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Analysis of Hospital Admissions of Neurological Patients in the COVID-19 Era: Comparison Between Hospitals

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Biomedical and Computational Biology (BECB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13637))

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Abstract

In the last few years, the COVID-19 pandemic has strongly affected different hospital departments, revealing their major weaknesses. For this reason, this emergency has been a driver for healthcare transformation in a very short interval of time in order to optimize the resources, minimize costs and simultaneously increase the caring services, also limiting over-occupancy in wards, especially emergency ones. One of the main factors for assessing the efficiency of a department is associated with how long a patient stays in the facility (LOS). This bicentric study investigated how COVID-19 has modified the activity of the Complex Operative Unit (C.O.U.) of Neurology and Stroke of the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) and the hospital A.O.R.N. “Antonio Cardarelli” of Naples (Italy). In the work data for the year 2019 (in the absence of Covid-19) and in the year of Covid-19 pandemic 2020 were considered. This work used the logistic regression technique to study the following variables: age, gender, length of stay (LOS), relative weight of DRG and mode of discharge. The analysis shows that in 2020 there was a greater adequacy of admissions, with an increase in the relative weight of DRG. And the statistical analysis obtained the following significant variables: gender, age, relative weight of DRG and discharge mode.

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References

  1. Eurosurveillance Editorial Team: Note from the editors: World Health Organization declares novel coronavirus (2019-nCoV) sixth public health emergency of international concern. Euro Surveill. 25(5), 200131e (2020)

    Google Scholar 

  2. Wilson, M.P., Jack, A.S.: Coronavirus disease 2019 (COVID-19) in neurology and neurosurgery: a scoping review of the early literature. Clin. Neurol. Neurosurg. 193, 105866 (2020). https://doi.org/10.1016/j.clineuro.2020.105866

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ozturk, C.N., Kuruoglu, D., Ozturk, C., Rampazzo, A., Gurunian Gurunluoglu, R.: Plastic surgery and the COVID-19 pandemic: a review of clinical guidelines. Ann. Plast. Surg. 85(2S Suppl 2), S155–S160 (2020). https://doi.org/10.1097/SAP.0000000000002443

  4. Wu, Z., McGoogan, J.M.: 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). Accessed 16 Mar 2020

    Google Scholar 

  5. Mao, L., Jin, H., Wang, M., et al.: Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China [published online ahead of print April 10, 2020]. JAMA Neurol. (2020). https://doi.org/10.1001/jamaneurol.2020.1127

  6. Asadi‐Pooya, A.A., Simani, L.: Central nervous system manifestations of COVID‐19: a systematic review. J. Neurol. Sci. 413, 116832 (2020). https://doi.org/10.1016/j.jns.2020.116832

  7. Moriguchi, T., Harii, N., Goto, J., et al.: A first case of meningitis/encephalitis associated with SARS-coronavirus-2. Int. J. Infect. Dis. 3(94), 55–58 (2020)

    Article  Google Scholar 

  8. Wee, L.E., Conceicao, E.P., Sim, X.Y.J., et al.: Minimising intra-hospital transmission of COVID-19: the role of social distancing. J. Hosp. Infect. 105, 113–115 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Houghton, A., Bowling, A., Jones, I., Clarke, K.: Appropriateness of admission and the last 24 h of hospital care in medical wards in an east London teaching group hospital. Int. J. Qual. Healthc. J. Int. Soc. Qual. Healthc. 8(6), 543–553 (1996). https://doi.org/10.1093/intqhc/8.6.543

    Article  CAS  Google Scholar 

  10. Coast, J., Peters, T.J., Ingles, A.: Factors associated with inappropriate emergency hospital admission in the UK. Int. J. Qual. Healthc. 8(1), 31–39 (1996). https://doi.org/10.1093/intqhc/8.1.31

    Article  CAS  Google Scholar 

  11. Aghajani, S., Kargari, M.: Determining factors influencing length of stay and predicting length of stay using data mining in the general surgery department. Hosp. Pract. Res. 1(2), 53–58 (2016). https://doi.org/10.20286/hpr-010251

    Article  Google Scholar 

  12. Belle, A., Thiagarajan, R., Soroushmehr, S.M., Navidi, F., Beard, D.A., Najarian, K.: Big data analytics in healthcare. BioMed Research International, 2015 (2015)

    Google Scholar 

  13. Bao, S.D., Zhang, Y.T., Shen, L.F.: Physiological signal based entity authentication for body area sensor networks and mobile healthcare systems. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 2455–2458. IEEE, January 2006

    Google Scholar 

  14. Scala, A., et al.: Regression models to study the total LOS related to valvuloplasty. Int. J. Environ. Res. Public Health 19(5), 3117 (2022)

    Google Scholar 

  15. Trunfio, T.A., et al.: Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy. BMC Med. Inform. Decis. Mak. 22(1), 1–8 (2022)

    Google Scholar 

  16. Esposito, C., Moscato, V., Sperlí, G.: Trustworthiness assessment of users in social reviewing systems. IEEE Trans. Syst. Man Cybern. Syst. 52(1), 151–165 (2022). https://doi.org/10.1109/TSMC.2020.3049082

    Article  Google Scholar 

  17. Sperlí, G.: A deep learning based chatbot for cultural heritage. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing, pp. 935–937, March 2020. https://doi.org/10.1145/3341105.3374129

  18. Ianni, M., Masciari, E., Sperlí, G.: A survey of big data dimensions vs social networks analysis. J. Intell. Inf. Syst. 57(1), 73–100 (2020). https://doi.org/10.1007/s10844-020-00629-2

    Article  PubMed  PubMed Central  Google Scholar 

  19. Sperlí, G.: A cultural heritage framework using a deep learning based Chatbot for supporting tourist journey. Expert Syst. Appl. 183, 115277 (2021). https://doi.org/10.1016/j.eswa.2021.115277

    Article  Google Scholar 

  20. Han, Q., Molinaro, C., Picariello, A., Sperli, G., Subrahmanian, V. S., Xiong, Y.: Generating fake documents using probabilistic logic graphs. IEEE Trans. Dependable Secure Comput. (2021).https://doi.org/10.1109/TDSC.2021.3058994

  21. Di Girolamo, R., Esposito, C., Moscato, V., Sperlí, G.: Evolutionary game theoretical on-line event detection over tweet streams. Knowl.-Based Syst. 211, 106563 (2021). https://doi.org/10.1016/j.knosys.2020.106563

    Article  Google Scholar 

  22. Albanese, M., et al.: Recognizing unexplained behavior in network traffic. In: Pino, R. (eds.) Network Science and Cybersecurity, vol. 55, pp. 39–62. Springer, New York, NY (2014). https://doi.org/10.1007/978-1-4614-7597-2_3

  23. Amato, F., et al.: Multimedia story creation on social networks. Futur. Gener. Comput. Syst. 86, 412–420 (2018). https://doi.org/10.1016/j.future.2018.04.006

    Article  Google Scholar 

  24. Obenshain, M.K.: Application of data mining techniques to healthcare data. Infect. Control Hosp. Epidemiol. 25(8), 690–695 (2004)

    Article  PubMed  Google Scholar 

  25. Benneyan, J.C.: The design, selection, and performance of statistical control charts for healthcare process improvement. Int. J. Six Sigma Compet. Adv. 4(3), 209–239 (2008)

    Google Scholar 

  26. Tu, J.V., Jaglal, S.B., Naylor, C.D.: Multicenter validation of a risk index for mortality, intensive care unit stay, and overall hospital length of stay after cardiac surgery. Circulation 91(3), 677–684 (1995)

    Article  CAS  PubMed  Google Scholar 

  27. Marcantonio, E., Goldman, L., Rohde, L.E., Orav, J., Mangione, C.M., Lee, T.H.: Impact of age on perioperative complications and length of stay in patients undergoing noncardiac surgery. Ann. Intern. Med. 134(8), 637–643 (2001)

    Article  PubMed  Google Scholar 

  28. Hein, O.V., Birnbaum, J., Wernecke, K., England, M., Konertz, W., Spies, C.: Prolonged intensive care unit stay in cardiac surgery: risk factors and long-term-survival. Ann. Thorac. Surg. 81(3), 880–885 (2006)

    Article  PubMed  Google Scholar 

  29. Velmahos, G.C., et al.: Management of the most severely injured spleen: a multicenter study of the research consortium of New England centers for trauma (ReCONECT). Arch. Surg. 145(5), 456–460 (2010)

    Article  PubMed  Google Scholar 

  30. Revetria, R., et al.: Improving Healthcare Using Cognitive Computing Based Software: An Application in Emergency Situation. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds.) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. LNCS, vol. 7345. Springer, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31087-4_50

  31. Trunfio, T.A., Borrelli, A., Improta, G.: Is it possible to predict the length of stay of patients undergoing hip-replacement surgery? Int. J. Environ. Res. Public Health 19(10), 6219 (2022)

    Google Scholar 

  32. Santini, S., et al.: Using fuzzy logic for improving clinical daily-care of β-thalassemia patients. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6 (2017). https://doi.org/10.1109/FUZZ-IEEE.2017.8015545

  33. Improta, G., et al.: Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐transplant patients. J. Eval. Clin. Pract. 26(4), 1224–1234 (2020)

    Google Scholar 

  34. Ponsiglione, A.M., Cosentino, C., Cesarelli, G., Amato, F., Romano, M.: A comprehensive review of techniques for processing and analyzing fetal heart rate signals. Sensors 21, 6136 (2021). https://doi.org/10.3390/s21186136

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ponsiglione, A.M., Amato, F., Romano, M.: Multiparametric investigation of dynamics in fetal heart rate signals. Bioengineering 9, 8 (2022). https://doi.org/10.3390/bioengineering9010008

    Article  Google Scholar 

  36. Cesarelli, M., et al.: An application of symbolic dynamics for FHRV assessment. MIE (2012)

    Google Scholar 

  37. Improta, G., et al.: Analytic hierarchy process (AHP) in dynamic configuration as a tool for health technology assessment (HTA): the case of biosensing optoelectronics in oncology. Int. J. Inf. Technol. Decis. Mak. 18(05), 1533–1550 (2019)

    Google Scholar 

  38. Improta, G., et al.: An innovative contribution to health technology assessment. In: Ding, W., Jiang, H., Ali, M., Li, M. (eds.) Modern Advances in Intelligent Systems and Tools. Studies in Computational Intelligence, vol. 431, pp. 127–131. Springer, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30732-4_16

  39. Ponsiglione, A.M., Amato, F., Cozzolino, S., Russo, G., Romano, M., Improta, G.: A hybrid analytic hierarchy process and likert scale approach for the quality assessment of medical education programs. Mathematics 10(9), 1426 (2022)

    Article  Google Scholar 

  40. Loperto, I., et al.: Appropriate admission in COVID-19 era: the case study of the COU neurology and stroke unit. In: 2021 International Symposium on Biomedical Engineering and Computational Biology (2021)

    Google Scholar 

  41. Improta, G., Borrelli, A., Triassi, M.: Machine learning and lean six sigma to assess how COVID-19 has changed the patient management of the complex operative unit of neurology and stroke unit: a single center study. Int. J. Environ. Res. Public Health 19(9), 5215 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Guarino, F., Improta, G., Triassi, M., Castiglione, S., Cicatelli, A.: Air quality biomonitoring through Olea europaea L.: the study case of Land of pyres. Chemosphere 282, 131052 (2021). https://doi.org/10.1016/j.chemosphere.2021.131052

  43. Guarino, F., Improta, G., Triassi, M., Cicatelli, A., Castiglione, S.: Effects of zinc pollution and compost amendment on the root microbiome of a metal tolerant poplar clone. Front. Microbiol. 11, 1677 (2020). https://doi.org/10.3389/fmicb.2020.01677

    Article  PubMed  PubMed Central  Google Scholar 

  44. Guarino, F., et al.: Genetic characterization, micropropagation, and potential use for arsenic phytoremediation of Dittrichia viscosa (L.) Greuter. Ecotoxicol. Environ. Saf. 148, 675–683 (2018). https://doi.org/10.1016/j.ecoenv.2017.11.010

  45. Guarino, F., Cicatelli, A., Brundu, G., Improta, G., Triassi, M., Castiglione, S.: The use of MSAP reveals epigenetic diversity of the invasive clonal populations of Arundo donax L PLoS One 14 (2019). https://doi.org/10.1371/journal.pone.0215096

  46. De Agostini, A., et al.: Heavy metal tolerance of orchid populations growing on abandoned mine tailings: A case study in Sardinia Island (Italy). Ecotoxicol. Environ. Saf. 189, 110018 (2020). https://doi.org/10.1016/j.ecoenv.2019.110018

  47. Moccia, E., et al.: Use of zea mays L. in phytoremediation of trichloroethylene. Environ. Sci. Pollut. Res. 24, 11053–11060 (2017). https://doi.org/10.1007/s11356-016-7570-8

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Authors and Affiliations

  1. Department of Public Health, University of Naples “Federico II”, Naples, Italy

    Emma Montella, Marta Rosaria Marino, Maria Triassi & Arianna Scala

  2. University of Rome “La Sapienza”, Rome, Italy

    Cristiana Giglio

  3. A.O.R.N. “A. Cardarelli”, Naples, Italy

    Eliana Raiola & Giuseppe Russo

  4. San Giovanni di Dio e Ruggi d’Aragona” University Hospital, Salerno, Italy

    Giovanni Rossi & Anna Borrelli

  5. Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, Naples, Italy

    Maria Triassi

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  1. Emma Montella
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Correspondence to Marta Rosaria Marino .

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Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Shiping Wen

  2. Nanchang Hangkong University, Nanchang, China

    Cihui Yang

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Montella, E. et al. (2023). Analysis of Hospital Admissions of Neurological Patients in the COVID-19 Era: Comparison Between Hospitals. In: Wen, S., Yang, C. (eds) Biomedical and Computational Biology. BECB 2022. Lecture Notes in Computer Science(), vol 13637. Springer, Cham. https://doi.org/10.1007/978-3-031-25191-7_50

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  • DOI: https://doi.org/10.1007/978-3-031-25191-7_50

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