ijid.2020.05.055. Epub 2020 May 18. PMID:

32425643; PMCID: PMC7233226.

  1. Smilowitz NR, Kunichoff D, Garshick M, Shah B, Pillinger M, Hochman JS, et al. C- reactive protein and clinical outcomes in patients with COVID-19. Eur Heart J. 2021 Jun 14;42(23):2270-9. https://doi.org/10.1093/eurheartj/ehaa1103. PMID:

33448289; PMCID: PMC7928982.

  1. Gao Y-D, Ding M, Dong X, et al. Risk factors for severe and critically ill COVID-19 pa- tients: a review. Allergy. 2021;76:428-55. https://doi.org/10.1111/all.14657.
  2. Izcovich A, Ragusa MA, Tortosa F, LavenaMarzio MA, Agnoletti C, Bengolea A, et al. Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review. PLoS One. 2020 Nov 17;15(11):e0241955.
  3. Rostami Mehrdad, Mansouritorghabeh Hassan. D-dimer level in COVID-19 infection: a systematic review. Expert Rev Hematol. 2020;13(11):1265-75. https://doi.org/10. 1080/17474086.2020.1831383.
  4. Cheng L, Li H, Li L, Liu C, Yan S, Chen H, et al. Ferritin in the coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis. J Clin Lab Anal. 2020 Oct;34 (10):e23618. https://doi.org/10.1002/jcla.23618. Epub 2020 Oct 19. PMID: 33078400; PMCID: PMC7595919.
  5. Henry BM, Aggarwal G, Wong J, Benoit S, Vikse J, Plebani M, et al. Lactate dehydro- genase levels predict coronavirus disease 2019 (COVID-19) severity and mortality: A pooled analysis. Am J Emerg Med. 2020 Sep;38(9):1722-6. https://doi.org/10.1016/ j.ajem.2020.05.073. Epub 2020 May 27. PMID: 32738466; PMCID: PMC7251362.
  6. Lippi G, Lavie CJ, Sanchis-Gomar F. Cardiac troponin I in patients with coronavirus disease 2019 (COVID-19): evidence from a meta-analysis. Prog Cardiovasc Dis. 2020 Mar 10;63(3):390-1.
  7. Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S, et al. The role of chest imaging in patient management during the COVID-19 Pandemic: a multina- tional consensus statement from the fleischner society. Radiology. 2020 Jul;296 (1):172-80. https://doi.org/10.1148/radiol.2020201365. Epub 2020 Apr 7. PMID:

32255413; PMCID: PMC7233395.

  1. Colombi D, Bodini FC, Petrini M, Maffi G, Morelli N, Milanese G, et al. Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 Pneumonia. Ra- diology. 2020 Aug;296(2):E86-96.
  2. Yu Qian Wang, Huang Yuan-Cheng, Liu Shan, Zhou Song, Zhang Zhen, Zhao Shijun, et al. Multicenter cohort study demonstrates more consolidation in upper lungs on initial CT increases the risk of adverse clinical outcome in COVID-19 patients. Thera- nostics. 2020;10:5641-8. https://doi.org/10.7150/thno.46465. 10.1148/ radiol.2020201433. Epub 2020 Apr 17. PMID: 32301647; PMCID: PMC7233411.
  3. Sun Dong Li, Guo Xiang, Wu Dajing, Chen Lan, Fang Ting, Chen Zheng, et al. CT quan- titative analysis and its relationship with clinical features for assessing the severity

of patients with COVID-19. Korean J Radiol. 2020;21. https://doi.org/10.3348/kjr. 2020.0293.

  1. Liu Fengjun Zhang, Huang Qi, Wang Chao, Shi Lin, Fang Nannan, Shan Cong, et al. CT quantification of pneumonia lesions in early days predicts progression to severe ill- ness in a cohort of COVID-19 patients. Theranostics. 2020;10:5613-22. https://doi. org/10.7150/thno.45985.
  2. Francone Marco Iafrate, Masci Franco, Coco Giorgio Maria, Cilia Simona, Manganaro Francesca, Panebianco Lucia, et al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur Radiol. 2020;30. https://doi. org/10.1007/s00330-020-07033-y.
  3. Ruichao Niu Shuming Ye, Li Yongfeng, Ma Hua, Xiaoting Xie Shilian Hu, Huang Xiaoming, Ou Yangshu, et al. Chest CT features associated with the clinical charac- teristics of patients with COVID-19 pneumonia. Ann Med. 2021;53(1):169-80. https://doi.org/10.1080/07853890.2020.1851044.
  4. Definition Task Force ARDS, Ranieri VM, Rubenfeld GD, et al. Acute respiratory dis- tress syndrome: the Berlin definition. JAMA. 2012;307:2526-33.
  5. Singer M, Deutschman CS, Seymour CW, et al. The third international consensus def- initions for Sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801-10. https:// doi.org/10.1001/jama.2016.0287.
  6. Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M, et al. Radiological Society of North America expert consensus statement on reporting chest CT findings related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA – Secondary Publication. J Thorac Imaging. 2020 Jul;35(4): 219-27. https://doi.org/10.1097/RTI.0000000000000524. PMID: 32324653; PMCID: PMC7255403.
  7. Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time course of lung changes at chest CT during recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020 Jun; 295(3):715-21. https://doi.org/10.1148/radiol.2020200370. Epub 2020 Feb 13. PMID: 32053470; PMCID: PMC7233367.
  8. Imam Z, Odish F, Gill I, et al. Older age and comorbidity are independent mortality predictors in a large cohort of 1305 COVID-19 patients in Michigan, United States. J Intern Med. 2020;4:469-76. 1111/joim.13119.
  9. Petrilli CM, Jones SA, Yang J, 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. https://doi.org/10.1136/ bmj.m1966. PubMed: 32444366.
  10. Duan J, Wang X, Chi J, et al. Correlation between the variables collected at admission and progression to severe cases during hospitalization among patients with COVID- 19 in Chongqing. J Med Virol. 2020. https://doi.org/10.1002/jmv.26082.
  11. Grasselli G, Zangrillo A, Zanella A, et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region. Italy JAMA. 2020;323:1574. https://doi.org/10.1001/jama.2020.5394. PubMed: 32250385.
  12. Centers for Disease Control and Prevention. People with Certain Medical Conditions. Updated Oct. 14, 2021. Available from. https://www.cdc.gov/coronavirus/2019- ncov/need-extraprecautions/people-with-medical-conditions.html.
  13. Kompaniyets L, Goodman AB, Belay B, et al. Body mass index and risk for COVID-19- related hospitalization, intensive care unit admission, invasive mechanical ventila- tion, and death — United States, March-December 2020. MMWR Morb Mortal Wkly Rep. 2021;70:355-61. https://doi.org/10.15585/mmwr.mm7010e4.
  14. Tanaka SI, Isoda F, Ishihara Y, Kimura M, Yamakawa T. T lymphopaenia in relation to body mass index and TNF-? in human obesity: adequate weight reduction can be corrective. Clin Endocrinol (Oxf). 2001;54(3):347-54.
  15. Alwarawrah Y, Kiernan K, MacIver NJ. Changes in Nutritional status impact immune cell metabolism and function. Front Immunol. 2018;9:1055.
  16. Dixon AE, Peters U. The effect of obesity on lung function. Expert Rev Respir Med. 2018 Sep;12(9):755-67. https://doi.org/10.1080/17476348.2018.1506331. Epub

2018 Aug 14. PMID: 30056777; PMCID: PMC6311385.

  1. Simonnet A, Chetboun M, Poissy J, Raverdy V, Noulette J, Duhamel A, et al. High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS- CoV-2) requiring invasive mechanical ventilation. Obesity. 2020;28:1195-9.
  2. Liu Y, Du X, Chen J, Jin Y, Peng L, Wang HHX, et al. Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19. J In- fect. 2020 Jul;81(1):e6-12. https://doi.org/10.1016/j.jinf.2020.04.002. Epub 2020

Apr 10. PMID: 32283162; PMCID: PMC7195072.

  1. Li X, Liu C, Mao Z, Xiao M, Wang L, Qi S, et al. Predictive values of neutrophil-to- lymphocyte ratio on disease severity and mortality in COVID-19 patients: a system- atic review and meta-analysis. Crit Care. 2020 Nov 16;24(1):647. https://doi.org/10. 1186/s13054-020-03374-8. PMID: 33198786; PMCID: PMC7667659.
  2. Zeng F, Huang Y, Guo Y, Yin M, Chen X, Xiao L, et al. Association of inflammatory markers with the severity of COVID-19: A meta-analysis. Int J Infect Dis. 2020 Jul; 96:467-74. https://doi.org/10.1016/j.ijid.2020.05.055. Epub 2020 May 18. PMID:

32425643; PMCID: PMC7233226.

  1. Tan C, Huang Y, Shi F, Tan K, Ma Q, Chen Y, et al. C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early. J Med Virol. 2020 Jul;92(7):856-62. https://doi.org/10.1002/jmv.25871. Epub 2020 Apr 25. PMID: 32281668; PMCID: PMC7262341.
  2. Gao Y-D, Ding M, Dong X, et al. Risk factors for severe and critically ill COVID-19 pa- tients: a review. Allergy. 2021;76:428-55. https://doi.org/10.1111/all.14657.
  3. Tian W, Jiang W, Yao J, Nicholson CJ, Li RH, Sigurslid HH, et al. Predictors of mortality in hospitalized COVID-19 patients: A systematic review and meta-analysis. J Med Virol. 2020 Oct;92(10):1875-83. https://doi.org/10.1002/jmv.26050. Epub 2020 Jul

11. PMID: 32441789; PMCID: PMC7280666.

  1. Poggiali E, Zaino D, Immovilli P, Rovero L, Losi G, Dacrema A, et al. Lactate dehydro- genase and C-reactive protein as predictors of respiratory failure in CoVID-19 pa- tients. Clinicachim Acta. 2020 Oct 1;509:135-8.
  2. E-Nicholson CJ, Wooster L, Sigurslid HH, Li RH, Jiang W, Tian W, et al. Estimating risk of mechanical ventilation and in-hospital mortality among adult COVID-19 patients admitted to mass general Brigham: the VICE and DICE scores. EClinicalMedicine. 2021 Mar 1;33:100765.
  3. Herold T, Jurinovic V, Arnreich C, Lipworth BJ, Hellmuth JC, von Bergwelt-Baildon M, et al. Elevated levels of IL-6 and CRP predict the need for mechanical ventilation in COVID-19. J Allergy Clin Immunol. 2020 Jul 1;146(1):128-36.
  4. Acar E, Demir A, Yildirim B, Kaya MG, Gokcek K. The role of hemogram parameters and C-reactive protein in predicting mortality in COVID-19 infection. Int J Clin Pract. 2021 Jul;75(7):e14256. https://doi.org/10.1111/ijcp.14256. Epub 2021 Apr 30. PMID: 33887100; PMCID: PMC8250321.
  5. Lentner Jacob, Adams Taylor, Knutson Valene, Zeien Sarah, Abbas Hassan, Moosavi Ryan, et al. C-reactive protein levels associated with COVID-19 outcomes in the United States. J Osteopath Med. 2021;121(12):869-73. https://doi.org/10.1515/ jom-2021-0103.
  6. Farhana A, Lappin SL. Biochemistry, Lactate Dehydrogenase. [Updated 2021 May 7]. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2021 Jan Available from:. https://www.ncbi.nlm.nih.gov/books/NBK557536/.
  7. Yan L, Zhang HT, Goncalves J, Xiao Y, Wang M, Guo Y, et al. An interpretable mortal- ity prediction model for COVID-19 patients. Nature Machine Intelligence. 2020 May; 2(5):283-8.
  8. Drent M, Cobben NA, Henderson RF, Wouters EF, van Dieijen-Visser M. Usefulness of lactate dehydrogenase and its isoenzymes as indicators of lung damage or inflam- mation. Eur Respir J. 1996 Aug;9(8):1736-42.
  9. Zhang JJ, Lee KS, Ang LW, Leo YS, Young BE. Risk factors for severe disease and effi- cacy of treatment in patients infected with COVID-19: a systematic review, meta- analysis, and Meta-regression analysis. Clin Infect Dis. 2020 Oct 15;71(16): 2199-206.
  10. Colombi D, Bodini FC, Petrini M, Maffi G, Morelli N, Milanese G, et al. Well-aerated Lung on admitting chest CT to predict adverse outcome in COVID-19 Pneumonia. Ra- diology. 2020 Aug;296(2):E86-96. https://doi.org/10.1148/radiol.2020201433. Epub 2020 Apr 17. PMID: 32301647; PMCID: PMC7233411.
  11. Yu Q, Wang Y, Huang S, Liu S, Zhou Z, Zhang S, et al. Multicenter cohort study dem- onstrates more consolidation in upper lungs on initial CT increases the risk of ad- verse clinical outcome in COVID-19 patients. Theranostics. 2020 Apr 27;10(12): 5641-8. https://doi.org/10.7150/thno.46465. PMID: 32373237; PMCID: PMC7196305.
  12. Zhang JJ, Cao YY, Tan G, Dong X, Wang BC, Lin J, et al. Clinical, radiological, and lab- oratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients. Allergy. 2021 Feb;76(2):533-50. https://doi.org/10.1111/all. 14496. Epub 2020 Aug 24. PMID: 32662525; PMCID: PMC7404752.
  13. Lieveld AW, Azijli K, Teunissen BP, van Haaften RM, Kootte RS, van den Berk IA, et al. Chest CT in COVID-19 at the ED: validation of the COVID-19 reporting and data sys- tem (CO-RADS) and CT severity score: a prospective, multicenter, observational study. Chest. 2021 Mar 1;159(3):1126-35.

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