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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 8
| Issue : 2 | Page : 108-115 |
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The prognostic role of inflammatory markers in COVID-19 patients: A retrospective analysis in a tertiary care hospital of southern India
Shivkumar Gopalakrishnan1, Babu Krishnan1, Malini Santhana Krishnan2, Sangeetha Kandasamy2, Peer Mohamed Sahul Hameed3, Velmurugan Karunakaran1
1 Department of Internal Medicine, Government Villupuram Medical College and Hospital, Viluppuram, Tamil Nadu, India
2 Department of Biochemistry, Government Sivagangai Medical College and Hospital, Sivagangai, Tamil Nadu, India
3 Department of Internal Medicine, Government Sivagangai Medical College and Hospital, Sivagangai, Tamil Nadu, India
| Date of Submission | 24-Jan-2022 |
| Date of Decision | 31-Mar-2022 |
| Date of Acceptance | 03-Apr-2022 |
| Date of Web Publication | 17-Sep-2022 |
Correspondence Address:
Sangeetha Kandasamy
No 54/55 Mount Kailash, Tamarai Street, Subiksha Garden, Pannampattu Road, Viluppuram - 605 602, Tamil Nadu
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jcrsm.jcrsm_4_22
Background: Approximately 5% of COVID-19 patients suffer from near-fatal disease. Clinical and radiologic features may predict severe disease although with limited specificity and radiation hazard. Laboratory biomarkers are specific, simple, and point-of-care triage tools that can be used to predict the severity of the disease. This research aimed to study the role of inflammatory markers (serum ferritin, lactate dehydrogenase [LDH], D-dimer, and C-reactive protein [CRP]) in prognosticating COVID-19 patients.
Methodology: This was a hospital-based retrospective study conducted on COVID-19 adult inpatients classified into three groups: mild disease-recovered (Group I), severe disease-recovered (Group II), and dead (Group III). Categorical outcomes were compared using the Chi-square test. Univariate binary logistic regression analysis was performed to test the association between the outcome and explanatory variables. An unadjusted odds ratio (OR) along with a 95% confidence interval was calculated. The utility of laboratory parameters (ferritin, LDH, neutrophil/lymphocyte ratio, D-dimer, and platelet/lymphocyte ratio) in predicting the severity of COVID-19 was assessed by the receiver operative curve analysis. P < 0.05 was considered statistically significant.
Results: A total of 500 case records were analyzed. The mean age was 49.32 ± 17.1 years. About 72.4% were <60 years and 301 male and 199 female patients were included. The comorbidity count included diabetes 168 (33.6%), hypertension 122 (24.4%), coronary artery disease 23 (4.6%), hypothyroidism 3 (6%), and others 33 (6.6%) The median levels of ferritin among the three groups differed significantly bearing higher levels in Group 3 (P < 0.001). Median LDH and D-dimer values of the three groups showed statistical significance (P < 0.001). Qualitative CRP was significantly associated with poor outcomes (P < 0.001). The odds of patients suffering severe COVID-19 rose with rising values of ferritin, LDH, and D-dimer (unadjusted OR: 1.007, 1.004, and 1.020).
Conclusion: Onetime measurement of serum ferritin, LDH, D-dimer, and CRP performed between 7th and 10th day of symptoms significantly predicted outcomes for COVID-19 inpatients.
Keywords: COVID-19, C-reactive protein, D-dimer, ferritin, inflammatory markers, lactate dehydrogenase, prognostic role
How to cite this article:
Gopalakrishnan S, Krishnan B, Krishnan MS, Kandasamy S, Sahul Hameed PM, Karunakaran V. The prognostic role of inflammatory markers in COVID-19 patients: A retrospective analysis in a tertiary care hospital of southern India. J Curr Res Sci Med 2022;8:108-15 |
How to cite this URL:
Gopalakrishnan S, Krishnan B, Krishnan MS, Kandasamy S, Sahul Hameed PM, Karunakaran V. The prognostic role of inflammatory markers in COVID-19 patients: A retrospective analysis in a tertiary care hospital of southern India. J Curr Res Sci Med [serial online] 2022 [cited 2023 May 31];8:108-15. Available from: https://www.jcrsmed.org/text.asp?2022/8/2/108/356213 |
| Introduction | |  |
The pandemic caused by the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has swept through the globe casting a shadow of death and disability. Each wave of rising infections reminds the scientific community of our limited capability and inadequate knowledge.[1] The fatality rate has a wide range with considerable international variance (from 2.1% to as high as 5.25%).[2] Most COVID-19 patients experience mild illness (80%); however, a few end up with moderate (15%) to severe (5%) disease endangering survival.[3] Researchers in the field have identified clinical predictors of adverse outcomes although with limited specificity.[4] Experience gained so far reveals advanced age (>65 years), comorbid illnesses like diabetes, hypertension, obesity, atherosclerotic cardiovascular diseases, and cancer to be significant associations that portend mortality.[5] Imaging modalities such as computerized tomography (CT) scans do add to the predictive accuracy of the clinical outcome but not without radiation hazard. This underscores the need for specific biomarkers which can predict outcomes early enough in the disease course to facilitate triage and improve survival.
Animal studies and laboratory observations have given preliminary insight into the pathogenicity of SARS-CoV-2. The surface spike protein, primed by transmembrane serine protease 2, binds to the human angiotensin-converting enzymes 2 (ACE 2) receptor, thereby gaining cellular entry.[6] The clinicopathologic mechanism involved in COVID-19 disease is divided into four phases – Phase 1 whence the patient becomes symptomatic, most commonly with fever and cough. Phase 2 marks uncontrolled viral replication leading to macrophage activation, neutrophil infiltration, and release of inflammatory cytokines such as interleukin-1, interleukin-6 [IL-6], interleukin-10, tumor necrosis factor-α, and interferon-γ. The ensuing chain reaction activates procoagulant pathways while suppressing the antithrombotic mechanisms simultaneously. Phase 3 is marked by a hyperinflammatory and hypercoagulable state causing hypoxic respiratory failure and may explode into multi-organ dysfunction, most notably the heart, kidneys, and vascular system [Figure 1]. Phase 4 is the recovery stage with the healing of lung lesions and resolution of hypoxia.[7],[8],[9],[10] | Figure 1: Interconnections between COVID-19 biomarkers of systemic inflammatory response leading to activation of the extrinsic coagulation pathway, macrophage activation, and oxidative stress. LDH: Lactate dehydrogenase, IL-1: Interleukin-1, IL-6: Interleukin-6, IL-8: Interleukin-8, PAI-I: Plasminogen activator inhibitor-1, IL-10: Interleukin-10, IL 1β: Interleukin 1 beta
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High levels of D-dimer, IL-6, C-reactive protein (CRP), troponin, lactate dehydrogenase (LDH), ferritin, and procalcitonin are detected in severely ill patients.[11] However, our knowledge about the optimal combination of these markers, the timing of the tests, their validity in clinically mild disease, and individual/cumulative predictive accuracy is far from complete. A single or combination of inflammatory markers which can forecast adverse results in time to modulate treatment is the need of the hour. We aimed to study the role of inflammatory markers in prognosticating COVID19 inpatients admitted to the hospital.
| Methodology | | |
This retrospective study was conducted at Government Medical College and Hospital, Sivagangai district, Tamil Nadu, India. All adult patients between the age group of 18–75 years who had severe transcription–polymerase chain reaction confirmed for COVID-19 admitted to the hospital between April 2020 and January 2021 were eligible participants. Inclusion criteria were symptom duration 7–10 days before admission, baseline investigations and inflammatory markers done on day 1 of admission, and complete follow-up records available up to death or discharge from the hospital. As part of the institutional protocol, basic blood investigations and inflammatory markers were performed for all patients at baseline. A total of 3089 patients were eligible, among whom detailed scrutiny shortlisted 500 case records that fulfilled predefined enrolment criteria. The remaining 2589 patients were excluded because of early admissions (symptoms lasting <5 days), hematological abnormalities, immune-compromised status, and incomplete case records (patients referred to other centers, prematurely discharged to home at request, discharged to COVID care centers, discharged against medical advice, wholesome inflammatory markers unavailable on admission day 1 and data attrition). From the final sample of 500, information retrieved included comorbidities, clinical signs, oxygen saturation, complete blood counts, neutrophil-to-lymphocyte and platelet–to-lymphocyte ratios (NLR and PLR), and inflammatory markers such as CRP, D-Dimer, LDH, and ferritin. The study sample was divided into three groups based on outcome-based criteria – mild COVID recovered (Group 1), severe COVID recovered (Group 2), and severe COVID died (Group 3) [Figure 2].[12] The primary outcomes studied were threefold, namely mild COVID patients who recovered with minimal supportive care (Group 1), severe COVID patients who survived with intensive care and prolonged oxygen support (Group 2), and severe COVID patients who died in hospital (Group 3). | Figure 2: Flowchart of patient enrollment. RT-PCR: Reverse transcription-polymerase chain reaction
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Ethical consideration
The Institutional Ethics Committee (IEC) of Government Medical College and Hospital, Sivagangai, waived informed consent from participants since this was a retrospective study. The IEC approval was obtained vide reference no. IRB no: 3991/ME4/2020.
Statistical analysis
Ferritin, LDH, CRP, and D-dimer were considered as primary outcome variables. The severity of COVID-19 was considered a primary explanatory variable. Age, gender, duration of hospitalization, clinical presentation, hemoglobin, neutrophil, lymphocyte and platelet count, packed cell volume, comorbidity, and (CT) thorax findings were considered as other study relevant variables.
Descriptive analysis was carried out by mean and standard deviation for quantitative variables, frequency, and proportion for categorical variables. Nonnormally distributed quantitative variables were summarized by the median and interquartile range (IQR) and were compared across study groups using the Kruskal–Wallis test (>2 groups). Shapiro–Wilk test was used to assess normal distribution. The power of the study was calculated using the formula n = (Z1-α/2 + Z1-β) 2* (σ1+ σ2) 2/(μ1-μ2) 2. As per the formula, assuming a margin of error/specificity of inflammatory markers as 5%, a sample of 154 was considered adequate to achieve a power of 80% with a significance level of 95%.
Categorical outcomes were compared across study groups using the Chi-square test. Univariate binary logistic regression analysis was performed to test the association between the explanatory variables and outcome variables. An unadjusted odds ratio (OR) along with a 95% confidence interval (CI) was calculated. The utility of laboratory parameters (such as ferritin, LDH, D-dimer, NLR, and PLR) in predicting the severity of COVID-19 was assessed by receiver operating characteristics (ROC) curve analysis. P < 0.05 was considered statistically significant. The software used was ADSS Corp. Released 2020. CoGuide Statistics software, Version 1.0, India.
| Results | | |
Case records of 500 patients were analyzed. The mean age was 49.32 ± 17.1 years. About 72.4% were <60 years. As per the inclusion criteria, 301 male and 199 female participant case records were studied. Among the study population, 378 (75.60%) had mild (Group 1), 66 (13.20%) moderate (Group 2), and 56 (11.20%) severe diseases (Group 3). The mean duration of hospital stay was 5.6 days ranging between 1 and 25 days. The comorbidity count included diabetes 168 (33.6%), hypertension 122 (24.4%), coronary artery disease 23 (4.6%), hypothyroidism 3 (6%), and others 33 (6.6%) [Table 1]. The values of ferritin, LDH, D-dimer, NLR, and PLR of patients in the three groups are depicted in [Table 2]. | Table 1: Demographics and baseline characteristics of patients with COVID-19
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The median levels of ferritin among three groups were 62 ng/mL (24.60 ng/mL–89 ng/mL) (mild), 388.50 ng/mL (195.50 ng/mL–541 ng/mL) (moderate), and 1199.50 ng/mL (854 ng/mL–1536 ng/mL) (severe). The median LDH of Group I was 95U/L (IQR 35–105), Group II: 720 U/L (IQR: 648.25–916), and Group III: 982.50 U/L (IQR: 739–1119.25) [(P < 0.001). D-dimer values of the three groups were 23.20 ng/mL, 104.30 ng/mL, and 197.10 ng/mL which on comparison had significance (P < 0.001) [Figure 3]. Qualitative CRP was positive in 2 (0.53%), 30 (45.45%), and 53 (94.64%) patients of Groups I, II, and III, respectively, assuming statistical significance P < 0.001 [Table 2]. Using ROC curve analysis and assuming 95% CI, the odds of patients suffering severe COVID-19 rose with rising values of ferritin, LDH and D-dimer (unadjusted OR: 1.007,1.004 and1.020) [Figure 4]. | Figure 3: Box whisker plot of comparison of ferritin, LDH, and D-Dimer across the severity of COVID (N = 500). LDH: Lactate dehydrogenase
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 | Figure 4: Predictive validity of laboratory parameters in predicting the severity of COVID-19 (ROC analysis) (N = 500). ROC: Receiver operative curve, LDH: Lactate dehydrogenase
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Other significant observations in the study population included a higher mean age (58.66 years), longer duration of hospitalization (7 days), and higher NLR (2.07 [1.44–9.86]) to be statistically associated with severe COVID-19 [Figure 5]. | Figure 5: Forest plot for the factors associated with severe COVID in 19 patients (N = 500). LDH: Lactate dehydrogenase
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| Discussion | | |
COVID-19 pandemic has affected all age groups, breached geo-political barriers, stunned the healthcare, economic and social systems of countries across the globe. The death toll is over a million to date[12]. The unpredictable course of COVID-19 mandates the need for early clinical and laboratory markers of outcome. In resource-limited settings triage could prioritize patients who need hospitalization and intensive monitoring. To date, the search for such specific and cost-effective biomarkers is elusive to the scientific community.[13] Accumulating body of evidence suggests that inflammatory mediators such as IL-6, ferritin, LDH, and CRP play a critical role in the progression of COVID-19.[14] Therefore, these biomarkers play an indispensable role in not just clinical management but the timely screening of patients who are on the trajectory to severe disease. Although measurement of inflammatory markers can bear prognostic implications, uncertainties exist on the optimal timing and right combination. Whether their cumulative validity is additive is another gray area. We studied the association between onetime measured biomarkers such as ferritin, LDH, CRP, and D-dimer on the 7th–10th day of illness and the observed mortality.
It is common knowledge that underlying organ damage occurs due to activation of the complement system, inflammatory cascades, and pro-inflammatory cytokines in severe COVID-19. Circulating levels of free thrombin activate platelets and cause dysregulated fibrinolysis as portrayed by elevated D-dimer levels. This pathogenic phenomenon has been observed to be associated with severe COVID disease in previous research work.[15] Moreover, they were instrumental in guiding anticoagulant therapy among COVID-19 patients. Akin to other published reports, we observed a significant rise in D-dimer levels in our patients as well who suffered severe COVID-19 (OR 1.020, P < 0.001). Interestingly, we also found out that performing D-dimer for all admitted patients was not cost-effective and the productivity was enhanced when done approximately 7th–10th day of symptoms and on those requiring oxygen therapy to maintain SpO2 of >94%. As per the institutional protocol, D-dimer was done for all inpatients upon admission. Although this was not the primary aim of the study, we did an internal comparison between patients who were admitted for <5 days with symptoms, hemodynamically stable not requiring oxygen support with our actual study sample which involved the three groups (7th–10th-day symptoms). Almost all patients who were admitted early and not in respiratory distress revealed D-dimer values (median: 23.20 ng/dL [IQR: 12.20–56.60]) within the normal range and no significant difference in their association with outcome. About 75.6% of them were discharged in a healthy status after a mean period of 5.3 days. Furthermore, it has been often proven in previous research studies that inflammatory markers carry more specificity after the initial viremic phase of COVID-19 infection when they escalate into the cytokine surge phase.[15] Hence, we concluded that performing a baseline D-dimer test for all inpatients who present early within 5 days of symptoms did not contribute much to the triage and significantly increased the cost of health care. Regional literature evaluating the cost-effectiveness of universal D-dimer for all inpatients is strikingly absent despite widespread practice of the same.
CRP is a sensitive marker of acute-phase response in inflammation, infection, and tissue damage[16] whose potential has been exploited in COVID as well. In the study by Chen et al., although no statistically significant difference was found in the level of CRP between the nonsevere and the severe group, the mean level of CRP was higher in the latter.[17] Henry et al. also reported significant increases in ferritin and CRP levels in patients with severe COVID-19, consistent with the earlier findings.[18] In our case series, we measured CRP only by qualitative method; however, we documented statistically significant CRP positivity with severe COVID-19 disease (P < 0.001). Two other pertinent findings surfaced in our study, first, the qualitative method of CRP was sufficient to triage patients which could considerably reduce the cost burden. Second, an internal comparison among the study participants revealed that onetime performance of the test had the same prognostic accuracy as repeat measurements done on days 15 and 20 of symptoms.
Markers such as ferritin and CRP have an important role to play in a country like India, where IL-6 cannot be widely estimated due to high cost. The elevated ferritin levels are probably due to secondary lymphohistiocytosis and severe cytokine release syndrome.[19],[20] Gómez-Pastora et al. in a systematic review on the utility of ferritin in COVID-19 found that ferritin concentrations of COVID-19 patients were generally within the normal range of <400 ng/ml in nonsevere disease.[21] However, hyperferritinemia (ferritin level >400 μg/L) was observed in patients with the severe disease on admission, precisely between 1.5 and 5.3 times higher. The author evaluated studies comparing ferritin levels on admission between COVID-19 survivors and nonsurvivors and demonstrated that nonsurvivors showed ferritin levels on admission around 1400 ng/mL, which is between three and four times higher than survivors. Our observations parallel that of other researchers with a mean ferritin value of 1199.50 ng/mL in the severe group. The unadjusted odds for patients suffering severe COVID were 1.007 in patients with elevated ferritin levels. The finding provides clinicians with a cost-effective, point-of-care triage tool to optimize outcomes in resource-limited settings.
A previous study found significantly higher levels of LDH in intensive care unit (ICU) patients than in non-ICU patients (248 U/L vs. 151 U/L, P = 0.002). Since high levels of LDH continued in the ICU patients for several days post admission (160 U/L vs. 218 U/L, P = 0.002), LDH was speculated to predict mortality. However, this being a single center study was prone to selection bias with doubtful validity.[22] A multicentric study involving 1099 patients reported strong evidence correlating the extent of tissue damage and inflammation with increasing levels of LDH, which corroborated well with CT scan scoring of severe COVID-19 pneumonia.[23] In our study, LDH charted a significant rise among patients with severe COVID-19 (OR: 1.004/P < 0.001). This increase observed in our series is consistent with the findings of Liu who correlated LDH, lymphocyte, neutrophil, and CRP abnormalities with severe COVID pneumonia.[24]
The viral particles spread through the respiratory mucosa, first using the ACE2 receptor at the level of ciliated bronchial epithelial cells and then infecting other cells. Cytotoxic lymphocytes and natural killer cells play a key role in controlling the spread of infection which becomes relentless if lymphocyte count depletes. The number of lymphocytes particularly CD4 type can serve as a surrogate marker of severity and mortality in COVID-19 disease.[25] Lymphocytopenia directly correlates with disease severity and death with a threefold higher risk of poor outcomes, in younger patients compared to older.[26] Other hematological markers of significance are high NLR and thrombocytopenia which have been associated with adverse outcomes in COVID-19 patients. Elevated NLR is conducive to a dysregulated cytokine elaboration, whereas low platelet counts resulted from microthrombi and vascular occlusion of pulmonary vessels.[27] In our study, we documented a strong predilection between high NLR and poor outcomes of COVID-19; however, thrombocytopenia was not statistically significant.
Other relevant findings in our study included older age and longer hospital duration being predictive of COVID mortality. Similar relevance to the age factor has been reiterated in research work both regional and global.[28]
Limitations
Although the sample size was adequate, the method adopted was convenient sampling which might not represent the general population. CRP test done in the study was qualitative; hence, the exact level of the parameter predicting severity could not be assessed.
| Conclusion | | |
Triage-oriented, specific, and point-of-care biomarkers are an elusive dream for COVID-19 care physicians. We identified onetime measurement of serum ferritin, LDH, D-dimer, and CRP performed between 7th and 10th day of symptoms as promising tests to predict outcomes for COVID-19 inpatients. The highest specificity was for the combination of NLR, lymphocyte percentage, and D-dimer levels. The authors urge the need for larger multicentric research on these lines to end the long trail for the optimal biomarkers.
Acknowledgment
The authors acknowledge the support of all staff, Department of Medical Records, Government Medical College and Hospital, Sivagangai.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323:1061-9.  |
| 2. | Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 2020;395:507-13. |
| 3. | Esakandari H, Nabi-Afjadi M, Fakkari-Afjadi J, Farahmandian N, Miresmaeili SM, Bahreini E. A comprehensive review of COVID-19 characteristics. Biol Proced Online 2020;22:19. |
| 4. | Sharma D, Mehndiratta M, Puri D. Laboratory changes in SARS-CoV-2 infection: A review. Indian J Med Biochem 2020;24:62-5. |
| 5. | Sanyaolu A, Okorie C, Marinkovic A, Patidar R, Younis K, Desai P, et al. Comorbidity and its impact on patients with COVID-19. SN Compr Clin Med 2020;2:1069-76. |
| 6. | D'Ardes D, Boccatonda A, Rossi I, Guagnano MT, Santilli F, Cipollone F, et al. COVID-19 and RAS: Unravelling an unclear relationship. Int J Mol Sci 2020;21:3003. |
| 7. | Bal A, Agrawal R, Vaideeswar P, Arava S, Jain A. COVID-19: An up-to-date review – From morphology to pathogenesis. Indian J Pathol Microbiol 2020;63:358-66. [ PUBMED] [Full text] |
| 8. | Siddiqi HK, Mehra MR. COVID-19 illness in native and immunosuppressed states: A clinical-therapeutic staging proposal. J Heart Lung Transplant 2020;39:405-7. |
| 9. | Ayres JS. Immunometabolism of infections. Nat Rev Immunol 2020;20:79-80. |
| 10. | Santos A, Magro DO, Evangelista-Poderoso R, Saad MJ. Diabetes, obesity, and insulin resistance in COVID-19: Molecular interrelationship and therapeutic implications. Diabetol Metab Syndr 2021;13:23. |
| 11. | Huang I, Pranata R, Lim MA, Oehadian A, Alisjahbana B. C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: A meta-analysis. Ther Adv Respir Dis 2020;14:1753466620937175. doi:10.1177/1753466620937175. |
| 12. | Cascella M, Rajnik M, Aleem A, Dulebohn SC, Cuomo A, Di Napoli R. Features, Evaluation, and Treatment of Coronavirus (COVID-19). In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2020. Available from: https://www.ncbi.nlm.nih.gov/books/NBK554776/. [Last accessed on 2020 Aug 10]. |
| 13. | |
| 14. | Kermali M, Khalsa RK, Pillai K, Ismail Z, Harky A. The role of biomarkers in diagnosis of COVID-19 – A systematic review. Life Sci 2020;254:117788. |
| 15. | Zeng F, Huang Y, Guo Y, Yin M, Chen X, Xiao L, et al. Association of inflammatory markers with the severity of COVID-19: A meta-analysis. Int J Infect Dis 2020;96:467-74. |
| 16. | Rodríguez-Morales AJ, MacGregor K, Kanagarajah S, Patel D, Schlagenhauf P. Going global - Travel and the 2019 novel coronavirus. Travel Med Infect Dis 2020;33:101578. doi: 10.1016/j.tmaid.2020.101578. |
| 17. | Pepys MB, Hirschfield GM. C-reactive protein: A critical update. J Clin Invest 2003;111:1805-12. |
| 18. | Chen W, Zheng KI, Liu S, Yan Z, Xu C, Qiao Z. Plasma CRP level is positively associated with the severity of COVID-19. Ann Clin Microbiol Antimicrob 2020;19:18. |
| 19. | Payán-Pernía S, Gómez Pérez L, Remacha Sevilla ÁF, Sierra Gil J, Novelli Canales S. Absolute lymphocytes, ferritin, C-reactive protein, and lactate dehydrogenase predict early invasive ventilation in patients with COVID-19. Lab Med 2021;52:141-5. doi: 10.1093/lab med/lmaa105. |
| 20. | Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ, et al. COVID-19: Consider cytokine storm syndromes and immunosuppression. Lancet 2020;395:1033-4. |
| 21. | Gómez-Pastora J, Weigand M, Kim J, Wu X, Strayer J, Palmer AF, et al. Hyperferritinemia in critically ill COVID-19 patients – Is ferritin the product of inflammation or a pathogenic mediator? Clin Chim Acta 2020;509:249-51. |
| 22. | Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020;382:1708-20. |
| 23. | Xiong Y, Sun D, Liu Y, Fan Y, Zhao L, Li X, et al. Clinical and high-resolution CT features of the COVID-19 infection: Comparison of the initial and follow-up changes. Invest Radiol 2020;55:332-9. doi: 10.1097/RLI.0000000000000674. |
| 24. | Liu Y, Yang Y, Zhang C, Huang F, Wang F, Yuan J, et al. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci 2020;63:364-74. doi: 10.1007/s11427-020-1643-8. |
| 25. | Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, et al. Dysregulation of immune response in patients with coronavirus 2019 (COVID-19) in Wuhan, China. Clin Infect Dis 2020;71:762-8. |
| 26. | Yu HH, Qin C, Chen M, Wang W, Tian DS. D-dimer level is associated with the severity of COVID-19. Thromb Res 2020;195:219-25. |
| 27. | Yang AP, Liu JP, Tao WQ, Li HM. The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int Immunopharmacol 2020;84:106504. |
| 28. | Iaccarino G, Grassi G, Borghi C, Ferri C, Salvetti M, Volpe M, et al. Age and multimorbidity predict death among COVID-19 patients: Results of the SARS-RAS study of the Italian Society of Hypertension. Hypertension 2020;76:366-72. |
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2]
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