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Abstract

This study primarily aimed to examine the significance of the C-reactive protein to lymphocyte ratio (CLR), a key marker of inflammation, in relation to the disease progression and management of COVID-19 patients admitted to the intensive care unit (ICU). A total of 464 patients aged 18 years or older, diagnosed with COVID-19 and admitted to the ICU between April 1, 2021, and February 1, 2022, were included in the study. Sociodemographic, laboratory, radiological, and clinical data were collected for each patient. The cohort was then divided into two groups—those who survived and those who did not—and analyzed accordingly. Among the patients included in the study, 58.2% were male, and the mean age was 62.39 ± 15.65 years. The mortality rate was 42%. The analysis revealed that the need for high-flow oxygen and mechanical ventilation increased the risk of death by 9.64 times. Furthermore, for each 1-point increase in the SOFA Score, Charlson Comorbidity Index, and Nutric Score, the risk of death increased by 1.27, 1.18, and 1.40 times, respectively. Intravenous immunoglobulin, administered to a select group of patients, reduced the risk of death by 23.8 times. The optimal threshold value for CLR was identified as 103.05, with values above this increasing the risk of death by 1.84 times. Critically ill patients with CLR values exceeding the identified threshold should receive more intensive monitoring and timely adjustments in treatment. Given that CLR is a simple, accessible, and cost-effective marker, it holds particular value in managing aggressive diseases like COVID-19.

Keywords

c-reactive protein COVID-19 intensive care unit lymphocyte mortality

What we already know: Numerous studies have identified risk variables for COVID-19 therapeutic management, follow-up, and mortality as well as disease progression. Key indicators associated with disease progression or mortality include low lymphocyte count, elevated C-reactive protein (CRP) and/or procalcitonin levels, and an increased neutrophil-to-lymphocyte ratio. However, no studies have been conducted to validate the significance of these factors in guiding treatment selection, management, or modification.

What this article adds: As far as we are aware, no research has been published in the literature that clarifies the relationship between CLR and mortality, management, and disease progression with a cut-off value. This sets the presented study apart. The main goal of management of COVID-19 is to prevent the onset of a cytokine storm, the ensuing ICU procedure, and death. The main aim, therefore, is that the CLR value over the treshold in the study may pave the way for early interventions by guiding therapy and follow-up in COVID-19 patients.

Introduction

According to recent data from the World Health Organization (WHO), there have been approximately 17 million confirmed cases in Turkey and 773 million confirmed cases worldwide due to COVID-19, with approximately 7 million deaths worldwide.¹ While measures such as personal hygiene and mask distancing reduce transmission in COVID-19, the best way to protect against the disease is to provide immunity through vaccines.² Numerous elements have been identified as contributing to the pathogenesis of COVID-19, with one of the most significant being the interplay between neutrophils (N), the primary innate immunity cells, and lymphocytes (L), the primary acquired immunity cells.³ A research by Buonacera et al. demonstrated that a heightened neutrophil-to-lymphocyte ratio (NLR) may be a crucial indicator and predictor of development to critical illness, the necessity for intensive care, and mortality in patients with COVID-19.⁴

This underlying inflammatory process progresses in some patient groups, leading to an abnormal cytokine response and cytokine storm, the development of a critical illness process, and ultimately to acute respiratory distress syndrome (ARDS) and intensive care unit (ICU) admission.³ While a significant proportion of confirmed COVID-19 patients survive the disease asymptomatically or as outpatients, approximately 15% develop severe illness and are hospitalized, and up to 5% develop critical illnesses closely associated with mortality, including ARDS, respiratory failure and multiorgan failure.³ A common outcome of studies on the development of critical illness and mortality in COVID-19 points to older age, male gender, comorbid diseases (especially diabetes mellitus (DM), hypertension (HT), chronic and progressive diseases of vital organs such as lungs, heart, and kidneys), immunosuppressive diseases or immunosuppressive drug use.^5–7 The Food and Drug Administration (FDA) issued Emergency Use Authorizations (EUAs) for numerous novel medications and medical devices in the early stages of the pandemic (without full FDA approval).⁸ Later, as COVID-19 was better understood, its pathophysiology was recognized, and multicenter randomized controlled studies demonstrated that these medications had no helpful effect on the disease, the majority of these medications were eliminated from the COVID-19 therapy protocol.^3,9,10 Currently, the main treatments with proven efficacy for COVID-19 disease are antiviral drugs, anti-inflammatory drugs, immunomodulators, neutralizing antibodies, cell and gene therapies.^9,10 In order to maintain vital functions in the ICU, treatment methods such as plasmapheresis-hemodiafiltration are applied in addition to drug therapies and supportive treatment methods.^11–13 Treatment is typically guided by the clinical and radiological presentation; however, uncertainties remain regarding which patients should receive additional medications, the timing and duration of such treatments, and when to discontinue them. The objective of this study was to identify risk factors associated with mortality in COVID-19 patients admitted to the ICU and to explore the potential use of the inflammatory marker C-reactive protein to lymphocyte ratio (CLR) as a predictor of disease progression and mortality, as well as a tool for guiding drug selection during treatment.

Methodology

Study design

This is a single-center and descriptive study based on retrospective data of patients with COVID-19 who were hospitalized in the ICU of Cukurova University between 01.04.2021 and 01.02.2022. Ethical approval was obtained from Cukurova University Faculty of Medicine Non-Interventional Clinical Research Ethics Committee on December 8, 2023 (40/139) and complies with the 1964 Declaration of Helsinki and Hospital ethical rules. Participants were included in the study after obtaining written informed consent from them or their legal heirs.

Participants

Inclusion criteria;

-To be over 18 years of age,

-Positive SARS-CoV-2 PCR in nasopharyngeal and/or oropharyngeal swabs

-Need to be monitored in intensive care unit due to COVID-19

-Receiving corticosteroid treatment before or in the ICU

Exclusion criteria;

-Patients who do not meet the inclusion criteria

-Patients with missing tests

A total of 716 patients were initially evaluated for the study; however, 252 patients were excluded. Among those excluded, 62 were under 18 years of age, 112 had incomplete investigations, 44 tested negative for PCR, and 34 were not receiving corticosteroid treatment. Ultimately, 464 patients were included in the study, which were subsequently divided into two groups: Group 1 (deceased) and Group 2 (survivors). The study flow is presented in Figure 1.

Figure 1.

Study flow.

Variables

Age, gender, comorbidities, acute physiology and chronic health evaluation (APACHE)-II score, charlson comorbidity index (CCI), modified nutric score (mNUTRIC), sequential organ failure assessment score (SOFA score), vital signs (temperature, heart rate, respiratory rate, systolic/diastolic blood pressure), oxygen saturation (SaO₂), PO₂/FiO₂ ratio at initial presentation as a marker of respiratory failure (Normal: PaO₂/FiO₂ > 300, Mild: 200 < PaO₂/FiO₂ ≤ 300, Moderate: 100 < PaO₂/FiO₂ ≤ 200, Severe: PaO₂/FiO₂ < 100), oxygen treatments (nasal oxygen, mask with reservoir, high flow oxygen (HFO) demand, noninvasive mechanical ventilator (NIV) or invasive mechanical ventilator (IMV) support), routine blood laboratory parameters (complete blood count, glucose, BUN, creatinine, ALT, AST, ferritin, fibrinogen, CRP, procalcitonin), chest X-ray and Computed Tomography (CT) findings, type and duration of treatments (antiviral, corticosteroid, anti-cytokine therapy, anticoagulant, intravenous immunoglobulin (IVIG), hemodiafiltration (HDF), plasmapheresis), length of hospital stay, discharge status were recorded.

Data source/measurement

Acute physiology and chronic health evaluation (APACHE) II score

Physiological variables assessed in APACHE-II include body temperature, mean arterial pressure, heart rate, respiratory rate, oxygenation, arterial pH, venous HCO3-, sodium, potassium, serum creatinine, hematocrit, leukocytes, and glasgow coma scale (GCS). APACHE-II is the sum of three sub-scores: acute physiology score, age and chronic health assessment, with a maximum value of 71. The mortality rate is 25% with a total score of 25 and increases to 80% with a score of 35 and above.¹⁴

Charlson comorbidity index (CCI)

According to the CCI, comorbidities are defined as “0 points low risk, 1–2 points moderate risk, 3–4 points high risk, 5 points and above very high risk.”¹⁵

Modified nutrition risk in critically Ill patients (mNUTRIC) score

Interleukin 6 (IL-6), which is used as an inflammatory marker in the NUTrition Risk in the Critically ill (NUTRIC) score developed using metabolic status, comorbidities, reduced energy intake, body mass index (BMI) and prognosis markers, is not routinely used in ICU. The scoring calculated without the use of IL-6 is called modified NUTRIC (mNUTRIC) score. According to the mNUTRIC score, patients are divided into low (0–4) and high (5–9) risk groups, and high mNUTRIC score is associated with poor prognosis.¹⁶

Sequential organ failure assessment score (SOFA score)

It is used to monitor a person’s condition during their ICU stay, to determine the degree of organ function or rate of failure.¹⁷ The score is based on six different scores, each for the respiratory, cardiovascular, hepatic, coagulation, renal, and neurological systems. The SOFA scoring system is useful in predicting the clinical outcome of critically ill patients. The score ranges from 0 (best) to 24 (worst).¹⁸

Management and treatment of patients

For the management and treatment of hospitalized patients, we adhered to the continuously updated local guidelines provided by the country’s Ministry of Health, along with current clinical recommendations.^9,10,19 In accordance with the recommendations available at the time, the initial treatments administered included antiviral medications (favipiravir), low molecular weight heparin (enoxaparin), and corticosteroids (either methylprednisolone or dexamethasone) for patients experiencing hypoxemia or clinical progression.

Anticytokine therapies, including Anakinra and Tocilizumab, were administered to patients who did not respond to steroid treatment and experienced worsening hypoxemia along with an intense cytokine storm. Patients with rapid clinical deterioration, who developed respiratory failure and required high-flow oxygen or mechanical ventilation support, were admitted to the ICU. In addition to respiratory support, these patients also received plasmapheresis, HDF or IVIG treatment. Due to budget constraints and regulations of the country’s social security agency, IVIG was difficult to obtain and could only be used in a select population of patients. Anticytokine therapies, plasmapheresis and HDF therapy were used more often than IVIG because they were more accessible. It is up to the critical care unit supervisor to make the decision.

Statistical analysis

SPSS 22 program was used for data analysis. Kolmogorov Smirnov test was used as normal distribution test. Mann Whitney U test, Kruskal Wallis test, binary logistic regression analysis, correlation tests, ROC analysis were used in the analysis. Forward LR logistic regression model was used for mortality prediction and Favipravir, Tociluzumab, Anakinra, Immune Plasma, IVIG, HDF, Anticoagulant, Plasmapheresis treatment status, lymphopenia status at presentation, oxygen requirement, APACHE-II score, CCI score, Sofa score, Nutric, NLR, CLR independent variables were included in the model. P < 0.05 was considered statistically significant.

Results

Among the patients included in the study, 58.2% were male, and the mean age was 62.39 ± 15.65 years. The overall mortality rate was 42%. According to the Charlson comorbidity index score, 2.4% of the patients were in the low risk group and 50.6% were in the very high risk group, which was statistically significant in terms of mortality (P < 0.001). The mortality rate was significantly higher among patients who received oxygen support via high-flow oxygen (HFO), non-invasive mechanical ventilation (NIMV), or invasive mechanical ventilation (IMV), as well as those who presented with chest CT findings compatible with COVID-19 at the time of initial assessment (P = 0.008 and P < 0.001, respectively). Similarly, the severity of respiratory failure (low PO₂/FiO₂ ratio) at the time of initial presentation was found to significantly increase mortality rates (P < 0.001). During the follow-up, mortality rates were higher in the patients who used antibiotics, antifungals, anakinra, plasmapheresis and hemodiafiltration and this difference was statistically significant (P values P < 0.001, P < 0.001, P = 0.025, P < 0.001, P < 0.001, P < 0.001, respectively), whereas mortality was lower in IVIG use (P = 0.008). Among the laboratory findings at initial presentation, the mean CRP value was 108.37 ± 90.69 and the mean lymphocyte value was 0.77 ± 0.53 in all patients, and high CRP and low lymphocyte levels were statistically significant for mortality (P < 0.001). Furthermore, among all patients, APACHEE II score at initial admission, mean length of hospitalization and mean length of ICU stay were higher in group 1 (those who died) and this was statistically significant (P values P < 0.001, P = 0.259, P < 0.001, respectively) (Table 1).

Table 1.

Association of socio-demographic and clinical findings and treatment methods with mortality.

Characteristics	All patients (n = 464)	Group 1 (deceased) (n = 195)	Group 2 (living) (n = 269)	P
Characteristics	Mean ± SD or n (%)			P
Age (year) (mean ± SD)	62.39 ± 15.65	65.86 ± 14.21	59.87 ± 16.19	0.080
Sex
Male	270 (58.2)	123 (63.1)	147 (54.6)	0.069
Female	194 (41.8)	72 (36.9)	122 (45.4)	0.069
CCI score
Low risk (0 points)	11 (2.4)	3 (1.5)	8 (3)	<0.001
Medium risk (1–2 points)	91 (19.6)	21 (10.8)	70 (26)
High risk (3–4 points)	127 (27.4)	44 (22.6)	83 (30.9)
Very high risk (5 points or more)	235 (50.6)	127 (65.1)	108 (40.1)
Computed tomography of the thorax
Compatible with COVID-19	356 (76.7)	163 (83.6)	193 (71.7)	0.008
Incompatible with COVID-19	106 (22.8)	32 (16.4)	74 (27.5)
No infiltration	2 (0.4)	0 (0)	2 (0.7)
O₂ support at admission
Nasal or reservoir mask	169 (36.4)	71 (36.4)	98 (36.4)	<0.001
HFO/NIV/IMV	295 (63.6)	124 (63.6)	171 (63.6)	<0.001
PO₂/FiO₂
Group 1 PO₂/FiO₂ > 300	58 (12.5)	10 (5.1)	48 (17.8)	<0.001
Group 2 300 ≥ PO₂/FiO₂ > 200	63 (13.6)	15 (7.7)	48 (17.8)
Group 3 200 ≥ PO₂/FiO₂ > 100	164 (35.3)	70 (35.9)	94 (34.9)
Group 4 PO₂/FiO₂ ≤ 100	179 (38.6)	100 (51.3)	79 (29.4)
Drugs and other treatments
Favipiravir	297 (64)	115 (38.7)	182 (61.3)	0.054
Antibiotics	393 (84.7)	184 (46.8)	209 (53.2)	<0.001
Antifungals	122 (26.3)	79 (64.8)	43 (35.2)	<0.001
Anakinra	118 (25.4)	60 (50.8)	58 (49.2)	<0.025
Tocilizumab	33 (7.1)	10 (30.3)	23 (69.7)	0.157
Plasmapheresis	145 (31.3)	81 (55.9)	64 (44.1)	<0.001
HDF	42 (9.1)	31 (73.8)	11 (26.2)	<0.001
IVIG	21 (4.5)	3 (14.3)	18 (85.7)	0.008
Immune plasma	28 (6)	14 (50)	14 (50)	0.378
Anticoagulants
Heparin prophylaxis	412 (88.8)	171 (87.7)	241 (89.6)	0.547
Standard heparin therapy	48 (10.3)	23 (11.8)	25 (9.3)
Enoxaparin sodium treatment	4 (0.9)	1 (0.5)	3 (1.1)
Laboratory findings at admission
Hemoglobin (g/dL)	11.90 ± 2.37	11.39 ± 2.32	12.26 ± 2.33	0.091
Hematocrit (%)	36.01 ± 17.61	35.81 ± 26.26	36.16 ± 6.07	0.316
Platelet count (×10³)	224.01 ± 122.41	180.59 ± 117.54	255.47 ± 116.44	0.203
WBC (×10³)	11.05 ± 6.32	11.09 ± 7.01	11.03 ± 5.79	0.442
Absolute lymphocyte count (µL)	0.77 ± 0.53	0.58 ± 0.38	0.91 ± 0.58	<0.001
CRP (mg/dL)	108.37 ± 90.69	123.69 ± 91.77	97.27 ± 88.42	0.435
APACHEE-II score	17.80 ± 8.46	22.06 ± 8.56	14.72 ± 6.93	<0.001
SOFA score	5.50 ± 3.51	7.55 ± 3.85	4.01 ± 2.29	<0.001
mNUTRIC score	3.87 ± 1.98	4.88 ± 2.01	3.13 ± 1.61	<0.001
Length of stay in hospital	14.63 ± 8.61	12.91 ± 7.39	15.87 ± 9.21	<0.259
Length of stay in the ICU	8.25 ± 5.65	9.74 ± 5.90	7.17 ± 5.21	<0.001

CCI: Charlson comorbidity index; HFO: high-flow nasal oxygen therapy; NIV: non-invasive ventilation; IMV: invasive mechanical ventilation; HDF: hemodiafiltration; IVIG: intravenous immunoglobulin; WBC: white blood cell count; CRP: c-reactive protein; APACHE II: acute physiology and chronic health evaluation II; SOFA: the sequential organ failure assessment; mNUTRIC: modified nutrition risk in critically Ill; ICU: intensive care unit; PO₂: arterial oxygen pressure; FiO₂: fraction of inspired oxygen.

In the ROC-Curve analysis, the CRP-to-lymphocyte ratio (CLR) was found to have a role in predicting mortality and to be a diagnostic test with moderate power [the area under the curve (AUC) = 0.684] (Table 2 and Figure 2).

Table 2.

Area of CRP to lymphocyte ratio under the curve.

Test result variable(s): CRP to lymphocyte ratio
Area	Std. error	P	Asymptotic 95% confidence interval
Area	Std. error	P	Lower bound	Upper bound
0.684	0.025	<0.001	0.636	0.732

CRP: c-reactive protein.

Figure 2.

CRP to lymphocyte ratio (area under the curve).

In the present study, the area under the curve for CLR was significant. According to the results, the optimum cut-off value for this parameter was found to be 103.05. The validity results for this cut-off value were as follows: sensitivity 75.3% and specificity 51.9% (Table 3).

Table 3.

Validity results for optimum cut-off value for CRP to lymphocyte ratio.

Positive if greater than or equal toa	Sensitivity	Specificity	Youden index	Likelihood ratio (+) DP/YP	Likelihood ratio (−) YN/DN
103.0556	0.753	0.519	0.272	1.56	0.47

CRP: c-reactive protein.

When CLR and length of hospitalization were compared according to treatment modalities, statistically significant differences were found between the groups. CRP-lymphocyte ratios were found to be higher in patients who received only anakinra treatment and plasmapheresis, HFO-NIV-IMV and in case of mortality (P values P = 0.002, P < 0.001, P < 0.001, P < 0.001, P < 0.001, respectively). In addition, patients receiving favipiravir, tocilizumab, anakinra, immunoplasma, plasmapheresis treatment and patients receiving respiratory support with HFO/NIV/IMV were found to have significantly longer hospitalization times (P values P < 0.001, P < 0.001, P < 0.001, P = 0.009, P = 0.007, P < 0.001, P = 0.008, respectively) (Table 4).

Table 4.

Comparison of CRP to lymphocyte ratio and length of hospitalization according to drug types.

Medications or other methods	Administration	CRP to lymphocyte ratio		Length of stay (days)
Medications or other methods	Administration	Median (IQR)	P	Median (IQR)	P
Favipiravir	Yes No	148(252) 140(303)	0.727	14(10) 11(11)	<0.001
Tociluzumab	Yes No	127(176) 147(275)	0.208	20(11) 12(11)	<0.001
Anakinra	Yes No	195(337) 132(234)	0.002	14(10) 12(11)	0.009
Immune plasma	Yes No	164(314) 144(268)	0.221	17(10.3) 12.5(11)	0.007
IVIG	Yes No	215(216) 144(272)	0.428	17(13) 13(11)	0.143
Hemodiafiltration	Yes No	220(268) 139(269)	0.171	12(10.5) 13(12)	0.738
Anticoagulants	-Heparin prophylaxis -Heparin therapy -Enoxaparin treatment	139(253) 224(358) 258(270)	0.341	13(12) 13.5(12) 12.5(7)	0.777
Plasmapheresis	Yes No	184(284) 284(252)	<0.001	15(11) 12(11)	<0.001
Mortality	Yes No	217(311) 97.4(208)	<0.001	11(10) 14(12)	<0.001
Oxygen support	-Nasal or reservoir mask -HFO-NIV-IMV	83.3(180) 193(292)	<0.001	12(9) 14(11)	0.008

IVIG: intravenous immunoglobulin; CRP: c-reactive protein.

A negative correlation was observed between CLR and PO₂/FiO₂, a positive correlation between CLR and NLR, and a negative correlation between NLR and PO₂/FiO₂, and this was statistically significant (Table 5).

Table 5.

Correlations between CLR and other parameters.

Correlations	CLR	NLR	Neutrophil	Lymphocyte	PO₂/FiO₂
CLR
Pearson correlation	1	294	−0.070	−0.430	−0.118
Sig. (2-tailed)		0.000	0.134	0.000	0.011
N	464	464	464	464	464
NLR
Pearson correlation	294	1	0.501	−0.458	−0.203
Sig. (2-tailed)	0.000		0.000	0.000	0.000
N	464	464	464	464	464
Neutrophil
Pearson correlation	−0.070	0.501	1	0.079	−0.149
Sig. (2-tailed)	0.134	0.000		0.091	0.001
N	464	464	464	464	464
Lymphocyte
Pearson correlation	−430	−0.458	0.079	1	0.139
Sig. (2-tailed)	0.000	0.000	0.091		0.003
N	464	464	464	464	464
PO₂/FiO₂
Pearson correlation	−118	−0.203	−0.149	0.139	1
Sig. (2-tailed)	0.011	0.000	0.001	0.003
N	464	464	464	464	464

CLR: CRP to lymphosit ratio; NLR: neutrophil to lymphocyte ratio; PO₂: arterial oxygen pressure; FiO₂: fraction of inspired oxygen.

Forward LR logistic regression model created for mortality prediction was found to be significant (omnibus test P < 0.001). Independent variables included in the model were Favipravir, Tociluzumab, Anakinra, Immune Plasma, IVIG, HDF, Anti-coagulant treatment, Plasmapheresis treatment, lymphopenia status at presentation, oxygen requirement, APACHE-II score, CCI score, Sofa score, mNutric score, NLR, CLR (risk group > 103.05). The dependent variable is mortality status.

Among the variables included in the model, IVIG, HDF, plasmapheresis, oxygen requirement, CCI score, SOFA score, mNutric score, PO₂/FiO₂ ratio and CLR were found to contribute significantly to the model. The mortality risk was found to decrease 23.8-fold in patients using IVIG, while it increased OR = 3.79-fold in patients using HDF and 1.87-fold in patients using plasmapheresis. Compared to patients using oxygen with a nasal reservoir mask, patients using HFO-NIV-IMV had a 9.64-fold increased risk of death. The risk of death decreased by 1.67 times for every unit increase in the PO₂/FiO₂ ratio; however, the risk of death increased by 1.014 times for each unit increase in age; 1.27 times for each unit increase in SOFA score; 1.18 times for each unit increase in CCI; 1.40 times for each unit increase in nutric score; and 1.84 times higher for patients whose CRP lymphocyte ratio was above 103.05 (table 6).

Table 6.

Logistic regression analysis of mortality prediction.

R² = 0.597. Accuracy = 82.4%	B	P	OR	95% CI for OR
Variables	B	P	OR	Lower	Upper
IVIG	−3.166	<0.001	0.042	0.009	0.208
HDF	1.335	0.010	3.799	1.380	10.457
Plasmapheresis	0.626	0.041	1.871	1.026	3.412
Oxygen support	2.266	<0.001	9.645	4.926	18.887
CCI score	0.166	0.013	1.180	1.035	1.346
SOFA score	0.242	<0.001	1.273	1.138	1.425
mNUTRIC score	0.336	0.001	1.400	1.148	1.706
CLR	0.610	0.033	1.840	1.051	3.221
PO₂/FiO₂	−0.515	0.000	0.598	0.480	0.744
Age	0.028	0.000	1.028	1.014	1.043
Constant	−4.439	<0.001	0.012

IVIG: intravenous immunoglobulin; HDF: hemodiafiltration; CCI: Charlson comorbidity index; SOFA: the sequential organ failure assessment; mNUTRIC: modified nutrition risk in critically Ill; CLR: CRP to lymphosit ratio; PO₂: arterial oxygen pressure; FiO₂: fraction of inspired oxygen.

Discussion

The presented study demonstrated that the C-reactive protein to lymphocyte ratio (CLR) can serve as a moderately effective diagnostic tool for predicting mortality in ICU patients, with an optimal cut-off value of 103.05. The validity results for this cut-off indicated a sensitivity of 75.3% and a specificity of 51.9%. Additionally, the study clarified that patients who received treatment with anakinra, plasmapheresis, high-flow oxygen (HFO), non-invasive ventilation (NIV), or invasive mechanical ventilation (IMV), and subsequently died during the progression of their condition, exhibited higher CLR values upon hospitalization.

According to a study looking at the severity of the disease, mortality, and risk factors associated with COVID-19, the overall mortality rate was found to be between 3.77 and 5.4%, and among 41.1–61.5% in patients who were extremely or critically sick.²⁰ According to a different study, the overall hospital death rate for patients treated for COVID-19 ranged from 15 to 20% (depending on the cohort), and it was almost 40% for patients who needed intensive care follow-up.²¹ In line with previous research, the present study discovered that 42% of COVID-19 patients in the ICU died.

Early in the pandemic, some research revealed that factors such as advanced age, underlying illnesses, high D-dimer levels, and multivariate biochemical abnormalities were strongly linked to COVID-19 patients’ disease severity and even death.^5,6,20,22 A retrospective analysis of 456 patients found that older age, male gender, and high neutrophil-to-lymphocyte ratio (NLR) and high CRP levels at presentation were significantly associated with poor prognosis in individuals with moderate COVID-19, suggesting that NLR and CRP levels at presentation may be good predictors of progression to critical illness and death.²³ A recent study based on a retrospective review of 228 patients hospitalized for COVID-19 identified risk factors for mortality as high creatinine levels, increased respiratory rate, inadequate COVID-19 vaccination, and high COVID-GRAM critical illness and 4-C Mortality scores.²⁴ In a study on CLR level in COVID-19 patients, CLR was found to be an independent risk factor for mortality in COVID-19 patients, and the sensitivity of CLR in detecting mortality for values of 23.4% and above was 75% and specificity was 70%.²⁵ The presented study concluded that high CCI score, compatible thorax CT compatible with COVID-19, HFO or mechanical ventilator support, antibiotic, antifungal, anakinra, plasmapheresis, HDF, IVIG use, low lymphocyte count, high APACHEE II, SOFA and NUTRIC scores and long ICU stay may be risk factors associated with increased mortality.

According to the current study, patients receiving IVIG in the intensive care unit had a lower risk of dying, whereas patients receiving HDF and plasmapheresis had a higher risk. More effective treatments are required at the next level for this patient group, as many patients admitted to the Intensive Care Unit require higher oxygen and respiratory support due to escalating clinical and radiological abnormalities caused by COVID-19. Due to budgetary limitations and the country’s social security institution’s regulations, IVIG is more difficult to obtain and can only be used on a specific patient population. More often than IVIG, anticytokine treatments, plasmapheresis, and HDF therapy were utilized because they are more accessible. It was up to the critical care unit monitorian to make the decision. Only 3 of the 21 patients who received IVIG treatment, however, died. Of the eighteen patients who survived, fourteen experienced a rapid anti-inflammatory response (during the first 48 h), and the other four experienced a delayed anti-inflammatory response (beyond 48 h). Apart from the decline in the clinical findings’ advancement, a particular association was noted between the clinical findings and the reduction in CRP and ferritin levels. While the lymphocyte count fluctuated over time, other laboratory indicators also showed improvement, and after discharge, normal or almost normal values were reported. Therefore, it can be said that timing IVIG treatment delivery is crucial to stopping the progression of the disease in patient groups with COVID-19 and similar disorders where systemic inflammation is prevalent and progressing. Expanding the IVIG therapy patient base will confirm the conclusion, nevertheless. Oxygen or mechanical ventilator requirement at initial presentation, SOFA score, CCI score and nutritional score were also found to increase mortality.

Various laboratory parameters that may be indicative of prognosis, such as low lymphocyte count, high serum CRP levels, D-Dimer-ferritin-fibrinogen elevations, IL-1, IL-6, IL-22 elevations, increases in neutrophil/lymphocyte, lymphocyte/platelet and lymphocyte/CRP ratios have been detected in COVID-19.^26,27 Previous studies have reported that lymphopenia is common in COVID-19 cases and is an important and reliable indicator of disease severity.^28,29 In a review examining the laboratory biomarkers for diagnosis and prognosis in COVID-19, it was noted that procalcitonin may be useful as an indicator of disease severity and mortality and in the management of antimicrobial therapy, neutrophil count may be a predictor of clinical outcome in hospitalized COVID-19 patients, and NLR may be strongly associated with the severity and mortality of COVID-19.³⁰ Of these laboratory parameters, NLR is known to be a good indicator of systemic inflammatory status and has inspired many studies.^31,32 In fact, prior to the COVID-19 pandemic, a study on patients with community-acquired pneumonia found that patients with NLR 10 and above had a statistically significant higher risk of ICU admission and mortality compared to patients with NLR less than 10, suggesting that NLR may be a marker of progressive disease in this regard.³³ In the same study, they stated that lymphocyte count and CRP level did not have a statistical significance in terms of survival among patients, and that NLR predicted mortality better than CRP level, WBC count, neutrophil count and lymphocyte count.³³ In another study on patients with community-acquired pneumonia, NLR outperformed PSI, CURB-65, C-reactive protein and white blood cell count in predicting 30-day mortality and prognosis, and predicted early discharge of individuals with NLR lower than 11.12, short-term hospitalization for those with an NLR between 11.12 and 13.4, intermediate hospitalization for those with an NLR between 13.4 and 28.3, and hospitalization in the respiratory intensive care unit for those with an NLR greater than 28.3.³⁴ In another study comparing the NLR, platelet-to-lymphocyte ratio (PLR) and CRP results of 411 patients with COVID-19, it was emphasized that NLR gave more significant results for disease prognosis and mortality compared to both PLR and CRP, NLR and CRP were elevated at the onset of COVID-19 disease, NLR remained elevated despite the decrease in CRP in later times and this may be important for progressive disease. In addition stated that NLR can largely predict the risk of ICU admission, there is an inverse relationship between NLR and PO₂/FiO₂, but high NLR and low PO₂/FiO₂ ratio are complementary steps in the same pathophysiological process and may be a warning for clinical deterioration.³⁵ Early in the pandemic, Tatum et al., in a cohort study of 125 patients, found that NLR may be a prognostic factor for endotracheal intubation on hospital admission and an independent predictor of mortality risk in SARS-CoV-2 patients on subsequent hospital days.³⁶ In COVID-19, Cai J. et al. found that NLR can be used as a very practical and cost-effective parameter for risk stratification during hospitalization and that there was no significant association between corticosteroid treatment and mortality at NLR values of 6.11 and below (less inflammation).³⁷ A retrospective prognosis study of 2648 COVID-19 patients with routine laboratory data, CRP, INR, PT, Procalcitonin, ESR, Troponin, Lymphocyte/CRP ratio, d-CL (CRP times Lymphocyte), d-CI (CRP times INR), d-CT (CRP times Troponin), d-TI (Troponin times INR), They found d-PPT (PT times Procalcitonin times Troponin) and d-CIT (CRP times INR times Troponin) values to be significant in the diagnosis of the disease, and d-CWL (CRP divided by WBC times LYM) and d-CFL (CRP times Ferritin divided by LYM) biomarkers to be the most important risk factors in the diagnosis of the disease.³⁸ Furthermore, these biomarkers were found to be more successful in determining the diagnosis of the disease than direct blood values and previously used biomarkers (NLR, d-NLR, PLR, LCR) (AUC: 77.8%, ACC: 77.6% for d-CWL, AUC: 78.3%, ACC: 79.54% for d-CFL). The d-CIT, d-CT, and d-PPT derived from these blood values were found to be more successful than direct blood values and previously used biomarkers in identifying patients for ICU admission (disease prognosis).³⁸ Vuillaume et al. reported that the lymphocyte/CRP ratio (LCR) of 1035 COVID-19 patients hospitalized in the emergency department was significantly lower in the group with severe disease compared to moderate disease and that LCR may be a predictive marker for severe forms of COVID-19.³⁹ In another study comparing LCR with traditional inflammatory markers to evaluate the clinical applicability of LCR in hospitalized patients with COVID-19, they reported that LCR was characterized by similar results to CRP in predicting mortality and composite endpoints, while it provided better prediction in lymphocyte, platelet and white cell counts. LCR cut-off values of 58 and below were associated with a worse prognosis.⁴⁰ In our study, it was found that there was a weak negative correlation between CLR and PO₂/FiO₂, a weak positive correlation between CLR and NLR, and a weak negative correlation between NLR and PO₂/FiO₂. The optimum cut-off value for CLR was found to be 103.05, and the mortality risk was found to be 1.84 times higher in individuals with values above this value. We found that the sensitivity and specificity of the validity results for this cut-off value of CLR were 75.3 and 51.9%, respectively, and the area under the curve was significant (AUC = 0.684). Based on these results, we suggest that CLR can be used as a moderate-power diagnostic test to predict clinical progression and mortality in ICU patients with COVID-19.

A meta-analysis examining the efficacy of tocilizumab, sarilumab, and anakinra in COVID-19 found that, overall, immunosuppressants significantly reduced mortality, had no significant effect on the increased risk of secondary infection, and tocilizumab treatment significantly increased the risk of fungal co-infection in COVID-19 patients.⁴¹ Another recent review of five randomized controlled trials of the efficacy of anakinra treatment in hospitalized COVID-19 patients, including a total of 1627 patients, showed that anakinra had no significant effect on mortality, clinical improvement, or deterioration in hospitalized adult patients with SARS-CoV-2 infection, and had little positive effect on safety outcomes compared to placebo or standard care alone.⁴² In our study, CLR at hospitalization was found to be higher in patients who received anakinra, required HFO-NIV-IMV, underwent plasmapheresis, and whose disease process resulted in death. In particular, patients with a cut-off value of 103.05 and above for CLR were found to be at higher risk in terms of mortality. Another important finding was that patients treated with favipiravir, tocilizumab, anakinra, immunoplasma, and plasmapheresis had significantly longer hospital stays compared to those who did not receive treatment. This was attributed to the fact that these drugs are preferred for critically ill patients and therefore the length of hospital stay is prolonged.

Limitations

The study has several limitations, including being a retrospective, single-center study with a short follow-up period and a patient group limited to patients admitted to tertiary critical care units who were typically older and had multiple comorbidities. As a result, not the complete population is represented in the study. An additional constraint on the research is that all patients admitted to the intensive care unit within the pertinent timeframe were incorporated into the study without any computation of the chosen sample size. The inability to quantify IL-1 and IL-6 levels, crucial indicators of the cytokine storm phase, is one of the study’s shortcomings. So that the management was solely based on clinical-radiological data. The limitation regarding IVIG results is that it was administered to a limited number of patients according to the preference of the responsible clinician due to local facilities and regional social security institution.

Conclusion

CLR with its affordability, convenience of use, and ability to detect inflammation, is a valuable, laboratory parameter for disorders like COVID-19 that are primarily caused by viral-induced systemic inflammation. High CLR (particularly those beyond the cut-off value) increases the risk of mortality, hence patients should be regularly followed for clinical progression and promptly reviewed for therapy modifications. Additionally, IVIG treatment may be helpful in certain patients due to its high clinical success rate, especially for critically ill patients in intensive care. Nevertheless, multicenter observational studies including a larger sample size are required to corroborate the results we found in our investigation.

Footnotes

Author’s note

The study was performed in Cukurova University, Faculty of Medicine, Adana, Turkey.

Authors’ contributions

EG, BM, OBT have made significant contributions to the working concept and design. EG, BM, OBT, NAA, AF, YB, SB, AOY, EO contributed to the data’s collection, analysis, and interpretation. EG, BM, OBT, EO have written the piece or significantly edited it, accepted the submitted version, and agreed to be held personally liable for their contributions. All authors have participated to drafting the manuscript, author EO revised it critically. All authors read and approved the final version of the manuscript.

All authors contributed equally to the manuscript and read and approved the final 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) received no financial support for the research, authorship, and/or publication of this article.

Ethical approval

Our study was approved by Çukurova University Ethics Committee with the date of December 8, 2023 (40/139).

Our study has not been published in any journal before.

Ethics approval

Ethical approval for this study was obtained from Non-Interventional Clinical Research Ethics Committee of Cukurova University Faculty of Medicine on December 8, 2023 (40/139) and was in accordance with 1964 Declaration of Helsinki and Hospital ethical guidelines).

Informed consent

Written informed consent was obtained from all subjects or legally authorized representatives before the study.

Trial registration

Not applicable.

ORCID iDs

Efraim Guzel

Burak Mete

References

World Health Organization (2023) WHO coronavirus (COVID-19) dashboard global situation. Available at: https://data.who.int/dashboards/covid19/cases?n=c/ (accessed on 25 December 2023).

Sharma

Ahmad Farouk

Lal

(2021) COVID-19: A review on the novel coronavirus disease evolution, transmission, detection, control and prevention. Viruses 13(2): 202.

Rahman

Montero

MTV

Rowe

, et al (2021) Epidemiology, pathogenesis, clinical presentations, diagnosis and treatment of COVID-19: A review of current evidence. Expert Review of Clinical Pharmacology 14(5): 601–621.

Buonacera

Stancanelli

Colaci

, et al (2022) Neutrophil to lymphocyte ratio: An emerging marker of the relationships between the immune system and diseases. International Journal of Molecular Sciences 23(7): 3636.

McGoogan

(2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese center for disease control and prevention. JAMA 323(13): 1239–1242.

Chen

Cai

, et al (2020) Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Internal Medicine 180(7): 934–943.

Kouhpayeh

(2022) Clinical features predicting COVID-19 mortality risk. European Journal of Translational Myology 32(2): 10268.

Gavriatopoulou

Ntanasis-Stathopoulos

Korompoki

, et al (2021) Emerging treatment strategies for COVID-19 infection. Clinical and Experimental Medicine 21(2): 167–179.

Drożdżal

Rosik

Lechowicz

, et al (2021) An update on drugs with therapeutic potential for SARS-CoV-2 (COVID-19) treatment. Drug Resistance Updates 59: 100794.

10.

Niknam

Jafari

Golchin

, et al (2022) Potential therapeutic options for COVID-19: An update on current evidence. European Journal of Medical Research 27(1): 6.

11.

Patton

Orihuela

Harrod

, et al (2023) COVID-19 bacteremic co-infection is a major risk factor for mortality, ICU admission, and mechanical ventilation. Critical Care 27(1): 34.

12.

Hermans

AJH

Laarhuis

Kouw

IWK

, et al (2023) Current insights in ICU nutrition: Tailored nutrition. Current Opinion in Critical Care 29(2): 101–107.

13.

Kowsar

Rahimi

Sroka

, et al (2023) Risk of mortality in COVID-19 patients: A meta- and network analysis. Scientific Reports 13(1): 2138.

14.

Knaus

Draper

Wagner

, et al (1958) APACHE II: A severity of disease classification system. Critical Care Medicine 13(10): 818–829.

15.

Charlson

Pompei

Ales

, et al (1987) A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases 40(5): 373–383.

16.

Heyland

Dhaliwal

Jiang

, et al (2011) Identifying critically ill patients who benefit the most from nutrition therapy: The development and initial validation of a novel risk assessment tool. Critical Care 15(6): R268.

17.

Singer

Deutschman

Seymour

, et al (2016) The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315(8): 801–810.

18.

Raith

Udy

Bailey

, et al (2017) Australian and New Zealand Intensive Care Society (ANZICS) centre for outcomes and resource evaluation (CORE). Prognostic accuracy of the SOFA score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA 317(3): 290–300.

19.

Republic of Turkey Ministry of Health COVID-19 information platform. https://covid19.saglik.gov.tr/TR-66926/eriskin-hasta-tedavisi.html. (accessed 5 January 2024)

20.

Zhang

Dong

Liu

, et al (2023) Risk and protective factors for COVID-19 morbidity, severity, and mortality. Clinical Reviews in Allergy & Immunology 64(1): 90–107.

21.

Wiersinga

Rhodes

Cheng

, et al (2020) Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): A review. JAMA 324(8): 782–793.

22.

Karimi

Shobeiri

Kulasinghe

, et al (2021) Novel systemic inflammation markers to predict COVID-19 prognosis. Frontiers in Immunology 12: 741061.

23.

Cheng

Zuo

, et al (2020) Predictors of progression from moderate to severe coronavirus disease 2019: A retrospective cohort. Clinical Microbiology and Infection 26(10): 1400–1405.

24.

Guzel

Aydin

Baydar Toprak

(2023) Prediction of mortality and the development of critical illness in the course of COVID-19 with tertiary hospital data: Vaccines? Critical illness scores? Mortality scores? European Review for Medical and Pharmacological Sciences 27(12): 5893–5908.

25.

Demirkol

Bilgin

Kahveci

, et al (2022) C-reactive protein-to-lymphocyte ratio is a reliable marker in patients with COVID-19 infection: The clear COVID study. Cirugía y cirujanos 90(5): 596–601.

26.

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. Clinical Chemistry and Laboratory Medicine 58(7): 1021–1028.

27.

Sun

Cai

Wang

, et al (2020) Abnormalities of peripheral blood system in patients with COVID-19 in Wenzhou, China. Clinica Chimica Acta 507: 174–180.

28.

Fan

Chong

VCL

Chan

SSW

, et al (2020) Hematologic parameters in patients with COVID-19 infection. American Journal of Hematology 95(6): E131–E134.

29.

Chen

, et al (2021) Clinical laboratory evaluation of COVID-19. Clinica Chimica Acta 519: 172–182.

30.

Battaglini

Lopes-Pacheco

Castro-Faria-Neto

, et al (2022) Laboratory biomarkers for diagnosis and prognosis in COVID-19. Frontiers in Immunology 13: 857573.

31.

Laforge

Elbim

Frère

, et al (2020) Tissue damage from neutrophil-induced oxidative stress in COVID-19. Nature Reviews Immunology 20(9): 515–516.

32.

Liao

Zhou

Luo

, et al (2020) Haematological characteristics and risk factors in the classification and prognosis evaluation of COVID-19: A retrospective cohort study. The Lancet Haematology 7(9): e671–e678.

33.

de Jager

Wever

Gemen

, et al (2012) The neutrophil-lymphocyte count ratio in patients with community-acquired pneumonia. PLoS One 7(10): e46561.

34.

Cataudella

Giraffa

Di Marca

, et al (2017) Neutrophil-to-lymphocyte ratio: An emerging marker predicting prognosis in elderly adults with community-acquired pneumonia. Journal of the American Geriatrics Society 65(8): 1796–1801.

35.

Regolo

Vaccaro

Sorce

, et al (2022) Neutrophil-to-lymphocyte ratio (NLR) is a promising predictor of mortality and admission to intensive care unit of COVID-19 patients. Journal of Clinical Medicine 11(8): 2235.

36.

Tatum

Taghavi

Houghton

, et al (2020) Neutrophil-to-lymphocyte ratio and outcomes in Louisiana COVID-19 patients. Shock 54(5): 652–658.

37.

Cai

Zhang

, et al (2021) The neutrophil-to-lymphocyte ratio determines clinical efficacy of corticosteroid therapy in patients with COVID-19. Cell Metabolism 33(2): 258–269.e3.

38.

Huyut

İlkbahar

(2021) The effectiveness of blood routine parameters and some biomarkers as a potential diagnostic tool in the diagnosis and prognosis of Covid-19 disease. International Immunopharmacology 98: 107838.

39.

Abensur Vuillaume

Lefebvre

Benhamed

, et al (2023) CREMS network (clinical research in emergency medicine and sepsis). Lymphocyte-to-C-reactive protein (LCR) ratio is not accurate to predict severity and mortality in patients with COVID-19 admitted to the ED. International Journal of Molecular Sciences 24(6): 5996.

40.

Liu

Hammond

Chan

, et al (2023) Comparison of lymphocyte-CRP ratio to conventional inflammatory markers for predicting clinical outcomes in COVID-19. Journal of Personalized Medicine 13(6): 909.

41.

Peng

Mei

, et al (2022) Efficacy and secondary infection risk of tocilizumab, sarilumab and anakinra in COVID-19 patients: A systematic review and meta-analysis. Reviews in Medical Virology 32(3): e2295.

42.

Dahms

Mikolajewska

Ansems

, et al (2023) Anakinra for the treatment of COVID-19 patients: A systematic review and meta-analysis. European Journal of Medical Research 28(1): 100.

Use of CRP/lymphocyte ratio as a predictor of treatment selection and mortality in COVID-19 patients in the intensive care unit

Abstract

Keywords

Introduction

Methodology

Study design

Participants

Variables

Data source/measurement

Acute physiology and chronic health evaluation (APACHE) II score

Charlson comorbidity index (CCI)

Modified nutrition risk in critically Ill patients (mNUTRIC) score

Sequential organ failure assessment score (SOFA score)

Management and treatment of patients

Statistical analysis

Results

Discussion

Limitations

Conclusion

Footnotes

Author’s note

Authors’ contributions

Declaration of conflicting interests

Funding

Ethical approval

Ethics approval

Informed consent

Trial registration

ORCID iDs

References