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Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning Cover

Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning

Open Access
|Mar 2022

References

  1. 1. World Health Organization. Pneumonia of unknown cause 2020, 5 January [Available from: https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/. Accessed 8 June 2020.
  2. 2. Zhuang Z, Cao P, Zhao S, Han L, He D, Yang L. The shortage of hospital beds for COVID-19 and nonCOVID-19 patients during the lockdown of Wuhan, China. Ann Transl Med 2021;9(3):200. https://doi.org/10.21037/atm-20-524810.21037/atm-20-5248
  3. 3. Li J, Yuan P, Heffernan J, et al. Observation wards and control of the transmission of COVID-19 in Wuhan. Bull World Health Organ 2020.
  4. 4. Sen-Crowe B, Sutherland M, McKenney M, Elkbuli A. A Closer Look in to Global Hospital Beds Capacity and Resource Shortages During the COVID-19 Pandemic. Journal of Surgical Research 2021;260:P53-63. https://doi.org/10.1016/j.jss.2020.11.06210.1016/j.jss.2020.11.062
  5. 5. Gerayelia FV, Milne S, Cheunga Ch, Lia X, Tony Yanga Ch. W, Tama A, Choia L.H, Baea A, Sin D.D. COPD and the risk of poor outcomes in COVID-19: A systematic review and meta-analysis. E Clinical Medicine 2021;33:100789. https://doi.org/10.1016/j.eclinm.2021.10078910.1016/j.eclinm.2021.100789
  6. 6. Deschepper M, Waegeman W, Vogelaers D, Eeckloo K. Using structured pathology data to predict hospital-wide mortality at admission. PLoS One. 2020;15(6):e0235117. https://doi.org/10.1371/journal.pone.023511710.1371/journal.pone.0235117
  7. 7. Bhattacharya S, Rajan V, Shrivastava H. ICU mortality prediction: a classification algorithm for imbalanced datasets. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. 2017;31(1):1288-94.10.1609/aaai.v31i1.10721
  8. 8. Tian, W. et al. Predictors of mortality in hospitalized COVID-19 patients: a systematic review and meta-analysis. J. Med. Virol. 2020;92(10):1875-1882. https://doi.org/10.1002/jmv.2605010.1002/jmv.26050
  9. 9. Chen T, Wu D, Chen HL, Yan WM, Yang DL, Chen G, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ. 2020; 368:m1091. https://doi.org/10.1136/bmj.m109110.1136/bmj.m1091
  10. 10. Zhou F, Yu t, Du R, et al. Clinical course and risk factors for mortality of adult in patients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054-1062. https://doi.org/10.1016/S0140-6736(20)30566-310.1016/S0140-6736(20)30566-3
  11. 11. Bikdeli B. et al. COVID-19 and thrombotic or thromboembolic disease: implications for prevention, antithrombotic therapy, and follow-up. J. Am. Coll. Cardiol. 2020;75(23):2950-2973. https://doi.org/10.1016/j.jacc.2020.04.03110.1016/j.jacc.2020.04.031716488132311448
  12. 12. Pourhomayoun M, Shakibi M. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health. 2021;20:100178. https://doi.org/10.1016/j.smhl.2020.10017810.1016/j.smhl.2020.100178783215633521226
  13. 13. Alballa N, Al-Turaiki I. Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Informatics in Medicine Unlocked. 2021;24(100564). https://doi.org/10.1016/j.imu.2021.10056410.1016/j.imu.2021.100564801890633842685
  14. 14. Gong J. et al. A tool to early predict severe corona virus disease 2019 (COVID-19): A multicenter study using the risk nomogram in Wuhan and Guangdong, China. Clin. Infect. Dis. 2020;71(15):833-840. https://doi.org/10.1093/cid/ciaa44310.1093/cid/ciaa443718433832296824
  15. 15. Yuan, M., Yin, W., Tao, Z., Tan, W. & Hu, Y. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PLoS ONE. 2020;15(3):e0230548. https://doi.org/10.1371/journal.pone.023054810.1371/journal.pone.0230548708207432191764
  16. 16. 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;80:639-645. https://doi.org/10.1016/j.jinf.2020.03.01910.1016/j.jinf.2020.03.019711852632240670
  17. 17. WHO Coronavirus disease (COVID-2019) situation reports (2020).
  18. 18. De Giorgi A, F. Fabbian, S. Greco, et al. Prediction of in-hospital mortality of patients with SARS-CoV-2 infection by comorbidity indexes: an Italian internal medicine single center study. Eur Rev Med Pharmacol Sci. 2020;24(19):10258-10266. https://doi.org/10.26355/eurrev_202010_23250
  19. 19. Dominguez-Ramirez L, Rodriguez-Perez F, Sosa-Jurado F, et al. The role of metabolic comorbidity in COVID-19 mortality of middle-aged adults. The case of Mexico. medRxiv 2020.12.15.20244160. https://doi.org/10.1101/2020.12.15.2024416010.1101/2020.12.15.20244160
  20. 20. Guan WJ, Liang WH, Zhao Y, Liang HR, Chen ZS, Li YM, et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J. 2020;55:2000547. https://doi.org/10.1183/13993003.01227-202010.1183/13993003.01227-2020723683132341104
  21. 21. 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;97:829-838, https://doi.org/10.1016/j.kint.2020.03.00510.1016/j.kint.2020.03.005711029632247631
  22. 22. Mazinani M., Rude B.J. The novel zoonotic coronavirus disease 2019 (COVID-19) pandemic: Health perspective on the outbreak. J Healthc Qual Res. 2020;36(1):47-51. https://doi.org/10.1016/j.jhqr.2020.09.00410.1016/j.jhqr.2020.09.004755680433162382
  23. 23. 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. https://doi.org/10.1001/jamainternmed.2020.099410.1001/jamainternmed.2020.0994707050932167524
  24. 24. Xie J, Covassin N, Fan Zh, Singh P, Gao W, Li G, et al. Association between Hypoxemia and Mortality in Patients With COVID-19. Mayo Clin Proc 2020;95(6):1138-1147. https://doi.org/10.1016/j.mayocp.2020.04.00610.1016/j.mayocp.2020.04.006715146832376101
  25. 25. Xiang G, Xie L, Chen Zh, Hao Sh, Fu C, Wu Q, Liu X, Li Sh. Clinical risk factors for mortality of hospitalized patients with COVID-19: systematic review and meta-analysis. Annals of Palliative Medicine. 2021;10(3). https://doi.org/10.21037/apm-20-127810.21037/apm-20-127833549005
  26. 26. Liang W, Liang H, Ou L, Chen B, Chen A, Li C, et al. China Medical Treatment Expert Group for COVID-19. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med. 2020;180:1081-1089. https://doi.org/10.1001/jamainternmed.2020.203310.1001/jamainternmed.2020.2033721867632396163
  27. 27. Wu JT, Leung K, Bushman M, Kishore N, Niehus R, de Salazar PM, et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med. 2020;26:506-10. https://doi.org/10.1038/s41591-020-0822-710.1038/s41591-020-0822-7709492932284616
  28. 28. Lin L, Lu L, Cao W, et al. Hypothesis for potential pathogenesis of SARS-CoV-2 infection-a review of immune changes in patients with viral pneumonia. Emerg Microbes Infect. 2020;9:1-14. https://doi.org/10.1080/22221751.2020.174619910.1080/22221751.2020.1746199717033332196410
  29. 29. Cheng A, Hu L,Wang Y Huang L, Zhao L, Zhang C et al. Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients. Int J Antimicrob Agents. 2020;56(3):106110. https://doi.org/10.1016/j.ijantimicag.2020.10611010.1016/j.ijantimicag.2020.106110737780332712332
  30. 30. Yang CJ, Chen J, Phillips AR, Windsor JA, Petrov MS. Predictors of severe and critical acute pancreatitis: a systematic review. Dig Liver Dis. 2014;46:446-451. https://doi.org/10.1016/j.dld.2014.01.15810.1016/j.dld.2014.01.15824646880
  31. 31. Wernly B, Lichtenauer M, Vellinga NAR, Boerma EC, Ince C, Kelm M. Blood urea nitrogen (BUN) independently predicts mortality in critically ill patients admitted to ICU: a multicenter study. Clin Hemorheol Microcirc. 2018;69:123-131. https://doi.org/10.3233/CH-18911110.3233/CH-18911129758935
  32. 32. Aronson D, Mittleman MA, Burger AJ. Elevated blood urea nitrogen level as a predictor of mortality in patients admitted for decompensated heart failure. Am J Med. 2004;116:466-473. https://doi.org/10.1016/j.amjmed.2003.11.01410.1016/j.amjmed.2003.11.01415047036
  33. 33. Tokgoz Akyil F, Yalcinsoy M, Hazar A, Cilli A, Celenk B, Kilic O. Prognosis of hospitalized patients with community-acquired pneumonia. Pulmonology. 2018;24(3):164-169. https://doi.org/10.1016/j.rppnen.2017.07.01010.1016/j.rppnen.2017.07.01029463455
  34. 34. Ryu S, Oh SK, Cho SU, You Y, Park JS, Min JH. Utility of the blood urea nitrogen to serum albumin ratio as a prognostic factor of mortality in aspiration pneumonia patients. Am J Emerg Med. 2021;43:175-179. https://doi.org/10.1016/j.ajem.2020.02.04510.1016/j.ajem.2020.02.04532122715
  35. 35. Chalmers JD, Singanayagam A, Hill AT. C-reactive protein is an independent predictor of severity in community-acquired pneumonia. Am J Med. 2008;121:219-225. https://doi.org/10.1016/j.amjmed.2007.10.03310.1016/j.amjmed.2007.10.03318328306
  36. 36. Sharifpour M, Rangaraju S, Liu M, Alabyad D, Nahab FB, Creel-Bulos CM, et al. C-Reactive protein as a prognostic indicator in hospitalized patients with COVID-19. PLoS ONE. 2020;15(11):e0242400. https://doi.org/10.1371/journal.pone.024240010.1371/journal.pone.0242400767915033216774
  37. 37. Cekerevac I, Turnic TN, Draginic N, Andjic M, Zivkovic V, Simovic S, et al. Predicting Severity and Intrahospital Mortality in COVID-19: The Place and Role of Oxidative Stress. Oxidative Medicine and Cellular Longevity. 2021:6615787. https://doi.org/10.1155/2021/661578710.1155/2021/6615787801937233854695
  38. 38. Gao Y, Cay GY, Fang W, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nature communications. 2020;11:5033. https://doi.org/10.1038/s41467-020-18684-210.1038/s41467-020-18684-2753891033024092
  39. 39. Yan L, Zhang HT, Goncalves J, et al. n interpretable mortality prediction model for COVID-19 patients. Nature Machine Intelligence. 2020;5(2):283-288. https://doi.org/10.1038/s42256-020-0180-710.1038/s42256-020-0180-7
  40. 40. Hu C, Liu Z, Jiang Y, et al. Early prediction of mortality risk among severe COVID-19 patients using machine learning. International Journal of Epidemiology. 2020;49(6):1918–1929. https://doi.org/10.1093/ije/dyaa17110.1093/ije/dyaa171754346132997743
  41. 41. Booth AL, Abels E, McCaffrey P. Development of a prognostic model for mortality in COVID-19 infection using machine learning. Modern Pathology. 2021;34:522-531. https://doi.org/10.1038/s41379-020-00700-x10.1038/s41379-020-00700-x756742033067522
  42. 42. Kim Y. Boosting and measuring the performance of ensembles for a successful database marketing. Expert Systems with Applications. 2009;36:2161-76. https://doi.org/10.1016/j.eswa.2007.12.03610.1016/j.eswa.2007.12.036
  43. 43. Piao Y, Park HW, Jin CH, Ryu KH. Ensemble method for classification of high-dimensional data. International Conference on Big Data and Smart Computing (BIGCOMP). 2014:245-249. https://doi.org/10.1109/BIGCOMP.2014.674144510.1109/BIGCOMP.2014.6741445
DOI: https://doi.org/10.2478/pjmpe-2022-0003 | Journal eISSN: 1898-0309 | Journal ISSN: 1425-4689
Language: English
Page range: 19 - 29
Submitted on: Sep 9, 2021
Accepted on: Jan 10, 2022
Published on: Mar 29, 2022
Published by: Polish Society of Medical Physics
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2022 Sadjad Shafiekhani, Sima Rafiei, Sina Abdollahzade, Saber Souri, Zeinab Moomeni, published by Polish Society of Medical Physics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.