|
 
 |
| ORIGINAL ARTICLE |
|
| Year : 2023 | Volume
: 6
| Issue : 1 | Page : 14-18 |
|
A study on coagulation profile and clinical outcomes in hospitalized COVID-19 patients in a tertiary care hospital in South India
Kartik Ramanathan1, Jagadeesh Chandrasekaran2, Prabu Pandurangan2
1 Department of General Medicine, Apollo Hospital, Chennai, Tamil Nadu, India
2 Department of Haematology, Apollo Hospital, Chennai, Tamil Nadu, India
| Date of Submission | 02-Feb-2023 |
| Date of Decision | 03-Apr-2023 |
| Date of Acceptance | 04-Apr-2023 |
| Date of Web Publication | 29-Apr-2023 |
Correspondence Address:
Dr. Kartik Ramanathan
Department of General Medicine, Apollo Hospital, Greams Road, Chennai, Tamil Nadu
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/japt.japt_3_23
Introduction: Coagulation abnormalities are a common occurrence in patients with COVID-19, of particular significance is the relationship between D-dimer levels and clinical outcomes. A higher D-dimer level at admission is found to have a poor clinical outcome with increased severity of the disease and increased mortality. Aim and Objectives: To analyze the coagulation profile and its relation to the outcome of patients admitted with COVID-19 pneumonia in a tertiary care center in South India. Subjects and Methods: We conducted a prospective observational study looking at the admission D-dimer, prothrombin time (PT)/international normalized ratio (INR), and platelet levels in 102 admitted COVID-19 patients from February 2021 to January 2022. The relationship between these parameters on admission and the clinical outcome in the form of oxygen requirement, duration of stay, survival, and need for domiciliary oxygen was studied. Discussion and Results: Higher D-dimer levels at admission were associated with poor survival and longer duration of hospital stay with increased requirement of oxygen support. There was a significant correlation between the mean admission D-dimer level and the need for supplemental oxygen (P < 0.05) with patients having a higher D-dimer level at admission (D-dimer of moderate and severe categories: 0.62 ± 0.88 μg/mL and 2.46 ± 4.22 μg/mL, respectively) requiring a higher concentration of oxygen in the form of noninvasive ventilation/high-flow nasal cannula. Furthermore, PT, INR, and platelet count on admission were not useful in predicting the clinical course, oxygen requirement, and overall survival in the study population. Conclusion: Admission levels of D-Dimer can predict the clinical course and outcome of COVID-19 patients.
Keywords: Coagulation, COVID-19, D-dimer
How to cite this article:
Ramanathan K, Chandrasekaran J, Pandurangan P. A study on coagulation profile and clinical outcomes in hospitalized COVID-19 patients in a tertiary care hospital in South India. J Assoc Pulmonologist Tamilnadu 2023;6:14-8 |
How to cite this URL:
Ramanathan K, Chandrasekaran J, Pandurangan P. A study on coagulation profile and clinical outcomes in hospitalized COVID-19 patients in a tertiary care hospital in South India. J Assoc Pulmonologist Tamilnadu [serial online] 2023 [cited 2023 May 29];6:14-8. Available from: https://www.japt.in//text.asp?2023/6/1/14/375458 |
| Introduction | |  |
COVID-19 started as a cluster of pneumonia cases of unknown etiology in late December of 2019 and subsequently was linked to a wet market in Wuhan, a city in the Hubei province of China. A novel coronavirus was isolated from the human airway epithelial cells of these patients and was called the SARS-CoV-2.[1] Coronaviruses are enveloped nonsegmented positive-sense RNA viruses and belong to the family Coronaviridae, the order Nidovirales, and are distributed broadly in humans and other mammals. Human coronavirus is one of the main pathogens of respiratory infection. SARS-CoV and MERS-CoV are two highly pathogenic viruses, causing severe respiratory syndrome in humans and four other human coronaviruses (HCoV-OC43, HCoV-229E, HCoV-NL63, and HCoV-HKU1) induce mild upper respiratory disease.[2],[3]
Common symptoms at the time of presentation are fever, cough, myalgia, and fatigue, along with less common symptoms like sputum production, headache, hemoptysis, and diarrhea.[2] It was soon reported that coagulation abnormalities were a major complication in COVID-19 patients resulting in a variety of thrombotic complications such as pulmonary embolism, deep vein thrombosis, ischemic stroke, and myocardial infarction.[4] Abnormal coagulation profiles were found at the time of admission. Lower mortality in hospitalized COVID-19 patients was noted with the administration of low molecular weight heparin for anticoagulation.[5]
It has been found that the inflammatory process, cytokine storm, and lung injury that are associated with COVID-19 put patients at an increased risk of thrombosis. A higher risk of thrombosis occurs with more severe disease and other factors, including but not limited to increasing age, male sex, obesity, cancer, comorbidities, and intensive care unit (ICU) admission.[6] Viral infections elicit a systemic inflammatory response and result in an imbalance between the procoagulant and anticoagulant homeostatic mechanisms. The pathogenetic mechanisms involved are multiple, including endothelial dysfunction, von Willebrand factor elevation, toll-like receptor activation, and tissue-factor pathway activation.[7],[8]
The unique problem associated with COVID-19-associated coagulopathy is the fact that the coagulation profiles of the patients do not satisfy the criteria for disseminated intravascular coagulation (DIC) in all the cases. The changes in the values of various coagulation factors revealed a different and complex picture.[9] Studies conducted by Han et al. and Tang et al. have shown that elevated levels of D-dimer and prothrombin time (PT) levels along with reduced levels of fibrinogen and AT in hospitalized patients were associated with the adverse outcomes in the terms of pulmonary embolism and ischemic stroke or death.[5],[7]
The data available, correlating the levels of various coagulation factors at admission and the clinical outcome of patients, are yet very limited. Our study is an effort to provide more data on the relationship between coagulation profiles and the end outcome of the hospitalization in patients admitted to a tertiary center located in a Southern State of India with reverse transcriptase polymerase chain reaction (RT-PCR)-confirmed COVID-19 infection.
| Subjects and Methods | | |
Study design
A prospective observational study was conducted among patients admitted to Apollo Hospitals, a tertiary care center in South India, from February 2021 to March 2022. The institutional review board approved this study. Informed consent was obtained from the patients. Privacy and patient confidentiality were maintained as per norms.
Data collection
The diagnosis of COVID-19 was based on the RT-PCR on the nasopharyngeal swab and sampling from the lower respiratory tract. Laboratory data and imaging studies were prospectively collected. Epidemiological data like age, gender, and co-morbidities of patients, laboratory parameters like complete blood picture with differential counts, coagulation parameters including PT, Platelet counts and D-dimer, and imaging studies like CT pulmonary angiogram were collected. The clinical course of the patient was noted during the hospital stay. The data were analyzed and tabulated.
Inclusion criteria
Patients aged 18 years and above with an RT-PCR-confirmed diagnosis of COVID-19 and admitted to Apollo Hospitals, Chennai, were included.
Exclusion criteria
Patients aged <18 years, treated as an outpatient, and patients on anticoagulation therapy prior to admission for other indications apart from COVID-19.
Statistical analysis
The normality of the data was assessed through Shapiro–Wilk test. Normally distributed variables were expressed as mean ± standard deviation, otherwise median (interquartile range). Categorical variables were represented by percentages. Comparison of normally distributed continuous variables was done by independent sample's t-test if there were two categories. ANOVA was used for comparing the clinical categories and the coagulation parameters. The Mann–Whitney U-test or Kruskal–Wallis H-test were used if the distribution was not normal.
Comparison of categorical variables was done by Chi-square test or Fisher's exact test based on the number of observations. Data entry was done in Microsoft Excel 2007. Data analysis was carried out by IBM SPSS statistics for windows version 25.0, Armonk, NY, USA: IBM Corp. “P” <0.05 was considered statistically significant.
| Results | | |
The mean age of the patients included in our study was 60.31 ± 14.85 years. The mean age of mild category was 58.78 ± 13.96 years. The average ages of moderate and severe categories were 58.63 ± 16.24 years and 63.05 ± 14.85 years, respectively [Table 1]. 47.05% of the patients fell in the age group of 61–80 years. Male (66.7%) patients were more compared to female patients (33.3%). Around 39.71% of males were classified severe category. Among females, 32.35% were classified as having severe category. | Table 1: Mean age with standard deviation among the different categories of patients
Click here to view |
Diabetes mellitus (57.8%) was the most common comorbidity followed closely by hypertension (54.9%). Hyperthyroidism and coronary artery disease were present in 14% and 16% of the study population, respectively. 9.8% had chronic kidney disease and 3.9% had asthma. Steroids were used in a total of 83 out of the 102 patients. Patients not requiring oxygen were not treated with steroids. Remdesivir was administered to 81 out of the 102 patients. Patients reporting late in the course of the disease were not treated with remdesivir.
In our study, the majority of the patients (75.5%) required oxygen support. Thirty-six patients (35.3%) were treated with nasal prongs (low-flow oxygen), 8 patients (7.8%) were treated with face mask, 4 patients (3.9%) were treated with nonrebreathing mask, 10 patients (9.8%) were treated with noninvasive mechanical ventilation, and 19 (18.6%) were treated with mechanical ventilation [Table 2]. Out of the 102 patients included in the study, 16 (15.7%) were nonsurvivors and 86 (84.3%) were survivors. Twenty-two patients (21.57%) were discharged with domiciliary oxygen and 64 patients (62.75%) were discharged without oxygen support. The average length of stay was 9.14 ± 5.81 days. The mean length of stay in the hospital for patients in the mild category was 5.61 ± 2.06 days. The mean duration of stay for the moderate and severe categories was 6.95 ± 3.02 days and 13.63 ± 6.79 days, respectively.
Out of the 102 patients, CT pulmonary angiogram (CTPA) was done on a total of 9 patients and 3 patients had evidence of pulmonary embolism. Most of the patients who underwent CTPA were in the moderate or severe category (8 out of 9 patients). All three patients who had evidence of pulmonary embolism belonged to the severe category. Most patients belonging to the severe category could not undergo CTPA as they were either severely dyspneic to lie flat, or if they were intubated, they were either on multiple vasopressor support or in prone ventilation. The number of patients with established pulmonary embolism was very less to do statistical analysis.
The average hemoglobin value in patients with mild COVID-19 pneumonia was 13.06 ± 1.65 g/dL. In the moderate and severe categories, the average hemoglobin level was 12.81 ± 1.74 g/dL and 12.79 ± 1.94 g/dL, respectively. The mean hemoglobin value in the survivors was 12.96 ± 1.77 g/dL and 12.34 ± 1.88 g/dL in the nonsurvivors. The average total leukocyte count in the survivors was 8.14 ± 4.06 × 103/mm3 and 10.86 ± 9.74 × 103/mm3 in the nonsurvivors. The mean total leukocyte count in mild, moderate, and severe categories was 6.18 ± 3.11 × 103/mm3, 8.37 ± 3.82 × 103/mm3, and 10.23 ± 7.14 × 103/mm3, respectively. In mild COVID-19 cases, the differential count showed an average neutrophil count of 66.70%, average lymphocyte count of 22.96%, and monocyte count of 8.39%. Patients with moderate disease had an average neutrophil count of 79.32%, lymphocyte count of 14.63%, and monocyte count of 5.07%. Severe COVID-19 disease was associated with 82.03%, 11.18%, and 6.18% of neutrophils, lymphocytes, and monocytes, respectively.
The average platelet count of the patients in our study was 268.55 ± 123.38 × 103/mm3. Patients with mild, moderate, and severe disease had a platelet count of 232.52 ± 77.92, 280.37 ± 118.62, and 277.61 ± 147.28 × 103/mm3, respectively. The average PT was 12.58 ± 1.47 s. Patients with mild, moderate, and severe disease had a PT of 12.48 ± 2.33, 12.51 ± 0.95, and 12.71 ± 1.37 s, respectively. The average international normalized ratio (INR) of mild, moderate, and severe cases was 1.10 ± 0.22, 1.08 ± 0.09, and 1.11 ± 0.13, respectively. The mean D-dimer of patients in our study was 1.27 ± 2.77 μg/mL. The average D-dimer of patients in the mild category was 0.46 ± 0.34 μg/mL. The average D-dimer of patients in the moderate and severe categories was 0.62 ± 0.88 μg/mL and 2.46 ± 4.22 μg/mL, respectively.
| Discussion | | |
This study included 102 hospitalized COVID-19 patients who were COVID-19 RT-PCR positive. The main goal of our study was to evaluate the correlation between the coagulation profile and the clinical outcome.
The purpose of this study is to identify potential hematological and coagulation parameters which can be used as a tool for prognosticating patients on arrival at the hospital. The correlation between the coagulation parameters and the severity and outcome of COVID-19 can help in triaging the patients early and anticipating the potential complications. The low cost of the tests used in our study and the fact that they are routinely done in many centers can help in identifying markers of severity and outcome which can be easily used even in resource-limited settings. Inflammatory markers such as interleukin-6, fibrinogen, and procalcitonin (PCT) are not readily available in many centers, especially in resource-limited regions like our country.
This study is an effort to identify commonly tested parameters of coagulation which can be used as a predictor of clinical outcome and severity of COVID-19. This can help in early referral to higher center when noninvasive or invasive ventilator need is anticipated based on the coagulation parameters.
The mean age of patients in our study was 60.31 ± 14.85 years ranging from 26 to 100 years. The mean age of patients in the mild and moderate categories was lower than the patients in the severe category, which shows that increasing age is a risk factor for severe COVID-19 disease. Elderly patients exhibited more clinical manifestations and increased mortality than younger patients in clinical.[10]
Our study clearly shows the increased incidence of COVID-19 in male patients and increased risk of developing severe disease in male patients compared to their female counterparts. A cross-sectional analysis found that the male gender was associated with a higher incidence of ICU admission, higher length of stay, and higher in-hospital mortality.[11]
Diabetes mellitus was the most common comorbidity in COVID-19 patients in our study. A higher proportion of patients with diabetes mellitus (25 out of 59) had severe disease compared with patients without diabetes mellitus (13 out of 43). Patients with diabetes mellitus were more likely to succumb to COVID-19 compared to patients without diabetes mellitus. Patients with comorbidities had greater disease severity risk compared to patients without comorbidities.[12]
Hemoglobin level did not have a significant impact on the clinical category or survival of COVID-19 patients.[2],[13] Most of the patients with an elevated total count had severe COVID-19 disease in our study (13 out of 22). There was a statistically significant correlation (P < 0.05) between the mean total count of mild and severe diseases. Studies have found that patients with an elevated total count at admission had a higher probability of developing severe COVID-19 infection compared to normal leukocyte count.[14] Neutrophilia and lymphocytopenia with resultant increased neutrophil: lymphocyte ratio (NLR) has been found to be a significant marker for the severity of COVID-19 disease. It has also been found to have significance in predicting mortality.[15] Our study had a similar finding with increased NLR being associated with a more severe manifestation of the disease (P < 0.05). Similar findings were observed in patients who succumbed to COVID-19. However, NLR was not significantly different among survivors and nonsurvivors (P > 0.05).
Platelet count was not found to be a consistent prognosticator in COVID-19.[16],[17] Our study found no significant difference between the platelet counts of survivors and nonsurvivors at the time of admission. There were no statistically significant differences between the platelet counts at admission of mild, moderate, and severe cases.
PT was found to be less affected by COVID-19 disease, especially in the early phase of the disease and there was no significant change in the PT as the disease progressed, in contrast to DIC which is usually associated with prolongation of PT.[18] Our study found no significant correlation between PT and INR on admission and the clinical severity or outcome of COVID-19 disease.
Meta-analysis of D-dimer levels at admission in COVID-19 has shown that D-dimer level is directly associated with severity of the disease [Table 3]. It has been found to be a significant indicator of mortality in COVID-19 patients [Table 4]. Studies have shown a significant difference between the D-dimer values of patients with mild and severe disease. Survivors had a lower D-dimer level compared to nonsurvivors.[16],[19] Our study found similar findings with severe COVID-19 patients having significantly higher D-dimer levels on admission than mild patients. Similar finding of elevated D-dimer level was found in nonsurvivors when compared with survivors.
The increased D-dimer level has been found to be associated with greater mortality in hospitalized patients with community-acquired pneumonia.[20] An analysis of D-dimer levels in inflammatory conditions found that D-dimer had a positive correlation with various inflammatory markers like CRP and PCT.[21] In our study, the D-dimer level at admission was able to predict the need for oxygen requirement along with the mode of oxygen supplementation [Figure 1], the clinical severity, and the outcome in terms of mortality. An elevated D-dimer level at admission was most likely to be associated with more severe disease with an increased risk of death (P < 0.05). However, it could not predict the need for oxygen requirement after discharge (P > 0.5). | Figure 1: Mean D-dimer and mode of oxygen delivery. NRBM = Non Re-breathing Mask, NIV = Noninvasive ventilation, HFNC = High-flow nasal cannula
Click here to view |
Neither PT nor platelet count on admission could predict the outcome in terms of severity, oxygen requirement, or mortality in the patients included in our study.
In conclusion, COVID-19 patients in our study were found to have a significant correlation between the admission D-dimer levels and the clinical course and outcome of COVID-19 disease.
Recommendation
D-dimer can be used as a prognostic marker for predicting severity and outcome in COVID-19 patients.
| Conclusion | | |
Among the basic coagulation parameters, D-dimer has the ability to consistently predict the clinical course and outcome of COVID-19 patients. The D-dimer level obtained on admission can be used as a reliable prognostic indicator of severity and overall survival. A higher D-dimer level on admission is more likely to be associated with severe COVID-19 disease, higher oxygen requirement, and increased chance of mortality.
Acknowledgment
I would like to thank the consultants in the Department Of Medicine for helping in data collection of their patients, the Departments of Haematology and Radiology for allowing access to the laboratory reports, and my colleagues and friends for providing emotional support.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020;382:727-33.  |
| 2. | Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497-506. |
| 3. | 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. |
| 4. | Klok FA, Kruip MJ, van der Meer NJ, Arbous MS, Gommers DA, Kant KM, et al. Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res 2020;191:145-7. |
| 5. | Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost 2020;18:844-7. |
| 6. | Khan IH, Savarimuthu S, Leung MS, Harky A. The need to manage the risk of thromboembolism in COVID-19 patients. J Vasc Surg 2020;72:799-804. |
| 7. | Han H, Yang L, Liu R, Liu F, Wu KL, Li J, et al. Prominent changes in blood coagulation of patients with SARS-CoV-2 infection. Clin Chem Lab Med 2020;58:1116-20. |
| 8. | Giannis D, Ziogas IA, Gianni P. Coagulation disorders in coronavirus infected patients: COVID-19, SARS-CoV-1, MERS-CoV and lessons from the past. J Clin Virol 2020;127:104362. |
| 9. | Adam EH, Zacharowski K, Miesbach W. A comprehensive assessment of the coagulation profile in critically ill COVID-19 patients. Thromb Res 2020;194:42-4. |
| 10. | Hu C, Li J, Xing X, Gao J, Zhao S, Xing L. The effect of age on the clinical and immune characteristics of critically ill patients with COVID-19: A preliminary report. PLoS One 2021;16:e0248675. |
| 11. | Vahidy FS, Pan AP, Ahnstedt H, Munshi Y, Choi HA, Tiruneh Y, et al. Sex differences in susceptibility, severity, and outcomes of coronavirus disease 2019: Cross-sectional analysis from a diverse US metropolitan area. PLoS One 2021;16:e0245556. |
| 12. | Wang B, Li R, Lu Z, Huang Y. Does comorbidity increase the risk of patients with COVID-19: Evidence from meta-analysis. Aging (Albany NY) 2020;12:6049-57. |
| 13. | Urrechaga E, Zalba S, Otamendi I, Zabalegui MA, Galbete A, Ongay E, et al. Hemoglobin and anemia in COVID19 patients. Hematol Med Oncol 2020;5:1-4. |
| 14. | Zhao K, Li R, Wu X, Zhao Y, Wang T, Zheng Z, et al. Clinical features in 52 patients with COVID-19 who have increased leukocyte count: A retrospective analysis. Eur J Clin Microbiol Infect Dis 2020;39:2279-87. |
| 15. | Chan AS, Rout A. Use of neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios in COVID-19. J Clin Med Res 2020;12:448-53. |
| 16. | 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. |
| 17. | Li Q, Cao Y, Chen L, Wu D, Yu J, Wang H, et al. Hematological features of persons with COVID-19. Leukemia 2020;34:2163-72. |
| 18. | Mackman N, Antoniak S, Wolberg AS, Kasthuri R, Key NS. Coagulation abnormalities and thrombosis in patients infected with SARS-CoV-2 and other pandemic viruses. Arterioscler Thromb Vasc Biol 2020;40:2033-44. |
| 19. | Zhang A, Leng Y, Zhang Y, Wu K, Ji Y, Lei S, et al. Meta-analysis of coagulation parameters associated with disease severity and poor prognosis of COVID-19. Int J Infect Dis 2020;100:441-8. |
| 20. | Querol-Ribelles JM, Tenias JM, Grau E, Querol-Borras JM, Climent JL, Gomez E, et al. Plasma d-dimer levels correlate with outcomes in patients with community-acquired pneumonia. Chest 2004;126:1087-92. |
| 21. | Bao W, Qi X, Li HL, Hou F, Zhang X, Wang R, et al. Correlation of D-dimer level with the inflammatory conditions: A retrospective study. AME Med J 2017;2:27. |
[Figure 1]
[Table 1], [Table 2], [Table 3], [Table 4]
|
Search Pubmed for