Article Data

  • Views 2591
  • Dowloads 227

Original Research

Open Access

Comparison of hematological and inflammatory mortality predictors between older and younger COVID-19 patients

  • Suna AVCI1
  • Vildan GURSOY2

1Department of Geriatrics, University of Health Sciences, Bursa Yuksek Ihtisas Education and Research Hospital,16310 Bursa, Turkey

2Department of Hematology, University of Health Sciences, Bursa Yuksek Ihtisas Education and Research Hospital,16310 Bursa, Turkey

DOI: 10.22514/sv.2021.125 Vol.18,Issue 1,January 2022 pp.68-75

Submitted: 11 March 2021 Accepted: 07 April 2021

Published: 08 January 2022

*Corresponding Author(s): Suna AVCI E-mail:


Background/objective: Several hematological and inflammatory parameters so far have been associated with COVID-19 disease severity; however, such evidence for particularly vulnerable elderly patients is lacking. This study aimed to investigate potential and practical biomarkers that could assist in predicting mortality at the presentation in a group of elderly and non-elderly patients.

Methods: This retrospective cohort study included 1820 COVID-19 patients hospi-talized for treatment. Clinical and mortality data as well as certain hematological and inflammatory parameters were retrieved from records. For analysis, patients were divided into two groups as geriatric (age ≥65 years) and non-geriatric subjects. The associated factors of the parameters on mortality were examined separately for elderly and younger patients.

Results: Following multivariate analysis, high neutrophil count and high troponin T levels emerged as significant independent predictors of mortality in both geriatric patients and younger patients. Low and high monocyte count was associated with increased mortality risk for geriatric and younger patients, respectively. In the geriatric population, high ferritin levels and high RBC count was associated with increased risk, but increased eosinophil count was associated with decreased risk. Low lymphocyte count emerged as a predictor of mortality among younger patients.

Conclusion: Several hematological and inflammatory parameters and indices may assist in predicting the mortality risk in patients with COVID-19; however, there appears to be some differences in terms of these predictors of mortality between elderly and younger patients. Larger prospective studies are warranted to support these findings.


Elderly patients; COVID-19; SARS-CoV-2; Hematologic parameters; Predictors; Mortality

Cite and Share

Suna AVCI,Vildan GURSOY. Comparison of hematological and inflammatory mortality predictors between older and younger COVID-19 patients. Signa Vitae. 2022. 18(1);68-75.


[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. New England Journal of Medicine. 2020; 382: 727–733.

[2] World Health Organization. WHO Coronavirus Disease (COVID-19) Dashboard. 2021. Available at: 22 June 2021).

[3] Dhama K, Patel SK, Kumar R, Rana J, Yatoo MI, Kumar A, et al. Geriatric population during the COVID-19 pandemic: problems, considerations, exigencies, and beyond. Frontiers in Public Health. 2020; 8: 574198.

[4] Onder G, Rezza G, Brusaferro S. Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. Journal of the American Medical Association. 2020; 323: 1775–1776.

[5] Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized With COVID-19 in the New York City area. Journal of the American Medical Association. 2020; 323: 2052–2059.

[6] Esme M, Koca M, Dikmeer A, Balci C, Ata N, Dogu BB, et al. Older adults with coronavirus disease 2019; a nationwide study in Turkey. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences. 2021; 76: e68–e75.

[7] Ponti G, Maccaferri M, Ruini C, Tomasi A, Ozben T. Biomarkers associated with COVID-19 disease progression. Critical Reviews in Clinical Laboratory Sciences. 2020; 57: 389–399.

[8] Zhang G, Zhang J, Wang B, Zhu X, Wang Q, Qiu S. Analysis of clinical characteristics and laboratory findings of 95 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a retrospective analysis. Respiratory Research. 2020; 21: 74.

[9] Bao J, Li C, Zhang K, Kang H, Chen W, Gu B. Comparative analysis of laboratory indexes of severe and non-severe patients infected with COVID-19. Clinica Chimica Acta. 2020; 509: 180–194.

[10] Danwang C, Endomba FT, Nkeck JR, Wouna DLA, Robert A, Noubiap JJ. A meta-analysis of potential biomarkers associated with severity of coronavirus disease 2019 (COVID-19). Biomarker Research. 2020; 8: 37.

[11] Elshazli RM, Toraih EA, Elgaml A, El-Mowafy M, El-Mesery M, Amin MN, et al. Diagnostic and prognostic value of hematological and immunological markers in COVID-19 infection: a meta-analysis of 6320 patients. PLoS ONE. 2020; 15: e0238160.

[12] Henry BM, de Oliveira MHS, Benoit S, Plebani M, Lippi G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clinical Chemistry and Laboratory Medicine. 2020; 58: 1021–1028.

[13] Liu Y, Sun W, Guo Y, Chen L, Zhang L, Zhao S, et al. Association between platelet parameters and mortality in coronavirus disease 2019: retrospective cohort study. Platelets. 2020; 31: 490–496.

[14] Tjendra Y, Al Mana AF, Espejo AP, Akgun Y, Millan NC, Gomez-Fernandez C, et al. Predicting disease severity and outcome in COVID-19 patients: a review of multiple biomarkers. Archives of Pathology & Laboratory Medicine. 2020; 144: 1465–1474.

[15] Pourbagheri-Sigaroodi A, Bashash D, Fateh F, Abolghasemi H. Labo-ratory findings in COVID-19 diagnosis and prognosis. Clinica Chimica Acta. 2020; 510: 475–482.

[16] Liu W, Tao Z, Wang L, Yuan M, Liu K, Zhou L, et al. Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease. Chinese Medical Journal. 2020; 133: 1032–1038.

[17] Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Medicine. 2020; 46: 846–848.

[18] Tan C, Huang Y, Shi F, Tan K, Ma Q, Chen Y, et al. C‐reactive protein correlates with computed tomographic findings and predicts severe COVID‐19 early. Journal of Medical Virology. 2020; 92: 856–862.

[19] Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 2020; 395: 1054–1062.

[20] Zhang J, Dong X, Cao Y, Yuan Y, Yang Y, Yan Y, et al. Clinical characteristics of 140 patients infected with SARS‐CoV‐2 in Wuhan, China. Allergy. 2020; 75: 1730–1741.

[21] Liu F, Xu A, Zhang Y, Xuan W, Yan T, Pan K, et al. Patients of COVID-19 may benefit from sustained Lopinavir-combined regimen and the increase of Eosinophil may predict the outcome of COVID-19 progression. International Journal of Infectious Diseases. 2020; 95: 183–191.

[22] Lippi G, Henry BM. Eosinophil count in severe coronavirus disease 2019. QJM: an International Journal of Medicine. 2020; 113: 511–512.

[23] Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. Journal of Thrombosis and Haemostasis. 2020; 18: 844–847.

[24] Rostami M, Mansouritorghabeh H. D-dimer level in COVID-19 infection: a systematic review. Expert Review of Hematology. 2020; 13: 1265–1275.

[25] Hanley B, Lucas SB, Youd E, Swift B, Osborn M. Autopsy in suspected COVID-19 cases. Journal of Clinical Pathology. 2020; 73: 239–242.

[26] Duerr GD, Heine A, Hamiko M, Zimmer S, Luetkens JA, Nattermann J, et al. Parameters predicting COVID-19-induced myocardial injury and mortality. Life Sciences. 2020; 260: 118400.

[27] Xia X, Wen M, Zhan S, He J, Chen W. An increased neu-trophil/lymphocyte ratio is an early warning signal of severe COVID-19. Nan Fang Yi Ke Da Xue Xue Bao 2020; 40: 333–336. (In Chinese)

[28] Liu Y, Du X, Chen J, Jin Y, Peng L, Wang HHX, et al. Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19. Journal of Infection. 2020; 81: e6–e12.

[29] Yang A, Liu J, Tao W, Li H. The diagnostic and predictive role of NLR, d- NLR and PLR in COVID-19 patients. International Immunopharma-cology. 2020; 84: 106504.

[30] Yan X, Li F, Wang X, Yan J, Zhu F, Tang S, et al. Neutrophil to lymphocyte ratio as prognostic and predictive factor in patients with coronavirus disease 2019: a retrospective cross‐sectional study. Journal of Medical Virology. 2020; 92: 2573–2581.

[31] Mahat RK, Panda S, Rathore V, Swain S, Yadav L, Sah SP. The dynamics of inflammatory markers in coronavirus disease-2019 (COVID-19) patients: a systematic review and meta-analysis. Clinical Epidemiology and Global Health. 2021; 11: 100727.

[32] Sayah W, Berkane I, Guermache I, Sabri M, Lakhal FZ, Yasmine Rahali S, et al. Interleukin-6, procalcitonin and neutrophil-to-lymphocyte ratio: Potential immune-inflammatory parameters to identify severe and fatal forms of COVID-19. Cytokine. 2021; 141: 155428.

[33] BG S, Gosavi S, Ananda Rao A, Shastry S, Raj SC, Sharma A, et al. Neutrophil-to-Lymphocyte, Lymphocyte-to-Monocyte, and Platelet-to-Lymphocyte Ratios: Prognostic Significance in COVID-19. Cureus. 2021; 13: e12622.

[34] Liu J, Liu Y, Xiang P, Pu L, Xiong H, Li C, et al. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. Journal of Translational Medicine. 2020; 18: 206.

[35] Yang Z, Shi J, He Z, Lü Y, Xu Q, Ye C, et al. Predictors for imaging progression on chest CT from coronavirus disease 2019 (COVID-19) patients. Aging. 2020; 12: 6037–6048.

[36] Qu R, Ling Y, Zhang Y, Wei L, Chen X, Li X, et al. Platelet‐to‐lymphocyte ratio is associated with prognosis in patients with coronavirus disease‐19. Journal of Medical Virology. 2020; 92: 1533–1541.

[37] 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. Clinical Infectious Diseases. 2020; 71: 762–768.

[38] Cummings MJ, Baldwin MR, Abrams D, Jacobson SD, Meyer BJ, Balough EM, et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study. The Lancet. 2020; 395: 1763–1770.

[39] Henderson LA, Canna SW, Schulert GS, Volpi S, Lee PY, Kernan KF, et al. On the alert for cytokine storm: immunopathology in COVID-19. Arthritis & Rheumatology. 2020; 72: 1059–1063.

[40] Al-Zahrani J. SARS-CoV-2 associated COVID-19 in geriatric population: a brief narrative review. Saudi Journal of Biological Sciences. 2021; 28: 738- 743.

[41] Koff WC, Williams MA. Covid-19 and immunity in aging populations— a new research agenda. New England Journal of Medicine. 2020; 383: 804–805.

[42] Tan L, Wang Q, Zhang D, Ding J, Huang Q, Tang Y, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduction and Targeted Therapy. 2020; 5: 33.

[43] Zheng Y, Liu X, Le W, Xie L, Li H, Wen W, et al. A human circulating immune cell landscape in aging and COVID-19. Protein & Cell. 2020; 11: 740–770.

Abstracted / indexed in

Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,200 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.

Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.

Chemical Abstracts Service Source Index The CAS Source Index (CASSI) Search Tool is an online resource that can quickly identify or confirm journal titles and abbreviations for publications indexed by CAS since 1907, including serial and non-serial scientific and technical publications.

Index Copernicus The Index Copernicus International (ICI) Journals database’s is an international indexation database of scientific journals. It covered international scientific journals which divided into general information, contents of individual issues, detailed bibliography (references) sections for every publication, as well as full texts of publications in the form of attached files (optional). For now, there are more than 58,000 scientific journals registered at ICI.

Geneva Foundation for Medical Education and Research The Geneva Foundation for Medical Education and Research (GFMER) is a non-profit organization established in 2002 and it works in close collaboration with the World Health Organization (WHO). The overall objectives of the Foundation are to promote and develop health education and research programs.

Scopus: CiteScore 1.0 (2022) Scopus is Elsevier's abstract and citation database launched in 2004. Scopus covers nearly 36,377 titles (22,794 active titles and 13,583 Inactive titles) from approximately 11,678 publishers, of which 34,346 are peer-reviewed journals in top-level subject fields: life sciences, social sciences, physical sciences and health sciences.

Embase Embase (often styled EMBASE for Excerpta Medica dataBASE), produced by Elsevier, is a biomedical and pharmacological database of published literature designed to support information managers and pharmacovigilance in complying with the regulatory requirements of a licensed drug.

Submission Turnaround Time