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2023 | OriginalPaper | Buchkapitel

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

verfasst von : Emma Montella, Marta Rosaria Marino, Cristiana Giglio, Eliana Raiola, Giuseppe Russo, Giovanni Rossi, Anna Borrelli, Maria Triassi, Arianna Scala

Erschienen in: Biomedical and Computational Biology

Verlag: Springer International Publishing

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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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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)

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.2020.105866CrossRefPubMedPubMedCentral

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

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

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

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

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)CrossRef

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)CrossRefPubMedPubMedCentral

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/8.6.543CrossRef

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/8.1.31CrossRef

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-010251CrossRef

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)

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

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)

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)

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.2020.3049082CrossRef

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/s10844-020-00629-2CrossRefPubMedPubMedCentral

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.2021.115277CrossRef

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.2020.106563CrossRef

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.2018.04.006CrossRef

24.

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

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)

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)CrossRefPubMed

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)CrossRefPubMed

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)CrossRefPubMed

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)CrossRefPubMed

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)

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)

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/s21186136CrossRefPubMedPubMedCentral

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/bioengineering9010008CrossRef

36.

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

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)

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)CrossRef

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)

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)CrossRefPubMedPubMedCentral

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.2020.01677CrossRefPubMedPubMedCentral

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

Titel: Analysis of Hospital Admissions of Neurological Patients in the COVID-19 Era: Comparison Between Hospitals
verfasst von: Emma Montella
Marta Rosaria Marino
Cristiana Giglio
Eliana Raiola
Giuseppe Russo
Giovanni Rossi
Anna Borrelli
Maria Triassi
Arianna Scala
Verlag: Springer International Publishing
Buch: Biomedical and Computational Biology
Print ISBN: 978-3-031-25190-0

Electronic ISBN: 978-3-031-25191-7

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-25191-7_50

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