PROGNOSTIC ROLE OF INFLAMMATORY INDICES IN RISK CLASSIFICATION OF PATIENTS WITH COVID-19

Abstract

Objective: The Covid-19 pandemic has revealed the importance of an evidence-based efficient triage system in the early identification of highrisk patients and the rational use of limited medical resources for reducing mortality. The aim of this study was to evaluate the role of various inflammatory indices that can be easily calculated using readily accessible, inexpensive routine test parameters in risk stratification and prediction of prognosis in patients with Covid-19. Material and Methods: The study was carried out retrospectively with the data of 8036 patients with Covid-19, who were grouped according to their prognosis in outpatient and inpatient follow-ups, and inpatients as survivors and death. Using the complete blood count and C-reactive protein baseline results of the patients at admission, neutrophillymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocytelymphocyte ratio (MLR), MVP-platelet ratio (MPR), platelet mass index (PMI), systemic immune-inflammatory index (SII), systemic inflammatory response index (SIRI), and multi-inflammatory indices (MII) were calculated. Results: Our results demonstrate that almost all of the inflammatory indices were significantly different in severe patients and in patients with high mortality risk, but not all of them had a predictive value. It has been seen that the most effective factors in determining the disease severity at the onset of Covid-19 are SIRI and age, and SII, MII-1 and MII-3 may also contribute to this prediction. Our results have also revealed that NLR is the most effective independent factor to predict mortality both at disease onset and for inpatients. Conclusion: Inflammatory indices, especially SIRI, NLR, SII, MII-1 and MII-3 can substantially contribute to clinical decisions in the early identification of high-risk patients and predicting mortality beginning from the onset of Covid-19.

Keywords

• Inflammatory indices
• platelet mass index (PMI)
• systemic immune-inflammatory index (SII)
• systemic inflammatory response index (SIRI)
• multi-inflammatory index (MII)

COVİD-19 HASTALARININ RİSK SINIFLAMASINDA ENFLAMATUVAR İNDEKSLERİN PROGNOSTİK ROLÜ

Abstract

Amaç: Pandemi süreci Covid-19’la etkin mücadele etmek, sınırlı hastane ve yoğun bakım kaynaklarının rasyonel kullanımı için yüksek riskli vakaların erkenden belirlenmesinde kanıta dayalı etkin bir triyaj sisteminin gerekliliğini ortaya koymuştur. Bu amaçla çalışmamızda Covid-19 tanısı konmuş hastalarda kolay ulaşılabilen, hızlı ve ucuz test parametreleri kullanılarak kolayca hesaplanabilen çeşitli enflamatuvar indeksler değerlendirilerek risk sınıflaması ve prognoz öngörüsündeki katkıları araştırılmıştır. Gereç-Yöntem:Çalışma, hastaların prognozlarına göre ayaktan ve yatarak takip edilenler, yatarak takip edilenlerin de sağ kalanlar ve vefat edenler şeklinde gruplandırıldığı toplam 8036 Covid-19 tanısı konulmuş hasta verisinde yürütülmüştür. Hastaların ilk başvuru sırasındaki tam kan sayımı ve C-reaktif protein sonuçları kullanılarak nötrofil-lenfosit oranı (NLR), platelet-lenfosit oranı (PLR), monosit-lenfosit oranı (MLR), MVP-platelet oranı (MPR), platelet kütle indeksi (PMI), sistemik immün-enflamatuvar indeksi (SII), sistemik enflamatuvar yanıt indeksi (SIRI), multi-enflamatuvar indeksler (MII) hesaplanmıştır. Bulgular: Enflamatuvar indekslerin hemen hepsinin hastalık şiddeti ve mortalite riski yüksek hastalarda anlamlı olarak farklı olduğunu ancak, hepsinin prediktif değere sahip olmadığını göstermiştir. Covid-19 başlangıcında hastalık şiddetinin belirlenmesinde en etkili faktörün SIRI ve yaş olduğu SII, MII-1 ve MII-3’ün de bu öngörüye katkı sağlayabileceği, NLR’nin ise hem hastalık başlanıcında hem de hastane içi mortalitenin öngörülmesinde en etkili bağımsız faktör olduğu saptanmıştır. Sonuç: Enflamatuvar indeksler özellikle SIRI, NLR, SII, MII-1 ve MII-3Covid- 19’da hastalığın başlangıcından itibaren yüksek riskli bireylerin erken saptanmasında ve mortalite öngörüsünde klinik kararlara önemli katkılar sağlayabilir.

Keywords

• Covid-19
• Enflamatuvar indeksler
• platelet kütle indeksi (PMI)
• sistemik immün-enflamatuvar indeksi (SII)
• sistemik enflamatuvar yanıt indeksi (SIRI)

References

1. 1. Zhou P, Yang XL, Wang XG,Hu B, Zhang L, Zhang W et al. A pneumonia out break associated with a new coronavirus of probable bat origin. Nature 2020;579(7798):270-3. google scholar
2. 2. Naming the coronavirus disease (COVİD-19) and the virus that causes it, Available from: URL: https://www.who.int / emergencies/ diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it, Erişim Tarihi: 11.02.2020 google scholar
3. 3. WHO Coronavirus (COVİD-19) Dashboard, Available from: URL: https://covid19.who.int, Erişim Tarihi: 20.06.2022 google scholar
4. 4. 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. google scholar
5. 5. Wang X, Fang J, Zhu Y, Chen L, Ding F, Zhou R, et al. Clinical characteristics of non-critically ill patients with novel coronavirus infection (COVİD-19) in a Fangcang Hospital. Clin Microbiol İnfect 2020;26(8):1063-8. google scholar
6. 6. Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA İntern Med 2020;180(7):1-18. google scholar
7. 7. Liu Y, Yan LM, Wan L, Xiang TX, Le A, Liu JM, et al. Viral dynamics in mild and severe cases of COVİD-19. Lancet İnfect Dis 2020;20(6):656-7. google scholar
8. 8. Ji D, Zhang D, Xu J, Chen Z, Yang T, Zhao P, et al. Prediction for Progression Risk in Patients with COVİD-19 Pneumonia: the CALL Score. Clin İnfect Dis 2020:71(6):1393-9. google scholar

9. 9. Hu H, Du H, Li J, Wang Y, Wu X, Wang C, et al. Early prediction and identification for severe patients during the pandemic of COVİD-19: A severe COVİD-19 risk model constructed by multivariate logistic regression analysis. J Glob Health 2020;10(2):020510. google scholar
10. 10. McGonagle D, Sharif K, O’Regan A, Bridgewood C. The role of cytokines including interleukin-6 in COVİD-19 induced pneumonia and macrophage activation syndrome-like disease. Autoimmun Rev 2020;19(6):102537. google scholar
11. 11. Liu Y, Du X, Chen J, Jin Y, Peng Lİ, Wang HHX, et al. Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVİD-19. J İnfect 2020;81(1):6-12. google scholar
12. 12. Schulte-Schrepping J,Reusch N, Paclik D,Ba(5ler K, Schlickeiser S, Zhang B, et al. Severe COVİD-19 is marked by a dysregulated myeloid cell compartment. Cell 2020;182(6):1419-40. google scholar
13. 13. Walter LO, Cardoso CC, Santos-Pirath İM, Costa HZ, Gartner R, Werle İ, et al. The relationship between peripheral immune response and disease severity in SARS-CoV-2-infected subjects: A cross-sectional study. İmmunology 2022;165(4):481-96. google scholar
14. 14. Casadei Gardini A, Scarpi E, Valgiusti M, Monti M,Ruscelli S, Matteucci L, et al. Prognostic role of a new index (multi inflammatory index) in patients with metastatic colorectal cancer: results from the randomized İTACa trial. Ther Adv Med Oncol 2020;12:doi: 10.1177/1758835920958363. google scholar
15. 15. Liu J, Li S, Zhang S, Liu Y, Ma L, Zhu J, et al. Systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio can predict clinical outcomes in patients with metastatic non-small-cell lung cancer treated with nivolumab. J Clin Lab Anal 2019;33(8):doi: 10.1002/jcla.22964. google scholar
16. 16. Ergenç H, Ergenç Z, Dog An M, Usanmaz M, Gozdas HT. C-reactive protein and neutrophil-lymphocyte ratio as predictors of mortality in coronavirus disease 2019. Rev Assoc Med Bras 2021;67(10):1498-502. google scholar
17. 17. Lattanzi S,Norata D, Divani AA, Napoli MD, Broggi S, Rocchi C, et al. Systemic İnflammatory Response İndex and Futile Recanalization in Patients with İschemic Stroke Undergoing Endovascular Treatment. Brain Sci 2021;11(9):1164. google scholar
18. 18. Mobarki AA, Dobie G, Saboor M, Madkhali AM, Akhter MS, Hakamy A, et al. MPR And NLR as prognostic markers in İCU-admitted patients with COVİD-19 in Jazan. Saudi Arabia. İnfect Drug Resist 2021;14:4859-64. google scholar
19. 19. Chan AS, Rout A. Use of Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in COVİD-19. J Clin Med Res 2020;12(7):448-53. google scholar
20. 20. Butler T, Newland AC. Haematological Problems in Older Adults İn: Proven D, editor. ABC of Clinical Haematology. New Jersey: John Wiley & Sons Ltd; 2018. p. 81-4. google scholar
21. 21. Zhao Z, Chen A, Hou W, Graham JM, Li H, Richman PS, et al. Prediction model and risk scores of İCU admission and mortality in COVİD-19. PLoS One 2020;15(7):doi: 10.1371/journal. pone.0236618. google scholar
22. 22. He F, Deng Y, Li W. Coronavirus disease 2019: What we know? J.Med Virol 2020;92(7):719-25. google scholar
23. 23. Erhabor O, Muhammad HA, Muhammad K, Onwuchekwa C, Egenti NB. İnterpretation of Full Blood Count Parameters in Health and Disease. Haematol İnt J 2021;5(1):00180. google scholar
24. 24. Chmielewski PP, Strzelec B. Elevated leukocyte count as a harbinger of systemic inflammation, disease progression, and poor prognosis: a review. Folia Morphol 2017;77(2):171-8. google scholar
25. 25. Goubran HA , Stakiw J., Radosevic M, Burnouf T. Platelets effects on tumor growth. Semin Oncol 2014;41(3):359-69. google scholar
26. 26. Bath PM, Butterworth RJ. Platelet size: Measurement, physiology and vascular disease. Blood Coagul Fibrinolysis 1996;7(2):157-61. google scholar
27. 27. Aly MM, Meshref TS, Abdelhameid MA, Ahmed SA, Shaltout AS, Abdel-Moniem AE, et al. Can Hematological Ratios Predict Outcome of COVİD-19 Patients? A Multicentric Study. J Blood Med 2021;12:505-15. google scholar
28. 28. Rathod BD, Amle D, Khot RS, Prathipati KK, Joshi PP. 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):doi: 10.7759/cureus.23696. google scholar
29. 29. Li X, Liu C, Mao Z, Xiao M, Wang L, Qi S, et al. Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis. Crit Care 2020;24(1):647. google scholar
30. 30. Singh Y, Singh A, Rudravaram S, Soni KD, Aggarwal R, Patel N, et al. Neutrophil-to-lymphocyte Ratio and Platelet-to-lymphocyte Ratio as Markers for Predicting the Severity in COVID-19 Patients: A Prospective Observational Study. Indian J Crit Care Med 2021;25(8):847-52. google scholar
31. 31. Citu C, Gorun F, Motoc A, Sas I, Gorun OM, Burlea B et al. The Predictive Role of NLR, d-NLR, MLR, and SIRI in COVID-19 Mortality. Diagnostics (Basel) 2022;12(1):122. google scholar
32. 32. Hamad DA, Aly MM, Abdelhameid MA, Shaltout AS, Abdel-Moniem AE et al. Combined Blood Indexes of Systemic Inflammation as a Mirror to Admission to Intensive Care Unit in COVİD-19 Patients: A Multicentric Study. J Epidemiol Glob Health 2022;12(1):64-73. google scholar
33. 33. Fois AG, Paliogiannis P, Scano V, Cau S, Babudieri S, Perra R, et al. The systemic inflammation index on admission predicts in-hospital mortality in COVİD-19 patients. Molecules 2020;25(23):5725. google scholar
34. 34. Nalbant A, Demirci T, Kaya T, Aydın A, Altındiş M, Güçlü E. Can prognostic nutritional index and systemic immune-inflammatory index predict disease severity in COVİD-19? Int J Clin Pract 2021;75(10):doi: 10.1111/ijcp.14544. google scholar
35. 35. Gozdas HT, Kayis SA, Damarsoy T, Ozsari E, Turkoglu M, Yildiz I, et al. Multi-inflammatory Index as a Novel Mortality Predictor in Critically III COVID-19 Patients. J Intensive Care Med. 2022:8850666221100411. doi: 10.1177/08850666221100411. google scholar
36. 36. Yurekli UF, Liste U, Ertunc B, Tercan M, Tahtabasi M. Could platelet mass index (PMI) be a new prognostic biomarker for COVID-19? Ann Clin Anal Med 2022;13(1):72-5. google scholar