Decision tree analysis as predictor tool for in-hospital mortality in critical SARS-CoV-2 infected patients
References
- 1. Wolff D, Nee S, Hickey NS, Marschollek M. Risk factors for Covid-19 severity and fatality: a structured literature review. Infection. 2021;49(1):15-28. DOI: 10.1007/s15010-020-01509-1
- 2. Myrstad M, Ihle-Hansen H, Tveita AA, Andersen EL, Nygård S, Tveit A, et al. National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from Covid-19 - a prospective cohort study. Scand J Trauma Resusc Emerg Med. 2020;28(1):66. DOI: 10.1186/s13049-020-00764-3
- 3. Guarino M, Perna B, Remelli F, Cuoghi F, Cesaro AE, Spampinato MD, et al. A New Early Predictor of Fatal Outcome for COVID-19 in an Italian Emergency Department: The Modified Quick-SOFA. Microorganisms. 2022;10(4):806. DOI: 10.3390/microorganisms10040806
- 4. Podgorelec V, Kokol P, Stiglic B, Rozman I. Decision Trees: An Overview and Their Use in Medicine. J Med Syst. 2002;26(5):445-63. DOI: 10.1023/A:1016409317640
- 5. Karthikeyan A, Garg A, Vinod PK, Priyakumar UD. Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction. Front Public Heal. 2021;9:626697. DOI: 10.3389/fpubh.2021.626697
- 6. Wang K, Zuo P, Liu Y, Zhang M, Zhao X, Xie S, et al. Clinical and laboratory predictors of in-hospital mortality in patients with COVID-19: a cohort study in Wuhan, China. Clin Infect Dis. 2020;71(16):2079-88. DOI: 10.1093/cid/ciaa538
- 7. McCormik K, Salcedo J. Peck J. WA. SPSS Statistics for Data Analysis and visualization. Indianapolis, IN: John Wiley & Sons, Inc. 2017:355-92.
- 8. Hsu M. Structural Equation Modeling with IBM SPSS Amos. In: IBM Corporation, editor. IBM Software Business Analytics. Somers NY; 2010.
- 9. Barclay D., Higgins C, Thompson R. The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration. Technol Stud. 1995;2(2):285-309.
- 10. Hair, Jr., J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., Danks, N.P., Ray, S. Partial least squares structural equation modeling (PLS-SEM) using R - a workbook, SPRINGER, 2021. DOI: 10.1007/978-3-030-80519-7
- 11. Khabaza T. 9 Laws of Data Mining [Internet]. 2010 [cited 2023 Apr 17]. Available from: http://khabaza.codimension.net/index_files/9laws.htm
- 12. Jöreskog KG, Wold H. The ML and PLS technique for modeling eith latent variables: historical and comparative aspects. In Wold H, Jöreskog KG (Eds.) Systems under indirect observations, part I. Amsterdam, North - Holland. 1982:263-270.
- 13. Prozan L, Shusterman E, Ablin J, Mitelpunkt A, Weiss-Meilik A, Adler A, et al. Prognostic value of neutrophil-to-lymphocyte ratio in COVID-19 compared with Influenza and respiratory syncytial virus infection. Sci Rep. 2021;11(1):21519. DOI: 10.1038/s41598-021-00927-x
- 14. Cîțu 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. 2022;12(1):122. DOI: 10.3390/diagnostics12010122
- 15. 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. J Infect. 2020;81(1):e6-12. DOI: 10.1016/j.jinf.2020.04.002
- 16. Önal U, Gülhan M, Demirci N, Özden A, Erol N, Işık S, et al. Prognostic value of neutrophile-to-lymphocyte ratio (NLR) and lactate dehydrogenase (LDH) levels for geriatric patients with COVID-19. BMC Geriatr. 2022;22(1):1-6. DOI: 10.1186/s12877-022-03059-7
- 17. Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, et al. Dysregulation of immune response in patients with COVID-19 in Wuhan, China. Clin Infect Dis. 2020;71(15):762-8. DOI: 10.1093/cid/ciaa248
- 18. Pál K, Molnar AA, Hu.anu A, Szederjesi J, Branea I, Timár Á, et al. Inflammatory Biomarkers Associated with In-Hospital Mortality in Critical COVID-19 Patients. Int J Mol Sci. 2022;23(18):10423. DOI: 10.3390/ijms231810423
- 19. Guan X, Zhang B, Fu M, Li M, Yuan X, Zhu Y, et al. Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study. Ann Med. 2021;53(1):257-66. DOI: 10.1080/07853890.2020.1868564
- 20. Andrijevic I, Matijasevic J, Andrijevic L, Kovacevic T, Zaric B. Interleukin-6 and procalcitonin as biomarkers in mortality prediction of hospitalized patients with community acquired pneumonia. Ann Thorac Med. 2014;9(3):162-7. DOI: 10.4103/1817-1737.134072
- 21. Jones SA, Hunter CA. Is IL-6 a key cytokine target for therapy in COVID-19? Nat Rev Immunol. 2021;21(6):337-9. DOI: 10.1038/s41577-021-00553-8
- 22. Rose-John S, Winthrop K, Calabrese L. The role of IL-6 in host defence against infections: immunobiology and clinical implications. Nat Rev Rheumatol. 2017;13:399-409. DOI: 10.1038/nrrheum.2017.83
- 23. Guirao JJ, Cabrera CM, Jiménez N, Rincón L, Urra JM. High serum IL-6 values increase the risk of mortality and the severity of pneumonia in patients diagnosed with COVID-19. Mol Immunol. 2020;128:64-8. DOI: 10.1016/j.molimm.2020.10.006
- 24. Hojyo S, Uchida M, Tanaka K, Hasebe R, Tanaka Y, Murakami M, et al. How COVID-19 induces cytokine storm with high mortality. Inflamm Regen. 2020;40:37. DOI: 10.1186/s41232-020-00146-3
- 25. Hirano T, Murakami M. COVID-19: A New Virus, but a Familiar Receptor and Cytokine Release Syndrome. Immunity. 2020;52(5):731-3. DOI: 10.1016/j.immuni.2020.04.003
- 26. Rodrigues PRS, Alrubayyi A, Pring E, Bart VMT, Jones R, Coveney C, et al. Innate immunology in COVID-19-a living review. Part II: dysregulated inflammation drives immunopathology. Oxford Open Immunol. 2020;1(1):iqaa005. DOI: 10.1093/oxfimm/iqaa005
- 27. Malik P, Patel U, Mehta D, Patel N, Kelkar R, Akrmah M, et al. Biomarkers and outcomes of COVID-19 hospitalisations: systematic review and meta-analysis. BMJ Evidence-Based Med. 2021;26(3):107-8. DOI: 10.1136/bmjebm-2020-111536
- 28. Kodavoor Vadiraj P, Thareja S, Raman N, Karantha SC, Jayaraman M, Vardhan V. Does Raised Transaminases Predict Severity and Mortality in Patients with COVID 19? J Clin Exp Hepatol. 2022;12(4):1114-23. DOI: 10.1016/j.jceh.2022.01.004
- 29. Pozzobon FM, Perazzo H, Bozza FA, Rodrigues RS, de Mello Perez R, Chindamo MC. Liver injury predicts overall mortality in severe COVID-19: a prospective multicenter study in Brazil. Hepatol Int. 2021;15(2):493-501. DOI: 10.1007/s12072-021-10141-6
- 30. Parohan M, Yaghoubi S, Seraji A. Liver injury is associated with severe coronavirus disease 2019 (COVID-19) infection: A systematic review and meta-analysis of retrospective studies. Hepatol Res. 2020;50(8):924-35. DOI: 10.1111/hepr.13510
- 31. Rothschild MA, Oratz M, Schreiber SS. Serum albumin. Hepatology. 1988;8(2):385-401. DOI: 10.1002/hep.1840080234
- 32. Huang J, Cheng A, Kumar R, Fang Y, Chen G, Zhu Y, et al. Hypoalbuminemia predicts the outcome of COVID-19 independent of age and co-morbidity. J Med Virol. 2020;92(10):2152. DOI: 10.1002/jmv.26003
- 33. Grasselli G, Greco M, Zanella A, Albano G, Antonelli M, Bellani G, et al. Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern Med. 2020;180(10):1345-55. DOI: 10.1001/jamainternmed.2020.3539
- 34. Leulseged TW, Hassen IS, Ayele BT, Tsegay YG, Abebe DS, Edo MG, et al. Laboratory biomarkers of COVID-19 disease severity and outcome: Findings from a developing country. PLoS One. 2021;16(3): e0246087. DOI: 10.1371/journal.pone.0246087
- 35. 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(11):1061-9. DOI: 10.1001/jama.2020.1585
- 36. Nadim MK, Forni LG, Mehta RL, Connor MJ, Liu KD, Ostermann M, et al. COVID-19-associated acute kidney injury: consensus report of the 25th Acute Disease Quality Initiative (ADQI) Workgroup. Nat Rev Nephrol. 2020;16(12):747-764. DOI: 10.1038/s41581-020-00356-5
- 37. Fabrizi F, Alfieri CM, Cerutti R, Lunghi G, Messa P. COVID-19 and Acute Kidney Injury: A Systematic Review and Meta-Analysis. Pathogens. 2020;9(12):1-16. DOI: 10.3390/pathogens9121052
- 38. Yildirim C, Ozger H S, Yasar E, Tombul N, Gulbahar O, et al. Early predictors of acute kidney injury in COVID-19 patients. Nephrology (Carlton). 2021;26(6), 513-521. DOI: 10.1111/nep.13856
- 39. Cheng Y, Luo R, Wang K, Zhang M, Wang Z, Dong L, et al. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int. 2020;97(5):829-38. DOI: 10.1016/j.kint.2020.03.005
- 40. Ley C, Martin RK, Pareek A, Groll A, Seil R, Tischer T. Machine learning and conventional statistics: making sense of the differences. Knee Surgery, Sport Traumatol Arthrosc. 2022;30(3):753-7. DOI: 10.1007/s00167-022-06896-6
- 41. Rakotomalala R. Arbres de Décision. Rev Modul. 2005;33:163-87.
- 42. Cole DA, Preacher KJ. Manifest variable path analysis: Potentially serious and misleading consequences due to uncorrected measurement error. Psychol Methods. 2014;19(2):300-15. DOI: 10.1037/a0033805
Language: English
Page range: 91 - 106
Submitted on: Mar 22, 2023
Accepted on: Apr 18, 2023
Published on: May 4, 2023
Published by: Romanian Association of Laboratory Medicine
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2023 Adina Hutanu, Anca A. Molnar, Krisztina Pal, Manuela R. Gabor, Janos Szederjesi, Minodora Dobreanu, published by Romanian Association of Laboratory Medicine
This work is licensed under the Creative Commons Attribution 4.0 License.