Utility of baseline D-dimer and neutrophil-to-lymphocyte ratio (NLR) in predicting the severity among COVID-19 patients in an Indian cohort during the first wave of the pandemic
DOI:
https://doi.org/10.51248/.v43i4.2679Keywords:
COVID-19, D-dimer, neutrophil-to-lymphocyte ratio (NLR), severity, predictionAbstract
Introduction and Aim: COVID-19 outbreak was declared as pandemic by WHO Director-General on 11th March 2020 in his opening remarks at the media briefing. The global population infected by Corona virus appeared to be responding at different levels in the first wave which warranted WHO to categorise the disease as mild, moderate, and severe. Haematological, biochemical, and radiological parameters played a crucial role in critically triaging and following-up disease progression. Amongst various laboratory parameters, this work aimed to identify the most specific marker in predicting disease severity.
Materials and Methods: Blood samples from study population of 510 laboratory confirmed COVID-19 cases admitted in our hospital were selected. The patients who were classified as having mild, moderate, and severe disease were analysed for biochemical and haematological inflammatory markers. Results were analysed by ANOVA and post-hoc tests. ROC curves were derived to determine the cut-off values between severe and non-severe groups. Correlation between D-dimer and NLR was done by Pearson’s correlation.
Results: Patients with co-morbidities were likely to develop severe complications which could lead to poor outcome. From ROC curves, we determine that NLR, with highest area under curve, is the best marker of disease severity. A significant positive correlation was found between D-dimer and NLR (p=0.000) across groups. Baseline cut-off values for D-dimer and NLR based to differentiate between severe and non-severe cases were 0.5 and 4.875 respectively.
Conclusion: We conclude that baseline NLR is a simple and most useful tool that would assist clinicians in designing treatment strategies for a COVID-19 infected patient.
References
Mamatha, S. D., Priya, S. A., Shashikala, L., Shivakumar, K. M., Chandrashekarappa, S. M. Assessment of the cognitive status of Covid-19 patients and its correlation to blood pressure. Biomedicine. 2023 Jul 20;43(3):1008-1012.
Kumar, N., Mathew, G., Huchchannavar, R. Home-based care programme (HBCP) for patients with COVID-19: Boon or burden during pandemic management?. Biomedicine. 2023 May 4;43(02):649-654.
Klok, F.A., Kruip, M.J.A., Van der Meer, N.J., Arbous, M.S., Gommers, D.A.M.P.J., Kant, K.M., et al., Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res. 2020;191:145-147.
Nahum, J., Morichau-Beauchant, T., Daviaud, F., Echegut, P., Fichet, J., Maillet, J. M., et al., Venous Thrombosis Among Critically Ill Patients with Coronavirus Disease 2019 (COVID-19). JAMA Net. Open. 2020;3(5): e2010478.
Dangis, A., Gieraerts, C., De Bruecker, Y., Janssen, L., Valgaeren, H., Obbels, D., et al., Accuracy and Reproducibility of Low-Dose Submillisievert Chest CT for the Diagnosis of COVID-19. Radiol Cardiothorac Imaging. 2020;2(2): e200196.
Ahmed, R. N. Utilization of chest imaging in COVID 19 pandemic. Biomedicine. 2023 Mar 28;43(01):386-390.
Amiri-Dashatan, N., Koushki, M., Parsamanesh, N., Chiti, H. Serum cortisol concentration and COVID-19 severity: a systematic review and meta-analysis. J Investig Med. 2022;70(3):766-772.
Johnson, E. D., Schell, J. C., Rodgers, G. M. The D-dimer assay. Am. J. Hematol.2019;94(7):833-839.
Simadibrata, D.M., Pandhita, B., Ananta, M.E., Tango, T. Platelet-to-lymphocyte ratio, a novel biomarker to predict the severity of COVID-19 patients: A systematic review and meta-analysis. J. Intensive Care Soc. 2022;23(1):20-26.
Buicu, A. L., Cernea, S., Benedek, I., Buicu, C. F., Benedek, T. Systemic Inflammation and COVID-19 Mortality in Patients with Major Noncommunicable Diseases: Chronic Coronary Syndromes, Diabetes and Obesity. J. Clin. Med. 2021; 10(8): 1545-1561.
Son, K.B., Lee, T.J., Hwang, S. S. Disease severity classification and COVID-19 outcomes, Republic of Korea. Bull. World Health Organ. 2021;99(1):62-66.
Adema, E., Gebert, U. Pooled patient samples as reference material for D-Dimer. Thromb Res. 1995;80(1):85-88.
Paliogiannis, P., Mangoni, A. A., Dettori, P., Nasrallah, G. K., Pintus, G., Zinellu, A. D-Dimer Concentrations and COVID-19 Severity: A Systematic Review and Meta-Analysis. Frontiers in Public Health. 2020; 8:432-439.
Oualim, S., Abdeladim, S., Ouarradi, A. E., Bensahi, I., Hafid, S., Naitlho, A., et al., Elevated levels of D-dimer in patients with COVID-19: prognosis value. Pan Afr Med J. 2020;35(Suppl 2):105-111.
Zhang, L., Yan, X., Fan, Q., Liu, H., Liu, X., Liu, Z. et al., D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19. J Thromb Haemost. 2020;18(6): 1324-1329.
Short, S. A. P., Gupta, S., Brenner, S. K., Hayek, S. S., Srivastava, A., Shaefi, S., et al., D-dimer and Death in Critically ill Patients with Coronavirus Disease 2019. Crit Care Med. 2020;49(5):e500-511.
Ye, W., Chen, G., Li, X., Lan, X., Ji, C., Hou, M., et al., Dynamic changes of D-dimer and neutrophil-lymphocyte count ratio as prognostic biomarkers in COVID-19. Respir Res. 2020;21 (1):169-176.
Mishra, Y., Pathak, B. K., Mohakuda, S. S., Tilak, T. V. S. V. G. K., Sen, S., P, H., Singh, R., et al., R. Relation of D-dimer levels of COVID-19 patients with diabetes mellitus. Diabetes Metab Syndr. 2020;14(6):1927-1930.
Zhou, P., Yang, X. L., Wang, X. G., Hu, B., Zhang, L., Zhang, W., et al., Addendum:A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;588(7836): E6.
Soni, M., Gopalakrishnan, R., Vaishya, R., Prabu, P. D-dimer level is a useful predictor for mortality in patients with COVID-19: Analysis of 483 cases. Diabetes Metab Synd. 2020;14(6):2245-2249.
Yildiz, H., Castanares-Zapatero, D., Pierman, G., Pothen, L., De Greef, J., Aboubakar Nana, F., et al., Validation of Neutrophil-to-Lymphocyte Ratio Cut-off Value Associated with High In-Hospital Mortality in COVID-19 Patients. Int J Gen Med. 2021;14: 5111-5117.
Yang, A. P., Liu, J. P., Tao, W. Q., Li, H. M. The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int Immunopharmacol. 2020; 84: 106504-106511.
Sun, S., Cai, X., Wang, H., He, G., Lin, Y., Lu, B., Abnormalities of peripheral blood system in patients with COVID-19 in Wenzhou, China. Clinica chimica acta. 2020;507:174-180.
Song, C.Y., Xu, J., He, J.Q., Lu, Y.Q., COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients. medRxiv. 2020; doi: 10.1101/2020.03.05.20031906.
Rathod, B. D., Amle, D., Khot, R. S., Prathipati, K. K., Joshi, P. P. Neutrophil-to-Lymphocyte Ratio as a Predictor of Disease Severity and Mortality in Coronavirus Disease 2019: Prospective Study from Central India. Cureus. 2022;14(3): e23696.
Bhat, T., Teli, S., Rijal, J., Bhat, H., Raza, M., Khoueiry, G., et al., Neutrophil to lymphocyte ratio and cardiovascular diseases: a review. Expert Rev. Cardiovasc. Ther. 2013;11(1):55-59.
Huang, Z., Fu, Z., Huang, W., Huang, K. Prognostic value of neutrophil-to-lymphocyte ratio in sepsis: A meta-analysis. Am. J. Emerg. Med. 2020;38(3):641-647.
Liu, Y., Du, X., Chen, J., Jin, Y., Peng, L., Wang, H. Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19. J. Infect. 2020;81(1): e6-e12.
Yan, Q., Li, P., Ye, X., Huang, X., Feng, B., Ji, T., et al., Longitudinal peripheral blood transcriptional analysis of COVID-19 patients captures disease progression and reveals potential biomarkers. J Immunol. 2020;206(9):2146-2159.
Jolicoeur, P., Lamontagne, L. Impairment of bone marrow pre-B and B cells in MHV3 chronically-infected mice. Adv Exp Med Biol. 1995; 380:193-195.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Biomedicine
This work is licensed under a Creative Commons Attribution 4.0 International License.
Plum Analytics
