Have a personal or library account? Click to login
Decision tree analysis as predictor tool for in-hospital mortality in critical SARS-CoV-2 infected patients Cover

Decision tree analysis as predictor tool for in-hospital mortality in critical SARS-CoV-2 infected patients

Open Access
|May 2023

References

  1. 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. 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. 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. 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. 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. 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. 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. 8. Hsu M. Structural Equation Modeling with IBM SPSS Amos. In: IBM Corporation, editor. IBM Software Business Analytics. Somers NY; 2010.
  9. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 31. Rothschild MA, Oratz M, Schreiber SS. Serum albumin. Hepatology. 1988;8(2):385-401. DOI: 10.1002/hep.1840080234
  32. 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. 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. 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. 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. 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. 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. 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. 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. 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. 41. Rakotomalala R. Arbres de Décision. Rev Modul. 2005;33:163-87.
  42. 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
DOI: https://doi.org/10.2478/rrlm-2023-0015 | Journal eISSN: 2284-5623 | Journal ISSN: 1841-6624
Language: English
Page range: 91 - 106
Submitted on: Mar 22, 2023
|
Accepted on: Apr 18, 2023
|
Published on: May 4, 2023
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 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.