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\setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Perveen RA} \author[2]{Nasir M} \author[3]{Ferdous J} \author[4]{urshed M} \author[5]{Nazneen R} \author[6]{Rahman MA} \affil[1]{ Holy Family Red Crescent Medical College} \renewcommand\Authands{ and } \date{\small \em Received: 12 February 2021 Accepted: 3 March 2021 Published: 15 March 2021} \maketitle \begin{abstract} The study aimed to observe and compare the demographic, comorbidities, biomarkers in different categories of diagnosed COVID-19 patients admitted to a COVID dedicated tertiary care hospital in the pic time of the pandemic, 2020, at Dhaka, Bangladesh.Methods: This retrospective study was conducted from May to September 2020 in 720 bed Holy Family Red Crescent Medical College Hospital. Four hundred seventy-three patients included in this study, diagnosed by RT-PCR of the nasopharyngeal swab, were divided into four groups. The mild group includes 254 patients, the moderate group has 82 patients, 38 patents in the severe group, and the critical group who were admitted to ICU, 99 patients. Demographic data, available investigation reports of individual patients, obtained from hospital records manually and compared between all four different categories of patients. \end{abstract} \keywords{COVID-19, biomarkers, co-morbidities, clinical features, severe, critical, Bangladesh.} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \begin{textblock*}{10cm}(1.05cm,3cm) {{\textit{CrossRef DOI of original article:}} \underline{10.34257/GJMRFVOL21IS4PG39}} \end{textblock*}\let\tabcellsep& \section[{Introduction}]{Introduction}\par ore than a year has passed since the first diagnosed SARS-CoV-2 infection in Wuhan; China was announced in December 2019. This was an unprecedented year with more than 15 billion documented infections and more than 3.2 million deaths worldwide due to SARS-CoV-2 \hyperref[b0]{1} . This large number of infected patients with a case fatality ratio ranges from 0.1\% to 25\% in different countries demonstrates that the coronavirus disease is extremely contagious \hyperref[b1]{2} . on 11th March 2020, WHO declared COVID-19 a pandemic situation. Near this announcement, Bangladesh reported their first case of COVD-19 on 8th March 2020. From then to 2nd May 2021, Bangladesh deals with 7,60,584 confirmed cases and 11.510 death \hyperref[b2]{3} . Besides Bangladesh, COVID became a concern in the densely populated South Asian region with more than 8 million confirmed cases and 1.2 million deaths up to 17th February, 2021 \hyperref[b3]{4} . SARS-COV-2 is a single-stranded enveloped RNA virus that produces symptoms like fever, myalgia, non-productive cough, fatigue, shortness of breath, diarrhea, and many others in affected patients \hyperref[b4]{5} . COVID-19 patients were categorized into mild, moderate, severe, and critical cases for proper management. Mild cases represent Influenza-like illness (ILI), moderate with pneumonia, severe patient with severe pneumonia, sepsis, and with ARDS, septic shock developed in those, considered as critical {\ref 6} .\par As the pandemic continues, global biomedical researchers are working urgently to identify coronavirus risk factors. Older age and underlying co-morbiditiesparticularly cardiovascular disease, diabetes, respiratory disease, chronic kidney disease, and many more are at high risk of severity \hyperref[b7]{7} .\par Besides symptoms and co-morbidities, change in some biomarkers level also reflects the disease severity. Though COVID-19 is a novel disease, Evidence shows severe inflammatory response, which contributes to weak adaptive immune response, thereby resulting in immune response balance in the patient body. Therefore, circulating biomarkers representing inflammation and immune status are potential predictors for the prognosis of COVID -19 patients 8 . Among hematological parameters, disease severity is associated with lymphopenia. Non-survivors of COVID 19 have had significantly less amount of lymphocyte counts than survivors \hyperref[b10]{9} -other blood cells -including white blood cells, neutrophils, and platelets, were partial predictors to differencing mild cases from severe COVID-19. Other than these, NLR, d-NLR. PLR are indicators of systemic inflammatory response \hyperref[b11]{10,}\hyperref[b12]{11} .\par Besides hematological markers, increased liver and cardiac biomarkers, which reflect dysfunction of these organs, were also observed in the critical group of patients than those with milder disease \hyperref[b13]{12,}\hyperref[b14]{13,}\hyperref[b16]{14} . C-reactive protein, serum ferritin level, levels of plasma D-dimers, and fibrin degradation products of COVID patients also correlate with disease severity \hyperref[b17]{15,}\hyperref[b18]{16,}\hyperref[b20]{17} .\par As this is a novel virus, scientific research is going on throughout the world to know more about how we can manage the patients affected by it. So, we conducted this retrospective study on 473 different categories of admitted COVID-19 patients to highlight their difference between a demographic profile, symptoms, comorbidities, and change on the biomarkers in a tertiary care dedicated hospital. \section[{II.}]{II.} \section[{Materials and Method}]{Materials and Method}\par Study design: This observational study was conducted in Holy Family Red Crescent Medical College Hospital (HFRCMCH) from May 17th to September 9th, 2020. HFRCMCH was a 720-bed tertiary care hospital located in Dhaka, Bangladesh. This hospital was assigned responsibility for treating patients with COVID-19 by the People's Republic of Bangladesh on May 15th May 2020, for five months. All RCT-PCR positive (by nasopharyngeal swab) patients treated in HFRCMCH within the period of the study were included. Patients who have insufficient information and discontinued or unavailability of any data, excluded from the study. \section[{Data collection method:}]{Data collection method:}\par The researcher screened all 1348 hospital record files of admitted patients. All the data recorded in a customized form. Researcher divided 473 patients' record files into four groups, the mild group includes 254 patients, the moderate group has 82 patients, the severe group has 38 patients, and the critical group have 99 patients.\par Case definition: National Guideline of Bangladesh published on 5th November 2020 categorized the confirmed COVID-19 cases. Mild cases present with fever, cough, sore throat, malaise, headache, muscle pain without shortness of breath, or abnormal imaging. Moderate group of patients have clinical sign of pneumonia with oxygen saturation of more than 90\% at ambient air. The severe group of patients have 30 breaths/ minute and finger oxygen saturation less than 90\% at rest. The critical group of patients admitted in ICU with respiratory failure or any other organ failure or shock and requiring mechanical ventilation. Though the clinical categories of the patients were discrete by the Triage zone (the zone where sorting of patients occur according to the urgency of their need for care), attending doctors, and attending critical care physicians. \section[{Ethical declaration:}]{Ethical declaration:}\par The hospital authority and the institutional ethics board of Holy Family Red Crescent Medical College approved the study. Though it is a retrospective study, formal consent was not taken from the patients. However ethical measures were taken throughout the study period to maintain a high standard of confidentiality of patient's hospital record files. \section[{Data acquisition and statistical analysis:}]{Data acquisition and statistical analysis:}\par We categorized age into eight groups with ten years' interval. We observed demographic data (age, gender, hospital stay, mortality), co-morbidities (DM, HTN, CKD, IHD, Bronchial asthma, Thyroid disease, cancer), symptoms (inflammatory and neurological), and laboratory biomarkers (hematological, inflammatory, hepatic, renal, metabolic). We expressed categorical variables like age range, comorbidities, and symptoms as the counts and percentage and continuous variables like age, hospital stay, and biomarkers as mean and standard deviation. We used SPSS version 21.0 for statistical analysis (chisquare test for qualitative variables and one-way ANOVA for quantitative variables), and all values were two-tailed, with p < .05 considered as statistically significant. \section[{III.}]{III.} \section[{Result}]{Result}\par Among 473 patients admitted in the hospital with COVID-19, the mean age of the mild group was 39.04(±12.24) years, gradually increasing in 52.35(±11.92) moderate group, 56.81(±15.51) in severe group and 61.08(±12.76) in critical group, with an age range from 18 to 91 years. Most of the severe and critical patients were in 60-69 years (23.68\% and 33.34\%), the moderate group were 50-59 years (42.68\%), and the mild group were 30-39 years (31.89\%). Out of all patients, 359 were male, and 115 were female. The male: female ratio was 1:3.12. Thirtynine patients (39.39\%) in ICU and only one patient (2.63\%) admitted in the general ward have died.\par (Table \hyperref[tab_0]{1}) The presenting symptoms of the patients were variable. The highest percentage of symptoms were shortness of breath (40.38\%), fever (33.61\%), cough (27.06\%) followed by anosmia (10.57\%), lethargy (08.03\%), diarrhea (06.34\%), myalgia (05.71\%), loss of taste (04.44\%) and sore throat (03.59\%). These symptoms were compared between four groups of patients and were not statistically significant. Fever Regarding co-morbidities, the highest number of patients in all four groups presents with diabetes Mellitus (35.09\%) and hypertension (32.55\%) than other co-morbidities like ischemic heart disease (09.09\%), chronic kidney disease (03.81\%), bronchial asthma (05.07\%), thyroid-related disorder (02.32\%) and neoplasm (01.06\%). Among all four groups, the highest (18.50 \%), anosmia (17.71\%), and cough (14.96\%) were the most common in the mild group of patients. Whereas, SOB (57.32\%), cough (46.34\%), and fever (45.12\%) in the moderate group of patients. The severe group of patients complain about similar symptoms in a higher percentage (76.31\%, 52.63\%, and 31.51\%). SOB (85.85\%) was the most common symptom, followed by fever (66.66\%), cough (32.32\%) and anosmia was absent in ICU admitted patients (Table \hyperref[tab_2]{: 3} \section[{Discussion}]{Discussion}\par The retrospective study revealed the difference in demographic data, age groups, gender, clinical symptoms, and change in the biomarkers in admitted four different clinical categories of COVID-19 patients. Data were recorded from May to September 2020 in the pick of the pandemic to distinguish the relevant factor of disease severity.\par The number of male patients (359) admitted to the hospital was much higher than the number of the female (114), which was similar to the other studies worldwide, including Bangladesh \hyperref[b14]{13,}\hyperref[b22]{18,}\hyperref[b23]{19,}\hyperref[b24]{20} . Patients mean age increased from 39 years to above 60 years according to disease severity. The severe and critical group of patients were above 60 years, found to be similar among the same categories patients in other studies \hyperref[b22]{18,}\hyperref[b23]{19,}\hyperref[b24]{20,}\hyperref[b25]{21} .\par COVID-19 patients who have co-morbid conditions such as diabetes mellitus (DM), hypertension (HTN), ischemic heart disease (IHD), chronic kidney disease (CKD), and bronchial asthma lead to disease severity, thus increases ICU admission and risk of mortality.\par Other observational studies of Bangladesh \hyperref[b26]{22,}\hyperref[b27]{23,} {\ref 24} , and china \hyperref[b7]{7,}\hyperref[b16]{14} support similar findings. In our study, mild category patients present with a lower percentage of co-morbid conditions than moderate to critical ones. A lower percentage of patients without comorbidities have a lower case fatality rate (0.9\%) \hyperref[b29]{25} .\par In this study, patients present with various inflammatory and neurological symptoms, which were almost similar in many studies. But the predominant symptoms vary in different categories of patients. Fever, anosmia, and cough were the most frequent symptoms in the mild group of patients. Whereas shortness of breath, cough, and fever was common and increased in percentage in the other three groups. Anosmia was absent in the critical group. Several studies in Bangladesh \hyperref[b23]{19,}\hyperref[b24]{20,}\hyperref[b26]{22,}\hyperref[b27]{23} and worldwide \hyperref[b7]{7,}\hyperref[b16]{14} show patients with similar symptoms.\par In this study, we observed and compared several biomarkers level like hematological, inflammatory, hepatic, renal, and metabolic between different clinical categories of the COVID-19 patients to focus on disease severity. We found a statistically significant rise of total WBC, NLR (neutrophillymphocyte ratio), d-NLR, PLR (platelet-lymphocyte ratio), and total platelet count, but Hb\% and HCT were not statistically remarkable in all four groups of patients. These hematological findings were associated with disease severity, clearly support our study findings \hyperref[b7]{7,}\hyperref[b10]{9,}\hyperref[b16]{14} .\par Different categories of COVID-19 patients show change in the level of biomarkers. Most of the biomarkers showed significant change except Hb\%, HCT, Serum Creatinine, HbA 1 C, and serum lipid profile level. (Table \hyperref[tab_3]{4}) Specially platelets, NLR, d-NLR. PLR were also discriminating mild cases from severe COVID-19 \hyperref[b11]{10,}\hyperref[b12]{11} .\par V. \section[{Conclusion}]{Conclusion}\par The pragmatic observations and outcomes of the study guides, age, co-morbid conditions, and changes in hematological, inflammatory, and hepatic biomarkers, influences the disease severity in COVID-19 cases. However, the commonly observed symptoms were fever, cough, breathlessness in severe and critical cases, whereas anosmia was the common predictor in mild cases. This clinical experience and correlation helped us adopt the management strategy, with the new variant and immune response against it, in our population.\par VI. \section[{Limitations}]{Limitations}\par The study has few limitations, including a short period, and data were not representing the information of all socioeconomic classes of the country.\par Among the inflammatory biomarkers (CRP, d-Dimer, and ferritin), we observed a statistically significant change in CRP levels in different clinical categories of COVID-19 patients. Several studies stated raised levels of the inflammatory marker has a clear connection with the severity of illness \hyperref[b17]{15,}\hyperref[b18]{16,}\hyperref[b20]{17} . We found a significant difference in increased SGPT, prothrombin time, and INR between all four categories of COVID-19 patients. Patients with severe COVID-19 appear to have more frequent signs of liver dysfunction than those with milder disease \hyperref[b13]{12,}\hyperref[b16]{14,}\hyperref[b20]{17,}\hyperref[b30]{26} . Changes in the renal and metabolic (Serum creatinine, HbA 1 C, lipid profile) biomarkers were also unremarkable. \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{P{0.0955223880597015\textwidth}P{0.1246268656716418\textwidth}P{0.3888059701492537\textwidth}P{0.12014925373134328\textwidth}P{0.12089552238805969\textwidth}} \tabcellsep Mild case\tabcellsep Moderate case\tabcellsep Severe case\tabcellsep Critical case\\ \tabcellsep (n= 254)\tabcellsep (n=82)\tabcellsep (n= 38)\tabcellsep (n=99)\\ Mean age\tabcellsep 39.04± 12.24\tabcellsep 52.35± 11.92\tabcellsep 56.81± 15.51\tabcellsep 61.08± 12.76\\ 10-19years\tabcellsep 03/ 254 (0.79\%)\tabcellsep -\tabcellsep -\tabcellsep -\\ 20-29years\tabcellsep 63/ 254 (24.80\%)\tabcellsep 02/ 82 (02.44\%)\tabcellsep 01/ 38 (02.63\%)\tabcellsep 01/ 99 (01.01\%)\\ 30-39 years\tabcellsep 81/ 254 (31.89\%)\tabcellsep 12/ 82 (14.63\%)\tabcellsep 03/ 38 (07.89\%)\tabcellsep 06/ 99 (06.06\%)\\ 40-49years\tabcellsep 57/ 254 (22.44\%)\tabcellsep 13/ 82 (15.85\%)\tabcellsep 07/ 38 (18.42\%)\tabcellsep 08/ 99 (08.08\%)\\ 50-59years\tabcellsep 36/ 254 (14.17\%)\tabcellsep 35/ 82 (42.68\%)\tabcellsep 10/ 38 (26.31\%)\tabcellsep 27/ 99 (27.28\%)\\ 60-69years\tabcellsep 12/ 254 (04.72\%)\tabcellsep 14/ 82 (14.07\%)\tabcellsep 09/ 38 (23.68\%)\tabcellsep 33/ 99 (33.34\%)\\ 70 and above\tabcellsep 04/ 254 (01.57\%)\tabcellsep 06/ 82 (07.32\%)\tabcellsep 08/ 38 (21.05\%)\tabcellsep 24/ 99 (24.25\%)\\ Male/ Female\tabcellsep 213/ 42\tabcellsep 48/34\tabcellsep 27/ 11\tabcellsep 71/ 28\\ Hospital stay in days\tabcellsep 12.19± 05.26\tabcellsep 12.24± 07.29\tabcellsep 10.96± 07.10\tabcellsep 12.44± 10.22\\ Mortality (\%)\tabcellsep -\tabcellsep -\tabcellsep 01/ 38 (02.63\%)\tabcellsep 39/ 99 (39.39\%)\\ \tabcellsep \tabcellsep \multicolumn{3}{l}{number of co-morbidities present in critical patients}\\ \tabcellsep \tabcellsep \multicolumn{3}{l}{(71.72\%, 64.65\%, 19.19\%, 18.19\%, 10.10\%) in}\\ \tabcellsep \tabcellsep \multicolumn{3}{l}{comparison with the other three groups, which were}\\ \tabcellsep \tabcellsep \multicolumn{3}{l}{statistically not significant. Patients with thyroid-related}\\ \tabcellsep \tabcellsep \multicolumn{3}{l}{disorder in lowest percentage (0.79\%, 04.88\%, 02.63\%,}\\ \tabcellsep \tabcellsep \multicolumn{3}{l}{04.04\%) in all four groups and cancer (02.63\%, 04.04\%)}\\ \tabcellsep \tabcellsep \multicolumn{3}{l}{in severe and critical patients. (Table: 2, Fig: I)}\end{longtable} \par \caption{\label{tab_0}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.0664804469273743\textwidth}P{0.16303538175046553\textwidth}P{0.15828677839851024\textwidth}P{0.15670391061452513\textwidth}P{0.15828677839851024\textwidth}P{0.14720670391061452\textwidth}} Characteristics\tabcellsep Mild case (n= 254)\tabcellsep Moderate case (n=82)\tabcellsep Severe case (n= 38)\tabcellsep Critical case (n=99)\tabcellsep Statistical Significance\\ DM\tabcellsep 48/ 254 (18.89\%)\tabcellsep 32/ 82 (39.02\%)\tabcellsep 15/ 38 (39.47\%)\tabcellsep 71/ 99 (71.72\%)\tabcellsep Chi-square =\\ HTN\tabcellsep 43/ 254 (16.93\%)\tabcellsep 37/ 82 (45.12\%)\tabcellsep 10/ 38 (26.31\%)\tabcellsep 64/ 99 (64.65\%)\tabcellsep 48.981.\\ IHD\tabcellsep 08/ 254 (03.14\%)\tabcellsep 12/ 82 (14.63\%)\tabcellsep 04/ 38 (10.52\%)\tabcellsep 19/ 99 (19.19\%)\tabcellsep p< 0.00001.\\ CKD\tabcellsep 04/ 254 (01.57\%)\tabcellsep 03/ 82 (03.66\%)\tabcellsep 03/ 38 (07.89\%)\tabcellsep 18/ 99 (18.19\%)\tabcellsep \\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Result is highly\\ Bronchial asthma\tabcellsep 07/ 254 (07.25\%)\tabcellsep 06/ 82 (07.32\%)\tabcellsep 01/ 38 (02.63\%)\tabcellsep 10/ 99 (10.10\%)\tabcellsep significant at p <\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep .001.\end{longtable} \par {\small\itshape [Note: Figure-1: Co-morbidities of different stages of COVID patients]} \caption{\label{tab_1}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.02909090909090909\textwidth}P{0.17363636363636364\textwidth}P{0.44909090909090904\textwidth}P{0.01818181818181818\textwidth}P{0.017272727272727273\textwidth}P{0.01818181818181818\textwidth}P{0.14454545454545453\textwidth}} \multicolumn{2}{l}{Symptoms}\tabcellsep Mild case (n= 254)\tabcellsep Moderate case (n=82)\tabcellsep Severe case (n= 38)\tabcellsep Critical case (n=99)\tabcellsep Statistical Significance\\ Inflammatory\tabcellsep Fever\tabcellsep \multicolumn{4}{l}{47/ 254 (18.50\%) 37/ 82 (45.12\%) 12/ 38 (31.51\%) 63/ 99 (63.64\%)}\\ \tabcellsep Cough\tabcellsep \multicolumn{4}{l}{38/ 254 (14.96\%) 38/ 82 (46.34\%) 20/ 38 (52.63\%) 32/ 99 (32.32\%)}\tabcellsep Chi-square =\\ \tabcellsep SOB\tabcellsep \multicolumn{4}{l}{30/ 254 (11.81\%) 47/ 82 (57.32\%) 29/ 38 (76.31\%) 85/ 99 (85.85\%)}\tabcellsep 43.2556.\\ \tabcellsep Sore Throat\tabcellsep \multicolumn{4}{l}{10/ 254 (03.94\%) 04/ 82 (04.88\%) 02/ 38 (05.26\%) 01/ 99 (01.01\%)}\tabcellsep p=.00002.\\ \tabcellsep Diarrhea\tabcellsep \multicolumn{4}{l}{10/ 254 (03.94\%) 12/ 82 (14.63\%) 04/ 38 (10.52\%) 04/ 99 (04.04\%)}\tabcellsep Result is highly\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep significant at p <\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep .001\\ Neurological\tabcellsep Myalgia\tabcellsep \multicolumn{4}{l}{13/ 254 (05.12\%) 08/ 82 (09.76\%) 03/ 38 (07.89\%) 03/ 99 (03.03\%)}\tabcellsep Chi-square =\\ \tabcellsep Lethargy\tabcellsep \multicolumn{4}{l}{05/ 254 (01.97\%) 12/ 82 (14.63\%) 08/ 38 (21.05\%) 12/ 99 (12.12\%)}\tabcellsep 76.9569.\\ \tabcellsep \multicolumn{5}{l}{Anosmia Loss of taste 07/ 254 (02.75\%) 06/ 82 (07.32\%) 02/ 38 (05.26\%) 06/ 99 (06.06\%) 45/ 254 (17.71\%) 04/ 82 (04.88\%) 01/ 38 (02.63\%) -}\tabcellsep p< .00001 Result is highly\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep significant at p <\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep .001\end{longtable} \par \caption{\label{tab_2}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.09245444191343964\textwidth}P{0.0842255125284738\textwidth}P{0.1795842824601367\textwidth}P{0.11278473804100228\textwidth}P{0.12198177676537585\textwidth}P{0.13843963553530753\textwidth}P{0.12052961275626423\textwidth}} \multicolumn{2}{l}{Biomarkers}\tabcellsep Mild case (n= 254)\tabcellsep Moderate case (n=82)\tabcellsep Severe case (n= 38)\tabcellsep Critical case (n=99)\tabcellsep Statistical Significance Test\\ Hematological\tabcellsep Hb\%\tabcellsep 13.28± 2.32\tabcellsep 12.12± 1.67\tabcellsep 12.55±1.33\tabcellsep 12.33±2.15\tabcellsep p= .385118.\\ \tabcellsep Total WBC\tabcellsep 6,622± 2,432\tabcellsep 7,778± 3,059\tabcellsep 8,766±3,641\tabcellsep 10,532±4,174\tabcellsep **p= .005149.\\ \tabcellsep NLR\tabcellsep 2.18± 2.37\tabcellsep 04.48± 03.17\tabcellsep 05.09±03.23\tabcellsep 07.56± 5.43\tabcellsep ***p= < .00001.\\ \tabcellsep d-NLR\tabcellsep 1.68± 1.65\tabcellsep 03.36± 02.03\tabcellsep 03.93±02.38\tabcellsep 05.68± 4.60\tabcellsep ***p= < .00001.\\ \tabcellsep PLR\tabcellsep \multicolumn{3}{l}{128.35± 62.84 216.81±131.48 206.99±78.99}\tabcellsep \multicolumn{2}{l}{266.92±178.18 ***p= .000018.}\\ \tabcellsep Platelet\tabcellsep 253 X 10\textasciicircum 9±\tabcellsep 287X 10\textasciicircum 9±\tabcellsep 295X 10\textasciicircum 9±\tabcellsep 298X 10\textasciicircum 9±\tabcellsep *p= .037806.\\ \tabcellsep (10\textasciicircum 9/LX)\tabcellsep 71 X 10\textasciicircum 9\tabcellsep 103X 10\textasciicircum 9\tabcellsep 83X 10\textasciicircum 9\tabcellsep 99X 10\textasciicircum 9\tabcellsep \\ \tabcellsep HCT\tabcellsep 41.13± 6.83\tabcellsep 37.93± 4.96\tabcellsep 39.47± 4.32\tabcellsep 38.69± 5.38\tabcellsep p= .073442.\\ Inflammatory\tabcellsep CRP (mg/ L)\tabcellsep 9.70± 10.57\tabcellsep 17.39± 13.76\tabcellsep 33.56± 28.42\tabcellsep 35.49± 27.55\tabcellsep ***p= .000226.\\ \multicolumn{2}{l}{D dimer (mg/ L)}\tabcellsep 0.21± 0.59\tabcellsep 0.72± 01.73\tabcellsep 0.91± 01.38\tabcellsep 01.43± 02.05\tabcellsep p= .106931.\\ \multicolumn{2}{l}{Ferritin (ng/ml)}\tabcellsep \multicolumn{4}{l}{295.39±322.41 561.34±560.36 761.43±1020.33 897.20±644.04}\tabcellsep **p= .006706\\ Hepatic\tabcellsep SGPT (IU/ L)\tabcellsep 49.78± 36.71\tabcellsep 57.73± 45.28\tabcellsep 87.50± 83.06\tabcellsep 61.82± 44.28\tabcellsep *p= .042316.\\ \tabcellsep Prothrombin\tabcellsep 13.97± 2.13\tabcellsep 14.49± 02.86\tabcellsep 14.64± 02.10\tabcellsep 15.97± 02.66\tabcellsep ***p= .000023.\\ \tabcellsep time (Sec)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep INR\tabcellsep 1.07± 0.17\tabcellsep 01.10± 0.13\tabcellsep 01.17± 0.22\tabcellsep 01.20± 0.27\tabcellsep ***p= .000065.\\ Renal\tabcellsep S. creatinine\tabcellsep 1.23± 1.19\tabcellsep 01.15± 0.31\tabcellsep 01.77± 03.33\tabcellsep 01.76± 01.91\tabcellsep p= .432518.\\ \tabcellsep (mg/ dl)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Metabolic\tabcellsep HbA 1 C (\%)\tabcellsep 6.12± 1.19\tabcellsep 06.45± 1.52\tabcellsep 06.39± 1.05\tabcellsep 07.45± 01.04\tabcellsep p= .336891.\\ \tabcellsep Total\tabcellsep \multicolumn{2}{l}{160.99± 38.77 149.23± 42.57}\tabcellsep 138.45± 48.22\tabcellsep 138.34± 71.32\tabcellsep p= .658324.\\ \tabcellsep Cholesterol\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep Triglyceride\tabcellsep \multicolumn{2}{l}{230.28±160.01 189.51±130.99}\tabcellsep 142.6±71.48\tabcellsep 225.54± 94.59\tabcellsep p= .677266.\\ \tabcellsep HDL\tabcellsep 31.61± 9.08\tabcellsep 34.08± 12.51\tabcellsep 34.63± 14.89\tabcellsep 28.42± 10.01\tabcellsep p= .079309.\\ \tabcellsep LDL\tabcellsep 83.83± 31.71\tabcellsep 79.89± 29.76\tabcellsep 77.64± 39.61\tabcellsep 73.32± 41.65\tabcellsep p= .699251.\\ \multicolumn{6}{l}{* stands for significance p<.05, ** stands for significance p<.01, *** stands for significance p<.001}\tabcellsep \\ IV.\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_3}Table 4 :}\end{figure} \backmatter \subsection[{Acknowledgments}]{Acknowledgments}\par The authors acknowledge the contribution and dedication of all the healthcare workers of Holy Family Red Crescent Medical College Hospital for their services and participation in keeping the manual records of patients' 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