Predictive value of hematologic indices in COVID-19 disease outcomes

Fatemeh Nejatifar¹, Ali Alavi Foumani ², Saman Maroufizadeh³, Bardia Afsharian⁴, Zahra Chegini ⁴, Amir Mohammad Ghanbari⁴*

¹ Associate professor, Hematology and Medical Oncology Department, Guilan University of Medical Sciences, Rasht, Iran

² Inflammatory Lung Diseases Research Center, Department of Pulmonology, Guilan University of Medical Sciences, Rasht, Iran

³ Department of Biostatistics and Epidemiology, School of Health, Guilan University of Medical Sciences, Rasht, Iran

⁴ Student Research Committee, Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran

Corresponding Authors: Amir Mohammad Ghanbari

* Email: amir.damash@yahoo.com

Abstract

Introduction: COVID-19 was declared a worldwide concern for public health in January 2020 by the World Health Organization. Most patients manifest mild symptoms. In more severe cases it can lead to sepsis, acute respiratory distress syndrome and other organ dysfunction. Lymphopenia, increased inflammatory markers and dysregulated liver enzymes are observed in many patients and is related to higher mortality rates.
Materials and Methods: We evaluated two hundred and sixty-eight patients in this study. All patients had dyspnea, and O2 saturation below 93% and were tested positive for COVID-19 through RT-PCR. Patients’ demographic, clinical and paraclinical information were obtained on admission and disease outcomes were assessed based on these data. The evaluated indices were previously shown to be altered in patients with different disease outcomes.
Results: From a total of 268 included patients, 40% had severe disease, 29% were admitted to ICU, 22% required mechanical ventilation and 24% died during hospitalization. WBC counts, neutrophil counts, NLR, serum LDH activity and serum albumin levels were the most powerful factors in predicting disease outcomes.
Conclusion: COVID-19 disease severity and outcomes were affected by hematologic indices and laboratory results.

Keywords: COVID-19, Neutrophil, White blood cell, Neutrophil lymphocyte ratio

Introduction

COVID-19 was first seen in December 2019 in Wuhan, China and was declared a worldwide concern for public health in January 2020 by World Health Organization (1). The disease was named “COVID-19” in February 2020 and the virus was named as “SARS-CoV-2” (2). SARS-CoV-2 was the third coronavirus in the past 20 years that can infect human species (3). About 81% of the patients manifest mild symptoms, the symptoms can be severe in 14% of the patients and it can lead to sepsis, acute respiratory distress syndrome and other organs’ failure in 5% of the patients (4).

Previous studies have shown lung involvement in CT-scan in most patients. Lymphopenia, increased inflammatory markers (like ferritin and C-reactive protein) and elevated AST and ALT levels are also observed in many patients (5, 6). It is shown that lymphopenia presents despite normal white blood cell count and lymphocyte count is related with disease severity and prognosis (7). Higher mortality rates are observed among patients with lymphopenia, thrombocytopenia, elevated inflammatory markers (like CRP, LDH and ferritin) and coagulopathies (8, 9).

As mentioned, these hematologic indices and inflammatory markers are shown to have a predictive role in determining the disease outcome. In this study we evaluated this predictive role in COVID-19 patients.

Materials and methods

Two hundred and sixty-eight patients were enrolled in this study. All patients were admitted to Razi Hospital, Rasht from March 2021 until March 2022. All patients had dyspnea, O₂ saturation below 93% and were tested positive for COVID-19 through RT-PCR. Patients with underlying medical condition (which is known to affect blood cell counts or other evaluated lab data e.g. hematologic malignancies) were excluded from this study. Demographic and clinical information were gathered from patients’ admission records. A blood test was administered in admission to evaluate hematologic and inflammatory indices.

Disease severity was classified as moderate (90 < SPO₂ < 94 or less than 50% lung involvement in CT-Scan) and severe (SPO₂ < 90 or respiratory rate over 30 or PCO₂/FIO₂ < 300). The patients were also classified by admission to intensive care unit, death within hospital admission and requiring ventilation. Patients’ demographic data, past medical records, inflammatory and hematologic indices in admission and clinical presentation were assessed based on the mentioned categories.

In this survey, quantitative data are shown as “mean (standard deviation)” and qualitative data are shown as “frequency (percentile)”. Man-Whitney test was done to compare the hematologic indices based on disease severity (moderate or severe), ICU admission (yes or no), death within hospital admission (yes or no) and requiring mechanical ventilation (yes or no). Area under the receiver operating characteristics curve (AUC for ROC curve) was shown to evaluate the potential of hematologic indices to predict disease severity, ICU admission, death within hospital admission and requirement of mechanical ventilation. All results were analyzed with a 95% confidence interval.

Results

Two hundred and sixty-eight patients, who were admitted to Razi hospital with a definite diagnosis of COVID-19, were enrolled in this study. 109 patients (41%) were male and the mean age was 56 ± 16.6. 105 patients (39%) had hypertension, 75 patients (28%) had diabetes mellitus,30 patients (11%) and ischemic heart disease, eleven patients (4%) had an underlying pulmonary disease and ten patients (4%) had chronic kidney disease. The mean systolic blood pressure in admission was 123.2 ± 19.4, the mean pulse rate and respiratory rate were 90.1 ±12.8 and 23.2 ±4.4 and the mean O2 saturation was 89.9 ± 8.3. All clinical and demographic data are shown in table 1.

Lab test results in admission are also shown in table 2.

As shown in table 3, 108 patients (40%) had severe disease, 78 patients (29%) were admitted in ICU, 58 patients (22%) required mechanical ventilation and 63 patients (24%) died during hospitalization.

Table 1. Patients' clinical and demographic data.

		Frequency (percentage) or mean ± standard deviation
Age (years)		56 ± 16.6
Gender	Male	109 (41%)
	Female	159 (59%)
HTN		105 (39%)
DM		75 (28%)
CKD		10 (4%)
Pulmonary disease		11 (4%)
IHD		30 (11%)
Temperature		37.1 ± 0.5
Systolic blood pressure		123.2 ± 19.4
Diastolic blood pressure		76.1 ± 12.7
Pulse rate		90.1 ± 12.8
Respiratory rate		23.2 ± 4.4
O₂ saturation		89.9 ± 8.3

Table 2. Patients' lab test results in admission.

	Mean ± SD	Median (IQR)
WBC (× 10⁶/mL)	8.1 ± 4.3	7.1 (5.1 – 10)
Hb (g/dL)	12 ± 1.9	12.2 (10.8 – 13.2)
RDW (%)	14 ± 2	13.5 (12.7 – 14.8)
MCV (fL)	84 ± 7.9	85 (80.5 – 88.4)
Platelets (10⁶/mL)	220.5 ± 97.1	203 (152 – 263.5)
Neutrophils (10⁶/mL)	6.61 ± 3.8	5.7 (4 – 8.5)
Lymphocytes (10⁶/mL)	1.1 ± 0.9	0.8 (0.6 – 1.3)
Monocytes (10⁶/mL)	0.4 ± 0.3	0.3 (0.2 – 0.5)
NLR	8.2 ± 6	6.8 (4 – 10.7)
PLR	288.5 ± 206	228.5 (158.8 – 356.6)
MLR	0.5 ± 0.4	0.3 (0.2 – 0.6)
PT (s)	12.7 ± 1.1	12 (12 – 12.7)
PTT (s)	34.6 ± 0.9	32 (30 – 37)
BS (mg/dL)	157.7 ± 82.3	133.5 (110.1 – 170)
BUN (mg/dL)	22.2 ± 18.6	16 (11.3 – 23)
Cr (mg/dL)	1.24 ± 1	1 (0.8 – 1.2)
AST (U/L)	54.8 ± 44.2	44 (31 – 65)
ALT (U/L)	43.8 ± 46.6	29 (21 – 45.8)
ALP (U/L)	198 ± 85	178 (145.3 – 230)
LDH (U/L)	904 ± 372.9	841 (654 – 1078.8)
Alb (g/dL)	3.6 ± 0.5	3.6 (3.5 – 3.9)
ESR (mm/h)	54.6 ± 22.9	55 (39 – 66)
pH	7.37 ± 0.07	7.38 (7.34 – 7.41)
PCO₂ (mmHg)	42.1 ± 8.3	41.8 (36.8 – 45.7)
HCO₃ (mmol/L)	25.1 ± 4.1	25.1 (22.4 – 27.9)

Table 3. Rates of disease severity, ICU admission, requiring mechanical ventilation and death during hospitalization.

		Frequency (Percent)
Disease severity	Moderate	160 (60%)
Disease severity	Severe	108 (40%)
ICU admission	No	190 (71%)
ICU admission	Yes	78 (29%)
Mechanical ventilation	Not required	210 (78%)
Mechanical ventilation	Required	58 (22%)
Death during hospitalization	No	205 (76%)
Death during hospitalization	Yes	63 (24%)

Table 4 & 5 show the comparison of lab test results based on disease severity, ICU admission, mechanical ventilation and death during hospitalization. White blood cells, neutrophil count, neutrophil to lymphocyte ratio, prothrombin time, random plasma glucose, blood urea nitrogen, AST, LDH and erythrocyte sedimentation rate were significantly higher in patients who had severe disease, required mechanical ventilation, were admitted to ICU or died during hospitalization.

Table 4. Comparison of lab test results between groups of disease severity and ICU admission.

	Disease severity		P value	ICU admission		P value
	Moderate	Severe	P value	No	Yes	P value
WBC (× 10⁶/mL)	6.7 (4.7 – 9.9)	7.9 (5.5 – 10.6)	0.010	6.7 (4.9 – 9.4)	8.7 (5.6 – 12.2)	0.001
Hb (g/dL)	12 (10.7 – 13.1)	12.4 (10.9 – 13.4)	0.139	12.1 (10.9 – 13.1)	12.4 (10.7 – 13.4)	0.400
RDW (%)	13.3 (12.5 – 15)	13.9 (12.8 – 14.7)	0.338	13.3 (12.6 – 14.8)	14.1 (12.9 – 14.6)	0.096
MCV (fL)	86.2 (82 – 89)	83.4 (79.1 – 86.9)	0.003	86.2 (82.2 – 89.5)	82.1 (78.5 – 85.8)	< 0.001
Platelets (10⁶/mL)	194 (146 – 257.8)	213 (169.3 – 283)	0.068	203 (147.8 – 262)	201.5 (157.5 – 271.5)	0.703
Neutrophils (10⁶/mL)	5.3 (3.5 – 7.6)	6.4 (4.5 – 9)	0.002	5.3 (3.6 – 7.5)	7.4 (4.8 – 10.3)	< 0.001
Lymphocytes (10⁶/mL)	0.9 (0.6 – 1.4)	0.8 (0.6 – 1.1)	0.239	0.9 (0.6 – 1.4)	0.8 (0.6 – 1.1)	0.170
Monocytes (10⁶/mL)	0.3 (0.2 – 0.5)	0.3 (0.2 – 0.5)	0.962	0.3 (0.2 – 0.5)	0.3 (0.2 – 0.5)	0.516
NLR	5.5 (3.8 – 8.8)	8.6 (4.7 – 11.3)	0.001	5.4 (3.8 – 8.8)	8.8 (5.1 – 14.5)	< 0.001
PLR	208.3 (148.3 – 319.9)	272.9 (167.8 – 376.7)	0.010	220.2 (151.1 – 339.6)	268.1 (160 – 384.3)	0.118
MLR	0.3 (0.2 – 0.6)	0.4 (0.2 – 0.6)	0.656	0.3 (0.2 – 0.6)	0.4 (0.2 – 0.6)	0.931
PT (s)	12 (12 – 12.7)	12.7 (12 – 13.4)	< 0.001	12 (12 – 12.7)	12.7 (12 – 13.4)	< 0.001
PTT (s)	31 (30 – 36)	33 (30 – 39)	0.036	31 (30 – 36)	33 (30 – 40)	0.007
BS (mg/dL)	129 (110 – 155.8)	139.5 (110.3 – 202.8)	0.025	129 (107.8 – 155.3)	150 (115.8 – 219.3)	0.003
BUN (mg/dL)	16 (11 – 21.8)	18 (12 – 25.8)	0.025	16 (11 – 21.3)	19 (12 – 26.8)	0.017
Cr (mg/dL)	1 (0.9 – 1.2)	1 (0.8 – 1.3)	0.958	1 (0.89 – 1.2)	1 (0.8 – 1.33)	0.330
AST (U/L)	38 (27.3 – 52)	50.5 (38 – 73.5)	< 0.001	39 (29 – 54)	52.5 (41 – 86.8)	< 0.001
ALT (U/L)	28 (21 – 45)	31.5 (22 – 49.3)	0.257	28 (21 – 44)	36 (23 – 56.3)	0.036
ALP (U/L)	185 (151 – 232.8)	170 (144.3 – 221)	0.180	177.5 (146 – 225)	184 (145 – 240.5)	0.489
LDH (U/L)	731.5 (580 – 938.5)	1007.5 (786 – 1305)	< 0.001	741.5 (607.5 – 950.5)	1070 (796 – 1390)	< 0.001
Alb (g/dL)	3.7 (3.4 – 4)	3.4 (3.2 – 3.8)	< 0.001	3.7 (3.4 – 4)	3.4 (3.2 – 3.8)	< 0.001
ESR (mm/h)	53.5 (35.3 – 63)	58 (40.5 – 75)	0.020	54.5 (39.8 – 65)	60 (37.8 – 78.5)	0.140
pH	7.39 (7.35 – 7.41)	7.37 (7.33 – 7.41)	0.245	7.39 (7.35 – 7.42)	7.36 (7.33 – 7.4)	0.001
PCO₂ (mmHg)	42.7 (38.9 – 46.2)	40.1 (35.3 – 45)	0.011	42.3 (38.2 – 46.2)	40.1 (35.1 – 44.2)	0.014
HCO₃ (mmol/L)	26 (23.5 – 28.2)	24.1 (21.5 – 27.3)	0.002	26 (23.5 – 28.3)	23.7 (20 – 26.2)	< 0.001

Table 5. Comparison of lab test results between groups of mechanical ventilation and death during hospitalization.

	Mechanical ventilation		P value	Death during hospitalization		P value
	Not required	Required	P value	No	Yes	P value
WBC (× 10⁶/mL)	6.7 (4.8 – 9.5)	9.15 (6.5 – 13.1)	< 0.001	6.7 (4.8 – 9.5)	8.7 (5.8 – 13)	0.001
Hb (g/dL)	12.2 (10.9 – 13.1)	12.15 (10.9 – 13.5)	0.547	12.2 (10.9 – 13.1)	12 (10.7 – 13.4)	0.968
RDW (%)	13.4 (12.6 – 14.8)	14 (12.8 – 14.7)	0.545	13.3 (12.6 – 14.7)	14.2 (12.9 – 14.8)	0.101
MCV (fL)	85.2 (81.4 – 89)	82.9 (78.3 - 87)	0.008	85.3 (81.5 – 89)	82.8 (77.9 – 87)	0.002
Platelets (10⁶/mL)	201.5 (149.5 – 261.3)	206.5 (164 - 281)	0.400	204 (152 – 262)	197 (154 – 271)	0.813
Neutrophils (10⁶/mL)	5.3 (3.6 – 7.8)	7.9 (5 – 10.5)	< 0.001	5.3 (3.6 – 7.9)	7.1 (4.9 – 10.1)	< 0.001
Lymphocytes (10⁶/mL)	0.9 (0.6 – 1.3)	0.9 (0.7 – 1.1)	0.719	0.9 (0.6 – 1.4)	0.8 (0.6 – 1.1)	0.677
Monocytes (10⁶/mL)	0.3 (0.2 – 0.5)	0.3 (0.2 – 0.5)	0.378	0.3 (0.2 – 0.5)	0.3 (0.2 – 0.5)	0.592
NLR	5.7 (3.9 – 9.8)	8.7 (5.1 – 12.7)	0.002	5.7 (3.9 – 9.6)	8.7 (5.1 – 11.4)	0.001
PLR	226.6 (159.1 – 359.8)	232.7 (156 - 350)	0.845	226.1 (159 – 354.8)	234.2 (157.2 – 364.1)	0.787
MLR	0.3 (0.2 – 0.6)	0.4 (0.2 – 0.6)	0.980	0.3 (0.2 – 0.6)	0.4 (0.2 – 0.6)	0.706
PT (s)	12 (12 – 12.7)	12.7 (12 – 13.4)	< 0.001	12 (12 – 12.7)	12.7 (12 – 13.4)	< 0.001
PTT (s)	32 (30 - 37)	33 (30 – 40)	0.073	32 (30 – 36)	35 (30 – 40)	0.005
BS (mg/dL)	128.5 (106 - 156)	153 (120.8 – 229.3)	< 0.001	130 (107.5 – 156)	150 (117 – 216)	0.004
BUN (mg/dL)	16 (11 - 22)	20 (13.8 – 40.8)	0.001	16 (11 – 22)	19 (12 – 48)	0.003
Cr (mg/dL)	0.97 (0.84 – 1.18)	1.1 (0.88 – 1.64)	0.036	0.96 (0.84 – 1.17)	1.1 (0.9 – 1.68)	0.018
AST (U/L)	42 (30 - 55)	53 (42.5 – 90.5)	< 0.001	41 (29 – 54)	54 (44 – 90)	< 0.001
ALT (U/L)	28 (21 – 46.8)	33.5 (22.5 – 45.5)	0.402	28 (21 – 45.5)	34 (23 – 47)	0.342
ALP (U/L)	177.5 (145 – 225.5)	184 (148.3 – 124.5)	0.365	178 (147.5 – 232)	178 (145 – 224)	0.915
LDH (U/L)	788 (623 - 989)	1084.5 (779.8 – 1412.5)	< 0.001	775 (611 – 974.5)	1085 (786 – 1400)	< 0.001
Alb (g/dL)	3.7 (3.4 – 3.9)	3.4 (3.2 – 3.8)	0.001	3.7 (3.4 – 3.9)	3.4 (3.2 – 3.8)	0.001
ESR (mm/h)	54 (38 – 64.3)	60 (42 – 50.5)	0.008	54 (38 – 64.5)	60 (42 – 80)	0.016
pH	7.39 (7.35 – 7.42)	7.35 (7.32 – 7.39)	0.001	7.39 (7.35 – 7.42)	7.35 (7.31 – 7.40)	< 0.001
PCO₂ (mmHg)	42.2 (38.2 – 46.2)	39.2 (34.5 – 44.4)	0.015	42.2 (38.4 – 46.1)	38.8 (34.3 – 44.6)	0.008
HCO₃ (mmol/L)	25.6 (23.4 – 28.2)	23 (20 – 26.1)	<0.001	25.6 (23.5 – 28.2)	22.7 (19 – 26.3)	< 0.001

Table 6. Effect of different factors on disease severity, ICU admission, requiring mechanical ventilation and death during hospitalization.

	Severity		ICU admission		Intubations		In-hospital mortality
	AUC (95% CI)	P value	AUC (95% CI)	P value	AUC (95% CI)	P value	AUC (95% CI)	P value
WBC (× 10⁶/mL)	0.593 (0.524 – 0.662)	0.010	0.636 (0.558 – 0.715)	0.001	0.624 (0.548 – 0.701)	0.001	0.672 (0.593 – 0.750)	<0.001
Hb (g/dL)	0.000 (0.000 – 0.000)	0.139	0.000 (0.000 – 0.000)	0.968	0.000 (0.000 – 0.000)	0.400	0.000 (0.000 – 0.000)	0.547
RDW (%)	0.535 (0.465 – 0.604)	0.338	0.568 (0.491 – 0.646)	0.101	0.565 (0.493 – 0.636)	0.096	0.526 (0.446 – 0.606)	0.545
MCV (fL)	0.608 (0.539 – 0.677)	0.003	0.629 (0.551 – 0.708)	0.002	0.678 (0.611 – 0.745)	<0.001	0.614 (0.534 – 0.695)	0.008
Platelets (× 10⁶/mL)	0.000 (0.000 – 0.000)	0.068	0.000 (0.000 – 0.000)	0.813	0.000 (0.000 – 0.000)	0.703	0.000 (0.000 – 0.000)	0.400
Neutrophils (× 10⁶/mL)	0.612 (0.544 – 0.679)	0.002	0.657 (0.581 – 0.733)	<0.001	0.654 (0.580 – 0.727)	<0.001	0.687 (0.611 – 0.763)	<0.001
Lymphocytes (× 10⁶/mL)	0.000 (0.000 – 0.000)	0.239	0.000 (0.000 – 0.000)	0.677	0.000 (0.000 – 0.000)	0.170	0.000 (0.000 – 0.000)	0.719
Monocytes (× 10⁶/mL)	0.000 (0.000 – 0.000)	0.962	0.000 (0.000 – 0.000)	0.592	0.000 (0.000 – 0.000)	0.516	0.000 (0.000 – 0.000)	0.378
NLR	0.625 (0.556 – 0.693)	<0.001	0.645 (0.570 – 0.720)	<0.001	0.675 (0.603 – 0.747)	<0.001	0.635 (0.558 – 0.713)	0.002
PLR	0.592 (0.522 – 0.662)	0.010	0.511 (0.428 – 0.595)	0.787	0.561 (0.483 – 0.639)	0.118	0.508 (0.423 – 0.594)	0.845
MLR	0.516 (0.445 – 0.587)	0.654	0.516 (0.435 – 0.596)	0.708	0.503 (0.426 – 0.580)	0.936	0.499 (0.415 – 0.582)	0.977
PT (s)	0.625 (0.556 – 0.694)	<0.001	0.637 (0.558 – 0.715)	0.001	0.641 (0.567 – 0.714)	<0.001	0.646 (0.566 – 0.726)	<0.001
PTT (s)	0.575 (0.504 – 0.646)	0.037	0.617 (0.535 – 0.700)	0.005	0.604 (0.538 – 0.682)	0.007	0.576 (0.489 – 0.664)	0.075
BS (mg/dL)	0.581 (0.510 – 0.652)	0.025	0.619 (0.535 – 0.703)	0.004	0.617 (0.516 – 0.696)	0.003	0.653 (0.569 – 0.736)	<0.001
BUN (mg/dL)	0.580 (0.511 – 0.650)	0.026	0.623 (0.541 – 0.705)	0.003	0.592 (0.458 – 0.669)	0.017	0.644 (0.562 – 0.726)	<0.001
Cr (mg/dL)	0.498 (0.426 – 0.570)	0.958	0.598 (0.511 – 0.685)	0.019	0.538 (0.499 – 0.618)	0.332	0.590 (0.499 – 0.680)	0.036
AST (U/L)	0.653 (0.585 – 0.720)	<0.001	0.665 (0.586 – 0.743)	<0.001	0.657 (0.584 – 0.731)	<0.001	0.652 (0.571 – 0.733)	<0.001
ALT (U/L)	0.541 (0.470 – 0.611)	0.257	0.540 (0.460 – 0.620)	0.342	0.582 (0.506 – 0.658)	0.036	0.536 (0.454 – 0.618)	0.402
ALP (U/L)	0.452 (0.382 – 0.521)	0.180	0.496 (0.413 – 0.578)	0.915	0.527 (0.450 – 0.604)	0.489	0.539 (0.455 – 0.623)	0.365
LDH (U/L)	0.731 (0.670 – 0.792)	<0.001	0.735 (0.666 – 0.804)	<0.001	0.749 (0.683 – 0.814)	<0.001	0.710 (0.635 – 0.785)	<0.001
Alb (g/dL)	0.640 (0.573 – 0.706)	<0.001	0.632 (0.556 – 0.709)	0.001	0.666 (0.598 – 0.734)	<0.001	0.643 (0.567 – 0.719)	<0.001
ESR (mm/h)	0.584 (0.513 – 0.654)	0.020	0.600 (0.517 – 0.683)	0.016	0.557 (0.477 – 0.638)	0.141	0.615 (0.527 – 0.702)	0.008
pH	0.542 (0.470 – 0.614)	0.246	0.650 (0.569 – 0.731)	<0.001	0.630 (0.555 – 0.705)	<0.001	0.645 (0.563 – 0.728)	<0.001
PCO2 (mmHg)	0.592 (0.521 – 0.662)	0.011	0.611 (0.526 – 0.696)	0.008	0.595 (0.520 – 0.671)	0.014	0.604 (0.517 – 0.691)	0.015
HCO3 (mmol/L)	0.612 (0.541 – 0.684)	0.002	0.677 (0.595 – 0.759)	<0.001	0.674 (0.601 – 0.747)	<0.001	0.669 (0.588 – 0.750)	<0.001

Figure 4. Predicting potential of WBC counts on disease severity, ICU admission, requiring mechanical ventilation and death during hospitalization.

Figure 5. Predicting potential of neutrophil counts on disease severity, ICU admission, requiring mechanical ventilation and death during hospitalization.

Figure 6. Predicting potential of NLR on disease severity, ICU admission, requiring mechanical ventilation and death during hospitalization.

Figure 7. Predicting potential of LDH on disease severity, ICU admission, requiring mechanical ventilation and death during hospitalization.

Figure 8. Predicting potential of serum Albumin levels on disease severity, ICU admission, requiring mechanical ventilation and death during hospitalization.

* ROC curve is shown on the left and Violin plot is shown on the right.

Discussion

In this study, we investigated 268 patients with mean age of 56 ± 16.6 years, of whom 59% were female. 39% of our patients had hypertension, 28% had diabetes mellitus, 11% had IHD, 4% had CKD and 4% had an underlying pulmonary disease.

In our study, WBC and neutrophil count was significantly higher in patients with severe disease, patients admitted to ICU, patients requiring mechanical ventilation and patients who died during hospitalizations; however, our findings didn’t show any significant difference of lymphocyte count between the two groups of our study outcomes (in-hospital death, ICU admission, mechanical ventilation and disease severity). An elevated neutrophil count may be an indicator of viremia or a bacterial co-infection, which can worsen the severity and prognosis of infected patients (10, 11). Compensatory hyperplasia of the bone marrow which happens due to prolonged hypoxia can also result in elevated WBC count (12). A meta analyze by Shi et al. suggested WBC count as a mortality predictor for COVID-19 (13). Many previous studies have shown elevated neutrophil and WBC counts, which supports our findings in this study (12, 14-21). Neutrophil infiltration in pulmonary capillaries in autopsy studies can confirm the role of neutrophil count in predicting disease severity and mortality (22, 23). In contrary to our findings, numerous previous studies have shown lymphopenia as a predicting factor of severity and different outcomes in COVID-19 (7, 16-21, 24-26). On the other hand, Zhou et al. demonstrated that after adjusting potential risk factors, lymphopenia didn’t have a significant effect on COVID-19 mortality (11). We only included moderate and severe patients who met the admission criteria for COVID-19 disease in this study. This can lead to a similar lymphocyte count in all our patients. The mean lymphocyte counts of 1070, which indicates lymphopenia, can confirm this hypothesis. The presence of lymphopenia in our patients is similar to previous findings in the literature (5-7, 15, 27-30). Angiotensin converting enzyme 2, which is expressed in lymphocytes, is the main surface receptor for SARS-CoV-2 (31); this characteristic can result in serious damage to lymphocytes by the virus. Dramatically reduced lymphocyte (CD8, CD4 and CD3) count can indicate the effect of virus on T-lymphocytes and cause a major malfunction in immune system. Immunosuppression caused by lymphocyte injury will worsen the prognosis and can cause more severe disease (7, 13).

An elevated neutrophil count and decreased lymphocyte count results in an elevated NLR in the patients with more severe disease and poor prognosis. The fact of correlation between NLR and disease severity and outcome is stated by many previous studies (12, 20, 32-34).

In our study, LDH was significantly higher in patients with severe disease, patients admitted to ICU, patients requiring mechanical ventilation and patients who died during hospitalizations. The mentioned result is stated in previous studies, too (7, 12, 17-20, 35). It is evident that LDH can be a reflecting parameter for the extent of lung injury in ARDS, including the patients infected with corona virus SARS (36). We also showed that ESR is significantly higher in patients with severe disease, patients admitted to ICU, patients requiring mechanical ventilation and patients who died during hospitalizations. The correlation between inflammatory biomarkers including ESR and disease outcomes is noted in a meta-analysis by Shi et al. (13).

In our patients, serum levels of albumin were significantly lower in patients with severe disease, patients admitted to ICU, patients requiring mechanical ventilation and patients who died during hospitalizations. Bastug et al. also showed lower levels of albumin in patients with more severe disease (13, 17, 33). The relevance between the levels of serum albumin and ICU admission had been shown in MERS infection too (37). Lower levels of serum albumin may indicate the effect of malnutrition on disease prognosis and suggests the benefits of nutritional support (13).

In this study, AST levels were significantly higher in patients with severe disease, patients admitted to ICU, patients requiring mechanical ventilation and patients who died during hospitalizations; on the other hand, ALT levels were only higher in patients requiring ICU admission. There wasn’t any significant difference in alkaline phosphatase in patients with different disease severity and outcomes. Altered liver function tests were documented in previous studies (17); however, other studies didn’t show any significant change in liver enzymes among different stages of disease severity (20). There are studies showing that AST elevates before other liver enzymes, so it can be used for patients monitoring and predicting the disease outcome (13, 38). The direct effect of SARS-CoV-2 on cholangiocytes is suggested as a reason of liver failure in some recent studies. Liver injuries can occur as a result of drugs and systemic inflammatory response, too (39). The exact reason causing liver injuries should be investigated in further studies.

Our findings showed longer PT and PTT in patients with severe disease, patients admitted to ICU and patients who died during hospitalizations. PT was also longer in patients requiring medical ventilation. Alteration in coagulation factors is evident in previous studies (16-18); however, Wang et al. showed that there is no difference of PT, PTT and INR among disease severities. These results suggest that intravascular and consumption coagulopathies can be present in COVID-19 patients with more severe disease and hence, lead to higher mortality rates (40). Previous studies on SARS indicate that inflammatory response may alter coagulation pathways and lead to disseminated infarcts and hemorrhages (41).

Based on the AUC of ROC curve we demonstrated that LDH, AST and serum albumin levels were the most powerful predicting factors for disease severity. LDH and serum albumin levels were also shown by Zhang et al. and HU et al. to be a potential predicting factor for disease severity (34, 42). We showed that LDH, WBC and neutrophil counts and NLR are significant predicting factor in ICU admission and requirement of mechanical ventilation. Previous studies support these results (33, 43).

Conclusion

This study shows higher values of hematologic indices in patients with severe disease and poor outcome. These indices can reflect inflammatory passages (neutrophilia) and viral infection by COVID-19 (lymphopenia). Evaluated inflammatory markers are also shown to be generally higher in patients with poor disease outcome. The existence of coagulopathies and altered LFT in patients with poor disease outcome can be the effect of direct viral infection of COVID-19 and needs to be further investigated.

Limitations

We evaluated patients with moderate and severe disease in this study. Evaluation of patients with mild disease will give us more accurate results. Also, a bigger sample size and a multicenter study can always help the accuracy of the survey. Determining the predicting factors can lead to earlier treatment for severe cases of COVID-19 and further studies to establish a cut-off for clinical interference can be clinically beneficial.

Author contribution

Conceptualization: FN, AAF; Methodology: FN, BA; Formal analysis and investigation: AMGh, SM; Writing - original draft preparation: BA, ZCh, AMGh; Writing - review and editing: AMGh, FN; Supervision: FN, AAF.

Acknowledgments

We declare our gratitude to all staff in Razi hospital which devoted their lives in COVID-19 pandemia. They contributed a huge role in gathering the information used in this study.

This study was approved by the Ethics Committee of Guilan University of Medical Sciences (Code: IR.GUMS.REC.1400.547, Date: 02-02-2022).

Conflict of interest

There is no Conflicts of interest/competing interests.

Funding

There is no funding.

References

1. Lai C-C, et al. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. International journal of antimicrobial agents. 2020;55(3):105924.

2. Gorbalenya AE, et al. Severe acute respiratory syndrome-related coronavirus: The species and its viruses–a statement of the Coronavirus Study Group. BioRxiv. 2020.

3. Lefkowitz EJ, et al. Virus taxonomy: the database of the International Committee on Taxonomy of Viruses (ICTV). Nucleic acids research. 2018;46(D1):D708-D17.

4. Zu ZY, et al. Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. 2020;296(2):E15-E25.

5. Huang C, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet. 2020;395(10223):497-506.

6. Chen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The lancet. 2020;395(10223):507-13.

7. Tan L, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal transduction and targeted therapy. 2020;5(1):33.

8. Tang N, et al. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. Journal of thrombosis and haemostasis. 2020;18(4):844-7.

9. Rahi MS, et al. Hematologic disorders associated with COVID-19: a review. Annals of hematology. 2021;100:309-20.

10. Cervellin G, et al. Toward a holistic approach for diagnosing sepsis in the emergency department. Advances in clinical chemistry. 2019;92:201-16.

11. Zhou F, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The lancet. 2020;395(10229):1054-62.

12. Wang C, Deng R, Gou L, Fu Z, Zhang X, Shao F, Wang G, Fu W, Xiao J, Ding X, Li T, Xiao X, Li C. Preliminary study to identify severe from moderate cases of COVID-19 using combined hematology parameters. Ann Transl Med. 2020 May;8(9):593.

13. Shi C, et al. Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis. BMC infectious diseases. 2021;21:1-15.

14. Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, Xie C, Ma K, Shang K, Wang W, Tian DS. Dysregulation of Immune Response in Patients With Coronavirus 2019 (COVID-19) in Wuhan, China. Clin Infect Dis. 2020;71(15):762-768.

15. Chen G, et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. The Journal of clinical investigation. 2020;130(5):2620-9.

16. Javanian M, et al. Clinical and laboratory findings from patients with COVID-19 pneumonia in Babol North of Iran: a retrospective cohort study. Romanian Journal of Internal Medicine. 2020;58(3):161-7.

17. Bonetti G, et al. Laboratory predictors of death from coronavirus disease 2019 (COVID-19) in the area of Valcamonica, Italy. Clinical Chemistry and Laboratory Medicine (CCLM). 2020;58(7):1100-5.

18. Wang K, et al. Clinical and laboratory predictors of in-hospital mortality in patients with coronavirus disease-2019: a cohort study in Wuhan, China. Clinical infectious diseases. 2020;71(16):2079-88.

19. Fan BE, et al. Hematologic parameters in patients with COVID-19 infection. American journal of hematology. 2020;95(6):E131-E4.

20. Dubey DB, et al. Hematological and serum biochemistry parameters as a prognostic indicator of severally ill versus mild Covid-19 patients: A study from tertiary hospital in North India. Clinical Epidemiology and Global Health. 2021;12:100806.

21. Yu C, et al. Clinical characteristics, associated factors, and predicting COVID-19 mortality risk: a retrospective study in Wuhan, China. American journal of preventive medicine. 2020;59(2):168-75.

22. Fox SE, et al. Pulmonary and cardiac pathology in African American patients with COVID-19: an autopsy series from New Orleans. The Lancet Respiratory Medicine. 2020;8(7):681-6.

23. Yao X, et al. A pathological report of three COVID-19 cases by minimal invasive autopsies. Zhonghua bing li xue za zhi= Chinese journal of pathology. 2020;49(5):411-7.

24. Lippi G, Plebani M. Laboratory abnormalities in patients with COVID-2019 infection. Clinical chemistry and laboratory medicine (CCLM). 2020;58(7):1131-4.

25. Terpos E, et al. Hematological findings and complications of COVID‐19. American journal of hematology. 2020;95(7):834-47.

26. Chen T, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. bmj. 2020;368.

27. Organization WH. Report of the WHO-China joint mission on coronavirus disease 2019 (COVID-19). 2020. 2020.

28. Guan W-j, et al. Clinical characteristics of 2019 novel coronavirus infection in China. MedRxiv. 2020.

29. Shi Q, et al. Clinical characteristics of 101 non-surviving hospitalized patients with COVID-19—A single center, retrospective study. MedRxiv. 2020:2020.03. 04.20031039.

30. Xu X-W, et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. bmj. 2020;368.

31. Xu H, et al. High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa. International journal of oral science. 2020;12(1):1-5.

32. Saurabh A, et al. Evaluation of hematological parameters in predicting intensive care unit admission in COVID-19 patients. SN Comprehensive Clinical Medicine. 2022;4(1):39.

33. Bastug A, et al. Clinical and laboratory features of COVID-19: Predictors of severe prognosis. International immunopharmacology. 2020;88:106950.

34. Zhang M, et al. An emerging marker predicting the severity of COVID-19: Neutrophil-Lymphocyte Count Ratio. 2020.

35. Wang L. C-reactive protein levels in the early stage of COVID-19. Medecine et maladies infectieuses. 2020;50(4):332-4.

36. Chiang C-H, et al., editors. Eight-month prospective study of 14 patients with hospital-acquired severe acute respiratory syndrome. Mayo Clinic Proceedings; 2004: Elsevier.

37. Saad M, et al. Clinical aspects and outcomes of 70 patients with Middle East respiratory syndrome coronavirus infection: a single-center experience in Saudi Arabia. International journal of infectious diseases. 2014;29:301-6.

38. Lei F, et al. Longitudinal association between markers of liver injury and mortality in COVID‐19 in China. Hepatology. 2020;72(2):389-98.

39. Chai X, et al. Specific ACE2 expression in cholangiocytes may cause liver damage after 2019-nCoV infection. biorxiv. 2020:2020.02. 03.931766.

40. Favaloro EJ, Lippi G. Recommendations for Minimal Laboratory Testing Panels in Patients with COVID-19: Potential for Prognostic Monitoring. Semin Thromb Hemost. 2020 Apr;46(3):379-382.

41. Enjuanes L, et al. Biochemical aspects of coronavirus replication and virus-host interaction. Annu Rev Microbiol. 2006;60:211-30.

42. Hu H, et al. Early prediction and identification for severe patients during the pandemic of COVID-19: a severe COVID-19 risk model constructed by multivariate logistic regression analysis. Journal of global health. 2020;10(2).

43. Li W, et al. Early predictors for mechanical ventilation in COVID-19 patients. Therapeutic advances in respiratory disease. 2020;14:1753466620963017.