Association of Clinical Characteristics, Hematological Indices, and Acute Inflammatory Markers in Hospitalized COVID-19 Patients with Disease Severity and Mortality: A Single-center Retrospective Study

Abstract

Objective:

To determine clinical and laboratory predictors of disease severity and mortality among patients with COVID-19 admitted to a tertiary care center in Saudi Arabia.

Methods:

This retrospective cohort study included patients with laboratory-confirmed SARS-CoV-2 infection admitted to King Fahd Hospital of the University, Al Khobar, Saudi Arabia, between May and August 2020. Disease severity was defined based on intensive care unit (ICU) admission. Demographic, clinical, and laboratory data were extracted from the patients’ electronic medical records and the laboratory information system.

Results:

The study included 207 patients (mean age: 51 ± 16 years; male: 72%), of which 56 were severe cases. Compared to non-severe patients, severe patients had significantly higher white blood cell count (9.3 K/μl vs. 7.1 K/μl; P < 0.001), median absolute neutrophil count (6.2 vs. 4.4 K/μl; P = 0.003), absolute lymphocyte count (1.4 K/μl vs. 1.0 K/μl; P = 0.002); neutrophil-to-lymphocyte ratio (5.6 vs. 3.2; P < 0.001), median D-dimer (1.0 vs. 0.6 μg/ml; P < 0.001), median C-reactive protein (8.7 vs. 2.9 mg/dl; P < 0.001), and prothrombin time (14.1 vs. 13.6 seconds; P = 0.003). Similarly, compared to survivors, non-survivors had significantly higher median absolute neutrophil count (6.9 K/μl vs. 4.8 K/μl; P = 0.016), neutrophil-to-lymphocyte ratio (6.9 vs. 3.5; P < 0.001), median D-dimer (1.6 vs. 0.6 μg/ml; P = 0.005), and median C-reactive protein (11.7 vs. 3.1 mg/dl; P < 0.001). However, in the multivariable analysis, none of the laboratory parameters remained independently associated with disease severity or mortality.

Conclusion:

This study found that among hospitalized COVID-19 patients in Saudi Arabia, several clinical and laboratory markers were associated with disease severity and mortality in univariable analyses; however, no independent predictors were identified after adjustment. These findings highlight the complexity of COVID-19 progression and the need for cautious interpretation of laboratory markers in clinical decision-making.

INTRODUCTION

The coronavirus disease (COVID-19) can result in a wide clinical spectrum, ranging from asymptomatic to severe respiratory complications such as viral pneumonia, respiratory failure, multiorgan dysfunction, and death in severe cases.[1,2] Early clinical reports from China, and later from Europe and the United States, revealed that in addition to respiratory involvement, patients often also developed systemic inflammation, hematologic abnormalities, and coagulation disturbances that contribute significantly to disease progression and mortality.[3,4]

COVID-19 was initially considered as a form of viral pneumonia;[5] however, it is now established as a complex interplay between viral replication, immunological dysregulation, endothelial injury, and altered hemostasis.[6] Hospitalized patients who progress to severe illness often have lymphopenia, neutrophilia, thrombocytopenia, and elevated markers of inflammation such as C-reactive protein (CRP) and ferritin, reflecting an exaggerated systemic inflammatory response.[7,8,9] Concurrently, elevated D-dimer levels, prolonged coagulation times, and hypercoagulable changes are associated with an elevated risk of thrombosis and poor prognosis, highlighting the hematologic system as a key target of SARS-CoV-2 infection.[10] Consequently, routine laboratory testing such as complete blood count, inflammatory biomarkers, and coagulation profiles have become significant tools for the initial evaluation and management of hospitalized patients.[10] Many studies worldwide have publicized that abnormal levels of CRP, D-dimer, neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), and interleukin-6 are associated with severe disease, the need for intensive care, and fatal outcomes.[9,11,12] Other biomarkers such as serum amyloid A have also been projected as significant indicators of rapid disease progression.[13] These observations suggest that clinical and laboratory data obtained at hospital admission may support early risk stratification and guide decisions regarding monitoring intensity, treatment escalation, and resource allocation.[14]

In March 2020 when first confirmed case of COVID-19 was reported in Saudi Arabia, the Saudi Arabian government implemented a series of national prevention and management strategies.[15] Despite the abundance of international evidence, regional data describing the clinical and hematologic profile of hospitalized COVID-19 patients remain relatively limited.[16] Understanding the local pattern of laboratory abnormalities and their association with disease severity is essential, particularly in tertiary care centers, where timely triage and intervention can influence patient outcomes.

This study aims to describe the clinical characteristics, hematological indices, and acute inflammatory markers in patients with confirmed SARS-CoV-2 infection admitted to King Fahd Hospital of the University, Al Khobar, Saudi Arabia. In addition, it examines the association between these laboratory parameters and disease severity and mortality. By identifying laboratory markers that predict poor outcomes in the local population, this work may contribute to more precise clinical decision-making and improved management strategies for COVID-19 in similar hospital settings. Although the data for the study were collected during the early phase of the pandemic, yet it provides important baseline insights into the hematological response to COVID-19 prior to widespread vaccination and evolving treatment protocols.

METHODS

Study design, setting, and participants

This study received ethical approval from the Institutional Review Board of Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia (Ref. no.: IRB-2025-03-0842). This retrospective study included patients with SARS-CoV-2 infection who were admitted to King Fahd Hospital of the University, Al Khobar, Saudi Arabia, between May 23 and August 30, 2020.

COVID-19 diagnosis was confirmed using reverse transcription polymerase chain reaction (RT-PCR) testing of nasopharyngeal swab samples, in accordance with institutional and national guidelines. All confirmed cases were included, and no exclusion criteria were applied. Patients who required admission to the intensive care unit (ICU) were classified as sever cases, whereas patients who were managed in general hospital wards were classified as non-sever cases. ICU admission during the study period was determined according to institutional clinical criteria, including severe respiratory distress, hypoxemia requiring high flow oxygen therapy or mechanical ventilation, or evidence of multi-organ dysfunction.

Data collection

Demographic, clinical, and laboratory data were extracted from the patients’ electronic medical records and the laboratory information system. Blood samples were analyzed for complete blood counts using the Alinity HQ analyzer (Abbott Laboratories, Diagnostics Division, Santa Clara, CA, USA). The blood routine indices included red blood cell count (RBC), hemoglobin (HB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), MCH concentration (MCHC), total white blood cell count (WBC), platelet count (PLT), absolute neutrophil count (NEU), absolute lymphocyte count (LYM), and erythrocyte sedimentation rate (ESR). Hemostasis assays were conducted using Star Max Stago analyzer (Diagnostica Stago, Asnières-sur-Seine, France) and comprised prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (INR), D-dimer (DD), fibrinogen (FIB), and fibrin degradation products (FDPs). In addition, derived inflammatory indices such as the NLR were calculated and analyzed. All comorbid conditions documented in the medical records, including chronic, acute, infectious, and malignant diseases, were recorded where available.

Acute inflammatory markers, including CRP and LDH levels, were tested using Alinity C-series analyzer. For each patient, baseline laboratory values were defined as the first available measurements obtained at hospital admission, typically within the first 24 hours of admission. Although repeated measurements were available during hospitalization, only the initial values were used for analysis to ensure consistency across patients. Given the nature of the study, not all laboratory parameters were measured for every patient. Laboratory investigations, including LDH, were performed based on clinical indications at the decision of the treating physicians, and were not uniformly available for all patients. The effective sample size for each parameter is indicated at the top of the columns in each TableMissing laboratory values were not imputed, and analysis were performed using the available data only.

Statistical analysis

Data were analyzed using SPSS version 26. A Shapiro–Wilk test was conducted to test the normality of the data. Categorical variables were presented as frequencies and percentages and were analyzed using Chi-square and Fisher’s exact tests, as appropriate. Normally distributed continuous variable (age) was described as mean ± standard deviation and analyzed using independent sample t-tests, whereas all remaining non-normally distributed continuous variables were reported as medians and interquartile ranges (IQRs) and were analyzed using Mann–Whitney U tests. Association of the cohort’s demographic, clinical, and laboratory data was done based on severity (admitted to ICU vs. not admitted to ICU) and mortality (survivor vs. non-survivor). Because several laboratory parameters were not available for all patients, statistical analyses were conducted using complete-case (available-case) analysis. For each parameter, only patients with available laboratory measurements were included in the corresponding comparisons and regression analyses. Binary logistic regression analysis was performed to determine the hematological indices and inflammatory markers as predictors of disease severity and mortality. Variables entered into the multivariable logistic regression models were selected based on clinical relevance and statistically significant associations observed in the univariable analysis. To reduce model instability and collinearity, closely related variables were not entered simultaneously in the same model. A two-sided P value <0.05 was considered statistically significant.

RESULTS

Demographic and clinical characteristics

A total of 207 patients were included. The demographic and clinical characteristics of the included patients by severity and mortality are presented in Tables 1 and 2, respectively. In total, 56 cases were severe and 151 were non-severe/mild/moderate. The mean age of the cohort was 51 ± 16 years, and the mean ages of the severe (57 ± 15.2 years) and non-survivor (63 ± 13.9 years) groups were statistically significantly higher than those of the non-severe (49 ± 15.8 years; P < 0.001) and survivor groups (49 ± 15.6 years; P < 0.001). Males accounted for 72% of the cohort, and male gender was statistically significantly associated with disease severity (P = 0.020) and mortality (P = 0.045).

Table 1.

Clinical characteristics of patients with COVID-19 by disease severity

Characteristic	Total (N=207), n (%)	Severe (n=56), n (%)	Non-severe (n=151), n (%)	P**
Age (years), mean±SD	51±16.0	57±15.2	49±15.8	0.001
Sex
Male	149 (72)	47 (83.9)	102 (67.5)	0.020
Female	58 (28)	9 (16.1)	49 (32.5)
Comorbidities*	134 (64.7)	35 (62.5)	99 (65.6)	0.682
Chest X-ray				0.068
Bilateral infiltrate	141 (68.1)	47 (83.9)	94 (62.3)	0.003
Unremarkable	32 (15.5)	1 (1.8)	31 (20.5)	0.001
Pleural effusion	18 (8.7)	5 (8.9)	13 (8.6)	0.944
Lateral infiltrate	16 (7.7)	3 (5.4)	13 (8.6)	0.435
Symptoms
Fever	136 (65.7)	32 (57.1)	104 (68.9)	0.114
Cough	118 (57)	34 (60.7)	84 (55.6)	0.512
Shortness of breath	115 (55.6)	42 (75)	73 (48.3)	0.001
Diarrhea	36 (17.4)	9 (16.1)	27 (17.9)	0.760
Vomiting	31 (15)	3 (5.4)	28 (18.5)	0.018
Myalgia	31 (15)	3 (5.4)	28 (18.5)	0.018
Headache	26 (12.6)	3 (5.4)	23 (15.2)	0.057
Chest pain	23 (11.1)	5 (8.9)	18 (11.9)	0.543
Sputum production	22 (10.6)	4 (7.1)	18 (11.9)	0.322
Sore throat	12 (5.8)	5 (8.9)	7 (4.6)	0.313
Hemoptysis	5 (2.4)	3 (5.4)	2 (1.3)	0.124
Wheezes	4 (1.9)	4 (7.1)	0	0.005
Shivering	3 (1.4)	0	3 (2.0)	0.565
Anticoagulant therapy
Clexane	182 (87.9)	47 (83.9)	135 (89.4)	0.271
Heparin	11 (5.3)	4 (7.1)	7 (4.6)	0.484
Warfarin	1 (0.5)	0	1 (0.7)	0.549
Clexane and heparin	3 (1.4)	2 (3.6)	1 (0.7)	0.110
None	10 (4.8)	3 (5.4)	7 (4.6)	0.841
Mortality
Survivors	181 (87.4)	34 (60.7)	147 (97.4)	<0.001
Non-survivors	26 (12.6)	22 (39.3)	4 (2.6)
Hospital length of stay (days), median (IQR)	10 (6–16)	20 (14–28)	8 (6–12)	<0.001

*Comorbidities: Diabetes, dyslipidemia, and hypertension were most common. Other conditions included osteoporosis, hypothyroidism, chronic obstructive pulmonary disease, hepatitis B and C, obesity, esophageal benign tumor, and bladder carcinoma, **Bold values are statistically significant. SD – Standard deviation; IQR – Interquartile range

Table 2.

Demographic and clinical characteristics of patients with coronavirus disease of 2019 by mortality

Characteristic	Survivors (n=181), n (%)	Non-survivors (n=26), n (%)	P*
Age (years), mean±SD	49±15.6	63±13.9	<0.001
Sex
Male	126 (69.6)	23 (88.5)	0.045
Female	55 (30.4)	3 (11.5)
Comorbidities	117 (64.6)	17 (65.4)	0.941
Chest X-ray			0.068
Unremarkable	32 (17.7)	0	0.020
Lateral infiltrate	15 (8.3)	1 (3.8)	0.430
Bilateral infiltrate	119 (65.7)	22 (84.6)	0.054
Pleural effusion	15 (8.3)	3 (11.5)	0.582
Symptoms
Fever	123 (68)	13 (50)	0.071
Shivering	3 (1.7)	0	1.000
Shortness of breath	95 (52.5)	20 (76.9)	0.019
Chest pain	22 (12.2)	1 (3.8)	0.321
Wheezes	3 (1.7)	1 (3.8)	0.418
Cough	106 (58.6)	12 (46.2)	0.232
Sputum production	22 (12.2)	0	0.084
Diarrhea	33 (18.2)	3 (11.5)	0.400
Vomiting	30 (16.6)	1 (3.8)	0.138
Myalgia	30 (16.6)	1 (3.8)	0.138
Headache	24 (13.3)	2 (7.7)	0.543
Sore throat	8 (4.4)	4 (15.4)	0.048
Hemoptysis	4 (2.2)	1 (3.8)	0.492
Anticoagulant therapy
Clexane	160 (88.4)	22 (84.6)	0.582
Heparin	10 (5.5)	1 (3.8)	0.719
Warfarin	1 (0.6)	0	0.704
Clexane and heparin	3 (1.7)	0	0.509
None	7 (3.9)	3 (11.5)
Severity
Severe	34 (18.8)	22 (84.6)	<0.001
Nonsevere	147 (81.2)	4 (15.4)
Hospital length of stay (days), median (IQR)	10 (6–15)	14 (9–21)	<0.001

*Bold values are statistically significant. IQR – Interquartile range; SD – Standard deviation

Underlying comorbidities were reported in 64.7% of patients, with no significant association with disease severity or mortality. Unremarkable chest X-ray findings were significantly more frequent among non-severe cases compared with severe cases (20.5% vs. 1.8%; P < 0.001). Similarly, unremarkable radiographic findings were observed only among survivors and were absent in non-survivors (17.7% vs. 0%, P = 0.020). Bilateral infiltrate findings were significantly associated with disease severity (P = 0.003), and a high number of non-severe patients had bilateral infiltration [94 (62.3%)]. No significant association was observed between bilateral infiltrate and mortality. In addition, no significant association was observed between disease severity and mortality and other chest X-ray findings.

Four clinical symptoms were significantly associated with disease severity. Shortness of breath was significantly more common among severe cases compared with non-severe cases (75% vs. 48.3%, P <0.001). In contrast, vomiting and myalgia were more frequently reported among non-severe patients. Wheezing was reported only among severe cases. There were reported symptoms of wheezing (P <0.001) in only severe cases. No significant association was observed between disease severity and other clinical symptoms (fever, shivering, chest pain, cough, sputum production, diarrhea, headache, sore throat, and hemoptysis). Conversely, survivors were more likely to have symptoms of shortness of breath (P = 0.019) and sore throat (P = 0.048) compared to non-survivors. No significant association was observed between mortality and other clinical symptoms (fever, shivering, chest pain, wheezing, cough, sputum production, diarrhea, vomiting, myalgia, headache, and hemoptysis). Moreover, anticoagulant therapy did not have a significant association across all comparative groups of patients.

Mortality and severity were significantly associated, with a higher proportion of survivors being non-severe patients (P <0.001). The median length of hospital stay was 10 days (IQR: 6–16 days) and was significantly associated with severity and mortality (P <0.001). The length of stay was significantly higher in severe cases (20 days, IQR: 14–28 days) and in non-survivors (14 days, IQR: 9–21 days).

Laboratory findings

Tables 3 and 4 show the laboratory findings and the number of patients tested for each parameter and their association with disease severity and mortality. The medians of RBC, HGB, HCT, MCV, MCH, MCHC, WBC, PLT, NEU, PT, APTT, INR, and DD were within the reference range. However, the median WBC of the severe group was significantly higher than that of the non-severe group (9.3 K/μl vs. 7.1 K/μl; P <0.001). The NEU and LYM were tested and available for only 158 of 207 patients, and the median NEU count was significantly higher in the severe group compared to the non-severe group (6.2 vs. 4.4 K/μl; P = 0.003) and in the non-survivors compared to the survivors (6.9 K/μl vs. 4.8 K/μl; P = 0.016). The median LYM count was significantly higher in the non-severe group compared to the severe group (1.4 K/μl vs. 1.0 K/μl; P = 0.002). Slight lymphocytopenia was reported in the non-survivors (0.9 K/μl; IQR 0.6–1.2), with a statistically significant difference between survivors and non-survivors (P < 0.001).

Table 3.

Comparison of hematologic parameters among patients with COVID-19 by disease severity

Parameter	Reference range	Total (N=207)	Severe (n=56)	Non-severe (n=151)	P*
RBC (mil/µl)	4.2–5.5	4.64 (4.3–5.1)	4.5 (4.2–4.9)	4.7 (4.3–5.1)	0.171
HB (g/dl)	12.0–16.0	12.8 (11.1–13.6)	12.8 (11.9–13.6)	13.5 (11.1–14.0)	0.384
HCT (%)	37.0–47.0	39.0 (34.2–42.8)	38.4 (33.6–42.1)	39.4 (34.4–43.6)	0.188
MCV (fl)	80–94	84.7 (78.4–89.2)	84.8 (78.1–89.3)	84.7 (78.5–89.2)	0.985
MCH (pg)	27–32	27.9 (25.4–29.4)	28.0 (25.3–29.6)	27.8 (25.4–29.3)	0.722
MCHC (g/dl)	32–36	32.4 (31.6–33.2)	32.7 (31.7–33.5)	32.3 (31.55–32.9)	0.068
WBC (K/µl)	4.0–11.0	7.5 (5.3–11.0)	9.3 (6.4–11.9)	7.1 (5.0–9.8)	0.001
PLT (K/µl)	140–450	249 (175–333)	228 (178–300)	258 (174–361)	0.249
		n=158	n=43	n=115
NEU (K/µl)	2.0–7.5	5.2 (3.4–7.2)	6.2 (4.3–8.5)	4.4 (3.0–6.8)	0.003
LYM (K/µl)	1.0–5.0	1.3 (0.9–1.8)	1.0 (0.8–1.4)	1.4 (0.9–2.0)	0.002
NLR		3.7 (2.2–6.5)	5.6 (3.7–8.3)	3.2 (2.1–5.4)	<0.001
PLR		183.5 (132.3–266.3)	221.8 (140.8–324.4)	176.7 (126.3–254.3)	0.068
		n=187	n=56	n=131
PT (s)	12.9–15.9	13.7 (13.1–14.5)	14.1 (13.4–15.2)	13.6 (13.0–14.2)	0.003
		n=189	n=56	n=133
APTT (s)	25.6–42.3	39.1 (35.2–44.0)	40.7 (36.0–44.8)	38.4 (35.1–42.9)	0.103
		n=137	n=51	n=86
INR	0.8–1.2	1.0 (0.9–1.1)	1.1 (0.9–1.2)	0.9 (0.9–1.0)	0.001
		n=153	n=44	n=109
DD (µg/ml)	<5.0	0.7 (0.4–1.5)	1.0 (0.7–1.9)	0.6 (0.4–1.2)	0.001
		n=16	n=12	n=4
Fibrinogen (mg/dl)	200–400	602 (435–662)	616 (456–677)	494 (407–619)	0.332
		n=10	n=8	n=2
FDPs (µg/ml)	<5.0	4.0 (4.0–11.3)	5.6 (4.0–13.9)	4.0 (4.0–4.0)	0.239
		n=173	n=45	n=128
CRP (mg/dl)	0.05–0.3	4.3 (1.0–9.6)	8.7 (3.1–17.7)	2.9 (0.9–6.7)	<0.001
		n=188	n=47	n=141
ESR (mm/h)	0–20	48.5 (29–62)	48 (30–68)	49 (28.5–62)	0.652
		n=182	n=50	n=132
LDH (µ/L)	140–280	294 (239.5–417.3)	400 (291.3–649.3)	267.5 (227.5–372.5)	<0.001

*Bold values are statistically significant. Data are presented as: median (IQR). RBC – Red blood cell; HB – Hemoglobin; HCT – Hematocrit; MCV – Mean corpuscular volume; MCH – Mean corpuscular hemoglobin; MCHC – Mean corpuscular hemoglobin concentration; WBC – White blood cell; PLT – Platelet; NEU – Neutrophils; LYM – Lymphocytes; NLR – Neutrophil-to-lymphocyte ratio; PLR – Platelet-to-lymphocyte ratio; PT – Prothrombin time; APTT – Activated partial thromboplastin time; INR – International Normalized Ratio; DD – D-dimer; FDPs – Fibrin degradation products; CRP – C-reactive protein; ESR – Erythrocyte sedimentation rate; LDH – Lactate dehydrogenase; IQR – Interquartile range

Table 4.

Comparison of hematologic parameters among patients with COVID-19 by mortality

Parameter	Reference range	Total (N=207)	Survivors (n=181)	Non-survivors (n=26)	P*
RBC (mil/µl)	4.2–5.5	4.64 (4.3–5.1)	4.7 (4.3–5.1)	4.2 (3.7–4.5)	<0.001
HB (g/dl)	12.0–16.0	12.8 (11.1–13.6)	12.9 (11.5–14.1)	11.3 (10.4–13.1)	0.003
HCT (%)	37.0–47.0	39.0 (34.2–42.8)	39.6 (34.9–43.5)	34.1 (31.2–38.8)	0.001
MCV (fl)	80–94	84.7 (78.4–89.2)	84.8 (78.7–89.2)	84.2 (75.1–89.4)	0.499
MCH (pg)	27–32	27.9 (25.4–29.4)	27.9 (25.2–29.4)	27.7 (26.4–29.5)	0.800
MCHC (g/dl)	32–36	32.4 (31.6–33.2)	32.3 (31.5–33.0)	33.0 (32.0–34.0)	0.014
WBC (K/µl)	4.0–11.0	7.5 (5.3–11.0)	7.3 (5.3–10.6)	9.4 (6.5–12.1)	0.079
PLT (K/µl)	140–450	249 (175–333)	256 (178–366)	223 (165–246)	0.019
		n=158	n=138	n=20
NEU (K/µl)	2.0–7.5	5.2 (3.4–7.2)	4.8 (3.2–7.0)	6.9 (4.9–8.8)	0.016
LYM (K/µl)	1.0–5.0	1.3 (0.9–1.8)	1.4 (0.9–1.9)	0.9 (0.6–1.2)	0.001
NLR		3.7 (2.2–6.5)	3.5 (2.1–5.8)	6.9 (4.8–11.1)	<0.001
PLR		183.5 (132.3–266.3)	181.9 (129.8–271.7)	205.8 (259.6–142.4)	0.621
		n=187	n=161	n=26
PT (s)	12.9–15.9	13.7 (13.1–14.5)	13.7 (13.1–14.4)	14.1 (13.2–15.7)	0.081
		n=189	n=163	n=26
APTT (s)	25.6–42.3	39.1 (35.2–44.0)	39.2 (35.2–44.0)	38.1 (34.9–44.6)	0.940
		n=137	n=113	n=24
INR	0.8–1.2	1.0 (0.9–1.1)	0.9 (0.9–1.1)	1.0 (0.9–1.3)	0.050
		n=153	n=44	n=109
DD (µg/ml)	<5.0	0.7 (0.4–1.5)	0.6 (0.4–1.3)	1.6 (0.7–2.9)	0.005
		n=16	n=10	n=6
Fibrinogen (mg/dl)	200–400	602 (435–662)	627 (442–670)	560 (333–693)	0.386
		n=10	n=6	n=4
FDPs (µg/ml)	<5.0	4.0 (4.0–11.3)	4.0 (4.0–9.2)	7.0 (4.0–58.6)	0.471
		n=173	n=151	n=22
CRP (mg/dl)	0.05–0.3	4.3 (1.0–9.6)	3.1 (0.9–8.0)	11.7 (7.5–18.6)	<0.001
		n=188	n=171	n=17
ESR (mm/h)	0–20	48.5 (29–62)	49 (29–62)	38 (23.5–63.5)	0.667
		n=182	n=158	n=24
LDH (µ/L)	140–280	294 (239.5–417.3)	282.5 (233.3–397.3)	509.5 (327.8–696.8)	<0.001

*Bold values are statistically significant. Data are presented as: median (IQR). RBC – Red blood cell; HB – Hemoglobin; HCT – Hematocrit; MCV – Mean corpuscular volume; MCH – Mean corpuscular hemoglobin; MCHC – Mean corpuscular hemoglobin concentration; WBC – White blood cell; PLT – Platelet; NEU – Neutrophils; LYM – Lymphocytes; NLR – Neutrophil-to-lymphocyte ratio; PLR – Platelet-to-lymphocyte ratio; PT – Prothrombin time; APTT – Activated partial thromboplastin time; INR – International Normalized Ratio; DD – D-dimer; FDPs – Fibrin degradation products; CRP – C-reactive protein; ESR – Erythrocyte sedimentation rate; LDH – Lactate dehydrogenase; IQR – Interquartile range

The NLR and platelet-to-lymphocyte ratio (PLR) were calculated. Median NLR was significantly higher in the severe group (5.6 vs. 3.2; P <0.001) and non-survivors (6.9 vs. 3.5; P <0.001). No significant difference was observed in the medians of the PLR across comparative groups. PT was available for 187 patients. The severe group had significantly higher median PT than the non-severe group (14.1 vs. 13.6 seconds; P = 0.003), while no significant difference was observed between survivors and non-survivors. Similarly, the median INR, which was available for 137 patients, was significantly higher in the severe group than in the non-severe group (1.1 vs. 0.9; P < 0.001), with no significant difference between survivors and non-survivors.

APTT was available for 189 patients, with no significant differences observed across the comparative groups. DD was analyzed in 153 patients, and the median was significantly higher in the severe group than in the non-severe group (1.0 vs. 0.6 μg/ml; P < 0.001) and in non-survivors compared to survivors (1.6 vs. 0.6 μg/ml; P = 0.005).

The median CRP was significantly higher in the severe group compared to the non-severe group (8.7 vs. 2.9 mg/dl; P < 0.001) and in non-survivors compared to survivors (11.7 vs. 3.1 mg/dl; P < 0.001). FIB and FDP medians were measured in 16 and 10 patients, respectively. Although FIB was higher than the reference range and in the severe cases and survivors compared to the non-severe cases and non-survivors, this difference was not statistically significant. Likewise, FDPs were above the reference range in the severe and non-survivor cases, but this was not statistically significant.

The multivariable logistic regression analysis for disease severity is presented in Table 5. A simplified model was constructed to reduce collinearity among related variables. In this adjusted model, none of the laboratory parameters remained significantly associated with disease severity (all P > 0.05). In contrast, no independent laboratory predictors of mortality were identified in the multivariable model [Table 6].

Table 5.

Binary logistic regression analysis to identify predictors COVID-19 severity

Variables in the equation	OR (95% CI)	P
WBC	0.87 (0.68–1.13)	0.303
NLR	0.92 (0.71–1.19)	0.51
PT	0.95 (0.71–1.25)	0.697
DD	1.01 (0.81–1.25)	0.939
CRP	1.04 (0.96–1.13)	0.32

OR – Odds ratio; CI – Confidence interval; WBC – White blood cell; NLR – Neutrophil-to-lymphocyte ratio; PT – Prothrombin time; DD – D-dimer; CRP – C-reactive protein

Table 6.

Binary logistic regression analysis to identify predictors of COVID-19 mortality

Variables in the equation	OR (95% CI)	P
RBC	5.746 (0.727–45.400)	0.097
HGB	1.065 (0.399–2.842)	0.900
HCT	0.948 (0.653–1.377)	0.780
MCHC	0.996 (0.652–1.521)	0.985
NEU ABS	0.895 (0.625–1.282)	0.545
LYM ABS	2.789 (0.274–28.348)	0.386
PT	1.137 (0.706–1.831)	0.597
NLR	1.063 (0.690–1.639)	0.780
DD	1.051 (0.722–1.529)	0.795
CRP	0.898 (0.806–1.001)	0.052
LDH	0.998 (0.995–1.001)	0.206

OR – Odds ratio; CI – Confidence interval; RBC – Red blood cell; HGB – Hemoglobin; HCT – Hematocrit; MCHC – Mean corpuscular hemoglobin; NEU ABS – Absolute neutrophil; LYM ABS – Absolute lymphocyte; PT – Prothrombin time; NLR – Neutrophil-to-lymphocyte ratio; DD – D-Dimer; CRP – C-reactive protein; LDH – Lactate dehydrogenase

DISCUSSION

The current study found that among 207 COVID-19 patients admitted to a tertiary hospital in Al Khobar, Saudi Arabia, age was significantly associated with disease severity and mortality, which is also consistent with previously published studies.[4,17,18] National studies conducted by Alqahtani et al., and AlSulaim et al., have shown that the median age of ICU patients was significantly higher than those of non-ICU patients;[19,20] these findings are similar to the findings of international studies done in China and Pakistan.[21,22] In our study, males comprised majority of the sample (72%), severe cases (83.9%), and non-survivors (88.5%). These findings are in line with those of previously published studies, which note that male sex is associated with higher severity and mortality rates.[20] Some studies have suggested potential sex-based differences in COVID-19 outcomes;[23] however, evidence remains inconsistent, and sex was not identified as an independent predictor in our analysis.

The percentage of severe non-survivor patients (84.6%) in the current study is higher than that reported in previous studies from other countries and is notably higher than severe outcome rates identified in large multicenter analyses of COVID-19 severity and mortality.[24]

The chest X-ray finding that was predominantly reported in our cohort was bilateral infiltrate (68.1%), which is similar to findings from China, wherein there was increased odds of bilateral lung involvement on chest imaging compared with other patterns (OR = 1.74).[25] Of note, in the current study, the proportion of patients with bilateral infiltrates was higher in non-survivors (84.6%) compared to survivors (65.7%); however, this difference was not statistically significant In our cohort, unremarkable chest X-ray findings were predominantly observed among non-severe patients (20.5% vs. 1.8%) and present only among survivors (17.7%). This indicates that absence of radiograph abnormalities may reflect milder pulmonary involvement in COVID-19, which is coherent with the findings of Virdi et al.[26]

The most common clinical manifestations reported were fever, cough, and shortness of breath. Similar findings on the most prevalent symptoms have been reported in multiple national[20,27] and international studies.[17,26] However, Telmesani et al. noted that sore throat was among the three most common symptoms, along with fever and cough.[28] In the present study, shortness of breath was significantly more common among patients with severe disease, reflecting the greater respiratory compromise observed in critically ill COVID-19 patients. Similar findings have been reported in previous studies where dyspnea was identified as an important clinical indicator of disease progression and severity.[20,27]

It has been previously reported that the total WBC count and LYM count can predict disease prognosis and outcome.[29] The median WBC counts across all groups were within the normal range. The severe cases had a significantly higher WBC count compared to the non-severe cases, and this is consistent with previous findings.[30] The WBC count in non-survivors was significantly higher compared to that in survivors, consistent with the findings of Li et al.[31] Another study also indicated NEU and LYM were significantly higher in severe cases.[32] Several studies have indicated that NLR is a useful potential biomarker for predicting COVID-19 infection severity and that it is helpful in the management of early-stage cases.[17,32,33,34] We observed a significant association between NLR and disease severity and mortality. Severe cases and non-survivors had significantly higher NLR than non-severe cases and survivors, and this is consistent with previously published literature.[32]

Coagulation parameters have been recognized as key indications of poor prognosis in patients with COVID-19 and are increasingly used in clinical risk assessment.[34] Prolonged PT-raised DD, and enhanced FDPs show activation of the coagulation cascade and underlying endothelial damage, both of which are essential aspects of COVID-19 associated coagulopathy.[34] Previous investigations have revealed considerably greater PT, DD, and FDP levels in severe cases compared with moderate disease, suggesting an elevated thrombotic burden and dysregulated hemostasis in critically ill patients.[35,36] Our findings are consistent with these reports, as patients with severe disease demonstrated significantly prolonged PT, higher INR, and elevated DD levels. In addition to the markers evaluated in this study, other laboratory and clinical patterns have been reported in COVID-19. These include lymphopenia, thrombocytopenia, elevated ferritin levels, and markers of coagulation dysfunction. Radiological findings such as bilateral ground-glass opacities on chest imaging have also been commonly described. However, the clinical significance of these patterns may vary, depending on disease stage, patient characteristics, and study population.

Several investigations have further established that non-survivor exhibit more pronounced coagulation abnormalities than survivors, with higher PT, DD, and FDP levels functioning as predictors of mortality.[17,37] These differences apparently indicate chronic thrombo-inflammation, microvascular thrombosis, and impaired fibrinolysis, which lead to organ dysfunction and unfavorable consequences. In the present investigation, while PT and INR were higher among severe patients, only DD was found to have a significant association with mortality. This finding indicates that DD may represent a more sensitive measure of disease severity and thrombotic activity, strengthening its clinical significance in the early diagnosis of high-risk COVID-19 patients.

Previous studies have found that the concentrations of acute inflammatory markers, such as CRP, ESR, and LDH, were significantly higher in severe groups than in non-severe groups, indicating that the inflammatory reaction is more notable in severe patients.[30,32,38] It has also been reported that the levels of these biomarkers are higher in non-survivors compared to survivors.[17] Consistent with previous literature, the levels of CRP in our cohort were significantly associated with mortality, as higher levels were observed in the severe and non-survivor groups.[38]

The current study did not find any of the studies variables to be a significant predictor of severity and mortality. These results are in contrary with previous studies that showed multiple predictors of mortality such as NEU and RBC indices at baseline and during early progression of disease; NLR (composite inflammatory indices); other inflammatory and hematological markers such as DD, CRP, PLT, and WBC count (baseline and early changes in laboratory parameters). Although several laboratory parameters, including DD and CRP, significantly differed between survivors and non-survivors in the univariable analysis, none remained associated with mortality in the multivariate model. This is likely because of the small number of mortality events relative to the number of variables included, incomplete availability of laboratory data, and collinearity among related inflammatory markers. These factors may have reduced the ability of the model to detect independent effects, and thus the results of the multivariable model should be interpreted with caution.

Limitations

The current study has several limitations. Reporting bias may have been introduced due to the retrospective nature of the study, its dependency on medical records, the relatively small sample size, incomplete laboratory data, and the fact that the study was conducted using data from a single center. In addition, repeated measurements of blood indices were performed for patients during their hospital stay; however, we selected a single data point in our analysis, which might not reflect the dynamic and continuous changes of blood parameters. Disease severity in this study was defined based on ICU admission, which may be influenced by institutional admission policies, physician judgment, and ICU availability during the initial phase of the COVID-19 pandemic. Another limitation is that some of the laboratory parameters were not available for all patients because testing was performed based on clinical indications. This selective testing may introduce selection bias, especially if biomarkers were more frequently measured in clinically severe patients. Furthermore, some analyses were based on smaller subsets of patients due to incomplete laboratory testing, and thus the observed associations should be interpreted with caution. Formal assessment of the model’s performance and calibration (e.g., area under the curve, Brier score, or calibration plots) were not performed because the regression analysis were exploratory and the sample size was relatively limited. In addition, the data were collected during the early phase of the COVID-19 pandemic, and thus may not fully reflect the current disease patterns influenced by vaccination, emerging variants, and improved treatment strategies.

CONCLUSION

In this retrospective cohort study of COVID-19 positive hospitalized patients, several laboratory parameters including white blood cells, neutrophil-to-lymphocyte ratio, D-dimer, and C-reactive protein were associated with the severity of disease and mortality; however, none of these markers remained statistically significantly in the multivariable regression models. These findings suggest that while commonly used laboratory markers reflect disease burden, they may not independently predict outcomes when considered together.

Ethical considerations

Ethical Approval for this study was obtained from Institutional Review Board at Imam Abdulrahman bin Faisal University (IRB-2025-03-0842). The study was conducted in accordance with the Declaration of Helsinki, as revised in 2024. Requirement for patient consent was waived owing to the study design.

Peer review

This article was peer-reviewed by two independent and anonymous reviewers.

Data availability statement

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

Author contributions

A.M.A-S. was solely responsible for conceptualization, methodology, data analysis, writing–original draft preparation, and writing – review and editing.

A.M.A-S. has read and agreed to the published version of the manuscript.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.