Study of CRP, Ferritin and D-Dimer in Covid-19 RICU Patients as per HRCT severity in Assiut University Hospitals

Study of CRP, Ferritin and D-Dimer in Covid-19 RICU Patients as per HRCT severity in Assiut University Hospitals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Study of CRP, Ferritin and D-Dimer in Covid-19 RICU Patients as per HRCT severity in Assiut University Hospitals Mohamed F. Abdel-Ghany, Atef Farouk, Mina Ibraheem Anis (corresponding author), and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4940615/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Patients with Coronavirus disease (COVID-19) was found to exhibit elevated levels of inflammatory cytokines, which were linked to pulmonary inflammation, lung damage, and end with multi-organ failure.C-reactive protein (CRP), serum ferritin and D dimer levels may predict severity and mortality. Radiology plays a key role in the diagnosis, management, and follow-up of this disease. We attempted to describe the radiological features of SARS-CoV-2 infection in its original form, to correlate the HRCT patterns with clinical findings, C-reactive protein (CRP), D-dimer and ferritin and to consider as predictors of morbidity and mortality in adult (ICU) patients with COVID-19. Methods This prospective cross-sectional analytic work had been conducted on 159 patients aged ≥ 18 years old, admitted at Assiut University Hospital Respiratory ICU from November 2021 to November 2022, diagnosed as COVID-19 by positive RT-PCR. All cases were categorized on bases of (HRCT chest) disease reporting and data system (CO-RADS) scoring classification.Oxygen saturation, and inflammatory markers such as CRP, Ferritin and D dimer were measured. Age, sex, comorbidities, use of MV mechanical ventilation, and outcomes as per HRCT severity were key observations. Results A total of 159 HRCT chest scans of symptomatic RT-PCR-positive ICU patients were recruited. Higher CRP and Ferritinserum levels, lower lymphocytic count, higher frequency of need for mechanical ventilation were significantly greater in the severe group as assessed by HRCT severity score (CORAD 4,5) (P 133 mg/dlserum level, had 65.7% overall accuracywith AUC: 0.673(OR:2.19(P < 0.0001)),DM (OR:3.45(P < 0.0001)), chronic chest disease (OR:2.22(P < 0.0001)). Mortality predictors were age (OR:1.78(P < 0.0001)), DM (OR:2.89(P < 0.0001)), chronic chest disease (OR:3.01(P < 0.0001)), serum CRP levels (OR:2.11(P < 0.0001)). Need for mechanical ventilation and mortality rate as regards CT severity score were 76(66%),75 (65%), versus 3(7%) 4(9%) between severe and non-severe groups respectively (P < 0.0001). Conclusions High-resolution computed tomography (HRCT) scan of the chest as well as CRP and ferritin plasma levels are valuable methodsand significant predictors for future prognosis in patients with covid19 at risk of death and in need for MV. ’- Reactive Protein Covid-19 Ferritin D-Dimer ICU Assiut Introduction COVID-19 is the illness produced by a novel coronavirus known as SARSCoV-2. WHO initiallygaining awareness of this novel virus on December 31, 2019, upon getting an alertabout a group of instances of "viral pneumonia" in Wuhan, China [1, 2] . Recent studies have investigated the importance of chest computed tomography (CT), especially high-resolution CT (HRCT), in COVID-19 patients who have the virus. According to previous literature, a sensitivity of 98% is present for diagnosis and monitoring the progression of the disease and efficacy of treatment. HRCT can categorize stage and severity of COVID Pneumonia [3] . Patients with COVID-19 frequently exhibit elevated concentrations of inflammatory markers, likeD-dimer, CRP, ferritin, lactate dehydrogenase (LDH), and IL-6 [4,5] . The distribution features of CRP varied across COVID-19 patients, with variances observed in different age groups, clinical types, and outcomes. The characteristics aligned with the advancement of the illness [6] . The presence of an excessive release of cytokines and elevated levels of ferritin suggests that COVID-19 may be a component of the spectrum of hyperferritinemic syndrome [5] . Individuals withCOVID-19 having more severe disease exhibited elevated levels of inflammatory cytokines, which were correlated to lung inflammations, destruction, and multiple organ failure. [7, 8] . The exact causes for the extensive presence of inflammatory cytokines remain uncertain, however, they may have a significant impact on cell death linked to organ damage [9,10] . Multiple studies conducted in Wuhan have revealed that COVID-19 individuals with increased levels of D-dimer exhibited a greater risk of mortality. However, the impact of anticoagulation on concentrations of D-dimer among COVID-19 individuals is not well understood. Typically, patients who receive anticoagulation treatment tend to have very low D-dimer levels [11] . While various research has indicated a potential link between elevated levels of Ferritin, CRP, and D-Dimer with the presence of severe disease, the findings across these studies are not completely uniform. Currently, it remains uncertain if there is a substantial difference in inflammatory markers between people with severe COVID-19 contrasted to individuals with mild disease [12] . The purpose of this work was to determine the role of C- reactive protein (CRP), D-dimer and ferritin as predictors of morbidity and mortality in adult (ICU) patients with COVID-19. Patients And Methods Ethical approval: Ethics committee of the Faculty of Medicine, Assiut University (IRB no.17101508) approved this work. A written informed consent form was permanently incorporated into the participants' study records and was stored in the same manner as other records. All identifying data regarding the patients was concealed, and each participant was allocated a code to ensure confidentiality and privacy of the data. Thiswork had been conducted in compliance with the recommendationsclearly definedin the Declaration of Helsinki. And registered in clinicaltrials.gov with the number (NCT05102695). This prospective cross-sectional analytic work had been conducted on159 patients aged ≥ 18 years old, both sexes, diagnosed as COVID-19. The study was done from November 2021 to November 2022. A total of 159 HRCT chest scans of symptomatic RT-PCR-positive ICU patients were recruited. Criteria for exclusion were patients diagnosed as COVID-19 not admitted to RICU or Discharged from emergency department for home isolation.Each participant had been exposed to complete taking of history, clinical examinations, laboratory tests [serum ferritin, CRP, D-dimer and Arterial Blood Gases (ABG)]. Arterial Blood Gas: We used IL-GEM-Premier3000 device.The patient’s wrist was held and extended by approximately 30°. The needle was inserted through the skin at the insertion site at an angle of 30-45°. The needle was advanced slowly towards the pulsation until we felt a sudden reduction in resistance and then a rush of blood is seen back into the ABG syringe (this is known as “flashback”). Once the required amount of blood has been collected, we removed the needle and applied immediate firm pressure over the puncture site with some gauze. Measurement of serum CRP, ferritin and plasma D-dimer levels: We utilized the Cobas® 6000 modular (c501 and e601) Biochemistry and Immunoassay analyser to measure the levels of serum ferritin and CRP. The reagent kits utilized were from Roche Diagnostics, a company based in Basel, Switzerland. The serum CRP was estimated using a particle enhanced immunoturbidimetric test, and its biological reference interval (BRI) was defined as being less than 5 mg/L. The blood ferritin level was determined by electrochemiluminescence immunoassay (ECLIA), with a BRI of 30 – 400 μg/L for males and 13 – 150 μg/L for females. The serum D-dimer was measured using the STAR Max® hemostasisanalyzer from Diagnostica Stago Inc, located in Asnières-sur-Seine, France. The measurement was performed using an immune-turbidimetric assay. The BRI for it was deemed to be less than 0.5 μg/mL. The BRI used were those introduced by the manufacturer and specified in the package inserts of the individual diagnostic kits. The assessment of quality control was conducted employing the internal quality control material "PreciControl" supplied by Cobas, Roche Diagnostics, based in Basel, Switzerland. Assessment of Covid-19 severity by HRCT chest and CO-RADS Scoring classification: The participants were categorized into non-severe group (CORAD 1, 2,3) and severe group CORAD (4,5). We used GE Scanner 64 slice machine. Real Time PCR System. Measurement of PCR level: PCR-Q96 Series machine, we used this it for analysis of the PCR Swabs Taken from each patient at least once. A thin and flexible elongated nasal swab (specifically a nasopharyngeal swab) was put into the patient's nostril, with a cotton tip at the end. Subsequently, another stick was used to brush the swab down the posterior part of the patient's throat, in order to get a sample of mucus. To get an adequate amount of mucus for the test, swabbing might be performed in both nostrils during the nasal sample collection. The swab was left in position momentarily before being delicately spun while being withdrawn. The specimen was securely enclosed within a tube and delivered to a laboratory for examination. Statistical analysis Statistical Analysis had been conducted employing (SPSS-Version 20) software. All Data was displayed as means and frequencies. Clinical characteristics were compared through student test for continuous variables for 2 groups. Proportions had been contrasted with Chi-square tests. Graphics had been conducted employing Microsoft Excel. P value was considered significant if < 0.05. Results Patients were categorized into 2 groups according to HRCT chest (CORAD) severity scoring system. Patients with Co-rad 1,2,3: were categorized as non-severe group while Co-rad 4,5: the severe group. No significant difference between both groups was found as regards gender, residence, smoking and comorbidities (HTN, CVS, hepatic affection and presence of malignancy). Age, DM and chronic chest disease were significantly greater among severe group contrasted to non-severe group (P<0.05). Table1 Table 1: Baseline data and comorbidities of the studied patients based on disease's severity Non-severe group (n= 44) Severe group (n= 115) P Age (years) 51.93±14.75 63.54±16.32 < 0.001* Sex Male 25 (56.8%) 65 (56.5%) 0.55 Female 19 (43.2%) 50 (43.5%) Residence Rural 25 (56.8%) 62 (53.9%) 0.44 Urban 19 (43.2%) 53 (46.1%) Smoking Cigarette 18 (40.9%) 44 (38.3%) 0.44 Goza 12 (27.3%) 33 (28.7%) 0.51 Comorbidities DM 16 (36.4%) 78 (67.8%) < 0.001* HTN 13 (29.5%) 37 (32.2%) 0.45 IHD 14 (31.8%) 45 (39.1%) 0.25 CVS 5 (11.4%) 19 (16.5%) 0.29 CKD 8 (18.2%) 25 (21.2%) 0.39 Chronic Chest disease 5 (11.4%) 45 (39.1%) < 0.001 Liver disease 4 (9.1%) 8 (7%) 0.43 Malignancy 1 (2.3%) 6 (5.2%) 0.37 Data are presented as mean ± SD or frequency (%). * Significant p value <0.05, DM: Diabetes mellitus, COPD: chronic obstructive pulmonary disease, HTN: hypertension, CVS: cerebrovascular stroke,IHD: ischemic heart disease, CKD: chronic kidney disease. At presentation severe group were more tachycardic, tachypnic and hypotensive as compared to non-severe group. Table 2 Table 2: Clinical data of the studied patients based on disease radiological severity Non-severe group (n= 44) Severe group (n= 115) P Body ache 44 (100%) 113 (98.3%) 0.52 Fever 25 (56.8%) 80 (69.6%) 0.09 Cough 40 (90.9%) 112 (97.4%) 0.34 Dyspnea 43 (97.7%) 113 (98.3%) 0.62 Loss of smell/taste 24 (54.5%) 67 (58.3%) 0.40 Diarrhea 22 (50%) 62 (53.9%) 0.39 Abdominal pain 22 (50%) 53 (46.1%) 0.40 Vomiting 23 (52.3%) 59 (51.3%) 0.52 Temperature (°C) 37.53 ± 0.79 37.67 ± 0.64 0.15 SBP (mmHg) 123.87 ± 21.21 115.48 ± 25.46 < 0.001* Heart rate (b/m) 93.65 ± 15.35 100.53 ± 20.57 < 0.001* DBP (mmHg) 77.89 ± 12.34 71.44 ± 16.97 < 0.001* RR (cycle/m) 28.93 ± 6.58 34.17 ± 7.75 < 0.001* Data are presented as mean ± SD or frequency (%). * Significant p value <0.05, DBP: diastolic blood pressure, RR: respiratory rate, SBP: systolic blood pressure. On laboratory assessment, higher CRP and ferritin levels, lower lymphocytic count and more desaturation was significantly found among the severe group (P<0.05). Table 3 Table 3: Arterial blood gases and laboratory data of the studied patients based on disease's radiological severity Non-severe group (n= 44) Severe group (n= 115) P ABG pH° 7.46 ± 0.07 7.43 ± 0.11 0.03* PCO 2 (mmHg) 35.03 ± 12.30 33.21 ± 15.99 0.38 PO 2 (mmHg) 47.65 ± 13.81 40.97 ± 12.45 <0.001* Lactate (mmol/l) 1.85 ± 1.23 2.98 ± 2.55 <0.001* HCO 3 (mmol/l) 24.64 ± 6.86 21.72 ± 7.82 <0.001* Basedeficit(mmol/l) 2.96 ± 0.87 2.17 ± 1.19 0.25 Oxygen saturation (%) 81.42 ± 13.81 72.58 ± 17.41 <0.001* Laboratory data Leucocytes (10 3 /ul) 9.48 ± 5.31 12.01 ± 6.45 < 0.001* Neutrophils (10 3 /ul) 7.56 ± 5.14 10.26 ± 5.82 < 0.001* Lymphocyte (10 3 /ul) 1.25 ± 0.75 0.81 ± 0.61 < 0.001* Hemoglobin (g/dl) 12.69 ± 2.22 12.15 ± 2.65 0.07 Platelets (10 3 /ul) 256.79 ± 113.26 254.14 ± 118.91 0.27 INR 1.10 ± 0.21 1.27 ± 0.73 < 0.001* Creatinine (mmol/l) 121.94 ± 24.80 178.25 ± 88.87 < 0.001* Urea (mmol/l) 9.94 ± 7.48 16.94 ± 9.65 < 0.001* Potassium (mmol/l) 4.14 ± 0.74 4.26 ± 0.76 0.20 Sodium (mmol/l) 136.68 ± 5.17 138.23 ± 5.65 0.74 Albumin (mg/dl) 35.10 ± 5.73 32.97 ± 5.33 < 0.001* Bilirubin (umol/l) 9.20 ± 3.45 9.82 ± 2.83 0.56 ALT (u/L) 44.09 ± 11.34 54.83 ± 8.98 0.20 AST (u/L) 45.96 ± 9.45 80.23 ± 32.23 < 0.001* ALP (u/L) 101.77 ± 63.44 114.45 ± 83.87 0.81 CRP (mg/dl) 89.36 ± 16.56 142.87 ± 25.57 < 0.001* Ferritin (ng/ml) 612.34 ± 34.76 1116.87 ± 123.45 < 0.001* D-dimer (mg/l) 2.30 ± 0.48 3.49 ± 0.35 0.06 Data are presented as mean ± SD. * Significant p value <0.05,ABG: arterial blood gases, INR: international randomized ratio, AST: aspartate transaminase, CRP: c reactive protein,ALT: alanine transaminase, ALP: alkaline phosphatase. Need for mechanical ventilation and mortality rate and as regards CT severity score were 76(66%),75 (65%), versus 3(7%)75(65%) between severe and non-severe groups respectively (P < 0.001). Table 4 Table 4: Need for mechanical ventilation and mortality outcome of the studied patients based on disease's radiological severity Non-severe group (n= 44) Severe group (n= 115) P Venturi mask 41 (93.2%) 41 (35.7%) < 0.001* Nasal canula 14 (31.8%) 39 (33.9%) 0.47 Need for NIV 17 (38.6%) 47 (40.9%) 0.40 Need for MV 3 (6.8%) 76 (66.1%) < 0.001* Outcome Survived 40 (90.9%) 40 (34.8%) < 0.001* Not survived 4 (9.1%) 75 (65.2%) Data are presented as mean ± SD or frequency (%). * Significant p value <0.05, NIV: non-invasive ventilation, MV: mechanical ventilation. Discussion December 2019, Chinese scientists first identified SARS-CoV-2 as the etiology of COVID-19 disease [13] . At first, COVID-19 was classified as a respiratory illness, with pneumonia to be the prevailing and most fatal consequence. However, SARS-CoV-2 had been revealed to elicit an exaggerated and unregulated immune responses, known as immune hemostasis, which leads to various complications including thrombosis, damage to tissues, DIC, ARDS, and MODS [14] ; Therefore, it is imperative to comprehend COVID-19 not only as a respiratory illness but additionally as a possible multisystem disorder [15] . In this study, as regard comorbidities: significantly higher DM (67.8% vs. 36.4%; p < 0.001) and chronic chest disease (39.1% vs. 11.4%; p < 0.001) among severe group was found. Hypertension as recorded was (29.5%; 32.2%) in non-severe group and severe group respectively. CVS was (11.4%; 16.5%) in non-severe group and severe group correspondingly. CKD was (18.2%; 21.2%) in non-severe group and severe group respectively. In accordance, Abdelfattah et al. [16] reported that a strong association existed among the COVID-19 infection severity and the prevalence of co-morbidities, particularly systemic HTNand DM. Ji et al. [17] also observed similar findings, A countrywide retrospective case-control research was undertaken in Korea, involving 219,961 participants with COVID-19. The work found that co-morbidities, particularly DM and HTN, had a substantial impact on the COVID-19 infectionseverity. According to Khadija et al. [18] , 32% of individuals with DM had HTN and 9.2% had underlying ischemic cardiac disease. Both of these conditions are known to be risk factors for negative outcomes among individuals with COVID infections. In this study, it was found that lymphocytic count was significantly lower among patients with severe group. Also, severe groups exhibited significantly greater serum CRP levels and serum ferritin levels. While no statistically significant variation existed as regards levels of D-dimer among non-severe and severe groups of patients. This agrees with Abdelfattah et al. [16] reported that A strong connection was seen between serum ferritin and D-dimer and the COVID-19 infection severity. Furthermore, Elsharawy et al. [19] found a correlation between serum ferritin levels and both the severity of the illness and the ability to predict admission to the ICU. This was in opposition to the findings published by Yao et al. [20] , The study discovered a significant association amonglevel of D-dimer and the severity of the illness. Furthermore, they observed that D-dimer level served as a dependable prognostic marker for predicting in-hospital mortality in individuals admitted for COVID-19. This was in contrast with what was reported by Yao et al. [20] found that D-dimer level correlates with disease severity and was a reliable prognostic marker for in-hospital mortality in subjects admitted for COVID-19. In this study, it was found that frequency of need for MV was significantly higher among patients with severe disease (66.1% vs. 6.8%; p < 0.001) while venture mask was the method of correction of desaturation among non-severe group (93.2% vs. 35.7%; p < 0.001). In accordance, Abdelfattah al. [16] reported that, a positive correlation was found between severity of COVID-19 infection, days of hospital stay, the need for ICU admission, and mechanical ventilation which seems a logic finding. Higher levels of serum CRP are associated with higher mortality in people with severe COVID-19 disease [20] . more specifically, CRP values above 77.35 mg/L [21] . Davoudi et al. [22] reported that, although the level of CRP was higher in non-survived patients, this difference was not statistically significant. This is the same as proved by Alroomi et al. [23] conducted a retrospective study on 595 COVID-19 subjects, where higher levels of serum ferritin were found to be an independent predictor of mortality. Blood picture of patients with COVID-19 characterized by normal or low count of WBCs and decreased level of lymphocytes. Increased levels of WBCs and neutrophils were found in 68% and 72% of patients [24] . Petrilli et al. [25] reported striking findings regarding the predictive value of inflammatory markers to distinguish future critical from non-critical illness. In this study, at cut off > 133 mg/dl, baseline serum CRP level had 65.7% overall accuracy in prediction of severe disease among the studied with area under curve was 0.673. Ahnachet al. [27] confirmed that the CRP was a robust predictor of adverse disease outcome. CRP was also an independent discriminator of severe/critical illness on admission in comparison with other biological factors. These results were in agreement with similar report of Luo et al. [28] found an AUC of CRP for discriminating disease severity on admission at 0.783. With a cut-off value of 41.3, CRP exhibited similar results of our study with sensitivity of 65%, specificity of 83.7%, PPV of 81.6%, and NPV of 68.2%. In this study, it was found that predictors for severe disease among the studied patients were DM (odd's ratio = 3.45), chronic chest disease (odd's ratio = 2.22) and CRP (odd's ratio = 2.19). Similarly, Wang et al. [29] and Wu et al. [30] reported a significant association of the COVID-19 severity with diabetes. In this study, it was found that predictors for mortality among the studied patients were age (odd's ratio = 1.78), DM (odd's ratio = 2.89), chronic chest disease (odd's ratio = 3.01), serum albumin (odd's ratio = 1.90), serum CRP levels (odd's ratio = 2.11) and d-dimer (odd's ratio = 2.98). This is consistent with the findings stated by Shi et al. [31] reported that, age ≥ 70 years was found to be an independent risk factor for in-hospital death for patients with diabetes as well as for patients without diabetes (hazard ratio (HR) 2.39 and 5.87, respectively). In this study, at cut off> 150 mg/dl, baseline serum CRP level had 63% overall accuracy in prediction of mortality among the studied with area under curve was 0.674. meanwhile, baseline d-dimer had 67.7% overall accuracy in prediction of mortality among the studied with area under curve was 0.706. In a cohort conducted by Smilowitz et al. [33] , including 2782 COVID-19 patients, CRP levels above 108mg/L we associated with disease severity (47,6% vs 25,9%) and a higher mortality (32,2% vs 17,8%) (277). Similarly, in a retrospective study conducted by Sadeghi et al. [33] including 429 patients, it has been shown that not only the severe cases had significantly higher CRP levels than non-severe patients, but also that patients with CRP > 64.75 mg/L were more likely to have severe complications. Limitations of the work involved the relatively small sample size. The work had been conducted in a single center. Conclusion Patients with high radiological severity of the disease, as determined by HRCT severity score system (CO-rad5,6) had lower lymphocyte counts, elevated CRP, and increased ferritin level but no D-dimer significance. Also, individuals with severe illness exhibited a greater mean of age, gender difference towards malesand comorbiditiesDM and chronic chest disease. High-resolution computed tomography (HRCT) scan of the chest as well as CRP and ferritin plasma levels are valuable methods and significant predictors for future prognosis in patients with covid19 which had an important role in early identification of patients at risk of death and need for MV. Declarations Fund: No financial support was required Conflict of interest: Authors declared no conflict in the current work. IRB no : 17101508 Author Contribution M.F and A.F contributed to the study design, conception, and manuscript revision. M.I contributed to literature search data collection and manuscript writing. (corresponding author)S.F contributed to statistical analysis, manuscript writing and revision. Acknowledgement none References Organization WH. COVID-19 weekly epidemiological update, 9 March 2021.J Intensive Care.2021;123:985–1010. Wiens KE, Mawien PN, Rumunu J, Slater D, Jones FK, Moheed S, et al. 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Clinical characteristics and risk factors for mortality of COVID-19 patients with diabetes in Wuhan, China: A two-center, retrospective study.Diabetes Care.2020;43:1382–91. Smilowitz NR, Kunichoff D, Garshick M, Shah B, Pillinger M, Hochman JS, et al. C-reactive protein and clinical outcomes in patients with COVID-19.Eur Heart J.2021;420:2270–9. Sadeghi-Haddad-Zavareh M, Bayani M, Shokri M, Ebrahimpour S, Babazadeh A, Mehraeen R, et al. C-reactive protein as a prognostic indicator in COVID-19 patients.Interdiscip Perspect Infect Dis.2021;2021:55–69. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Oct, 2024 Reviews received at journal 04 Oct, 2024 Reviewers agreed at journal 04 Oct, 2024 Reviews received at journal 23 Sep, 2024 Reviewers agreed at journal 20 Sep, 2024 Reviewers invited by journal 22 Aug, 2024 Editor assigned by journal 22 Aug, 2024 Submission checks completed at journal 21 Aug, 2024 First submitted to journal 19 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4940615","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":354997393,"identity":"4f47c07f-e3f1-4e42-90a7-e17b766a415a","order_by":0,"name":"Mohamed F. Abdel-Ghany","email":"","orcid":"","institution":"Assiut University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"F.","lastName":"Abdel-Ghany","suffix":""},{"id":354997394,"identity":"414d47c3-14bc-4b35-a341-74f96d5ee136","order_by":1,"name":"Atef Farouk","email":"","orcid":"","institution":"Assiut University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Atef","middleName":"","lastName":"Farouk","suffix":""},{"id":354997395,"identity":"aea22860-e1d6-4208-949f-6171469c7e0a","order_by":2,"name":"Mina Ibraheem Anis (corresponding author)","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDACZhBhwMDAL3/+4wMgk4ePaC2SMxiMDUBa2Ii2zeAGg5kEiEFQi24778FPNwruyDPcbkir/JpjJ8PGwPzw0Q08WswO8yVL5xg8M2ycc+DYbdltyUCHsRkb5+DVwmMA1HKYsZkhse225DZmoBYeNmkCWox/A7XYtzEksxVLbqsnSosZyJbEHok0NsaP2w4Tp8UaqCV5Bs8ZZmnGbcd52JgJ+eX8GePbOX8O2+4/3sP48ee2ant+9uaHj/FpQQHMPGCSWOUgwPiDFNWjYBSMglEwYgAAVItEil+0e8sAAAAASUVORK5CYII=","orcid":"","institution":"Assiut University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mina","middleName":"Ibraheem Anis (corresponding","lastName":"author)","suffix":""},{"id":354997396,"identity":"185bc753-4872-4104-9fd0-dc587e07b7d5","order_by":3,"name":"Sahar Farghly Youssif","email":"","orcid":"","institution":"Assiut University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"Farghly","lastName":"Youssif","suffix":""}],"badges":[],"createdAt":"2024-08-19 19:29:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4940615/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4940615/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64864232,"identity":"009efd56-efe4-40f5-976d-13ce64051e63","added_by":"auto","created_at":"2024-09-19 17:42:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":923064,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4940615/v1/b7c3ecf0-f6dc-4028-a6fb-44ac58d1ff92.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study of CRP, Ferritin and D-Dimer in Covid-19 RICU Patients as per HRCT severity in Assiut University Hospitals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCOVID-19 is the illness produced by a novel coronavirus known as SARSCoV-2. WHO\u0026nbsp;initiallygaining awareness\u0026nbsp;of this novel virus on December 31, 2019,\u0026nbsp;upon getting an alertabout a group of instances of \u0026quot;viral pneumonia\u0026quot; in Wuhan, China\u0026nbsp;\u003csup\u003e[1, 2]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRecent studies have investigated the importance of chest computed tomography (CT), especially high-resolution CT (HRCT), in COVID-19 patients who have the virus. According to previous literature, a sensitivity of 98% is present for diagnosis and monitoring the progression of the disease and efficacy of treatment. HRCT can categorize stage and severity of COVID Pneumonia\u003csup\u003e[3]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePatients with COVID-19 frequently exhibit elevated concentrations of inflammatory markers, likeD-dimer, CRP,\u0026nbsp;ferritin,\u0026nbsp;lactate dehydrogenase (LDH),\u0026nbsp;and IL-6\u0026nbsp;\u003csup\u003e[4,5]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe distribution features of CRP varied across COVID-19 patients, with variances observed in different age groups, clinical types, and outcomes. The characteristics aligned with the advancement of the illness\u0026nbsp;\u003csup\u003e[6]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe presence of an excessive release of cytokines and elevated levels of ferritin suggests that COVID-19 may be a component of the spectrum of hyperferritinemic syndrome\u0026nbsp;\u003csup\u003e[5]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIndividuals withCOVID-19 having more severe disease exhibited elevated levels of inflammatory cytokines, which were correlated to lung inflammations, destruction, and multiple organ failure.\u003csup\u003e[7, 8]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe exact causes for the extensive presence of inflammatory cytokines remain uncertain, however, they may have a significant impact on cell death linked to organ damage\u0026nbsp;\u003csup\u003e[9,10]\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultiple studies conducted in Wuhan have revealed that COVID-19 individuals with increased levels of D-dimer exhibited a greater risk of mortality. However, the impact of anticoagulation on concentrations of D-dimer among COVID-19 individuals is not well understood. Typically, patients who receive anticoagulation treatment tend to have very low D-dimer levels\u0026nbsp;\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile various research has indicated a potential link between elevated levels of Ferritin, CRP, and D-Dimer with the presence of severe disease, the findings across these studies are not completely uniform. Currently, it remains uncertain if there is a substantial difference in inflammatory markers between people with severe COVID-19 contrasted to individuals with mild disease\u0026nbsp;\u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe purpose of this work was to determine the role of C- reactive protein (CRP), D-dimer and ferritin as predictors of morbidity and mortality in adult (ICU) patients with COVID-19.\u0026nbsp;\u003c/p\u003e"},{"header":"Patients And Methods","content":"\u003cp\u003eEthical approval:\u003c/p\u003e\n\u003cp\u003eEthics committee of the Faculty of Medicine, Assiut University (IRB no.17101508) approved this work. A written informed consent form was permanently incorporated into the participants\u0026apos; study records and was stored in the same manner as other records. All identifying data regarding the patients was concealed, and each participant was allocated a code to ensure confidentiality and privacy of the data. Thiswork had been conducted in compliance with the recommendationsclearly definedin the Declaration of Helsinki. And registered in clinicaltrials.gov with the number (NCT05102695).\u003c/p\u003e\n\u003cp\u003eThis prospective cross-sectional analytic work had been conducted on159 patients aged \u0026ge; 18 years old, both sexes, diagnosed as COVID-19. The study was done from November 2021 to November 2022. A total of 159 HRCT chest scans of symptomatic RT-PCR-positive ICU patients were recruited. Criteria for exclusion were patients diagnosed as COVID-19 not admitted to RICU or Discharged from emergency department for home isolation.Each participant had been exposed to complete taking of history, clinical examinations, laboratory tests [serum ferritin, CRP, D-dimer and Arterial Blood Gases (ABG)].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArterial Blood Gas:\u003c/strong\u003eWe used IL-GEM-Premier3000 device.The patient\u0026rsquo;s wrist was held and extended by approximately 30\u0026deg;. The needle was inserted through the skin at the insertion site at an angle of 30-45\u0026deg;. The needle was advanced slowly towards the pulsation until we felt a sudden reduction in resistance and then a rush of blood is seen back into the ABG syringe (this is known as \u0026ldquo;flashback\u0026rdquo;). Once the required amount of blood has been collected, we removed the needle and applied immediate firm pressure over the puncture site with some gauze.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of serum CRP, ferritin and plasma D-dimer levels:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the Cobas\u0026reg; 6000 modular (c501 and e601) Biochemistry and Immunoassay analyser to measure the levels of serum ferritin and\u0026nbsp;CRP. The reagent kits utilized were from Roche Diagnostics, a company based in Basel, Switzerland. The serum CRP was estimated using a particle enhanced immunoturbidimetric test, and its biological reference interval (BRI) was defined as being less than 5 mg/L. The blood ferritin level was determined by electrochemiluminescence immunoassay (ECLIA), with a BRI of 30 \u0026ndash; 400 \u0026mu;g/L for males and 13 \u0026ndash; 150 \u0026mu;g/L for females.\u003c/p\u003e\n\u003cp\u003eThe serum D-dimer was measured using the STAR Max\u0026reg; hemostasisanalyzer from Diagnostica Stago Inc, located in Asni\u0026egrave;res-sur-Seine, France. The measurement was performed using an immune-turbidimetric assay.\u0026nbsp;\u003cbr\u003e\u0026nbsp;The\u0026nbsp;BRI\u0026nbsp;for it was deemed to be less than 0.5 \u0026mu;g/mL. The\u0026nbsp;BRI\u0026nbsp;used were those introduced by the manufacturer and specified in the package inserts of the individual diagnostic kits. The assessment of quality control was conducted employing the internal quality control material \u0026quot;PreciControl\u0026quot; supplied by Cobas, Roche Diagnostics, based in Basel, Switzerland.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Covid-19 severity by HRCT chest and CO-RADS Scoring classification:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants were categorized into non-severe group (CORAD 1, 2,3) and severe group CORAD (4,5). We used GE Scanner 64 slice machine. Real Time PCR System.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of PCR level:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePCR-Q96 Series machine, we used this it for analysis of the PCR Swabs Taken from each patient at least once.\u0026nbsp;A thin and flexible elongated nasal swab (specifically a nasopharyngeal swab) was put into the patient\u0026apos;s nostril, with a cotton tip at the end. Subsequently, another stick was used to brush the swab down the posterior part of the patient\u0026apos;s throat, in order to get a sample of mucus. To get an adequate amount of mucus for the test, swabbing might be performed in both nostrils during the nasal sample collection. The swab was left in position momentarily before being delicately spun while being withdrawn. The specimen was securely enclosed within a tube\u0026nbsp;and delivered to a laboratory for examination.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eStatistical Analysis had been conducted employing (SPSS-Version 20) software. All Data was displayed as means and frequencies. Clinical characteristics were compared through student test for continuous variables for 2 groups. Proportions had been contrasted with Chi-square tests. Graphics had been conducted employing Microsoft Excel. P value was considered significant if \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePatients were categorized into 2 groups according to HRCT chest (CORAD) severity scoring system. Patients with Co-rad 1,2,3: were categorized as non-severe group while Co-rad 4,5: the severe group.\u003c/p\u003e\n\u003cp\u003eNo significant difference between both groups was found as regards gender, residence, smoking and comorbidities (HTN, CVS, hepatic affection and presence of malignancy). Age, DM and chronic chest disease were significantly greater among severe group contrasted to non-severe group (P\u0026lt;0.05).\u003cstrong\u003e\u0026nbsp;Table1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1: Baseline data and comorbidities of the studied patients based on disease\u0026apos;s severity\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.45360824742268%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-severe group (n= 44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere group (n= 115)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.45360824742268%\" colspan=\"2\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e51.93\u0026plusmn;14.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e63.54\u0026plusmn;16.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e25 (56.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e65 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.28358208955224%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.865671641791046%\"\u003e\n \u003cp\u003e19 (43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003e50 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e25 (56.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e62 (53.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.28358208955224%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.865671641791046%\"\u003e\n \u003cp\u003e19 (43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.850746268656717%\"\u003e\n \u003cp\u003e53 (46.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCigarette\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e18 (40.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e44 (38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.70886075949367%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGoza\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.78481012658228%\"\u003e\n \u003cp\u003e12 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\"\u003e\n \u003cp\u003e33 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e16 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e78 (67.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.70886075949367%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHTN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.78481012658228%\"\u003e\n \u003cp\u003e13 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\"\u003e\n \u003cp\u003e37 (32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.70886075949367%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.78481012658228%\"\u003e\n \u003cp\u003e14 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\"\u003e\n \u003cp\u003e45 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.70886075949367%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.78481012658228%\"\u003e\n \u003cp\u003e5 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\"\u003e\n \u003cp\u003e19 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.70886075949367%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.78481012658228%\"\u003e\n \u003cp\u003e8 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\"\u003e\n \u003cp\u003e25 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.70886075949367%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Chest disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.78481012658228%\"\u003e\n \u003cp\u003e5 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\"\u003e\n \u003cp\u003e45 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.70886075949367%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.78481012658228%\"\u003e\n \u003cp\u003e4 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\"\u003e\n \u003cp\u003e8 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.70886075949367%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.78481012658228%\"\u003e\n \u003cp\u003e1 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\"\u003e\n \u003cp\u003e6 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.189873417721518%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eData are presented as mean \u0026plusmn; SD or frequency (%). * Significant p value \u0026lt;0.05, DM: \u0026nbsp;Diabetes mellitus, COPD: chronic obstructive pulmonary disease, HTN: hypertension, CVS: cerebrovascular stroke,IHD: ischemic heart disease, CKD: chronic kidney disease.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt presentation severe group were more tachycardic, tachypnic and hypotensive as compared to non-severe group. \u0026nbsp;\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2: Clinical data of the studied patients based on disease radiological severity\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eNon-severe group (n= 44)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSevere group (n= 115)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eP\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eBody ache\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e44 (100%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e113 (98.3%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.52\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eFever\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e25 (56.8%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e80 (69.6%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.09\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eCough\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e40 (90.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e112 (97.4%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eDyspnea\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e43 (97.7%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e113 (98.3%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.62\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eLoss of smell/taste\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e24 (54.5%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e67 (58.3%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eDiarrhea\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e22 (50%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e62 (53.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eAbdominal pain\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e22 (50%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e53 (46.1%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eVomiting\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e23 (52.3%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e59 (51.3%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.52\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eTemperature (\u0026deg;C)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e37.53 \u0026plusmn; 0.79\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e37.67 \u0026plusmn; 0.64\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.15\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSBP (mmHg)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e123.87 \u0026plusmn; 21.21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e115.48 \u0026plusmn; 25.46\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt; 0.001*\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eHeart rate (b/m)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e93.65 \u0026plusmn; 15.35\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e100.53 \u0026plusmn; 20.57\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt; 0.001*\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eDBP (mmHg)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e77.89 \u0026plusmn; 12.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e71.44 \u0026plusmn; 16.97\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt; 0.001*\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eRR (cycle/m)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e28.93 \u0026plusmn; 6.58\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e34.17 \u0026plusmn; 7.75\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt; 0.001*\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eData are presented as mean \u0026plusmn; SD or frequency (%). * Significant p value \u0026lt;0.05, DBP: diastolic blood pressure, RR: respiratory rate, SBP: systolic blood pressure.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn laboratory assessment, higher CRP and ferritin levels, lower lymphocytic count and more desaturation was significantly found among the severe group (P\u0026lt;0.05). \u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3: Arterial blood gases and laboratory data of the studied patients based on disease\u0026apos;s radiological severity\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-severe group (n= 44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere group (n= 115)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eABG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003epH\u0026deg;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\" colspan=\"2\"\u003e\n \u003cp\u003e7.46 \u0026plusmn; 0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\"\u003e\n \u003cp\u003e7.43 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCO\u003csub\u003e2\u003c/sub\u003e (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\" colspan=\"2\"\u003e\n \u003cp\u003e35.03 \u0026plusmn; 12.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\"\u003e\n \u003cp\u003e33.21 \u0026plusmn; 15.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePO\u003csub\u003e2\u003c/sub\u003e (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\" colspan=\"2\"\u003e\n \u003cp\u003e47.65 \u0026plusmn; 13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\"\u003e\n \u003cp\u003e40.97 \u0026plusmn; 12.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLactate (mmol/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\" colspan=\"2\"\u003e\n \u003cp\u003e1.85 \u0026plusmn; 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\"\u003e\n \u003cp\u003e2.98 \u0026plusmn; 2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCO\u003csub\u003e3\u003c/sub\u003e (mmol/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\" colspan=\"2\"\u003e\n \u003cp\u003e24.64 \u0026plusmn; 6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\"\u003e\n \u003cp\u003e21.72 \u0026plusmn; 7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasedeficit(mmol/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\" colspan=\"2\"\u003e\n \u003cp\u003e2.96 \u0026plusmn; 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\"\u003e\n \u003cp\u003e2.17 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOxygen saturation (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\" colspan=\"2\"\u003e\n \u003cp\u003e81.42 \u0026plusmn; 13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.804123711340207%\"\u003e\n \u003cp\u003e72.58 \u0026plusmn; 17.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeucocytes (10\u003csup\u003e3\u003c/sup\u003e/ul)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e9.48 \u0026plusmn; 5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e12.01 \u0026plusmn; 6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophils (10\u003csup\u003e3\u003c/sup\u003e/ul)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e7.56 \u0026plusmn; 5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e10.26 \u0026plusmn; 5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocyte (10\u003csup\u003e3\u003c/sup\u003e/ul)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e1.25 \u0026plusmn; 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e0.81 \u0026plusmn; 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e12.69 \u0026plusmn; 2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e12.15 \u0026plusmn; 2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelets (10\u003csup\u003e3\u003c/sup\u003e/ul)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e256.79 \u0026plusmn; 113.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e254.14 \u0026plusmn; 118.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eINR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e1.10 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e1.27 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine (mmol/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e121.94 \u0026plusmn; 24.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e178.25 \u0026plusmn; 88.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrea (mmol/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e9.94 \u0026plusmn; 7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e16.94 \u0026plusmn; 9.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotassium (mmol/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e4.14 \u0026plusmn; 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e4.26 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSodium (mmol/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e136.68 \u0026plusmn; 5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e138.23 \u0026plusmn; 5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e35.10 \u0026plusmn; 5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e32.97 \u0026plusmn; 5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBilirubin (umol/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e9.20 \u0026plusmn; 3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e9.82 \u0026plusmn; 2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (u/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e44.09 \u0026plusmn; 11.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e54.83 \u0026plusmn; 8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (u/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e45.96 \u0026plusmn; 9.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e80.23 \u0026plusmn; 32.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eALP (u/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e101.77 \u0026plusmn; 63.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e114.45 \u0026plusmn; 83.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e89.36 \u0026plusmn; 16.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e142.87 \u0026plusmn; 25.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFerritin (ng/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e612.34 \u0026plusmn; 34.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e1116.87 \u0026plusmn; 123.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-dimer (mg/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2.30 \u0026plusmn; 0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"2\"\u003e\n \u003cp\u003e3.49 \u0026plusmn; 0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.878186968838527%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.77903682719547%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.2492917847025495%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"26.912181303116146%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.181303116147308%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eData are presented as mean \u0026plusmn; SD. * Significant p value \u0026lt;0.05,ABG: arterial blood gases, INR: international randomized ratio, AST: aspartate transaminase, CRP: c reactive protein,ALT: alanine transaminase, ALP: alkaline phosphatase.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeed for mechanical ventilation and mortality rate and as regards CT severity score were 76(66%),75 (65%), versus 3(7%)75(65%) between severe and non-severe groups respectively (P\u0026nbsp;\u0026lt; 0.001).\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;4: Need for mechanical ventilation and mortality outcome of the studied patients based on disease\u0026apos;s radiological severity\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eNon-severe group (n= 44)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSevere group (n= 115)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eP\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eVenturi mask\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e41 (93.2%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e41 (35.7%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt; 0.001*\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eNasal canula\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e14 (31.8%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e39 (33.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.47\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eNeed for NIV\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e17 (38.6%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e47 (40.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" colspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eNeed for MV\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3 (6.8%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e76 (66.1%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt; 0.001*\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eOutcome\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eSurvived\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e40 (90.9%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e40 (34.8%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" rowspan=\"2\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e\u0026lt; 0.001*\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.78787878787879%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eNot survived\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4 (9.1%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.84848484848485%\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e75 (65.2%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eData are presented as mean \u0026plusmn; SD or frequency (%). * Significant p value \u0026lt;0.05, NIV: non-invasive ventilation, MV: mechanical ventilation.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDecember 2019, Chinese scientists\u0026nbsp;first identified\u0026nbsp;SARS-CoV-2 as the etiology of COVID-19 disease\u0026nbsp;\u003csup\u003e[13]\u003c/sup\u003e. At first, COVID-19 was classified as a respiratory illness, with pneumonia to be the prevailing and most fatal consequence. However, SARS-CoV-2 had been revealed to elicit an exaggerated and unregulated immune responses, known as immune hemostasis, which leads to various complications including thrombosis, damage to tissues, DIC, ARDS, and MODS\u0026nbsp;\u003csup\u003e[14]\u003c/sup\u003e; Therefore, it is imperative to comprehend COVID-19 not only as a respiratory illness but additionally as a\u0026nbsp;possible\u0026nbsp;multisystem disorder\u0026nbsp;\u003csup\u003e[15]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, as regard comorbidities: significantly higher DM (67.8% vs. 36.4%; p \u0026lt; 0.001) and chronic chest disease (39.1% vs. 11.4%; p \u0026lt; 0.001) among severe group was found. Hypertension as recorded was (29.5%; 32.2%) in non-severe group and severe group respectively. CVS was (11.4%; 16.5%) in non-severe group and severe group correspondingly. CKD was (18.2%; 21.2%) in non-severe group and severe group respectively. In accordance, Abdelfattah et al.\u0026nbsp;\u003csup\u003e[16]\u003c/sup\u003ereported that\u0026nbsp;a strong association existed among the COVID-19 infection severity and the prevalence of co-morbidities, particularly systemic HTNand DM. Ji et al.\u0026nbsp;\u003csup\u003e[17]\u003c/sup\u003ealso observed similar findings,\u0026nbsp;A countrywide retrospective case-control research was undertaken in Korea, involving 219,961 participants with COVID-19. The work\u0026nbsp;found that co-morbidities, particularly DM and HTN, had a substantial impact on the COVID-19 infectionseverity. According to Khadija et al.\u0026nbsp;\u003csup\u003e[18]\u003c/sup\u003e, 32% of individuals with DM had HTN\u0026nbsp;and 9.2% had underlying ischemic cardiac disease. Both of these conditions are known to be risk factors for negative outcomes among individuals with COVID infections.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, it was found that lymphocytic count was significantly lower among patients with severe group. Also, severe groups exhibited significantly greater serum CRP levels and serum ferritin levels. While no statistically significant variation existed as regards levels of D-dimer among non-severe and severe groups of patients. This agrees with Abdelfattah et al.\u003csup\u003e[16]\u003c/sup\u003e reported that\u0026nbsp;A strong connection was seen between serum ferritin and D-dimer and the COVID-19 infection severity. Furthermore, Elsharawy et al.\u0026nbsp;\u003csup\u003e[19]\u003c/sup\u003e found a correlation between serum ferritin levels and both the severity of the\u0026nbsp;illness\u0026nbsp;and the ability to predict admission to the ICU. This was in opposition to the findings published by Yao et al.\u0026nbsp;\u003csup\u003e[20]\u003c/sup\u003e,\u0026nbsp;The study discovered a significant association amonglevel of\u0026nbsp;D-dimer and the severity of the illness. Furthermore, they observed that D-dimer level served as a dependable prognostic marker for predicting in-hospital mortality in individuals admitted for COVID-19.\u0026nbsp;This was in contrast with what was reported by Yao et al.\u003csup\u003e[20]\u003c/sup\u003efound that D-dimer level correlates with disease severity and was a reliable prognostic marker for in-hospital mortality in subjects admitted for COVID-19.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, it was found that frequency of need for MV was significantly higher among patients with severe disease (66.1% vs. 6.8%; p \u0026lt; 0.001) while venture mask was the method of correction of desaturation among non-severe group (93.2% vs. 35.7%; p \u0026lt; 0.001). In accordance, Abdelfattah al.\u003csup\u003e[16]\u003c/sup\u003ereported that, a positive correlation was found between severity of COVID-19 infection, days of hospital stay, the need for ICU admission, and mechanical ventilation which seems a logic finding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHigher levels of serum CRP are associated with higher mortality in people with severe COVID-19 disease\u003csup\u003e[20]\u003c/sup\u003e. more specifically, CRP values above 77.35\u0026thinsp;mg/L\u0026nbsp;\u003csup\u003e[21]\u003c/sup\u003e. Davoudi et al.\u0026nbsp;\u003csup\u003e[22]\u003c/sup\u003e reported that, although the level of CRP was higher in non-survived patients, this difference was not statistically significant. This is the same as proved by Alroomi et al.\u003csup\u003e[23]\u003c/sup\u003econducted a retrospective study on 595 COVID-19 subjects, where higher levels of serum ferritin were found to be an independent predictor of mortality.\u003c/p\u003e\n\u003cp\u003eBlood picture of patients with COVID-19 characterized by normal or low count of WBCs and decreased level of lymphocytes. Increased levels of WBCs and neutrophils were found in 68% and 72% of patients\u003csup\u003e[24]\u003c/sup\u003e. Petrilli et al.\u003csup\u003e[25]\u003c/sup\u003ereported striking findings regarding the predictive value of inflammatory markers to distinguish future critical from non-critical illness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study,\u0026nbsp;at cut off \u0026gt; 133 mg/dl, baseline serum CRP level had 65.7% overall accuracy\u0026nbsp;in prediction of severe disease among the studied with area under curve was 0.673. Ahnachet al.\u003csup\u003e[27]\u003c/sup\u003econfirmed that the CRP was a robust predictor of adverse disease outcome. CRP was also an independent discriminator of severe/critical illness on admission in comparison with other biological factors. \u0026nbsp;These results were in agreement with similar report of Luo et al.\u0026nbsp;\u003csup\u003e[28]\u003c/sup\u003efound an AUC of CRP for discriminating disease severity on admission at 0.783. With a cut-off value of 41.3, CRP exhibited similar results of our study with sensitivity of 65%, specificity of 83.7%, PPV of 81.6%, and NPV of 68.2%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, it was found that predictors for severe disease among the studied patients were DM (odd\u0026apos;s ratio = 3.45), chronic chest disease (odd\u0026apos;s ratio = 2.22) and CRP (odd\u0026apos;s ratio = 2.19). Similarly, Wang et al.\u003csup\u003e[29]\u003c/sup\u003e and Wu et al.\u003csup\u003e[30]\u003c/sup\u003e reported a significant association of the COVID-19 severity with diabetes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, it was found that predictors for mortality among the studied patients were age (odd\u0026apos;s ratio = 1.78), DM (odd\u0026apos;s ratio = 2.89), chronic chest disease (odd\u0026apos;s ratio = 3.01), serum albumin (odd\u0026apos;s ratio = 1.90), serum CRP levels (odd\u0026apos;s ratio = 2.11) and d-dimer (odd\u0026apos;s ratio = 2.98). This is consistent with the findings stated by Shi et al.\u003csup\u003e[31]\u003c/sup\u003ereported that, age \u0026ge; 70 years was found to be an independent risk factor for in-hospital death for patients with diabetes as well as for patients without diabetes (hazard ratio (HR) 2.39 and 5.87, respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, at cut off\u0026gt; 150 mg/dl, baseline serum CRP level had 63% overall accuracy in prediction of mortality among the studied with area under curve was 0.674. meanwhile, baseline d-dimer had 67.7% overall accuracy in prediction of mortality among the studied with area under curve was 0.706.\u0026nbsp;In a cohort conducted by Smilowitz et al.\u003csup\u003e[33]\u003c/sup\u003e, including 2782 COVID-19 patients, CRP levels above 108mg/L we associated with disease severity (47,6% vs 25,9%) and a higher mortality (32,2% vs 17,8%) (277). Similarly, in a retrospective study conducted by Sadeghi et al.\u003csup\u003e[33]\u003c/sup\u003e including 429 patients, it has been shown that not only the severe cases had significantly higher CRP levels than non-severe patients, but also that patients with CRP \u0026gt; 64.75\u0026thinsp;mg/L were more likely to have severe complications.\u003c/p\u003e\n\u003cp\u003eLimitations of the work involved the relatively small sample size. The work had been conducted in a single center.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePatients with high radiological severity of the disease, as determined by HRCT severity score system (CO-rad5,6) had lower lymphocyte counts, elevated CRP, and increased ferritin level but no D-dimer significance. Also, individuals with severe illness exhibited a greater mean of age, gender difference towards malesand comorbiditiesDM and chronic chest disease.\u003c/p\u003e\n\u003cp\u003eHigh-resolution computed tomography (HRCT) scan of the chest as well as CRP and ferritin plasma levels are valuable methods and significant predictors for future prognosis in patients with covid19 which had an important role in early identification of patients at risk of death and need for MV.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eFund:\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003eNo financial support was required\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eConflict of interest:\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003eAuthors declared no conflict in the current work.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eIRB no\u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003e: 17101508\u003c/span\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.F and A.F contributed to the study design, conception, and manuscript revision. M.I contributed to literature search data collection and manuscript writing. (corresponding author)S.F contributed to statistical analysis, manuscript writing and revision.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003enone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOrganization WH. COVID-19 weekly epidemiological update, 9 March 2021.J Intensive Care.2021;123:985\u0026ndash;1010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiens KE, Mawien PN, Rumunu J, Slater D, Jones FK, Moheed S, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Juba, South Sudan: a population-based study.medRxiv.2021;79:12\u0026thinsp;\u0026ndash;\u0026thinsp;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhamed, M., Sikder, M. R., Rahman, M. M., Sumon, S. R., Rahman, M. M., et al. (2024). 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Effect of Underlying Comorbidities on the Infection and Severity of COVID-19 in Korea: a Nationwide Case-Control Study.J Korean Med Sci.2020;35:237\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHafidh K, Abbas S, Khan A, Kazmi T, Nazir Z, Aldaham T. The clinical characteristics and outcomes of COVID-19 infections in patients with diabetes at a tertiary care center in the UAE.Int J Diabetes Metab.2021;26:158\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElsharawy S, Amer I, Salama M, El-Lawaty W, Abd Elghafar M, Ghazi A, et al. Clinical and laboratory predictors for ICU admission\u0026lrm; among COVID-19 infected egyptian patients, A\u0026lrm; multi-center study\u0026lrm;.AEJI.2021;11:284\u0026thinsp;\u0026ndash;\u0026thinsp;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A meta-analysis.Clin Chim Acta.2020;506:145\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan F, Yang L, Li Y, Liang B, Li L, Ye T, et al. Factors associated with death outcome in patients with severe coronavirus disease-19 (COVID-19): a case-control study.Int J Med Sci.2020;17:1281\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavoudi Z, Darazam IA, Saberian F, Homaee S, Shokouhi S, Shabani M, et al. Clinical course and outcome in diabetic patients with COVID-19.Immunopathologia Persa.2020;7:23\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlroomi M, Rajan R, Omar AA, Alsaber A, Pan J, Fatemi M, et al. Ferritin level: A predictor of severity and mortality in hospitalized COVID-19 patients.Immun Inflamm Dis.2021;9:1648\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. The clinical and chest CT Features associated with severe and critical COVID-19 pneumonia.Invest Radiol.2020;55:327\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrilli CM, Jones SA, Yang J, Rajagopalan H, O'Donnell L, Chernyak Y, et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study.BMJ.2020;369:1966\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai W, Li S, Du Z, Ma X, Lu J, Gao WD, et al. Severe patients with ARDS with COVID-19 treated with extracorporeal membrane oxygenation in china: A retrospective study.Front Med (Lausanne).2021;8:69\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhnach M, Zbiri S, Nejjari S, Ousti F, Elkettani C. C-reactive protein as an early predictor of COVID-19 severity.J Med Biochem.2020;39:500\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo X, Zhou W, Yan X, Guo T, Wang B, Xia H, et al. Prognostic value of C-reactive protein in patients with coronavirus 2019.Clin Infect Dis.2020;71:2174\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L. C-reactive protein levels in the early stage of COVID-19.Med Mal Infect.2020;50:332\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China.JAMA Intern Med.2020;180:934\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Q, Zhang X, Jiang F, Zhang X, Hu N, Bimu C, et al. Clinical characteristics and risk factors for mortality of COVID-19 patients with diabetes in Wuhan, China: A two-center, retrospective study.Diabetes Care.2020;43:1382\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmilowitz NR, Kunichoff D, Garshick M, Shah B, Pillinger M, Hochman JS, et al. C-reactive protein and clinical outcomes in patients with COVID-19.Eur Heart J.2021;420:2270\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadeghi-Haddad-Zavareh M, Bayani M, Shokri M, Ebrahimpour S, Babazadeh A, Mehraeen R, et al. C-reactive protein as a prognostic indicator in COVID-19 patients.Interdiscip Perspect Infect Dis.2021;2021:55\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-bronchology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Bronchology](https://ejb.springeropen.com/)","snPcode":"43168","submissionUrl":"https://submission.nature.com/new-submission/43168/3","title":"The Egyptian Journal of Bronchology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"’- Reactive Protein, Covid-19, Ferritin, D-Dimer, ICU, Assiut","lastPublishedDoi":"10.21203/rs.3.rs-4940615/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4940615/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePatients with Coronavirus disease (COVID-19) was found to exhibit elevated levels of inflammatory cytokines, which were linked to pulmonary inflammation, lung damage, and end with multi-organ failure.C-reactive protein (CRP), serum ferritin and D dimer levels may predict severity and mortality. Radiology plays a key role in the diagnosis, management, and follow-up of this disease. We attempted to describe the radiological features of SARS-CoV-2 infection in its original form, to correlate the HRCT patterns with clinical findings, C-reactive protein (CRP), D-dimer and ferritin and to consider as predictors of morbidity and mortality in adult (ICU) patients with COVID-19.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective cross-sectional analytic work had been conducted on 159 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years old, admitted at Assiut University Hospital Respiratory ICU from November 2021 to November 2022, diagnosed as COVID-19 by positive RT-PCR. All cases were categorized on bases of (HRCT chest) disease reporting and data system (CO-RADS) scoring classification.Oxygen saturation, and inflammatory markers such as CRP, Ferritin and D dimer were measured. Age, sex, comorbidities, use of MV mechanical ventilation, and outcomes as per HRCT severity were key observations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 159 HRCT chest scans of symptomatic RT-PCR-positive ICU patients were recruited. Higher CRP and Ferritinserum levels, lower lymphocytic count, higher frequency of need for mechanical ventilation were significantly greater in the severe group as assessed by HRCT severity score (CORAD 4,5) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)). Predictors of severity revealedCRP at cut off \u0026gt;\u0026thinsp;133 mg/dlserum level, had 65.7% overall accuracywith AUC: 0.673(OR:2.19(P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)),DM (OR:3.45(P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)), chronic chest disease (OR:2.22(P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)). Mortality predictors were age (OR:1.78(P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)), DM (OR:2.89(P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)), chronic chest disease (OR:3.01(P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)), serum CRP levels (OR:2.11(P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)). Need for mechanical ventilation and mortality rate as regards CT severity score were 76(66%),75 (65%), versus 3(7%) 4(9%) between severe and non-severe groups respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigh-resolution computed tomography (HRCT) scan of the chest as well as CRP and ferritin plasma levels are valuable methodsand significant predictors for future prognosis in patients with covid19 at risk of death and in need for MV.\u003c/p\u003e","manuscriptTitle":"Study of CRP, Ferritin and D-Dimer in Covid-19 RICU Patients as per HRCT severity in Assiut University Hospitals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-19 17:33:54","doi":"10.21203/rs.3.rs-4940615/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-04T06:19:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-04T06:16:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100841289544413760200673137098204448196","date":"2024-10-04T05:52:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-23T07:51:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334207651862073275848100201428540474017","date":"2024-09-20T11:25:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-22T12:43:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-22T06:46:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-21T05:10:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Journal of Bronchology","date":"2024-08-19T19:28:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-bronchology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Bronchology](https://ejb.springeropen.com/)","snPcode":"43168","submissionUrl":"https://submission.nature.com/new-submission/43168/3","title":"The Egyptian Journal of Bronchology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"feb41c3d-699c-45ed-a646-acd93c234ef6","owner":[],"postedDate":"September 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-08T03:53:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-19 17:33:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4940615","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4940615","identity":"rs-4940615","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}