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\begin{document}

\title{Neutrophil/Lymphocyte Ratio -- A Marker of COVID-19 Pneumonia Severity}



\author[1]{Mehar Muhammad  Imran}%
\author[2]{Umair Ahmed}%
\author[1]{Umer Usman}%
\author[3]{Majid Ali}%
\author[1]{Aamir Shaukat}%
\author[1]{Noor Gul}%
\affil[1]{District Headquarter Hospital}%
\affil[2]{Allied Hospital}%
\affil[3]{Umm Al-Qura University College of Pharmacy}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Aim: To determine the efficacy of neutrophil/lymphocyte ratio (NLR) as a
marker of the severity of COVID-19 pneumonia in the South-Asian
population. Methods: This was a prospective, cross-sectional, analytic
study conducted at HDU/ICU of District Headquarter Hospital, Faisalabad,
Pakistan, from May through July 2020. Sixty-three eligible patients,
admitted to the HDU/ICU, were prospectively enrolled in the study. Their
NLR, C-reactive protein, serum albumin, and serum fibrinogen were
measured. Patients' demographic characteristics, comorbidities, clinical
manifestations of COVID-19 infection, medication use, and history of
lung malignancy were retrieved from their medical history. Patients were
categorized into either a general group (with mild COVID-19) or a heavy
group (with moderate to severe COVID-19). Results: There were
significant differences between the two groups in diabetes prevalence,
NLR, C-reactive protein, and serum albumin. NLR and C-reactive protein
were positively correlated (P \textless{} 0.001, P = 0.04 respectively)
whereas serum albumin was negatively correlated (P = 0.009) with severe
COVID-19. NLR was found to be an independent risk factor for severe
COVID-19 pneumonia in the heavy group (OR = 1.264, 95\% CI:
1.046\textasciitilde{}1.526, P = 0.015). The calculated AUC using ROC
for NLR was 0.831, with an optimal limit of 4.795, sensitivity of 0.83
and specificity of 0.75, which is highly suggestive of NLR being a
marker for early detection of deteriorating severe COVID-19 infection.
Conclusion: NLR can be used as an early warning signal for deteriorating
severe COVID-19 infection and can provide an objective basis for early
identification and management of severe COVID-19 pneumonia.%
\end{abstract}%



\sloppy




\textbf{What is already known about this subject?}

\begin{itemize}
\tightlist
\item
  Patients with Severe COVID-19 infection have a high mortality rate.
\item
  Neutrophil/lymphocyte ratio (NLR) may be used as an early warning
  signal of severe and deteriorating COVID-19 infection and can help the
  physicians to intervene and manage the patients ' clinical situation
  early.
\end{itemize}

\textbf{What does this study contribute to the literature?}

\begin{itemize}
\tightlist
\item
  Our study further adds to the evidence that NLR has been associated
  with severe and deteriorating COVID-19 infection in the South-Asian
  population also.
\item
  It can be used as an early warning sign in this population and provide
  an objective basis for early identification and management of severe
  COVID-19 pneumonia.
\end{itemize}

\textbf{1 INTRODUCTION}

The new coronavirus disease 2019 (COVID-19), caused by severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2), is a highly
communicable infectious disease. It has rapidly and widely spread
worldwide,\textsuperscript{1} and was declared a pandemic in March 2020
by World Health Organization.\textsuperscript{2} As of
27\textsuperscript{th} August 2020, the cumulative number of confirmed
cases in Pakistan is 294,193 with 8,987 active cases and 6,267
deaths.\textsuperscript{3}

Despite significant advancements in medicine and science, we are still
in the age of infancy regarding COVID-19 infection and its management.
COVID-19 infection can be categorized as asymptomatic, mild, moderate,
or severe disease. Moderate and severe COVID-19 infections have been
associated with long hospital stays, difficult clinical management and
high mortality rate.\textsuperscript{4} Clinically, if early warning
signs of severe COVID-19 infection can be identified, timely
intervention and treatment may help reduce the mortality, improve the
cure rate, shorten the hospital stay and reduce the consumption of
resources.

Different immunity parameters in the human body have been identified to
combat the disease.\textsuperscript{5,6} These tests are often expensive
and time delay occurs in the results of these tests, jeopardizing the
patient's treatment. Therefore, we ought to have a clinical indicator or
marker that is inexpensive, has a rapid turnaround time, and is
simultaneously specific and sensitive. One such marker is a complete
blood count (CBC) profile, out of which neutrophils and lymphocytes are
especially important. The neutrophils are increased in bacterial
infection, and lymphocytes are reduced during viremia. Examining these
two parameters can greatly help assess the COVID-19 infection.

A recent systematic review and metanalysis by Feng \emph{et
al.}regarding immune-inflammatory parameters in COVID-19 infection
concluded that neutrophil/lymphocyte ratio (NLR) is associated with the
progression of the infection and can be utilized by the physicians to
identify high risk or deteriorating patients at an early
stage.\textsuperscript{7} Several other studies have reported their
findings that NLR can be used as an early warning signal of severe
COVID-19 infection,\textsuperscript{8} and cohnsidered as an independent
marker for poor clinical outcomes and mortality in COVID-19
infection.\textsuperscript{9-13} However, the majority of these studies
have been reported from China and some from European countries. To our
knowledge, no study has been conducted in this regard in the South-Asian
population. Our study aimed to prospectively analyze the clinical data
of COVID-19 patients admitted to the high dependency unit/intensive care
unit (HDU/ICU) at District Headquarter (DHQ), Faisalabad (Pakistan) in
order to determine the risk factors and markers, with particular focus
on NLR, associated with severity of COVID-19 infection in the
South-Asian population.

\textbf{2. METHODS}

\textbf{2.1 Study design}

This was a prospective, cross-sectional, analytic study conducted at
HDU/ICU DHQ Hospital, Faisalabad (Pakistan), from May through July 2020.
This facility was designated as a referral center by the provincial
health department to cater to approximately 13 million population from
the surrounding districts. The patients from these districts were
referred to this facility for the management of COVID-19 patients.

\textbf{2.2 Sample}

We screened all the hospitalized COVID-19 patients who were admitted to
HDU/ICU of DHQ Hospital, for eligibility, based on the inclusion and
exclusion criteria listed below.

\textbf{2.3 Inclusion criteria}

Following patients were considered for enrolment in the study:

\begin{enumerate}
\tightlist
\item
  Adult patients age \emph{\textgreater{}} 18 years.
\item
  All the~patients who meet the diagnostic criteria of the National
  Institute of Health (NIH) Pakistan for COVID-19
  infection,\textsuperscript{14} which are as follows:
\end{enumerate}

\begin{enumerate}
\tightlist
\item
  Clinical manifestations that include fever and pulmonary symptoms
  (cough, shortness of breath, chest pain, and tightness).
\item
  Radiological findings of consolidation, ground-glass opacities (GGOs)
  either on chest X-ray or high-resolution computed tomography (HRCT).
\item
  Real-time fluorescent reverse transcription-polymerase chain reaction
  (RT-PCR) of respiratory samples (nasal/oropharyngeal swab or tracheal
  secretions) positive for SARS-CoV-2.
\end{enumerate}

After being enrolled in the study, the eligible patients were then
segregated into the following two groups, based on diagnostic criteria
and classification criteria of the NIH Pakistan:\textsuperscript{14}

\begin{enumerate}
\tightlist
\item
  The `general group' that includes asymptomatic and mild cases.
\item
  The `heavy group' that includes moderate and severe cases.
\end{enumerate}

The following criteria were used to define the two groups.

\textbf{2.4 General group diagnostic criteria}

Patients presented with fever, respiratory symptoms, and radiological
findings consistent with COVID-19 infection.

\begin{enumerate}
\tightlist
\item
  Respiratory rate \emph{\textless{}} 30/min at rest.
\item
  Oxygen saturation (SpO\textsubscript{2}) \emph{\textgreater{}} 93\% on
  room air.
\item
  Arterial oxygen partial pressure (PaO\textsubscript{2})/oxygen uptake
  concentration (FiO\textsubscript{2}) \emph{\textgreater{}} 300.
\end{enumerate}

\textbf{2.5 Heavy group diagnostic criteria}

Patients presented with general group diagnostic criteria and any one of
the following:

\begin{enumerate}
\tightlist
\item
  Respiratory rate [?] 30/min at rest.
\item
  Oxygen saturation (SpO2) [?] 93\% on room air.
\item
  Arterial oxygen partial pressure (PaO\textsubscript{2})/oxygen uptake
  concentration (FiO\textsubscript{2})
  {[}PaO\textsubscript{2}/FiO\textsubscript{2}{]} (PF ratio) [?] 300.
\end{enumerate}

\textbf{2.6 Severe disease criteria}

Patients from any group presented with any one of the following were
categorized to have a severe disease:

\begin{enumerate}
\tightlist
\item
  Oxygen requirement of more than 10 liters for 90\% saturation.
\item
  50\% of lung involvement on either chest X-ray or HRCT.
\item
  Multi-organ dysfunction
\item
  CRP \textgreater{} 10 mg/L, D-dimer \textgreater{} 1000 mg/mL, serum
  ferritin \textgreater{} 1000 ng/mL.
\item
  Secondary infection (diagnosed by blood culture and sensitivity test
  or raised procalcitonin).
\item
  Shock or use of inotropes.
\item
  {[}PaO2/FiO2{]} (PF ratio) \textless{} 118 mmHg.
\end{enumerate}

\textbf{2.7 Exclusion criteria}

Following patients were excluded from the study:

\begin{enumerate}
\tightlist
\item
  Patients with negative detection of novel coronavirus nucleic acid.
\item
  Patients in whom monitoring of blood oxygen saturation
  (SpO\textsubscript{2} or PaO\textsubscript{2}) was not feasible due to
  any reason.
\item
  Patients with suspected bacterial pneumonia (confirmed by sputum
  bacterial culture).
\item
  Patients with interstitial pneumonia (previously diagnosed based on
  radiological findings).
\item
  Patients with heart failure associated with pulmonary edema
  (non-COVID-19 heart disease).
\item
  Patients with allergic pneumonia (acute or chronic eosinophilic
  pneumonia).
\item
  Patients with active lung tumors.
\item
  Patients on immunosuppressive drugs (including long-term steroids).
\item
  Patients with incomplete chest computed tomography (CT) examination
  due to any reason.
\item
  Patient refusal to be enrolled in the study.
\end{enumerate}

\textbf{2.8 Sample size}

A total of 106 cases were screened for eligibility based on the
inclusion and exclusion criteria. Sixty-three patients were found to be
eligible and enrolled in the study.

\textbf{2.9 Data collection and procedure}

After taking informed consent from the enrolled patients, their blood
sample was drawn according to standard operating procedures within 24
hours of admission and sent to the laboratory for routine laboratory
tests that included complete blood count (CBC), liver function tests
(LFTs), renal functional tests (RFTs) and serum electrolytes. CBC was
analyzed using the hospital laboratory system, 'Sysmex analyzer'. The
inflammatory markers such as neutrophil/lymphocyte ratio (NLR),
C-reactive protein (CRP), serum albumin (ALB), and serum fibrinogen
(FIB) were also ordered. The patients were then sent for a chest X-ray.
HRCT chest scan was conducted on the advice of the respiratory
physicians.

NLR ratio was calculated using the simple formula:

If a patient belonged to the heavy group, additional investigations
(D-dimer level and serum ferritin) were also ordered. Where indicated,
their sputum or blood culture, or serum procalcitonin was also ordered.

Simultaneously, patient demographic characteristics (gender and age),
comorbidities (hypertension (HTN), diabetes mellitus (DM), coronary
heart disease (CHD), lung diseases such as asthma or chronic obstructive
pulmonary disease (COPD)), clinical manifestations of COVID-19 (fever,
chest pain, cough and shortness of breath), medication history
(including the use of long-term steroid), and history of lung malignancy
were recorded on a data collection form.

\textbf{2.10 Ethical approval}

The study was reviewed and approved by the DHQ hospital research ethics
committee.

\textbf{2.11 Data analysis}

The data was analyzed using Statistical Package for Social Sciences
(SPSS) version 25.~For comparison of the measurements between the two
groups, independent samples~\emph{t} -test was used for the data that
met normal distribution, and the Mann-Whitney U test was used for the
data that did not meet normal distribution. Chi-square test was used for
comparison of categorical data between the groups; Pearson correlation
test was used for linear correlation analysis; Spearman correlation test
was used for rank correlation analysis;~\emph{Z} ~test~was used for the
area under Receiver Operating Characteristic (ROC) curve (AUC-ROC)
comparison; multiple logistic regression was used for regression
analysis. P values of less than 0.05 were considered statistically
significant unless otherwise stated.

\textbf{3. RESULTS}

\textbf{3.1 Demographic characteristics}

A total of 63 patients met the inclusion and exclusion criteria and
consented to be enrolled in the study, out of whom 32 patients were in
the general group and 31 patients in the heavy group. The average age of
the patients in the general group was 62.25\selectlanguage{ngerman}±15.07 years with 15 male
patients, whereas the average age of the patients in the heavy group was
64.55±14.88 years with 18 male patients. The differences in age and
gender between the two groups were not found to be statistically
significant (P~\textgreater{} 0.05).

\textbf{3.2 Comorbidities, clinical manifestations and laboratory
parameters}

As presented in Table 1, the difference in diabetes prevalence between
the two groups was statistically significant (P \textless{} 0.05).
However, the difference in HTN, CHD, lung malignancy, and other lung
diseases was not statistically significant (P ~\textgreater{} 0.05).~As
shown in Table 2, no statistically significant difference in cough,
fever, chest pain, tightness, and breathlessness was found between the
two groups (P~\textgreater{} 0.05). Moreover, no statistically
significant difference in the FIB index was seen between the two groups
(P~\textgreater{} 0.05). However, significantly increased NLR and CRP,
and significantly decreased ALB were observed in the heavy group
(P~\textless{} 0.05) as compared to the general group (Table 3).

\textbf{3.3 Correlation analysis and regression analysis}

Correlation analysis showed that diabetes, NLR, CRP and ALB had a
significant correlation with severe COVID-19 pneumonia in the heavy
group (P \textless{} 0.05) (Table 4). Diabetes, NLR and CRP were
positively correlated, whereas ALB was negatively correlated with severe
COVID-19 pneumonia in the heavy group. Further, multi-factor logistic
regression analysis of diabetes, NLR, CRP, ALB and severe COVID-19
pneumonia found that NLR was an independent risk factor for severe
COVID-19 pneumonia in the heavy group (OR = 1.264, 95\% CI:
1.046\textasciitilde{}1.526, P = 0.015) (Table 5).

\textbf{3.4 NLR prediction of heavy group COVID-19 pneumonia and
critical value judgment}

Using the ROC curve to calculate the area under the NLR curve, the
results showed that: AUC was 0.831 (95\%~CI
0.730\textasciitilde{}0.932), the Youden index was the largest 0.589,
corresponding to the best cut-off value of 4.795, the sensitivity was
0.839, and the specificity was 0.750 (Figure 1).

\textbf{4. DISCUSSION}

The results of our study show that the two groups of patients had a
significant difference in diabetes prevalence, NLR, CRP, and ALB. There
was a positive correlation~between~diabetes, NLR, CRP and severe
COVID-19 infection. However, ALB had a negative correlation with severe
COVID-19 infection. Moreover, NLR was found to be an independent risk
factor for severe COVID-19 infection.

Neutrophils are one of the human body's vital immune cells. When
pathogenic microorganisms invade the body, immune cells tend to rapidly
chemotactically gather to the infection site and play the role of host
defense and immune regulation.\textsuperscript{15}~When the body's
neutrophils are significantly reduced, the body's immunity is
compromised, and thus the risk of infection is significantly
increased.\textsuperscript{16}~Lymphocytes are the main effector cells
of the human immune response. The number of lymphocytes in the body is
closely related to the body's immunity and defense system against
pathogenic microorganisms and is negatively correlated with the degree
of inflammation.\textsuperscript{9}~NLR encompasses two types of
leukocyte subtypes, reflecting the balance of the body's neutrophil and
lymphocyte count levels and the degree of systemic inflammation. More
accurately, it reflects the balance between the severity of the
inflammation and the body's immunity status,\textsuperscript{17} and is
thus considered an important marker of systemic inflammatory response.
Based on this, we speculated that heavy COVID-19 infection can have
significant systemic inflammation and that NLR may have a role in
predicting the infection's severity.

The outbreak of COVID-19 is caused by the SARS-CoV-2 virus infection.
The disease spectrum varies from no symptoms to severe disease and
death.~Clinical observation has found that some patients with mild
disease progress to severe disease within a short period of time with a
high risk of mortality. The specific pathological mechanism is
unknown.~Some believe that the sudden aggravation of the condition is
due to the rapid emergence of ARDS, and subsequent multiple-organ
dysfunction in the later period which may be related to the ''cytokine
release storm''.\textsuperscript{18}~The concept of an inflammatory
factor storm was first proposed in 1993.\textsuperscript{19} In 2003,
the SARS-CoV virus infection caused ARDS and multiple-organ failure,
resulting in an exceedingly high rate of mortality -- the underlying
pathology of inflammatory factor storm that gradually attracted the
attention of the medical community.\textsuperscript{20}~The SARS-CoV-2
virus is extremely closely related to the SARS-CoV virus. In the virus
spectrum, both belong to \selectlanguage{greek}β-\selectlanguage{english}CoV coronavirus~family,\textsuperscript{21}in
terms of genetic characteristics, both have 79.5\% sequence
similarity,\textsuperscript{22} and with regards to clinical
manifestations, it is easy to progress to ARDS and multiple-organ
dysfunction in infections caused by these viruses.~Therefore, based on
the close similarities between the two viruses, COVID-19 patients'
clinical condition changes from mild to critical, which may be related
to the inflammatory factor storm. Li \emph{et al} found in their study
that the patients with COVID-19 pneumonia admitted to ICU had higher
levels of inflammatory markers such as IL2, IL7, IL10, GSCF, IP10, MCP1,
MIP1A, and TNF\selectlanguage{greek}α \selectlanguage{english}in the plasma, reflecting severe
inflammation.\textsuperscript{23} This is due to an apparent
inflammatory response in the patient's body, which is consistent with
our findings.

In summary, our study conducted a prospective analysis of common
clinical risk factors or parameters that can be easily obtained from the
laboratory. Our study found that there was a significant difference in
NLR between general and heavy group COVID-19 patients with heavy group
patients tending to have significantly higher NLR. In addition to NLR
being an independent risk factor for severe COVID-19 infection, the ROC
curve calculation of AUC showed a high predictive value of NLR for
severe COVID-19 infection.~As per these results, we believe that
increasing NLR can be used as a warning signal for rapidly deteriorating
COVID-19 infection and can provide us with a certain objective basis for
the early identification of severe COVID-19 pneumonia. This can further
allow us to be prepared for the exploration of subsequent treatment
mechanisms.~However, this study had certain limitations, such as having
a small sample size and being a single-center study. For more accurate
and precise results, and wider generalizability of the findings,
multicentred and larger sample size clinical studies are required to
validate our results.

\textbf{5. CONCLUSION}

NLR can be used as an early warning signal for deteriorating severe
COVID-19 infection and can provide an objective basis for early
identification and management of severe COVID-19 pneumonia. This marker
is especially important in our setting, where lack of resources often
prevents costly testing.

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  Med.}2020;382(13):1199-1207. doi:10.1056/NEJMoa2001316
\end{enumerate}

\textbf{TABLE 1} Comorbidities in the two groups of patients\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Comorbidities} & \textbf{General group (n=32)} & \textbf{Heavy
group (n=31)} & \selectlanguage{greek}\textbf{χ\textsuperscript{2}}\selectlanguage{english} & P value\tabularnewline
\midrule
\endhead
Hypertension & 9 & 15 & 2.741 & 0.098\tabularnewline
Diabetes & 3 & 9 & 3.946 & 0.047\tabularnewline
Coronary heart disease & 0 & 2 & NA & 0.238\tabularnewline
Tumor & 1 & 2 & 0.001 & 0.978\tabularnewline
Lung Disease & 2 & 1 & 0 & 1\tabularnewline
\bottomrule
\end{longtable}

NA: Not applicable.

\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}: Chi-square value.

\textbf{TABLE 2} Clinical manifestations in the two groups of patients\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Symptoms} & \textbf{General group (n=32)} & \textbf{Heavy group
(n=31)} & \selectlanguage{greek}\textbf{χ\textsuperscript{2}}\selectlanguage{english} & P value\tabularnewline
\midrule
\endhead
Fever & \emph{26} & \emph{25} & 0.004 & 0.951\tabularnewline
Cough & 22 & 21 & 0.007 & 0.932\tabularnewline
Chest tightness & 9 & 12 & 0.794 & 0.373\tabularnewline
Dyspnea & 14 & 20 & 2.733 & 0.098\tabularnewline
Chest Pain & 19 & 16 & 0.384 & 0.535\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}: Chi-square value.

\textbf{TABLE 3} Mean laboratory test results in the two groups of
patients\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Blood Tests} & \textbf{General group (n~=32)} & \textbf{Heavy
group (n~=31)} & \textbf{t~/~Z} & P value\tabularnewline
\midrule
\endhead
NLR & 2.88 (1.77-5.55) & 8.78 (5.76-25.10) & -4.51 &
\textless{}0.001\tabularnewline
CRP (\selectlanguage{greek}μ\selectlanguage{english}g/L) & 26.25 (7.40-75.15) & 81.49 (19.40-107.42) & -2.104 &
0.035\tabularnewline
FIB (g/L) & 3.58 (3.08-3.99) & 3.74 (2.91-4.42) & -0.653 &
0.514\tabularnewline
ALB (mg/L) & 36.16\selectlanguage{ngerman}±4.64 & 33.23\selectlanguage{ngerman}±3.98 & -2.688 & 0.009\tabularnewline
\bottomrule
\end{longtable}

NLR: Neutrophil/lymphocyte ratio; CRP: C-reactive protein; ALB: Albumin.

t: t score

Z: Z score

\textbf{TABLE 4} Correlation of diabetes, NLR, CRP, ALB with severe
COVID-19\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Statistics} & \textbf{Diabetes} & \textbf{NLR} & \textbf{CRP} &
\textbf{ALB}\tabularnewline
\midrule
\endhead
\emph{R value} & 0.25 & 0.5 & 0.27 & -0.33\tabularnewline
\emph{P value} & 0.048 & \textless{}0.001 & 0.04 & 0.009\tabularnewline
\bottomrule
\end{longtable}

NLR: Neutrophil/lymphocyte ratio;

CRP: C-reactive protein; ALB: Albumin.

\textbf{TABLE 5} Regression analysis of risk factors for severe COVID-19\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Risk factors} & P value & \textbf{OR} & \textbf{OR (95\%~CI)} &
\textbf{OR (95\%~CI)}\tabularnewline
\midrule
\endhead
& & & Lower limit & Upper limit\tabularnewline
Diabetes & 0.191 & 0.305 & 0.052 & 1.804\tabularnewline
NLR & 0.015 & 1.264 & 1.046 & 1.526\tabularnewline
CRP & 0.891 & 0.999 & 0.981 & 1.017\tabularnewline
ALB & 0.868 & 0.987 & 0.841 & 1.157\tabularnewline
\bottomrule
\end{longtable}

OR: Odds ratio; NLR: Neutrophil/lymphocyte ratio;

CRP: C-reactive protein; ALB: Albumin

\textbf{FIGURE 1} NLR prediction of severe COVID-19\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

