The Impact of Hematological Parameters on Survival for Patients with COVID-19

Abstract

Purpose: Coronavirus disease 2019 is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clinical and laboratory predictors may provide identification of patients at risk of mortality and guide treatment. This study aims to analyze laboratory parameters in COVID-19 patients and to determine which parameters affect mortality and hospitalization.

Materials and Methods: Demographic characteristics, the parameters including complete blood count (CBC) parameters, bio-chemical tests, coagulation parameters, duration of hospitalization, and final status (discharge or death) were recorded in patients diagnosed with COVID-19.

Results: This retrospective study was conducted with 101 patients diagnosed with COVID-19. The 101 patients included in the analysis comprised 52(51.5%) males and 49(48.5%) females with a mean age of 65.7±14.7 years. Comparisons were made between survivors and non-survivors at the end of the follow-up period. Multiple analyses showed mean platelet volume (MPV), platelet distribution width (PDW), and lactate dehydrogenase (LDH) to be significant predictors of mortality. The cut-off value of the hospitalization period was found to be 10 days; therefore, the patients were divided into two groups. In the univariate and multiple models, no significant independent parameter was observed for the prediction of hospitalization duration.

Conclusion: The results of the current study demonstrated that MPV, PDW and LDH were significant independent variables for the prediction of mortality. As SARS-CoV-2 and SARS-CoV are known to use the same receptor, there may be similar structures and receptors for mutant variants and the first variant, so these predictive parameters can be considered effective in mutant variants.

Keywords

• lactate dehydrogenase
• mean platelet volume
• mortality
• platelet distribution width
• SARS-CoV-2

COVID-19 Hastalarında Hematolojik Parametrelerin Sağ Kalıma Etkisi

Abstract

Giriş: Coronavirus hastalığı 2019, şiddetli akut solunum sendromu coronavirüs 2'nin (SARS-CoV-2) sebep olduğu viral bir antitedir. Klinik ve laboratuvar belirleyicileri, mortalite riski altındaki hastaları belirleyebilir ve tedaviye rehberlik edebilir. Bu çalışmanın amacı, COVID-19 hastalarında laboratuvar parametrelerini analiz etmek ve hangi parametrelerin mortalite ve hastaneye yatışı etkilediğini belirlemektir.

Araçlar ve Yöntem: Demografik özellikler, tam kan sayımı (CBC) parametrelerini içeren laboratuvar parametreleri, biyokimyasal testler, pıhtılaşma parametreleri, hastanede kalış süresi ve son durum (taburculuk veya ölüm) kaydedildi.

Bulgular: Bu retrospektif çalışma, COVID-19 teşhisi konan 101 hasta üzerinde yapıldı. Analize dahil edilen 101 hasta 52(%51.5) erkek ve 49(%48.5) kadından oluşmaktaydı ve ortalama yaşları 65.7±14.7 idi. İzlem süresi sonunda hayatta kalanlar ve hayatta kalmayanlar arasında karşılaştırmalar yapıldı. Çok değişkenli analiz, ortalama trombosit hacmi (MPV), trombosit dağılım genişliği (PDW) ve laktat dehidrogenazın (LDH) mortalitenin önemli belirleyicileri olduğunu gösterdi. Hastanede kalış süresinin cut-off değeri 10 gün olarak saptandı ve hastalar iki gruba ayrıldı. Tek değişkenli ve çok değişkenli modellerde, hastanede yatış süresinin öngörülmesi için anlamlı bağımsız parametre gözlenmedi.

Sonuç: Mevcut çalışmanın sonuçları, MPV, PDW ve LDH' nin mortaliteyi öngörmede önemli bağımsız değişkenler olduğunu göstermiştir. SARS-CoV -2 ve SARS-CoV'nin aynı reseptörü kullandığı bilindiğinden, mutant varyantlar ve ilk varyant için benzer bir yapı ve reseptör olabilir, dolayısıyla bu öngörücü parametrelerin mutant varyantlarda da etkili olduğu düşünülebilir.

Keywords

• laktat dehidrogenaz;
• ortalama trombosit hacmi
• ölüm oranı
• SARS-CoV-2
• trombosit dağılım genişliği

References

1. 1. Fauci AS, Lane HC, Redfield RR. Covid-19 navigating the uncharted. 2020;382(13):1268-1269.
2. 2. Ciftciler, R., Haznedaroğlu, İ. C., Tufan, A., Öztürk, M. A. Covid-19 scientific publications from Turkey. Turk J Med Sci. 2021;51 (3):877-889.
3. 3. Yang X, Yu Y, Xu J, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir. Med. 2020;8(5):475-481.
4. 4. Peleg Y, Kudose S, D’Agati V, et al. Acute kidney injury due to collapsing glomerulopathy following COVID-19 infection. Kidney Int. Rep. 2020;5(6):940-945.
5. 5. Jin Y-H, Cai L, Cheng Z-S, et al. A rapid ad-vice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Mil Med Res. 2020;7(1):1-23.
6. 6. Terpos E, Ntanasis‐Stathopoulos I, Elalamy I, et al. Hematological findings and complications of COVID‐19. Am J Hematol. 2020;95(7):834-847.
7. 7. Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a meta-analysis. Clinica chimica acta. 2020;506:145-148.
8. 8. Henry BM, Aggarwal G, Wong J, et al. Lactate dehydrogenase levels predict coronavirus disease 2019 (COVID-19) severity and mortality: A pooled analysis. Am J Emerg Med. 2020;38(9): 1722-1726.

9. 9. IBM Corp.(2017).IBM SPSS Statistics for Windows.Armonk,NY:IBM Corp.Retrieved from https://hadoop.apache.org. Access date 09 Eylül, 2021.
10. 10. Yanga J, Zheng Y, Gou X, et al. Prevalence of comorbidity and its effects in patients infected with SARS-CoV-2: a systematic review and metaanalysis. Int J Infect Dis. 2020;94:91-95.
11. 11. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. Jama. 2020;323(13):1239-1242.
12. 12. Guan W-j, Ni Z-y, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708-1720.
13. 13. Richardson S, Hirsch JS, Narasimhan M, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. Jama. 2020;323(20):2052-2059.
14. 14. Cai H. Sex difference and smoking predisposition in patients with COVID-19. Lancet Respir Med. 2020;8(4):e20.
15. 15. Wu C, Chen X, Cai Y, 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(7):934-943.
16. 16. Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus infected pneumonia in Wuhan, China. Jama. 2020;323(11):1061-1069.
17. 17. Tang X, Wu C, Li X, et al. On the origin and continuing evolution of SARS-CoV-2. National Science Review. 2020;7(6):1012-1023.
18. 18. Tufan A, Güler AA, Matucci-Cerinic M. COVID-19, immune system response, hyperinflammation and repurposing antirheumatic drugs. Turk. J. Med. 2020;50(SI-1):620-632.
19. 19. Guan W-j, Ni Z-y, Hu Y, et al. Clinical characteristics of 2019 novel coronavirus infection in China. MedRxiv. 2020;58(4):711-712.
20. 20. Chang D, Lin M, Wei L, et al. Epidemiologic and clinical characteristics of novel coronavirus infections involving 13 patients outside Wuhan, China. Jama. 2020;323(11):1092-1093.
21. 21. Pyo J-S, Sohn JH, Kang G. Diagnostic and prognostic roles of the mean platelet volume in malignant tumors: a systematic review and meta-analysis. Platelets. 2016;27(8):722-728.
22. 22. Tayman C, Tonbul A, Akca H, Bilici M. Mean platelet volume (MPV) may simply predict the severity of sepsis in preterm infants. Clin Lab. 2014;60(7):1193-2000.
23. 23. Tok D, Canpolat U, Tok D, et al. Association of mean platelet volume level with in-hospital major adverse events in infective endocarditis. Wien. Klin. Wochenschr. 2015;127(5):197-202.
24. 24. Kader Ç, Yolcu S, Erbay A. Evaluation of mean platelet volume (MPV) levels in brucellosis patients. Cumhur. Medical J. 2013;35(4):488-494.
25. 25. Erturk A, Cure E, Cure M, Parlak E, Kurt A, Ogullar S. The association between serum YKL-40 levels, mean platelet volume, and c-reactive protein in patients with cellulitis. Indian J. Med. Microbiol. 2015;33(5):61-66.
26. 26. Kaito K, Otsubo H, Usui N, et al. Platelet size deviation width, platelet large cell ratio, and mean platelet volume have sufficient sensitivity and specificity in the diagnosis of immune thrombocytopenia. Br. J. Haematol. 2005;128 (5):698-702.
27. 27. Wang Y, Fan Z, Wang S, Zhuang C. The diagnostic value of platelet distribution width in patients with mild COVID‐19. Journal of clinical laboratory analysis. 2021;35(4):e23703.
28. 28. Güçlü E, Kocayiğit H, Okan HD, et al. Effect of COVID-19 on platelet count and its indices. Rev. Assoc. Med. Bras. 2020;66(8):1122-1127.
29. 29. Xu P, Zhou Q, Xu J. Mechanism of thrombocytopenia in COVID-19 patients. Ann. Hematol. 2020;99(6):1205-1208.
30. 30. Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ. COVID-19: consider cytokine storm syndromes and immunosuppression. The lancet. 2020;395(10229):1033-1034.
31. 31. Wang L, He W, Yu X, et al. Coronavirus disease 2019 in elderly patients: characteristics and prognostic factors based on 4-week follow-up. J. Infect. 2020;80(6):639-645.
32. 32. Benoit JL, Benoit SW, de Oliveira MH, Lippi G, Henry BM. Anemia and COVID‐19: A prospective perspective. J Med Virol. 2021;93(2):708-711.
33. 33. Tao R-J, Luo X-L, Xu W, et al. Viral infection in community acquired pneumonia patients with fever: a prospective observational study. J. Thorac. Dis. 2018;10(7):4387.
34. 34. Meena P, Bhargava V, Rana DS, Bhalla AK, Gupta A. COVID-19 and the kidney: A matter of concern. CMRP. 2020;10(4):165-168.
35. 35. Ye B, Deng H, Zhao H, Liang J, Ke L, Li W. Association between an increase in blood urea nitrogen at 24 h and worse outcomes in COVID-19 pneumonia. Renal Failure. 2021;43(1):347-350.
36. 36. Werion A, Belkhir L, Perrot M, et al. SARS-CoV-2 causes a specific dysfunction of the kidney proximal tubule. Kidney international. 2020;98(5):1296-1307.