Sage Journals: Discover world-class research

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic resulted in more than five hundred million infected cases worldwide. The current study aimed to screen the correlation of different laboratory findings with disease severity and clinical outcomes of coronavirus disease (COVID-19) among Egyptian patients to obtain prognostic indicators of disease severity and outcome.

A total of 112 laboratory-confirmed COVID-19 patients were examined. According to the severity of the disease, these patients were divided into three main groups: mild, moderate and severe cases. In addition, clinical characteristics and laboratory findings, including Hb, platelet count, white blood cell count, lymphocyte percentage, neutrophil percentage, neutrophil lymphocyte ratio (NLR), D-dimer, highly sensitive C-reactive protein (HS-CRP), alanine aminotransferase (ALT), lactate dehydrogenase (LDH) and creatinine, were measured.

The presence of hypertension and/or diabetes was found to be a significant risk factor for disease severity and poor outcome. Increased respiratory rate, levels of SpO₂, HS-CRP, D-dimer, NLR, ALT, LDH, lymphopenia and neutrophilia, as well as changes in chest computed tomography (CT), were associated with increased disease severity and fatal consequences. Highly sensitive C-reactive protein, D-dimer, NLR and LDH constituted excellent predictors for both disease severity and death.

Laboratory biomarkers, such as HS-CRP, D-dimer, NLR and LDH, are excellent predictors for both disease severity and death. They can predict mortality in patients at the time of admission secondary to SARS-CoV-2 infection and can help physicians identify high-risk patients before clinical deterioration.

Keywords

highly sensitive C-reactive protein D-dimer COVID-19 neutrophil lymphocyte ratio and lactate dehydrogenase

Introduction

Coronavirus disease (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in Wuhan, China, in December 2019¹. In March 2020, the World Health Organization (WHO) declared a worldwide pandemic.² This disease usually starts with flu-like symptoms, and about two-thirds of infected subjects remain asymptomatic^3–4 The classical symptoms of the disease include fever, fatigue and cough.⁴ In many cases, the disease progresses to severe pneumonia or acute respiratory distress syndrome (SARS), up to multi-organ failure with fatal consequences.⁵ On the other hand, some patients might develop severe respiratory distress with fatal consequences, especially elderly patients with more comorbidities, such as hypertension,⁶ diabetes,⁷ dementia^8,⁹ and Parkinson disease^10,¹¹ as well as those with immunocompromised disorders¹² and cancer patients.¹³

The levels of many biomarkers are increased during the disease and are highly suggestive of the infection, including D-dimer, C-reactive protein (CRP), highly sensitive CRP (HS-CRP) and high-density lipoprotein.^3,12,14–17 In addition, the pathological findings of chest computed tomography (CT) exhibit good consistency, and their combination can reflect the disease severity and progression, as well as therapeutic effects.^3,¹⁸ A haemogram derived marker, NLR, has been studied in various conditions and found to be related to inflammation in type 2 diabetes mellitus,¹⁹ Hashimoto’s disease,²⁰ ulcerative colitis²¹ and COVID-19 infection.²² Moreover, it is correlated with plasma glucose and glycated haemoglobin (HbA1c) levels in diabetic patients. Therefore, it can be assumed that NLR could be related to the prognosis of COVID-19 subjects. Accordingly, there is a need to determine prognostic parameters, including laboratory biomarkers, clinical manifestations and factors affecting patient survival, for better disease management to predict the disease severity in a trial to reduce mortality among COVID-19 patients.^17,23,24 Therefore, in the current study, we aim to determine biomarkers that can be used as prognostic indicators of the clinical outcomes of the disease.

Materials and methods

Patients

This is a case–control study included 112 hospitalised patients whose infection with the SARS-CoV-2 virus was confirmed by real-time polymerase chain reaction through throat and/or nasal swabs. The control group included 45 age-matched normal subjects. Eligibility criteria were all COVID-19 patients who were admitted to Cairo University Hospitals between April and October 2020 with complete baseline clinical and laboratory data and were on treatment and follow up. Exclusion criteria were patients with incomplete medical records or those refused to sign the informed consent.

The patients were classified into mild, moderate and severe/critical cases according to the procedure described by WHO⁴⁰ and the outcomes were recorded. Mild cases were defined by the presence of clinical symptoms and no changes observed in chest CT scans, and moderate cases included all those with respiratory symptoms associated with changes observed in CT scans. Severe cases were defined by the presence of the following three criteria: respiratory distress, with a respiratory rate ≥ 30/min, resting blood oxygen saturation ≤ 93% or partial pressure of arterial blood oxygen (PaO₂)/oxygen concentration (FiO₂) ≤ 300 mmHg. Critically ill cases included all severe cases that deteriorated due to respiratory failure and required mechanical ventilation, cases that involved shock and cases in which other organ failure required treatment with monitoring in intensive care units (ICUs).

The data were carefully collected from medical records, including personal data, history of comorbidities, general examination findings, oxygen saturation at admission, laboratory test reports (i.e. complete blood count (CBC), lactate dehydrogenase (LDH), HS-CRP, D-dimer and liver and kidney functions) and chest CT findings at admission. The outcome indicators of interest of this study were disease severity and mortality.

Statistical analysis

Statistical analysis was performed using SPSS version 16.0 (IBM, NY, USA). The differences in the levels of laboratory and clinical findings were analysed using a chi-square test and analysis of variance (ANOVA). In addition, Spearman’s rho correlation of clinical and radiological findings and biochemical and haematological parameters with disease severity and outcome was evaluated. An ANOVA M analysis was used to identify independent prognostic factors. Receiver operating characteristic (ROC) curves were used to specify possible parameters that could be used as indicators of disease severity and clinical outcomes (clinical improvement, cure and death).

Results

Patients’ demography and clinical data

The patients’ demographic data and clinical findings of 112 consecutively hospitalised patients are presented in Table 1. In the current study, disease severity and outcome did not differ significantly with the sex and age group of the patients but differed significantly based on their health conditions. Diabetes, hypertension, respiratory rate, SpO₂ and changes in radiological findings were significantly raised in severely affected and fatal cases and mortalities (Table 1). SpO₂ differed significantly among the different diseased groups and affected the outcome and lifespan of the patients. In addition, 22/30 (73.3%) patients showed low SpO₂, from whom 3 (10%) died, while 46 showed very low SpO₂ and 26/46 (56.5%) died, while none of the patients who showed normal SpO₂ died or developed severe disease (Table 1). Similarly, an increase in the respiratory rate significantly increased disease severity and outcome. Furthermore, 33/63 (52.4%), 22/63 (34.9%) and 8/63 (12.9%) patients with slight increases in the respiratory rate showed mild, moderate and severe disease manifestation, respectively, and 7/63 (11.1%) died. In addition, 3/23 (13%), 7/23 (25.9%) and 13/23 (56.5%) patients with moderate increases in the respiratory rate showed mild, moderate and severe disease manifestations, respectively, and 6/23 (26%) died. Meanwhile, all (n:25) patients who showed a high increase in the respiratory rate showed severe disease manifestation, and 16/25 (64%) died (Table 1).

Table 1.

Patients’ demographic data and clinical findings in different groups of patients based on the disease severity and disease outcome.

Patients’ characteristics		Disease severity				Disease outcome
Patients’ characteristics		Mild (N:37)	Moderate (N:29)	Severe (N:46)	p Value	Live (N:83)	Dead (N:29)	p Value
Age (Years)	0–9	1	0	0	.377	1	0	.194
	10–19	0	2	1		2	1
	20–29	3	5	4		12	0
	30–39	6	6	5		14	3
	40–49	7	5	8		13	7
	51–59	11	5	15		22	9
	60–69	8	4	5		13	4
	70–79	1	1	7		4	5
	≥ 80	0	1	1		2	0
Sex	Male	20	21	25	.230	51	15	.360
Sex	Female	17	8	21	.230	32	14	.360
Diabetes	No	31	25	22	.001*	67	11	.001*
Diabetes	Yes	6	4	24	.001*	16	18	.001*
Hypertensive	No	27	23	20	.002*	59	11	.002*
Hypertensive	Yes	10	6	26	.002*	24	18	.002*
SpO₂	Normal	29	7	0	.001*	36	0	.001*
	Low	8	22	0		27	3
	Very low	0	0	46		20	26
Respiratory rate	Normal	1	0	0	.003*	1	0	.001*
	Slight increase in RR	33	22	8		56	7
	Moderate increase in RR	3	7	13		17	6
	High increase in the RR	0	0	25		9	16
Radiological findings	Normal	27	0	0	.001*	27	0	.001*
	Pneumonia	0	18	45		35	28
	Ground-glass opacity	10	11	1		21	1

Data expressed as the mean ± standard deviation, *p-values were obtained using Chi Square.

Disease severity and outcome were significantly affected by radiological findings. Patients who showed normal radiological findings suffered from mild disease with no mortality. Patients who developed pneumonia or ground-glass opacity showed a high rate of severe disease (45/63) (71.4%), with high fatal consequences (25/63) (39.7%) (Table 1).

Laboratory findings and disease severity and disease outcome

No significant differences were observed among the Hb, platelet count, WBC count, disease severity and mortality rate among the different diseased groups. However, significant decreases in the lymphocyte and neutrophil percentages were detected in the severely affected group compared to the moderate and mild affected groups. In contrast, marked and significant increases in the neutrophils, absolute neutrophilic count, neutrophil lymphocyte ratio (NLR), D-dimer, HS-CRP, ALT, LDH and creatinine were detected in the severely affected group compared to the moderately and mildly affected groups. The same findings were detected in fatal cases in contrast to non-fatal cases (Table 2).

Table 2.

Laboratory findings in different groups of patients based on the disease severity and disease outcome.

Item	Disease severity				Outcome of the disease
Item	Mild (N:37)	Moderate (N:29)	Severe(N:46)	p Value	Live (N:83)	Dead (N:29)	p Value
Hb	12.13 ± 1.59	12.66 ± 1.66	11.65 ± 2.836	.154	12.37 ± 1.61	11.22 ± 3.32	.017
Platelets	205.4 ± 96.4	230.5 ± 76.0	236.9 ± 98.8	.288	218.58 ± 82.39	242.69 ± 117.77	.230
White blood cell count, ^×	6.85 ± 2.09	6.13 ± 2.15	6.55 ± 5.82	.775	6.37 ± 2.30	7.05 ± 7.01	.440
Lymphocytes %	29.08 ± 10.97	15.52 ± 8.59	12.74 ± 9.35	.z000	21.20 ± 12.17	12.17 ± 9.18	.001
Lymphocytes	2054.0811 ± 976.16	938.7241 ± 528.82	917.6304 ± 1098.18	.000	1456.21 ± 1098.15	847.24 ± 868.05	.008
Neutrophils %	65.32 ± 11.90	77.66 ± 9.53	79.50 ± 9.62	.000	72.63 ± 12.56	79.24 ± 9.42	.011
Neutrophils	4424.1081 ± 1474.67	4726.52 ± 1703.04	5126.57 ± 4503.95	.595	4503.00 ± 1476.30	5614.97 ± 5597.64	.099
NLR	2.94 ± 2.14	7.09 ± 5.32	10.18 ± 6.76	.000	5.88 ± 5.62	10.14 ± 6.30	.001
D-dimer	0.46 ± 0.33	3.21 ± 2.89	11.29 ± 5.42	.000	2.73 ± 3.28	13.88 ± 4.78	.001
Highly sensitive CRP	34.89 ± 22.81	139.21 ± 47.94	262.35 ± 54.33	.000	109.74 ± 83.06	285.79 ± 44.37	.001
ALT	29.33 ± 5.58	33.35 ± 6.81	40.20 ± 14.27	.000	31.5692 ± 6.41037	43.9615 ± 16.21	.001
LDH	198.40 ± 53.53	279.21 ± 74.84	433.61 ± 153.29	.000	257.25 ± 91.4327	483.50 ± 161.817	.001
Creatinine	0.77 ± 0.18	0.94 ± 0.68	1.68 ± 1.81	.002	0.8311 ± 0.20788	2.1821 ± 2.18711	.001

ALT, alanine aminotransferase; LDH, lactic dehydrogenase. Data expressed as the mean ± standard deviation, P-values were obtained using ANOVA analysis.

Correlation of clinical and laboratory findings with the disease severity and outcome

Spearman’s rho correlation of clinical and radiological findings with disease severity and outcome revealed a significant reverse correlation between lifespan and age (R = −0.406), diabetes (R = −0.408), hypertension (R = −0.3), increase in SpO₂ (R = −0.567), respiratory rate (R = −0.456) and disease severity (R = −0.568) (Table 3).

Table 3.

Spearman’s rho correlation of clinical and radiological findings with the disease severity and outcome.

		Age	Sex	Diabetes	Hypertension	SpO₂	Respiratory rate	Radiological findings	Severity	Disease outcome
Age	Correlation coefficient	1.000	-0.045	0.679^**	0.581^**	0.262^**	0.267^**	0.183	0.282^**	-0.406^**
Age	Sig. (2-tailed)		0.636	0.000	0.000	0.005	0.004	0.053	0.003	0.000
Sex	Correlation coefficient		1.000	0.159	0.028	0.083	0.091	-0.190^*	0.012	-0.087
Sex	Sig. (2-tailed)			0.093	0.769	0.383	0.341	0.045	0.903	0.364
Diabetes	Correlation coefficient			1.000	0.572^**	0.391^**	0.385^**	0.081	0.352^**	-0.408^**
Diabetes	Sig. (2-tailed)				0.000	0.000	0.000	0.398	0.000	0.000
Hypertension	Correlation coefficient				1.000	0.293^**	0.316^**	0.167	0.278^**	-0.300^**
Hypertension	Sig. (2-Tailed)					0.002	0.001	0.078	0.003	0.001
SpO₂	Correlation coefficient					1.000	0.697^**	0.207^*	0.921^**	-0.567^**
SpO₂	Sig. (2-tailed)						0.000	0.028	0.000	0.000
Respiratory rate	Correlation coefficient						1.000	0.104	0.700^**	-0.456^**
Respiratory rate	Sig. (2-tailed)							0.276	0.000	0.000
Radiological findings	Correlation coefficient								0.295^**	-0.081
Radiological findings	Sig. (2-tailed)								0.002	0.396
Severity	Correlation coefficient									-0.568^**
Severity	Sig. (2-tailed)									0.000
Disease outcome	Correlation coefficient									1.000
Disease outcome	Sig. (2-tailed)									.

*Correlation is significant at the 0.05 level (2-tailed).

**Correlation is significant at the 0.01 level (2-tailed).

There was a highly significant correlation between the lymphocyte percentage and disease severity and outcome. The lower the lymphocyte percentage, the higher was the severity (R = −0.527) and worse was the disease outcome (0.299). Severity and NLR showed a high correlation (R = 0.578), while disease outcome and NLR showed a significant reverse correlation (−0.351) (Table 4).

Table 4.

Spearman’s rho correlation of haematological parameters with the disease severity and outcome.

		Age	Sex	Hb	Platelets	TLC	Lymphocyte%	Segmented cells %	Absolute lymph	AbsoluteNeut.	NLR	Severity	Disease outcome
Age	Correlation coefficient	1.000	-0.045	-0.205^*	0.022	-0.147	-0.347^**	0.316^**	-0.287^**	-0.019	0.344^**	0.282^**	-0.406^**
Age	Sig. (2-tailed)	.	0.636	0.030	0.816	0.122	0.000	0.001	0.002	0.844	0.000	0.003	0.000
Sex	Correlation coefficient			-0.616^**	0.080	0.056	0.042	-0.055	0.055	0.028	-0.049	0.012	-0.087
Sex	Sig. (2-tailed)			0.000	0.400	0.556	0.661	0.566	0.567	0.767	0.607	0.903	0.364
Hb	Correlation coefficient			1.000	-0.008	0.107	0.152	-0.139	0.152	0.085	-0.156	-0.136	0.311^**
Hb	Sig. (2-tailed)			.	0.934	0.262	0.109	0.144	0.109	0.374	0.100	0.153	0.001
Platelets	Correlation coefficient					0.012	-0.199^*	0.205^*	-0.152	0.117	0.207^*	0.158	-0.035
Platelets	Sig. (2-tailed)					0.896	0.036	0.030	0.109	0.220	0.028	0.097	0.712
TLC	Correlation coefficient					1.000	0.436^**	-0.353^**	0.766^**	0.873^**	-0.438^**	-0.287^**	0.169
TLC	Sig. (2-tailed)					.	0.000	0.000	0.000	0.000	0.000	0.002	0.076
Lymphocyte %	Correlation coefficient							-0.883^**	0.886^**	0.048	-0.996^**	-0.582^**	0.363^**
Lymphocyte %	Sig. (2-tailed)							0.000	0.000	0.616	0.000	0.000	0.000
Segmented cells %	Correlation coefficient							1.000	-0.780^**	0.067	0.909^**	0.492^**	-0.257^**
Segmented cells %	Sig. (2-tailed)							.	0.000	0.481	0.000	0.000	0.006
Absolute lymphocyte	Correlation coefficient									0.425^**	-0.888^**	-0.527^**	0.299^**
Absolute lymphocyte	Sig. (2-tailed)									0.000	0.000	0.000	0.001
Absolute neutrophil	Correlation coefficient									1.000	-0.042	-0.062	0.100
Absolute neutrophil	Sig. (2-tailed)									.	0.662	0.519	0.296
NLR	Correlation coefficient											0.578^**	-0.351^**
NLR	Sig. (2-tailed)											0.000	0.000
Severity	Correlation coefficient											1.000	-0.568^**
Severity	Sig. (2-tailed)											.	0.000
Disease outcome	Correlation coefficient
Disease outcome	Sig. (2-tailed)

*Correlation is significant at the 0.05 level (2-tailed).

**Correlation is significant at the 0.01 level (2-tailed).

Alanine aminotransferase (ALT), LDH and creatinine showed a significant correlation with disease severity (direct correlation). Meanwhile, a very high correlation was observed with D-dimer (R = 0.89) and HS-CRP (R = 909). Disease outcome showed a significant but reverse correlation for most variables (Table 5).

Table 5.

Spearman’s rho correlation of biochemical findings with the disease severity and outcome.

		Age	Sex	ALT	LDH	Creatinine	D-dimer	Highly sensitive CRP	Severity	Disease outcome
Age	Correlation coefficient	1.000	-0.045	0.282^**	0.363^**	0.371^**	0.382^**	0.348^**	0.282^**	-0.406^**
Age	Sig. (2-tailed)	.	0.636	0.007	0.000	0.000	0.000	0.000	0.003	0.000
Sex	Correlation coefficient			-0.087	-0.019	0.007	0.000	0.050	0.012	-0.087
Sex	Sig. (2-tailed)			0.410	0.858	0.946	0.998	0.601	0.903	0.364
ALT	Correlation coefficient			1.000	0.519^**	0.517^**	0.488^**	0.468^**	0.478^**	-0.471^**
ALT	Sig. (2-tailed)			.	0.000	0.000	0.000	0.000	0.000	0.000
LDH	Correlation coefficient				1.000	0.750^**	0.743^**	0.724^**	0.753^**	-0.628^**
LDH	Sig. (2-tailed)				.	0.000	0.000	0.000	0.000	0.000
Creatinine	Correlation coefficient						0.530^**	0.535^**	0.545^**	-0.545^**
Creatinine	Sig. (2-tailed)						0.000	0.000	0.000	0.000
D dimer	Correlation coefficient							0.881^**	0.890^**	-0.698^**
D dimer	Sig. (2-tailed)							0.000	0.000	0.000
Highly sensitive CRP	Correlation coefficient							1.000	0.909^**	-0.698^**
Highly sensitive CRP	Sig. (2-Tailed)							.	0.000	0.000
Severity	Correlation coefficient								1.000	-0.568^**
Severity	Sig. (2-tailed)								.	0.000
Disease outcome	Correlation coefficient									1.000
Disease outcome	Sig. (2-tailed)									.

**Correlation is significant at the 0.01 level (2-tailed).

Prognostic parameters of disease severity and disease outcome

The results of the multivariate analysis revealed that HS-CRP and D-dimer are independent prognostic factors that can differentiate among mild, moderate and severe cases. The respiratory rate, SpO₂, lymphocyte percentage, NLR, ALT and LDH are other independent prognostic factors which can denote severe forms of the disease. Creatinine, Hb, platelet count and TLC showed no significant differences among the groups (Figure 1).

Figure 1.

ANOVA multivariate analysis of different parameters in relation to the disease severity estimated as marginal means. (a) SpO₂, (b) Respiratory rate (c) Platelets (d) Hb, (e) Segmented cell percentage, (f) Lymphocyte percentage, (g) Total leucocyte count (TLC), (h) ALT, (i) D-dimer, (j) HS-CRP, (k) LDH, (l) Creatinine.

Receiver operating characteristic curves were analysed to reach the best cut-off values for predicting disease severity and outcome. Upon evaluating the ROC curve of fatal consequences and different parameters, D-dimer, HS-CRP and LDH showed excellent test values of 0.951, 0.961 and 0.900, respectively. Creatinine showed a good value of 0.88, and ALT showed a fair value of 0.797; however, the remaining parameters did not show promising predictive values.

The ROC curve of HS-CRP versus disease severity and outcome showed excellent test values with area values of 0.978 and 0.96 (p = .00), respectively. At 167.5, which was the cut-off for HS-CRP, 95.7% of the cases were correctly identified as a severe disease and only 6.1% were incorrectly classified. At 252.5 cut-off, 82.8% of the cases were correctly classified as fatal and 8.4% were incorrectly classified.

The ROC curve of D-dimer versus disease severity and outcome showed excellent test values with 0.964 and 0.96 area values (p > .01), respectively. At 5.3 cut-off of HS-CRP, 82.6% of the cases were correctly identified and only 7.6% were incorrectly classified. At 10.2 cut-off, 82.8% of the cases were correctly classified as fatal and 4.8% were incorrectly classified. The ROC curve of LDH versus disease severity and outcome also showed excellent test values with area values of 0.906 and 0.900, respectively. At 317.5 cut-off, 73.2% of the cases were correctly classified as severe and 10.2% were incorrectly classified. At 400.5, 76.9% of the cases were correctly classified as fatal and 9.4% were incorrectly classified.

The ROC curve of respiratory rate versus disease severity also showed excellent test value with an area value of 0.916. At 27.5 cut-off, 73.9% of the cases were correctly classified as severe and 9.1% were incorrectly classified. At 30.5, 55.2% cases were correctly classified as fatal and 10.8% were incorrectly classified (Table 6).

Table 6.

Roc curves results of different laboratory and clinical parameters in relation to the disease severity and disease outcome.

Test result Variable(s)		Area under the curve
		Area	Std. Error^a	Asymptotic Sig.^b	Asymptotic 95% confidence interval
		Area	Std. Error^a	Asymptotic Sig.^b	Lower bound	Upper bound
Platelets	Severity	0.592	0.061	0.133	0.472	0.713
Platelets	Disease outcome	0.535	0.073	0.606	0.392	0.678
SpO₂	Severity	0.000	0.000	0.000	0.000	0.000
SpO₂	Disease outcome	0.052	0.023	0.000	0.007	0.096
Radiological findings	Severity	0.588	0.064	0.150	0.462	0.715
Radiological findings	Disease outcome	0.575	0.058	0.266	0.462	0.689
Hb	Severity	0.399	0.061	0.100	0.278	0.519
Hb	Disease outcome	0.283	0.067	0.001	0.151	0.415
TLC	Severity	0.352	0.061	0.016	0.232	0.472
TLC	Disease outcome	0.397	0.077	0.126	0.246	0.547
Lymphocyte %	Severity	0.186	0.044	0.001	0.100	0.272
Lymphocyte %	Disease outcome	0.233	0.056	0.001	0.124	0.343
Segmented cells %	Severity	0.762	0.050	0.001	0.664	0.861
Segmented cells %	Disease outcome	0.688	0.060	0.005	0.571	0.804
NLR	Severity	0.770	0.046	0.001	0.680	0.860
	Disease outcome	0.731	0.054	0.001	0.626	0.838
ALT	Severity	0.748	0.052	0.001	0.646	0.850
ALT	Disease outcome	0.797	0.052	0.001	0.696	0.899
Creatinine	Severity	0.829	0.043	0.001	0.744	0.914
Creatinine	Disease outcome	0.880	0.048	0.001	0.786	0.975
LDH	Severity	0.906	0.033	0.001	0.841	0.971
LDH	Disease outcome	0.900	0.042	0.001	0.817	0.982
HS-CRP	Severity	0.979	0.013	0.001	0.954	1.005
HS-CRP	Disease outcome	0.961	0.017	0.001	0.928	0.995
D-dimer	Severity	0.973	0.013	0.001	0.947	0.999
D-dimer	Disease outcome	0.951	0.024	0.001	0.904	0.997
Respiratory rate	Severity	0.916	0.013	0.001	0.856	0.976
Respiratory rate	Disease outcome	0.818	0.052	0.001	0.715	0.920

^aUnder the nonparametric assumption.

^bNull hypothesis: true area = 0.5.

Discussion

The severity of COVID-19 is a crucial problem in patient treatment and outcome. Many studies and meta-analysis studies investigated the possible role of different laboratory biomarkers for predicting COVID-19.^11,25 The current study demonstrates the relationship between the different demographics and laboratory data, chest CT findings and disease severity and outcome. There was no significant effect of gender or age among the studied subjects. Some studies have also reported no gender variation among COVID-19 patients.^18,26 However, only 26/112 patients over 60 years old were included in the current study, which can explain the contrast between our results and those obtained by previous studies that reported a significant effect of both variables on disease severity.^23,27–31

Similar to previous results, the presence of hypertension and/or diabetes is considered an associated risk factor for disease severity and poor outcome.^{1,6,7,16,28,32–34} Low oxygen saturation and high respiratory rate increased the liability of ICU disease severity and fatal consequences, which agrees with other previous findings.^27,32,34,35 Abnormal chest CT findings of the patients were significantly correlated with disease severity and outcome. Patients who presented with normal radiological findings suffered from mild disease with no mortalities, while those who developed pneumonia or had ground-glass opacity showed severe disease with highly fatal consequences. This finding is also in agreement with those obtained by previous studies.^3,18

Our study reported highly significant lymphopenia and neutrophilia associated with severe and fatal cases, which was also evident in other studies.^17,28,36,37 However, the ROC curve analysis did not find any of them sensitive and specific enough to be good predictors of disease severity or fatal consequences. Although a significant increase in WBC count in fatal cases is well-documented,¹⁷ we did not find a significant increase in WBC count among severe or fatal cases. This finding can be explained by the fact that the increase in neutrophils compensates for the relative decrease in lymphocytes. A better assessment method is NLR. We found that NLR is an independent prognostic factor that correlates with disease severity and outcome, which agrees with other previous findings.^28,29

In the current study, an increase in both ALT and LDH was detected in severely affected subjects as well as fatal COVID-19 patients, and both showed a significant correlation to disease severity. An increase in the ALT level was reported in 50% of fatal cases and 20% of COVID-19 survivors.³ Meanwhile, an increase in both ALT and AST was reported in approximately 20% of COVID-19 patients.³ Increased levels of serum LDH have also been reported in fatal SARS-CoV-2 cases.^12,³⁸ In the current study, we found LDH to be an excellent predictor of both disease severity and death. Interestingly, it was also found to be a death predictor due to sepsis.³⁹

In the current study, HS-CRP and D-dimer were found to be strong independent prognostic factors for predicting disease severity and outcome. For example, the values of 167.5 and 5.8 could predict a severe disease, and those of 252.5 and 8.3 could predict fatal cases for HS-CRP and D-dimer, respectively. These findings agree with other previous findings that found a significant correlation between high levels of CRP and D-dimer with increased disease severity and poor prognosis.^18,27,32 In contrast, Maddani et al.²⁸ reported a strong association between them and the COVID-19 rather than being an independent prognostic factor. This might be because our study dealt with mild, moderate and severe cases, while Maddani et al. studied only severe cases and compared them to mild cases.²⁸

Limitation

The main limitation of this study was the unavailability of involving patients from centres other than Cairo University hospitals. In addition, we did not conduct any power analysis to calculate the sample size selected for this study.

Conclusion

The presence of hypertension and/or diabetes was considered an associated risk factor for disease severity and poor outcome. Increased levels of HS-CRP, D-dimer, NLR, ALT, LDH, lymphopenia and neutrophilia, as well as changes in the chest CT, were associated with increased disease severity and fatal consequences. The ROC curves of HS-CRP, D-dimer, NLR and LDH suggested that they constitute excellent predictors for both disease severity and death.

Footnotes

Author contributions

Conceptualization, M. M. K., A.S.A., and Y. M. E.; methodology, L. A.F., L. M. K., O. S., M. A. K., M. M. K., and H. H.F.; formal analysis, L. A. F., Y. A. S. A., J. A. A..; data curation, J. A. A., L. A. F., and Y. A. S. A.; writing—original draft preparation, L. M. K., O.S. and; writing—review and editing, A. S. A., L. A. F., M. M. K. All authors have read and agreed to the published version of the manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Taif University Researchers Supporting Project Number (TURSP-2020/11), Taif University, Taif, Saudi Arabia.

Ethics approval

The protocol for this study was approved by the Institutional Review Board of National Cancer Institute, Cairo University, EGYPT. The ethical approval number: CP1937-30783 on 10 March 2020; all patients & control group gave written informed consent.

Informed consent

Each participant of this study provided informed written consent before the study.

Trial registration

Not applicable.

ORCID iDs

Lamiaa A Fathalla

Mahmoud M Kamel

Ahmed S Abdel-Moneim

References

Zhu

Zhang

Wang

, et al. (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382: 727–733. DOI: 10.1056/NEJMoa2001017.

Abdel-Moneim

Abdelwhab

Memish

(2021) Insights into SARS-CoV-2 evolution, potential antivirals, and vaccines. Virology 558: 1–12. DOI: 10.1016/j.virol.2021.02.007.

Guan

, et al. (2020) Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 382: 1708–1720. DOI: 10.1056/NEJMoa2002032.

Lavezzo

Franchin

Ciavarella

, et al. (2020) Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'. Nature 584: 425–429. DOI: 10.1038/s41586-020-2488-1.

Chen

Zhou

Dong

, et al. (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 395: 507–513. DOI: 10.1016/s0140-6736(20)30211-7.

Basu

Agwu

Barlow

, et al. (2021) Hypertension is the major predictor of poor outcomes among inpatients with COVID-19 infection in the UK: A retrospective cohort study. BMJ Open 11: e047561. DOI: 10.1136/bmjopen-2020-047561.

Norouzi

Ruggiero

, et al. (2021) Type-2 diabetes as a risk factor for severe COVID-19 infection. Microorganisms 9: 1211. DOI: 10.3390/microorganisms9061211.

Wang

Davis

Gurney

, et al. (2021) COVID‐19 and dementia: Analyses of risk, disparity, and outcomes from electronic health records in the US. Alzheimer’s Dementia 17: 1297–1306. DOI: 10.1002/alz.12296.

Hariyanto

Putri

Arisa

, et al. (2021) Dementia and outcomes from coronavirus disease 2019 (COVID-19) pneumonia: A systematic review and meta-analysis. Arch Gerontol Geriatr 93: 104299. DOI: 10.1016/j.archger.2020.104299.

10.

Del Prete

Francesconi

Palermo

, et al. (2021) Prevalence and impact of COVID-19 in Parkinson’s disease: evidence from a multi-center survey in Tuscany region. J Neurol 268: 1179–1187. DOI: 10.1007/s00415-020-10002-6.

11.

Hariyanto

Japar

Kwenandar

, et al. (2021) Inflammatory and hematologic markers as predictors of severe outcomes in COVID-19 infection: A systematic review and meta-analysis. Am J Emerg Med 41: 110–119. DOI: 10.1016/j.ajem.2020.12.076.

12.

Liu

Yang

Zhang

, et al. (2020) Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci 63: 364–374. DOI: 10.1007/s11427-020-1643-8.

13.

Al-Quteimat

Amer

(2020) The impact of the COVID-19 pandemic on cancer patients. Am J Clin Oncol 43: 452–455. DOI: 10.1097/coc.0000000000000712.

14.

Mariappan

Manoharan

, et al. (2021) Potential biomarkers for the early prediction of SARS-COV-2 disease outcome. Microb Pathog 158: 105057. DOI: 10.1016/j.micpath.2021.105057.

15.

Huang

Wang

, et al. (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395: 497–506. DOI: 10.1016/s0140-6736(20)30183-5.

16.

Kompaniyets

Pennington

Goodman

, et al. (2021) Underlying medical conditions and severe illness among 540,667 adults hospitalized with COVID-19, March 2020-March 2021. Prev Chronic Dis 18: E66. DOI: 10.5888/pcd18.210123.

17.

Henry

de Oliveira

MHS

Benoit

, et al. (2020) Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): A meta-analysis. Clin Chem Lab Med 58: 1021–1028. DOI: 10.1515/cclm-2020-0369.

18.

Abdelsalam

Althaqafi

RMM

Assiri

, et al. (2021) Clinical and laboratory findings of COVID-19 in high-altitude inhabitants of Saudi Arabia. Front Med 8: 670195–672021. DOI: 10.3389/fmed.2021.670195.

19.

Duman

Aktas

Atak

, et al. (2019) Neutrophil to lymphocyte ratio as an indicative of diabetic control level in type 2 diabetes mellitus. Afr Health Sci 19: 1602–1606. DOI: 10.4314/ahs.v19i1.35.

20.

Aktas

Sit

Dikbas

, et al. (2017) Elevated neutrophil-to-lymphocyte ratio in the diagnosis of Hashimoto’s thyroiditis. Rev Assoc Méd Bras 63: 1065–1068. DOI: 10.1590/1806-9282.63.12.1065.

21.

Posul

Yilmaz

Aktas

, et al. (2015) Does neutrophil-to-lymphocyte ratio predict active ulcerative colitis? Wien Klin Wochenschr 127: 262–265. DOI: 10.1007/s00508-014-0683-5.

22.

Aktas

(1992) Hematological predictors of novel Coronavirus infection. Rev Assoc Méd Bras 67(suppl 1): 20211–20212. DOI: 10.1590/1806-9282.67.Suppl1.20200678.

23.

Richardson

Hirsch

Narasimhan

, et al. (2020) Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA 323: 2052–2059. DOI: 10.1001/jama.2020.6775.

24.

Velavan

TTP

Meyer

(2020) Mild versus severe COVID-19: Laboratory markers. Int J Infect Dis 95: 304–307. DOI: 10.1016/j.ijid.2020.04.061.

25.

Soraya

Ulhaq

(2020) Crucial laboratory parameters in COVID-19 diagnosis and prognosis: An updated meta-analysis. Med Clínica 155: 143–151. DOI: 10.1016/j.medcli.2020.05.017.

26.

Ramatillah

Isnaini

(2021) Treatment profiles and clinical outcomes of COVID-19 patients at private hospital in Jakarta. PLoS One 16: e0250147. DOI: 10.1371/journal.pone.0250147.

27.

Sharma

Dayama

Banerjee

, et al. (2020) To study the role of absolute lymphocyte count and RDW in COVID 19 patients and their association with appearance of symptoms and severity. J Assoc Phys India 68: 39–42.

28.

Maddani

Gupta

Umakanth

, et al. (2021) Neutrophil-lymphocyte ratio in patients with COVID-19 as a simple tool to predict requirement of admission to a critical care unit. In J Critic Care Med 25: 535–539. DOI: 10.5005/jp-journals-10071-23801.

29.

Soni

Kajal

Yaddanapudi

, et al. (2021) Demographic & clinical profile of patients with COVID-19 at a tertiary care hospital in north India. Ind J Med Res 153: 115.

30.

Guzzi

Tradigo

Veltri

(2021) Regional resource assessment during the COVID-19 pandemic in Italy: Modeling study. JMIR medical informatics 9: e18933. DOI: 10.2196/18933.

31.

Cannistraci

Valsecchi

Capua

(2021) Age-sex population adjusted analysis of disease severity in epidemics as a tool to devise public health policies for COVID-19. Sci Rep 11: 11787. DOI: 10.1038/s41598-021-89615-4.

32.

Bhadade

Harde

deSouza

, et al. (2020) Appraisal of critically Ill COVID-19 patients at a dedicated COVID hospital. J Assoc Phys India 68: 14–19.

33.

Ravichandran

Grimm

Krüger

, et al. (2021) SARS‐CoV‐2 and hypertension. Physiological reports 9: e14800. DOI: 10.14814/phy2.14800.

34.

Charoenngam

Alexanian

Apovian

, et al. (2021) Association between hyperglycemia at hospital presentation and hospital outcomes in COVID-19 patients with and without type 2 diabetes: A retrospective cohort study of hospitalized inner-city COVID-19 patients. Nutrients 13: 2199. DOI: 10.3390/nu13072199.

35.

Thakur

Rai

(2021) Study to identify predictor of hypoxia in COVID-19 infection: A single-center, retrospective study. J Fam Med Prim Care 10: 1852–1855. DOI: 10.4103/jfmpc.jfmpc_2252_20.

36.

Azarkar

Salehiniya

Kazemi

, et al. (2021) Epidemiological, imaging, laboratory, and clinical characteristics and factors related to mortality in patients with COVID-19: A single-center study. Osong public health and research perspectives 12: 169–176. DOI: 10.24171/j.phrp.2021.0012.

37.

Dosi

Jain

Mehta

(2020) Clinical characteristics, comorbidities, and outcome among 365 patients of coronavirus disease 2019 at a tertiary care centre in central India. J Assoc Phys India 68: 20–23.

38.

Wang

, et al. (2020) Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323: 1061–1069. DOI: 10.1001/jama.2020.1585.

39.

Wei

Jiang

, et al. (2018) Lactate dehydrogenase is associated with 28-day mortality in patients with sepsis: A retrospective observational study. J Surg Res 228: 314–321. DOI: 10.1016/j.jss.2018.03.035.

40.

WHO (2021) Living guidance for clinical management of COVID-19: living guidance . World Health Organization. Available at: https://apps.who.int/iris/rest/bitstreams/1394399/retrieve.

Laboratory biomarker predictors for disease progression and outcome among Egyptian COVID-19 patients

Abstract

Keywords

Introduction

Materials and methods

Patients

Statistical analysis

Results

Patients’ demography and clinical data

Laboratory findings and disease severity and disease outcome

Correlation of clinical and laboratory findings with the disease severity and outcome

Prognostic parameters of disease severity and disease outcome

Discussion

Limitation

Conclusion

Footnotes

Author contributions

Declaration of conflicting interests

Funding

Ethics approval

Informed consent

Trial registration

ORCID iDs

References