Hematological and biochemical parameters as diagnostic and prognostic markers in SARS-COV-2 infected patients of Pakistana retrospective comparative analysisKhalid, Atiqa; Ali Jaffar, Muhammad; Khan, Tabinda; Abbas Lail, Raees; Ali, Sana; Aktas,Gulali; Waris, Abdul; Javaid, Amnah; Ijaz, Nouman; Muhammad, Nasir

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Publication details:Khalid, A., Ali Jaffar, M., Khan, T., Abbas Lail, R., Ali, S., Aktas, G., Waris, A., Javaid, A., Ijaz, N., & Muhammad,N. (2021). Hematological and biochemical parameters as diagnostic and prognostic markers in SARS-COV-2infected patients of Pakistan: a retrospective comparative analysis. Hematology, 26(1), 529-542.https://doi.org/10.1080/16078454.2021.1950898

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Hematological and biochemical parameters asdiagnostic and prognostic markers in SARS-COV-2infected patients of Pakistan: a retrospectivecomparative analysis

Atiqa Khalid, Muhammad Ali Jaffar, Tabinda Khan, Raees Abbas Lail,Sana Ali, Gulali Aktas, Abdul Waris, Amnah Javaid, Nouman Ijaz & NasirMuhammad

To cite this article: Atiqa Khalid, Muhammad Ali Jaffar, Tabinda Khan, Raees Abbas Lail, Sana Ali,Gulali Aktas, Abdul Waris, Amnah Javaid, Nouman Ijaz & Nasir Muhammad (2021) Hematologicaland biochemical parameters as diagnostic and prognostic markers in SARS-COV-2 infectedpatients of Pakistan: a retrospective comparative analysis, Hematology, 26:1, 529-542, DOI:10.1080/16078454.2021.1950898

To link to this article: https://doi.org/10.1080/16078454.2021.1950898

© 2021 The Author(s). Published by InformaUK Limited, trading as Taylor & FrancisGroup

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Hematological and biochemical parameters as diagnostic and prognosticmarkers in SARS-COV-2 infected patients of Pakistan: a retrospectivecomparative analysisAtiqa Khalid a, Muhammad Ali Jaffar a, Tabinda Khan a, Raees Abbas Lail a, Sana Ali b,Gulali Aktas c, Abdul Waris d, Amnah Javaid a, Nouman Ijaz a and Nasir Muhammad e

aDepartment of Pathology, Sahiwal Medical College Sahiwal, affiliated with University of Health Sciences Lahore, Lahore, Pakistan;bAllama Iqbal Open University Islamabad, Islamabad, Pakistan; cAbbant Izzet Baysal University, Bolu, Turkey; dQuaid-e-Azam UniversityIslamabad, Islamabad, Pakistan; eUniversity of Health Sciences Lahore, Lahore, Pakistan

ABSTRACTObjectives: This study was conducted to investigate alteration in blood parameters and theirassociation with the presence, severity, and mortality of COVID-19 patients as the data onhematological abnormalities associated with the Pakistani COVID-19 patients is limited.Methodology: A double-centered, hospital-based comparative retrospective case study wasconducted, to include all the admitted patients (n = 317) having COVID-19 Polymerase chainreaction (PCR) positive. The control group (n = 157) tested negative for COVID-19.Results: Of 317 admitted cases, the majority were males n = 198 (62.5%). Associatedcomorbidities, lower lymphocytes, platelets, and higher White blood cells, neutrophil,neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were found inCOVID-19 cases as compared to healthy controls (p < 0.001 for all). The biochemicalparameters of cases including Ferritin, D-Dimer, CRP, IL-6, LDH, ALT, AST, and APTT alsoshowed a statistically significant difference compared with standard values (p < 0.001 forall). However, their comparison with a severity level of the severe and non-severe groupsshowed significance for WBCs, neutrophils, NLR (p < 0.001 for all), and PLR (p = 0.06) only.Receiver operating characteristic curve analysis showed that NLR had the highest area undercurve (0.84) followed by 1/lymphocyte (0.82), neutrophils (0.74), PLR (0.67),1/platelets (0.68)and WBC’s (0.65). Comparison of cases and controls with recommended cut-off valuesderived from sensitivity and 1-specificity was also done (p < 0.001).Conclusion: Monitoring all the hematological and biochemical parameters including novelhemograms NLR, PLR can aid clinicians to identify potentially severe cases at early stagesand initiate effective management in time which may reduce the overall mortality of COVID-19 patients.

Abbreviations: ALT, Alanine Aminotransferase; APTT, Activated Partial Thromboplastin Time;AST, Aspartate Aminotransferase; CBCs, complete blood counts; COVID-19, coronavirusdisease 2019; CRP, C-Reactive Protein; GRA, granulocytes; HGB, hemoglobin; LDH, Lactatedehydrogenase; Lymph, lymphocyte; MLR, Monocyte to Lymphocyte ratio.; NLR, Neutrophilto Lymphocyte ratio; PLR, Platelet to lymphocyte ratio; PLTs, platelets count; PT,Prothrombin time; RBCs, red blood cells; SARS-CoV-2, Severe acute respiratory syndromecoronavirus 2; WBCs, white blood cells.

KEYWORDSCOVID-19 SARS-CoV-2;biomarkers; hematologicalparameters; disease severity;diagnostic criteria; cutoffvalues; NLR; PLR

What is already known about this subject?. Hematological and biochemical parameters can

serve as prognostic markers as they show changeswith disease severity depicted in different studies.

. While they can be a source of diagnosing COVID-19infections, not all patients show the samechanges. Different studies show the difference intheir mean values, with values becoming moredispersed as the disease worsened and also in thedeceased patients.

. In other countries, changes show lymphopenia, leu-kocytosis, eosinopenia, and neutrophilia in severely

affected patients, while changes in platelet countwere differently reported in different studies.

. Elevated D-dimers, fibrin degradation products, PT,APTT, ferritin, CRP,NLR, PLR,were reported in the litera-ture, with the values increasing as the diseaseworsens.

What are the new findings?

. In the Sahiwal division, an underdeveloped divisionof Pakistan, this is the first study of its kind reportingchanges in hematological and biochemical par-ameters of patients and disease severity.

© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis GroupThis is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), whichpermits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

CONTACT Atiqa Khalid atiqakkhalid@gmail.comThis article has been republished with minor changes. These changes do not impact the academic content of the article.

HEMATOLOGY2021, VOL. 26, NO. 1, 529–542https://doi.org/10.1080/16078454.2021.1950898

. The data set of Comorbidities (Table 2) gives valu-able information for assessing mortality and severityin patients with comorbid conditions and their age,gender, race, quality of healthcare delivered tothem, and socioeconomic conditions.

. Healthy controls were compared with cases for CBCparameters. Further, biochemical parameters werecompared with normal values, and disease severitywas also determined between cases and controls.

. An optimal cutoff value was recommended for stat-istically significant parameters.

How might it impact clinical practice in theforeseeable future:

. Potential changes in patient’s complete bloodcounts and inflammatory and biochemical markerscan serve as a potential diagnostic tool as they areaccessible and readily available and they canreplace rRT-PCR in those countries which sufferfrom a significant short supply of rRT-PCR reagents.

. CBCs can serve as a very significant laboratoryexamination. Since, their level changes after theonset of disease, they can truly identify the stageof the disease. Additionally, they are a key indicatorto provide information for the diagnosis and treat-ment basis for health professionals.

1. Introduction

The start of 2020 has become a horrifying and unfor-gettable memory because of a novel virus, COVID-19,which abolished the world’s normal lifestyle andhealth condition [1]. The virus responsible for the pan-demic is the SARS-CoV-2 RNA virus (severe acute res-piratory syndrome coronavirus 2). The virus goes topulmonary epithelial cells via surface ACE2 receptors,causing viral pneumonia, followed by a systemicinflammation phase which further goes on to anadvanced stage to develop respiratory failure andmulti-organ dysfunction. To control the rapid trans-mission of the COVID-19 pandemic, diagnostic toolswould be of great value to detect cases accurately[2]. Different molecular techniques were developed,but the healthcare professionals in developingcountries have limited access to these specializedinstruments. Limitations of real-time reverse transcrip-tase-PCR include a long turnaround time, need forexpensive equipment, balancing cost on a largescale, and trained staff, as well as 20% false-negativeresults [3–5]. Hence, there is a need for readily availabletests for large-scale screening to rapidly diagnose thecases and prevent the overwhelming spread of thispandemic [6].

Different biochemical and hemocytometric markershave been used in routine practice as changes in thesemarkers have been reported in various studies andcould serve as prognostic markers in disease severity.Moreover, patients having hematological abnormal-ities are at increased risk of having different infectionsand other comorbidities [7]. According to internationalguidelines, hemocytometric markers show specificchanges as the COVID-19 disease gets worsened.Chinese diagnostic criteria show fever/respiratorysymptoms or decreased/normal white blood cellcount and decreased lymphocyte count. Additional cri-teria include computerized tomography-based pneu-monia along with contact with a suspected personsuffering from the disease or travel history [8].

Other diagnostic tools like X rays and CT scans andchest ultrasounds also have diagnostic value but arenot established as effective in terms of cost, logistics,and expertise availability as well as a prognostic toolin disease management [9]. Nucleic acid detectionthrough samples taken through nasopharyngealswabs serves as a gold standard. However, limitationsinclude 20% false-negative rates, experienced staff,high cost and turnaround time (3–4 h), and limitedaccess to underdeveloped areas. Moreover, approxi-mately 50% of COVID-19 patients are asymptomaticand pre-symptomatic carriers. Hence, continuous sur-veillance and contact tracing could help detect suchpatients and carriers [10].

A complete blood count serves as the most accessi-ble, potent, and readily available test as most routinelaboratories are equipped with hematology analyzers.Routine hematological and biochemical parametershave also shown changes in COVID-19 patients, asdepicted in various studies. Various hematological par-ameters in a complete blood count show changes asthe disease worsens. COVID-19 infection manifestedleukocytosis, leukopenia, lymphocytopenia, eosinope-nia, neutrophilia, thrombocytopenia, and raised D-Dimers, Ferritin, CRP, LDH, pro-calcitonin, ALT, AST,PT, APTT, and these parameters are widely used forrisk stratification [11–13]. Hence, knowledge aboutthe prognosis of infection and its relevance to comor-bidities could provide valuable information on riskstratification and decision making in severely affectedCOVID-19 patients [14].

This study aimed to evaluate and review variationswhile analyzing data from COVID-19 and comparingit with healthy controls to determine changesinduced in different hematological parameters. Novelinflammatory markers NLR and PLR were also exploredto help predict the outcome of COVID-19 infection.Moreover, they were defined as significant markers indifferent infections and inflammatory conditions inthe literature [13]. NLR was first presented in the che-motherapeutic response of esophageal carcinoma in2012 by dividing the relative percentage of neutrophils

530 A. KHALID ET AL.

by lymphocytes [12]. The normal range should be <3 inhealthy population but >3 value, suggests an ongoinginfection, and a ratio >9 reveals sepsis. The cut-offvalue was set at 4 [13]. NLR seems to be associatedwith inflammation as reported in the literature. NLRwas also identified as a prognostic marker of neuro-logical deterioration after acute cerebral hemorrhage,[15], as a marker of glucose control in type 2 diabeticsin addition to HbA1c [16], and as an independent prog-nostic marker of symptomatic hemorrhagic transform-ation (sHT), a complication of acute ischemic stroke(AIS) [17]. Additionally, serving as a disease predictortool in inflammatory bowel disease [18] along withserving as a significant predictor of outcomes ofvarious coronary arterial diseases and malignancies.There is overt inflammation in all these diseases, justlike COVID-19 infection.

PLR (platelet count divided by lymphocyte count)comes out in the range of 50–150, with mean valuesvarying in the population [7,19] While asymptomaticor moderately affected patients or recovered one hadlower values in comparison, coinciding with recentlyreported studies. Alternatively, a decreased lympho-cyte count (lymphocytopenia) was noted in criticalphase patients. Consequently, elevating the platelet-to-lymphocyte ratio among critical ICU patients. Inter-estingly, the ratio declined in the initial stages ofdisease (at the time of admission to isolation wards);hence supporting the notion of platelet-to-lymphocyteratio as an independent predictor of mortality andprognosis in critically patients in parallel with the out-comes of recent studies [20,21]. Biochemical par-ameters including D-dimers, Ferritin, Lactatedehydrogenase, C-reactive protein, Interleukin-6,Alanine transaminase, Aspartate transaminase, andprothrombin time, activated partial thromboplastintime of COVID-19 patients were also comparedbetween a non-severe and severe group of COVID-19infected patients to find their predictive significancein clinical practice.

In the present study, we aimed to observe the hem-atological and biochemical parameters of the patientswith Covid-19 infection, comparing them with healthycontrols. Figure 1 illustrates the graphical abstract ofthe current study.

2. Materials and methods

2.1. Study design

We conducted a double-centered, hospital-based com-parative retrospective case study from April 2020 toApril 2021 in the Department of Pathology, SahiwalMedical College. At the time of analysis, we followedthe hospital course along with the outcome of thedisease. We followed the ‘Strengthening the Reportingof Observational Studies in Epidemiology (STROBE)’,

[22,23] and ‘Standards for Reporting Diagnostic accu-racy Studies STARDS’ guidelines for reporting of diag-nostic studies to examine the ability of medical teststo correctly categorize the participants of studyhaving that target medical condition [24].

2.2. Patients

Study participants included cases and controls, testedpositive and negative for COVID-19 through real-timereverse transcriptase-PCR by nucleic acid detection ofsample swabs from the oro-nasopharynx, respect-ively. These patients were admitted to various iso-lation wards of tertiary care hospitals in the Sahiwaldivision, Pakistan, namely District Head Quarter Hos-pital (DHQ) and GHAQ, Sahiwal. All patients andhealthy controls were of Pakistani ethnicity. Thecontrol group was observed for three weeks, andthey showed no severe or apparent symptoms ofrespiratory infections. The following variables wererecorded for each COVID-19 patient: age, gender,ethnicity and associated comorbidities assessmentof disease severity on admission, laboratory findingsat the start (the first day of hospital admission), andsubsequently during the hospital stay. We followeddischarged and recovered patients by telephoneand appointments.

2.3. Laboratory procedures and parameters

About 3 ml of venous blood was collected from eachCOVID-19 positive individual in an EDTA vacutainertube on the 2nd day of admission of patients to thehospital. Only the reports of the first CBC’s of thecases on the first day of admission to the hospitalwere considered. A complete blood count (CBC) wasperformed using the Automatic Hematology AnalyzerSwelab Alfa Standard (Boule Medical AB, Sweden). Bio-chemical parameters were measured for some patientsusing Beckman Coulter Chemistry Analyser AU680(Beckman Coulter, Inc, Japan), Semi automated Chem-istry Analyser, Microlab 300, Merck, and Wiener LabCoagulation Analyser Fibrintimer 4. The integrity ofreagents and samples was regularly monitored. Onadmission, patients with COVID-19 were categorizedinto four groups: (mild, moderate, severe, critical)according to the National Institutes of Health (NIH)classification based on disease severity. Because ofthe large number of patients, associated comorbiditieswere retrieved for only severe and critical individualsand those having ICU admission. The hematologicalparameters, including WBC’s, lymphocyte count andpercentage, monocyte count and percentage, neutro-phils count and percentage, hemoglobin, RBC’s andplatelets count of both positive and negative patients,were compared.

HEMATOLOGY 531

2.4. Exclusion, inclusion criteria

All the patients diagnosed with COVID-19 throughreal-time reverse transcriptase-polymerase chain reac-tion (rRT-PCR) were included in the cases. In contrast,all the healthy individuals screened negativelythrough rRT-PCR were included in the control group.Individuals <18 years and those having missing dataor died before admission to the hospital were excludedfrom both cases and controls. Additionally, patientswith known hematological abnormalities and otherinflammatory conditions were also not included.

2.5. Diagnostic criteria

Fever, oxygen saturation, respiratory rate, and lunginfiltrates were the clinical parameters to stratify thedisease’s severity. According to the ‘novel coronavirusinfected pneumonia treatment scheme-Sixth edition’issued by the National Health Commission of thePeople’s Republic of China [11], the condition of thepatients at the time of admission was classified as

. ‘the regular group (symptoms related to respiratorytract infection, fever, and radiographic evidence ofpneumonia) including mild and moderate grouppatients.

. the severe group [meet any of the followingcriteria]:(1) at a quiet rest, peripheral blood oxygen satur-

ation is less than 93%;(2) pulmonary imaging indicated that the lesion

progression was more significant than 50%

within 24–48 h and at a quiet rest, peripheralblood oxygen saturation is less than 93%;

(3) shortness of breath, a respiratory rate of morethan 30 breaths per minute;

(4) PaO2/FiO2 of 300 mmHg or less.. the critically ill group [satisfy any of the following]:

(1) complicated with vital organ failure requires ICUtreatment.

(2) shock;(3) respiratory failure occurs, and mechanical venti-

lation support is required’ [11];

This study also agrees with the principles of theDeclaration of Helsinki of the World MedicalAssociation.

2.6. Statistical analysis

We used the IBM SPSS Ver. 25 and Med Calc Ver.15.8 statistical package programs for data analysis.We determined the categorical variables of thepatients by using the Analysis of Variance whichwas expressed as a number and percentage n(%). To assess the normality of the data distri-bution, the Shapiro–Wilk test, Q–Q plots, and histo-gram were used. Continuous variables werereported as mean and standard deviation, and cat-egorical variables were described as frequency andpercentage. Median and interquartile range (IQR)were described by applying the Kruskal–Wallistest. For parametric continued variables, two-tailed

Figure 1. Graphical illustration of our study propositions.

532 A. KHALID ET AL.

independent samples t-test was used for analysis,and they were presented as mean ± standard devi-ation. Nonparametric variables were analyzedusing the Mann–Whitney U-test. Correlationsbetween variables were examined using Spearman’srank correlation analysis. A receiver-operatingcharacteristic (ROC) curve was also generated todetermine the efficacy of different parameters indistinguishing COVID-19 patients from healthy con-trols. The area under the curve (AUC) was calcu-lated. The 95% CI was calculated wheneverappropriate, and a two-tailed p < 0.05 was con-sidered statistically significant. Various comparisonsamong different groups were also performedusing the Bonferroni adjustment method. Binominallogistic regression is used to adjust for gender as aconfounder. Differences between the two groupswere compared using the Mann–Whitney U-test.The significant (p < 0.05) results from the Mann–Whitney U-test (with post-hoc Bonferroni correction)were analyzed.

3. Results

In our study, three hundred seventeen (n = 317)patients diagnosed with COVID-19 after testing posi-tive with PCR for SARS-CoV-2 were enrolled. The com-parative control group n = 157 includes healthyindividuals who tested negative for COVID-19through rRT-PCR, and they had no respiratory tractinfection and other related symptoms.

3.1. Demographical characteristics

To examine the demographical characteristics of thepatients, we executed descriptive statistics for calculat-ing the frequencies and percentages as shown inTable 1. Descriptive statistics helps to summarize thedata understandably and comprehensively. Table 1summarizes the frequencies and percentages ofrespondents’ demographical characteristics. As visiblethat, a total of n = 317 (68.0%) positive cases hadmajority of males n = 198 (62.5%), and n = 111(35.0%) were females (M= 1.71 ± 0.457) Furthermore,in the control group, we had n = 157 participants.Out of 157 control subjects 111 (70.7%) were female,

and 46 (29.3%) were male (M = 1.36 ±0.481). Moreover,regarding the age of our study sample, most of thepatients were of the age 26–33 years (n = 102 or21.5%) (M = 3.46 ±1.75). The age group of controlsranges from 18 to 45 years and it was matched regard-ing the age and sex. Besides, we also executed theAnalysis of Variance to examine any potential meandifferences based on the participants’ demographicalcharacteristics. As visible in Table 1, based on thegender of both the groups (cases, controls), we didnot find any discrepancies in the sampled variables(p≥ .000). Likewise, we did not find any mean differ-ences based on age of the study participants. There-fore, the Analysis of Variance results showed thatthere were no demographical differences in theresponses.

Table 2 shows the percentages of individuals withcomorbidities and their outcome as survived ordeceased. Out of 260, who survived, 102 (39.2%)were hypertensive while 22 (36.6%) out of 57 deceasedpatients were hypertensive. Further, 99 (38%) survivedand 13 (21.6%) of the deceased patients had diabetesmellitus.

3.2. Hematological parameters of cases andcontrols

Table 3 shows a statistically significant difference (p-value <0.001) was found in all the mentioned CBCmarkers except monocytes, hemoglobin and RBCs.White blood cells measured in 474 patients showed asignificant elevation (leukocytosis) (p < 0.001) in thePositive group (m= 11.53, ±7.3) as compared to thecontrol group (7.74 ± 2.21). Lymphocytopenia wasseen in cases (M= 1.7, ±1.63) as compared to controls(1.71 ± 1.63) (p < 0.001). Interestingly, Neutrophilia wasprominent in COVID-19 affected patients (9.16 ± 6.44)as compared to controls (4.62 ± 1.66) (p < 0.001).Monocyte showed no significant differences in thecases and controls (p = 0.833). Hemoglobin levelswere not statistically different among study groups(p = 0.66). Novel hematological parameters, Neutrophilto Lymphocyte ratio (NLR) (reference range: 1–3) andPlatelet to Lymphocyte ratio (PLR) (reference range:36.63–149.13 in males and 43.36–172.68 in females)were also determined for both cases and controls,

Table 1. Summary of participants’ demographical data and analysis of variance.Variable Construct F % M SD Levene Statistics Sign.

Positive cases Male 198 62.5% 1.71 .457 .000 .000Female 111 35.0%

Negative cases Male 46 29.3% 1.36 .481 .000 .000Female 111 70.7%

Age 18–25 73 15.4% 3.46 1.75 111.86 .00026–33 102 21.5%34–42 83 17.5%43–50 61 12.9%51–60 59 12.4%61 or above 96 20.3%

HEMATOLOGY 533

and the mean of both these ratios was found to beraised in our patients compared to controls (p <0.001), as depicted in Table 3. Similarly, a comparisonof NLR and PLR in cases and controls shows significantdifferences (Figures 2 and 3).

3.3. Biochemical parameters determination incases compared to SI normal values

Due to limited resources, not all the biochemical par-ameters were measured in healthy controls, and thevalues of Ferritin, D-Dimer, CRP, IL-6, ALT, AST, PT,APTT, Pro-calcitonin were measured in a limitednumber of cases and compared with internationalreference ranges as shown in Table 4. The missingvalues were excluded while analyzing the data. Allthese parameters showed significant differences.Further, significantly elevated ferritin levels >300 ng/ml (816.24 ± 734.2, p < 0.001) were found more com-monly in critical individuals. Coagulation Profile includ-ing markedly raised D-dimer levels >550 ng/ml(1624.9 ± 2067.2, p < 0.001), thrombocytopenia (plate-let count <100 × 109 cells and prolonged prothrombintime >16 s; p = 0.049) and APTT (36.0 ± 2.92, p < 0.001)were seen in cases as compared to normal values.

Further, CRP (49.73 ± 53.59, p < 0.001), IL-6 (24.55 ±27.99, p < 0.001) and LDH (normal range=120–246)also show a significant increase (845.83 ± 424.8; p <0.001).

3.4. ROC curve analysis between significantmarkers

Due to statistically significant difference between casesand controls in the CBC markers, Receiver-OperatingCharacteristics curve analysis was done. As shown inTable 5 and Figures 4 and 5. The cut-off point for plate-let values was found to be ≥0.0039 (AUC = 0.682; p <0.001; 95% CI: 0.260–0.368). The cut-off point forhemoglobin was found to be >13.6 (AUC = 0.495; p <0.001; 95% CI: 0.436–0.554). The cut-off point forWBC’s was found to be ≤7.55 (AUC = 0.647; p < 0.001;95% CI: 0.705–0.59). The cut-off point for neutrophilvalues was found to be ≤4.65 (AUC = 0.741; p < 0.001;95% CI: 0.688–0.790). The cut-off point for NLR wasfound to be ≤2.98 (AUC = 0.837; p < 0.001; 95% CI:0.796–0.878). The cut-off point for PLR values wasfound to be ≤104 (AUC = 0.667; p < 0.001; 95% CI:0.612–0.721). The cut-off values for significantmarkers including 1/Platelets, WBC’s, Neutrophils,

Table 3. Mann–Whitney U-test to examine the laboratory parameters of cases and controls.Hematological parameters Units SI reference range Negativea (n = 157) Positivea (n = 317) p-valueb

White blood cells ×109/L 4.5–11.0 7.74 ± 2.217.4 (6.3–9.0)

11.53 ± 7.310 (6.3–14.6)

0.000*

Lymphocytes ×109/L 1.50–3.0 2.59 ± 8.02.5 (1.4–3.9)

1.71 ± 1.631.3 (1.0–2.0)

0.000*

Lymphocyte percentage 16–46% 34.39 ± 8.234.5 (28.6–39.9)

17.68 ± 12.6813.2 (8.0–25.0)

0.000*

Monocytes ×109/L 0.3–0.5 0.55 ± 0.420.4 (0.2–0.8)

0.55 ± 0.520.4 (0.2–0.6)

0.833

Monocytes percentage 4–11% 7.36 ± 4.695.8 (3.9–9.4)

5.23 ± 2.794.5 (3.5–6.3)

0.000*

Neutrophils ×109/L 3.00–5.80 4.62 ± 1.664.3 (3.6–5.4)

9.16 ± 6.448.2 (4.4–11.9)

0.000*

Neutrophil percentage 45–75% 57.85 ± 9.9458.5 (53.0–54.6)

75.32 ± 15.7680.3(64.27–87.27)

0.000*

Hemoglobin g/L Males: 140–180Females: 120–160

126.6 ± 2127.2 (114.6–139.8)

125.8 ± 2.2612.8 (11.2–14.0)

0.656

Red blood cells ×1012/L Males: 4.6–6.2Females: 4.2–5.4

4.79 ± 1.174.60 (4.23–5.02)

4.66 ± 1.034.64 (4.0–5.16)

0.494

Platelets ×109/L 150–400 260 ± 63.6262 (223–303)

212.9 ± 92.4203 (160.5–262.0)

0.000*

NLR – 1–3 1.91 ± 0.831.77 (1.31–2.31)

7.82 ± 6.906.08 (2.44–11.29)

0.000*

PLR – M= 36.63–149.13F = 43.36–172.68

108 ± 35.81103.6 (81.3–131.7)

181 ± 150145.8 (88.4–235.6)

0.000*

aMean ± standard deviation.bMann–Whitney U-test.*p < 0.05.The values in bold are statistically significant.

Table 2. Comorbidities in COVID-19 patients and their association with mortality.Comorbid diseases

Categories Hypertension Diabetes mellitus Liver diseases (HCV + ve, HBV + ve, CLD) Ischemic heart disease Asthma p-valuea

Survived 102 99 31 17 1139.2% 38.0% 11.9% 6.5% 4.2% 0.003

Deceased 22 13 9 11 236.6% 21.6% 15.0% 19.2% 3.5%

aChi-Square test.

534 A. KHALID ET AL.

NLR, PLR and 1/LYMPHOCYTES in the prediction ofCOVID-19 positive patients were also recommendedas shown in the Table 6. Additionally, the comparisonof results according to cut-off points was also per-formed (Table 7).

4. Discussion

The present study showed that hemogram parametersand hemogram-derived indices; such as NLR and PLRof the COVID-19 subjects were significantly differentfrom those in healthy controls.

Figure 2. Comparison of NLR in cases and controls.

Figure 3. Comparison of PLR in cases and controls.

HEMATOLOGY 535

COVID-19 disease has become a serious healthconcern for the whole world since it emerged fromWuhan in 2019 [1]. After being declared a globalpandemic by WHO, it has affected millions ofpeople, causing huge numbers of deaths andexhaustion of global health resources in our fightagainst COVID-19. The scarcity and limitation ofmedical equipment and resources have been felt allaround the globe, even in the developed countries,due to immense pressure on the health systems asa result of the rapidly spreading nature of thedisease and the resultant patient burden [6,25].Due to this, it is of immense importance to diagnosethe disease timely and effectively with readily avail-able resources so that wastage of time and medicalresources and technology can be avoided and thepatients of COVID-19 can be triaged quickly for

further management. In developing countries likePakistan, the COVID-19 disease burden on thehealth facilities was felt even more severely due tothe already shortage of equipment and low capacityof the health facilities in diagnosing and managingthe patients.

This scarcity of testing facilities and patient man-agement expertise and equipment is even more sig-nificant in small towns and cities away fromsignificant metropolitans of the country where thehealth resources are segregated. Sahiwal, the focusof our study population, is also a small peripheral cityof Punjab where many different commonly usedCOVID-19 standard reference diagnostic tests, devel-oped to get a reliable diagnosis, like Nasopharyngealswab rRT-PCR (gold standard), CT-scan, and chest x-ray, are not in common reach of the general popu-lation. This is due to the limited facilities and diagnos-tics centers, where these tests are available.Additionally, due to limited human resource to prop-erly conduct and interpret the results and expensivebills which the low income and poor populationcannot afford. The equipment availability and its sus-tainability and maintenance are not usually met regu-larly because of the need to get supplies of parts, films,and test kits from larger cities, creating a dangerousgap interval in which tests could not be performed.This scenario, which is the prevalent case in many Paki-stan and many countries of the world, creates anurgent need for an alternate way to diagnose andtriage the COVID-19 patient and monitor the progno-sis. Therefore, the development of easy to assess andinexpensive markers for the severity of COVID-19

Table 4. Biochemical parameters in COVID-19 positive patients.

Hematological parameters Units Reference range Patients’ dataa tNumber of patients

n Sign.b

Ferritin ng/ml 20–291 816.24 ± 734.2527.0 (334.0–1108.0)

11.683 111 .000***

D-Dimer ng/mL <550 1624.9 ± 2067.2990.0 (478–1750.0)

8.492 117 .000***

CRP mg/L <10 49.73 ± 53.5930.65(15.42–61.95)

9.509 114 .000***

IL-6 pg/ml 0–3.4 24.55 ± 27.9919.60(13.2–27.85)

7.783 92 .000***

LDH U/L 120–246 845.83 ± 424.8770.50(511.75–1111.25)

21.937 122 .000***

ALT U/L <50 60.63 ± 49.446.0 (29.0–75.5)

8.948 57 .000***

AST U/L <35 76.63 ± 63.360.5 (39.5–96.5)

8.972 58 .000***

PT sec 10–16 31.0 ± 54.016.5 (14.25–19.0)

2.145 16 .049**

APTT sec 21–32 36.0 ± 2.9237.5 (34.0–39.0)

44.317 14 .000***

Pro-calcitonin mcg/L Less than 0.1 mcg/L 1.52 ± 6.610.1140(0.047–0.460)

−1.509 436 0.132

PLT to D-Dimer ratio 0.6–0.88 23.7 ± 119.233.684 (1.09–69.0)

1.954 115 0.03

aMean ± standard deviation.bOne sample t-test.***p≤ 0.001.**p≤ 0 .049.In healthy adults, the reference range of PCT is below the level of detection.

Table 5. Comparison of hematological parameters amongpatients of COVID-19 disease based on severity.

ParametersTotal(n)

Non-severegroup

Severegroup

p-value

1/Platelets 219 99.59 115.42 0.079White Blood cells 220 81 125 0.000*Neutrophils 219 74.91 128.27 0.000*NLR 219 70.86 130.3 0.000*PLR 218 98.19 115.42 0.0551/LYMPHOCYTES 310 144.41 161.18 0.119Ferritin 109 56.39 54.45 0.773D-Dimer 115 59.01 57.57 0.832CRP 112 46.85 60.19 0.052IL-6 91 43.47 47.19 0.531LDH 121 73.13 57 0.029*ALT 57 11.50 29.31 0.287AST 58 11.00 29.82 0.269PT 16 0.00 8.50 –APTT 14 0.00 7.50 –

536 A. KHALID ET AL.

disease has utmost interest. CBC (complete bloodcounts) is a commonly used and readily available testin medical laboratories everywhere. They are widelyused in clinical practice serving as a routine lab testas they are performed easily and quickly, requirelittle medical expertise to perform and interpretresults, and are also easily available and cost-effectiveproviding significant prognostic and diagnostic valuein many clinical conditions [26]. NLR and PLR mayyield such advantages in the pandemic era, which

have been shown to be increased in COVID-19 patientscompared to the control subjects in the present study.

Various biomarkers in expanded CBC’s have beenproved to serve as valuable tools in diagnosingvarious infectious diseases, including COVID-19.Several studies have associated leukocytosis, lympho-penia, and neutrophilia with the diagnosis of COVID-19 [21]. In our study, leukocytosis was also significantin confirmed patients (cases). These results were con-sistent with a study conducted in China which

Figure 4. Receiver-operating characteristic curve for significant markers in the prediction of COVID-19 positive patients.

Figure 5. Receiver-operating characteristic curve for significant markers in the prediction of COVID-19 negative patients (controls).

HEMATOLOGY 537

showed that leukocytosis was associated with anincreased risk of death in the hospital setting [27].Zhou et al. also noted that leukocytosis was morecommon in deceased patients of COVID-19 [28].Further, lymphopenia and neutrophilia were men-tioned in the guidelines released by Australia andNew Zealand [29].

Regarding age and gender, we did not find anydifferences among the study participants. Therefore,the results of the Analysis of Variance showed thatthere were no demographical differences in theresponses. Moreover, mortality rate was found in15.8% in our study, which is much greater than theoverall disease mortality reported in Pakistani popu-lation. But this could be a false negative because itonly projects the mortality among the admittedpatients in our hospital.

Additionally, our results showed lymphopenia incases as compared to the healthy controls (Table 3).CDC and Huang et al in China have identified lympho-penia as the most common finding in COVID-19affected patients [30,31]. Studies show that the leuco-cyte and lymphocyte count is normal in asymptomaticpatients and also when the COVID-19 virus is in itsincubation period in a person. SARS-COV-2 infectscells of gastrointestinal tract (GIT), heart, and lungsthat express ACE-2 receptors following viremia [32]As lymphocytes also express ACE-2 receptors, thevirus causes their lysis. After one to two weeks, thereis a surge in cytokines producing ‘cytokine storm’, lym-phopenia then becomes more prominent due toatrophy of lymphoid organs and hence their decreasedproduction and turnover [33]. Hence, lymphopenia is

identified as one of the most important prognosticmarkers in COVID-19 patients [34]. Our results are con-sistent with a study conducted in 148 patients in Singa-pore in which neutrophilia was reported mostcommonly in serious COVID-19 patients requiring hos-pital admission (11.6 × 109 /L vs. 3.5 × 109 /L) [26].Gong et al. [35] and Qin et al. [36] also identified neu-trophilia in critical and severe patients (p < 0.001). Simi-larly, Li et al. also reported neutrophilia in non-survivors more commonly than in the survivors ofCOVID-19 infection [7] The possible cause of neutro-philia may be the ‘cytokine storm’ as the mean valueof IL-6 is also markedly raised, justifying a significant‘cytokine storm’. Additionally, the superimposed bac-terial infection and drugs used in the treatment canalso cause this presentation.

Raised LDH levels in our patients can be explainedby the coexisting lactic acidosis, specifically presentin cancer patients and patients with comorbiditieslike heart disease, hence increasing associated com-plications (Table 4). This further inhibits lymphocyteproliferation. Consequently, decreased lymphocytecount should be monitored and vigilantly gaugedregularly in COVID-19 patients to determine diseaseprogression [37].

Moreover, NLR and PLR ratios also play an essen-tial role in diagnosing COVID-19 disease [38]. Ourresults of NLR and PLR show a significant differencebetween cases and controls. Since, there is a strongsystemic inflammatory response in COVID-19 infec-tion, virus-induced inflammatory markers IFN-Y, IL-8, IL-6, GCSF, TNF-α activates neutrophils. Conver-sely, helper T-lymphocytes and other immune cellsare considerably declined, causing an overallincrease in NLR and hence disease progression. Pre-vious investigations have also identified NLR andPLR as valuable prognostic factors for disease pro-gression [39,40].

NLR has been suggested as a prognostic factor inother conditions, too. For example, Sit et al. notedthat thyroid nodules patients have increased NLR inthe preoperative period serving as a prognosticmarker in case of underlying malignant nodulardisease [41]. Additionally, in healthy individualsincreased NLR, MLR, PDW can be associated withhepatic steatosis [42]. All these conditions are

Table 6. Recommended cut-off values for significant markers in the prediction of Coronavirus Disease of 2019 (+) patients.Markers AUC Cut off Sensitivity Specificity 95% CI* p-valueb

1/Platelets 0.682 ≤0.0039 74 51 0.628–0.737 0.000White blood cells 0.647 ≤7.55 65 53.5 0.591–0.702 0.000Neutrophils 0.741 ≤4.65 75 60 0.690–0.792 0.000NLR 0.837 ≤2.98 75 61 0.796–0.878 0.000PLR 0.667 ≤104 70 50 0.612–0.721 0.0001/LYMPHOCYTES 0.823 0.42 86 55 0.780–0.866 0.000

The values in bold are statistically significant.bReceiver operating characteristic curve.*Lowest value - highest value.

Table 7. Comparison of results according to cut-off points.Hematologicalparameters

Cut-offvalue

Negative (n= 157)

Positive (n= 317)

p-value

1/Platelets >0.0039 69 195 0.000≤0.0039 88 122

White blood cells >7.55 85 247 0.000≤7.55 72 70

Neutrophils >4.65 75 262 0.000≤4.65 82 55

NLR >2.98 81 281 0.000≤2.98 76 36

PLR >104 77 266 0.000≤104 80 51

1/LYMPHOCYTES >0.42 53 224 0.000≤0.42 104 93

538 A. KHALID ET AL.

associated with inflammation just as in COVID-19 infec-tion. Similarly, patients with severe COVID-19 infectionreported an increased NLR than those with non-severeor mild COVID-19 infection [43,44].

In COVID-19 infection an increase in D-dimer witha minute decrease in platelet count has beenobserved. In our study, Elevated platelet count(thrombocytosis) was noted in COVID-19 patients atthe time of admission to hospital (initial phase ofthe disease) and admission to ICU (critical phase)and expired patients. Tang et al. noted that amongthe more concerning features of COVID-19 infectionis a coagulopathy characterized by high D-dimerand fibrinogen concentrations with minor changesin prothrombin time and platelet count. Thissignifies that the ratio of platelet count and d-dimer values changes in the COVID-19 patients, andthe results of mean D-Dimer/Platelet ratio as shownin (Table 4) in our study are consistent with theabove study [45] We statistically analyzed this ratiofor our cases and found some variations fromnormal. In our study, biochemical parameters includ-ing ferritin, CRP, Pro-calcitonin, and PT also show asignificant increase, as shown by Huang et al.,suggesting that increased serum CRP, PCT, D-dimer,and ferritin were associated with a poor outcomein COVID-19 and superimposed co-infection [46].Additionally, in a retrospective study conducted inWuhan, China, deceased patients from COVID-19showed a higher LDH (p < 0.0001), increased serumpro-calcitonin (p < 0.0001), elevated IL-6 (p < 0.0001),and raised serum ferritin (p < 0.0008) than non-severe patients [47] Higher ferritin and CRP may beassociated with ARDS. Ferritin levels were elevatedin our cases (816.24 ± 734.2) (p < 0.0001), which maybe related to elevated hepcidin levels due to inflam-matory reaction. Kell et al. [48] noted that ferritinlevels greater than 600 ng/dL suggest cellulardamage. Moreover, CRP is also considered a markerof inflammatory reaction and is raised in COVID-19as it is an inflammatory condition.

In our findings, the platelet count was decreased incases compared to controls, but the values were stillwithin normal limits. However, few patients had mildthrombocytopenia, which was comparable with thefindings of a study conducted by Liu et al., who notedthat most hospitalized patients had normal plateletcounts, only a few had decreased platelets [49].

Interestingly, PT and APTT were abnormally elev-ated due to disseminated intravascular coagulopathylike state as shown by different studies [50]. Moreover,raised PT was associated with raised ARDS and signifi-cantly raised D-dimer entails ARDS, sepsis and death.D-dimers are the degradation product of fibrin, andthey are routinely used to diagnose thrombotic state.Hence, any inflammatory condition that changesfibrin levels is associated with elevated d-dimers

levels [51]. Tang et al. noted that non-survivors havesignificantly increased D-dimers (p < 0.05), fibrindegradation products (p < 0.05), and PT (p < 0.05),APTT (p < 0.05). Moreover, fibrinogen was significantlydecreased consequently with deranged coagulationprofile [45]. Hence, DIC and venous thromboembolismare the most common complications, and the resultsmay complement our understanding of this compli-cation [52]. Unfractionated or low molecular weightheparins (LMWH), instead of direct oral anticoagulants(DOACs), should be given due to possible drug-druginteractions with subsequent antibiotic (such as azi-thromycin) and antiviral (especially anti-HIV proteaseinhibitors such as ritonavir) treatment [53].

Increased platelet activation and relative lymphope-nia due to apoptosis of lymphocyte cause markedlyelevated PLR in COVID-19 positive patients. Theresults of NLR and PLR in our study are consistentwith a study conducted by Yang et al. [34]. A studyfrom Pakistan conducted by Asghar et al. [54] reporteda mean PLR at the admission to the ward as 169.81 ±105.30, which was lower than our findings 181 ± 150,which may indicate that there may be variation ininflammatory activity at the time of presentation.Similar to our study, comorbidities including diabetes,coronary heart disease, hypertension, COPD, andasthma were also reported in different studies, predict-ing the severity of COVID-19 infection [2,36]. Addition-ally, associated comorbidities increase the risk of DIC,consequently elevating the d-dimers level [55,56].The deceased group in our study was most commonlyaffected by hypertension and diabetes.

By the evaluation of the trends and variations ofCBC parameters in the patients of COVID-19 andtheir comparative analysis with CBC parameters ofthe controls, we have concluded that by using a com-monplace and easily available test like CBC (index test)we can find reliable evidence of the presence ofCOVID-19 disease in the absence or unavailability ofother reference standard tests like rRT-PCR, CT-scan,etc. This can be extremely useful in the response ofour health systems against the COVID-19 pandemicin developing countries and areas which are strugglingwith increasing patient burden due to limitedresources and weak health infrastructure, CBC par-ameters being commonly available in the majority ofthe medical laboratories, even smaller ones. We usedthe ROC curve to find the cut-off values of differentindividual CBC parameters in predicting COVID-19disease successfully (Figures 4 and 5), so that the infor-mation derived from a regular CBC report can be easilyanalyzed and applied for COVID-19 triage, even by aperson with limited medical expertise and backgroundknowledge. We also derived sensitivity and specificitymeasures of these parameters to strengthen our argu-ment of effective application of this data for practicalpurposes in clinical scenario (Tables 6 and 7). We

HEMATOLOGY 539

compared the values of CBC parameters in confirmedCOVID-19 patients, classified according to their sever-ity, and concluded the effectivity of our index test inpredicting and giving valuable information about theprognosis and nature of the disease. Individuals weredivided into four groups based on their severitylevels mild, moderate, severe, and critical, as shownin Figure 6 according to Chinese criteria [11]. Table 5shows the comparison between non-severe (includingmild, moderate group) and severe (including severeand critical group). WBC’s, neutrophils NLR, LDH, PLRCRP showed a statistically significant differenceamong severe and non-severe groups.

We have also inferred that expanded CBC par-ameters can provide very significant informationabout the prognosis and nature of the disease. Thismakes the index test of this study beneficial inspecial settings in response to COVID-19. We rec-ommend exploring the potential CBC markers inCOVID-19 as current literature is extremely deficientin this respect. To our knowledge, this is the firststudy from Pakistan, reporting findings on the com-parison of hematological and biochemical parametersof COVID-19 patients. Adoption of expanded CBC test(index test) in the above-discussed context can proveto be very helpful in our opinion.

5. Limitations of the study

Small study population is a limitation of the presentstudy. Moreover, the study cohort included onlypatients presented to the tertiary care hospitals of

the Sahiwal division as the overall case rise inSahiwal was gradual as compared to other districts ofPunjab, Pakistan.

6. Conclusion

COVID-19 shows significant hematopoietic changesassociated with a hypercoagulable state. Careful moni-toring of all the CBC-associated hematological and bio-chemical parameters can assist clinicians in identifyingand determining the course of the disease, which canhelp provide prompt treatment and ICU admission tothose in need. It will elevate the burden on thehealth system its resources, especially in countrieswith limited resources. The patient outcome can befurther improved by continuous vigilance and prevent-ing hypercoagulation and DIC by thromboprophylaxis.The abnormal hematopoietic values not only serve as adiagnostic and prognostic value in determining thecourse of the disease but also the outcome and sever-ity of COVID-19 infection. Overall mortality and mor-bidity can be lowered in critical patients and thosehaving comorbidities. We suggest that, due to theirinexpensive and easy to assess nature, elevated NLRand PLR could be useful in the diagnosis of COVID-19along with other reference tests, especially when clini-cal suspicion continues despite negative PCR results.

Acknowledgements

The authors are indebted to all the healthcare pro-fessionals, nurses, and patients who are fighting with

Figure 6. Changes in the parameters based on the severity level of cases compared with normal values in controls.

540 A. KHALID ET AL.

COVID-19 together. A special thanks to Dr. Waseem, Assist-ant Professor Pulmonology, DHQ Teaching HospitalSahiwal, Dr. Tania Shakoori, Associate Professor, Instituteof Molecular Biology and Biotechnology, University ofLahore, Dr. Rana Amir Diwan, Head of Community Medi-cine, Sahiwal Medical College, Sahiwal & Dr. Akram Riaz,Department of Psychology, University of Lahore. Authorcontributions: Atiqa Khalid and Muhammad Ali Jaffar con-ceptualized and designed the study, collected data, andconstructed a first draft. Atiqa Khalid also planned method-ology, administered project & took responsibility for thearticle. Sana Ali, Tabinda Khan and Gulali Aktas conductedliterature search, elaborated arguments and contributed tosubsequent drafts of the paper. Raees Abbas Lail, AbdulWaris, Aamnah Javed and Nouman Ijaz helped in data col-lection. Muhammad Nasir contributed towards literaturesearch. All authors revised the document for critical intel-lectual input, and all authors approved the final version.Compliance with ethical standards: Data Set: The datasetsused and analyzed during the current study are availablefrom the corresponding author on reasonable request.Consent for publication: Informed consent was grantedfrom all the study participants and sources whose dataand figures were used. Informed consent: Informedconsent was received from all the participants whosedata are utilized. Ethical Approval: This study was carriedout in line with research regulations, including approvalby the Sahiwal medical college ethical committee (videletter SLMC/DME/103) and Government of Pakistan. Thisstudy is in accordance with the principles of the ‘WorldMedical Association Helsinki Declaration’.

Disclosure statement

The authors have no relevant affiliations or financial involve-ment with any organization or entity with a financial interestin or financial conflict with the subject matter or materialsdiscussed in the manuscript. This includes employment, con-sultancies, honoraria, stock ownership or options, expert tes-timony, grants or patents received or pending, or royalties.No writing assistance was utilized in the production of thismanuscript.

ORCID

Atiqa Khalid http://orcid.org/0000-0003-2712-5930Muhammad Ali Jaffar http://orcid.org/0000-0003-1022-967XTabinda Khan http://orcid.org/0000-0003-4257-5825Raees Abbas Lail http://orcid.org/0000-0003-3365-0707Sana Ali http://orcid.org/0000-0003-3474-000XGulali Aktas http://orcid.org/0000-0001-7306-5233Abdul Waris http://orcid.org/0000-0002-8593-5379Amnah Javaid http://orcid.org/0000-0003-3294-7648Nouman Ijaz http://orcid.org/0000-0003-2871-3662Nasir Muhammad http://orcid.org/0000-0002-8488-1591

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