Abstract
In the last years, the entire world has been affected by the SARS-COV-2 pandemic, that represents the etiologic agent of Coronavirus disease 2019 (CoViD-19), which degenerated into a global pandemic in 2020. CoViD-19 has also had a strong impact on cancer patients. Our analysis has been performed at the Department of Oncology of the AORN “Cardarelli” in Naples, collecting data from all patients who had access in 2019–2020. We aim to understand how CoViD-19 affected hospital admissions. The statistical analysis showed that between 2019 and 2020 there was an increase in urgent hospitalizations and a decrease in scheduled hospitalization, probably to decrease the risk of infection, particularly in this category of susceptible patients. Indeed, as recommended by the European Society of Medical Oncology, during the pandemic, it was necessary to reorganize healthcare activities, ensure adequate care for patients infected with CoViD-19. Therefore telemedicine services were implemented and clinic visits were reduced.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lu, R., Zhao, X., et al.: Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395(10224), 565–574 (2020). https://doi.org/10.1016/S0140-6736(20)30251-8
Fehr, A.R., Perlman, S.: Coronaviruses: an overview of their replication and pathogenesis. In: Maier, H.J., Bickerton, E., Britton, P. (eds.) Coronaviruses. MMB, vol. 1282, pp. 1–23. Springer, New York (2015). https://doi.org/10.1007/978-1-4939-2438-7_1
Guan, W., Ni, Z., et al.: Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 382(18), 1708–1720 (2020). https://doi.org/10.1056/NEJMoa2002032
Huang, C., Wang, Y., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China Lancet 395(10223), 497–506 (2020). https://doi.org/10.1016/S0140-6736(20)30183-5
Chen, N., Zhou, M., et al.: Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395(10223), 507–513 (2020). https://doi.org/10.1016/S0140-6736(20)30211-7
Shi, H., et al.: Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect. Dis. 20(4), 425–434 (2020). https://doi.org/10.1016/S1473-3099(20)30086-4
Raymond, E., Thieblemont, C., Alran, S., Faivre, S.: Impact of the COVID-19 Outbreak on the management of patients with cancer. Target Oncol. 15(3), 249–259 (2020). https://doi.org/10.1007/s11523-020-00721-1
Pentheroudakis G.: CoViD-19 and Cancer - ESMO. https://www.esmo.org/covid-19-and-cancer/q-a-on-covid-19
Kamboj, M., Sepkowitz, K.A.: Nosocomial infections in patients with cancer. Lancet Oncol. 10(6), 589–597 (2009). https://doi.org/10.1016/S1470-2045(09)70069-5
Longbottom, E.R., et al.: Features of postoperative immune suppression are reversible with interferon gamma and independent of interleukin-6 pathways. Ann. Surg. 264(2), 370–377 (2016). https://doi.org/10.1097/SLA.0000000000001484
Liang, W., et al.: Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. Lancet Oncol. 21(3), 335–337 (2020). https://doi.org/10.1016/S1470-2045(20)30096-6
Zhang, L., Zhu, F., et al.: Clinical characteristics of COVID-19-infected cancer patients: a retrospective case study in three hospitals within Wuhan. China Ann. Oncol. 31(7), 894–901 (2020). https://doi.org/10.1016/j.annonc.2020.03.296
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
Sperlí, G.: A deep learning based chatbot for cultural heritage. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing, pp. 935–937 (2020). https://doi.org/10.1145/3341105.3374129
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
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
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
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
Albanese, M., et al.: Recognizing unexplained behavior in network traffic. In: Network Science and Cybersecurity, pp. 39–62. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-7597-2_3
Petrillo, A., Picariello, A., Santini, S., Scarciello, B., Sperli, G.: Model-based vehicular prognostics framework using big data architecture. Comput. Ind. 115, 103177 (2020). https://doi.org/10.1016/j.compind.2019.103177
Sperlí, G.: A deep learning based community detection approach. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 1107–1110 (2019). https://doi.org/10.1145/3297280.3297574
Hall, G.H., Round, A.P.: Logistic regression–explanation and use. J. Roy. Coll. Phys. London 28(3), 242–246 (1994)
Trunfio, T.A., et al.: Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy. BMC Med. Inf. Decis. Making 22(1), 1–8 (2022)
Schober, P., Vetter, T.R.: Logistic regression in medical research. Anesth. Analg. 132(2), 365–366 (2021). https://doi.org/10.1213/ANE.0000000000005247
Dhillon, A., Singh, A.: Machine learning in healthcare data analysis: a survey. J. Biol. Today’s World 8(6), 1–10 (2019)
Colella, Y., et al.: Studying variables affecting the length of stay in patients with lower limb fractures by means of Machine Learning. In: 2021 5th International Conference on Medical and Health Informatics, pp. 39–43 (2021).https://doi.org/10.1145/3472813.3472821
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. Publ. Health 19(10), 6219 (2022)
Ponsiglione, A.M., Cesarelli, G., Amato, F., Romano, M.: Optimization of an artificial neural network to study accelerations of foetal heart rhythm. In: 2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI), pp.159–164 (2021). https://doi.org/10.1109/RTSI50628.2021.9597213
Ponsiglione, A.M., Romano, M., Amato, F.: A finite-state machine approach to study patients dropout from medical examinations.In: 2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI), pp. 289–294 (2021). https://doi.org/10.1109/RTSI50628.2021.9597264
Romano, M., et al.: Symbolic dynamics in cardiotocographic monitoring. In: 2013 E-Health and Bioengineering Conference (EHB). IEEE (2013)
Cesarelli, M., et al.: Prognostic decision support using symbolic dynamics in CTG monitoring. EFMI-STC 186, 140–144 (2013)
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)
Cesarelli, G., Montella, E., Scala, A., Raiola, E., Triassi, M., Improta, G.: DMAIC approach for the reduction of healthcare-associated infections in the neonatal intensive care unit of the university hospital of naples ‘federico ii.’ In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds.) EMBEC 2020. IP, vol. 80, pp. 414–423. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64610-3_48
di Laura, D., et al.: Efficiency measures of emergency departments: an Italian systematic literature review. BMJ Open Qual. 10(3), e001058 (2021). https://doi.org/10.1136/bmjoq-2020-001058
LeBlanc, V.R., Manser, T., Weinger, M.B., Musson, D., Kutzin, J., Howard, S.K.: The study of factors affecting human and systems performance in healthcare using simulation. Simul. Healthc.: J. Soc. Simul. Healthc. 6(7), S24–S29 (2011). https://doi.org/10.1097/SIH.0b013e318229f5c8
Vázquez-Serrano, J.I., Peimbert-García, R.E., Cárdenas-Barrón, L.E.: Discrete-event simulation modeling in healthcare: a comprehensive review. Int. J. Environ. Res. Publ. Health 18(22), 12262 (2021). https://doi.org/10.3390/ijerph182212262
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
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
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
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
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Montella, E. et al. (2023). The Effect of CoViD-19 Pandemic on the Hospitalization of a Department of Oncology of an Italian Hospital. 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_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-25191-7_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25190-0
Online ISBN: 978-3-031-25191-7
eBook Packages: Computer ScienceComputer Science (R0)