Abstract
The pandemic related to the Covid-19 virus that began in 2019 in China and then extended to the rest of the world has led to changes in the management of almost all clinical specializations. The main adaptations are due not only to changes in managerial management to better address organizational difficulties but there have also been variations from a treatment and care management point of view with respect to different clinical sectors including that relating to the Neurosurgery sector. In our analysis, the activity of the Department of Neurosurgery in AORN “A. Cardarelli” in Naples (Italy) was analysed. In particular, our analysis aims to investigate variables pre and post pandemic, comparing information gathered in 2019 and 2020. In the specific case, the hospitalizations of 2177 patients were considered in order to understand the influences that the Department has suffered due to the difficulties linked to the pandemic.
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References
Wang, D., Hu, B., Hu, C., et al.: Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323(11), 1061 (2020)
Trunfio, T.A., et al.: Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy. BMC Med. Inform. Decis. Mak. (2022)
Bernasconi, A., Sadile, F., Smeraglia, F., Mehdi, N., Laborde, J., Lintz, F.: Tendoscopy of Achilles, peroneal and tibialis posterior tendons: an evidence-based update. Foot Ankle Surg. 24(5), 374–382 (2018). https://doi.org/10.1016/j.fas.2017.06.004
Smeraglia, F., Del Buono, A., Maffulli, N.: Endoscopic cubital tunnel release: a systematic review. Br. Med. Bull. 116, 155–163 (2015). https://doi.org/10.1093/bmb/ldv049. Epub 2015 Nov. 24 PMID: 26608457
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 (2022)
Paterlini, M.: On the front lines of coronavirus: the Italian response to covid-19. BMJ (Clin. Res. Ed.) 368, m1065 (2020)
https://www.who.int/emergencies/diseases/novel-coronavirus-2019
https://hbr.org/2020/03/lessons-from-italysresponse-to-coronavirus
Sheldon, T.: Promoting health care quality: what role performance indicators? Qual. Health Care 7, 45–50 (1998). ISSN 0963-8172
Lamberti, A., Balato, G., Summa, P.P., Rajgopal, A., Vasdev, A., Baldini, A.: Surgical options for chronic patellar tendon rupture in total knee arthroplasty. Knee Surg. Sports Traumatol. Arthrosc. 26(5), 1429–1435 (2016). https://doi.org/10.1007/s00167-016-4370-0
Kenarkoohi, A., et al.: Hospital indoor air quality monitoring for the detection of SARS-CoV-2 (COVID-19) virus. Sci. Total Environ. 748, 141324 (2020)
Jiang, Y., et al.: Clinical data on hospital environmental hygiene monitoring and medical staff protection during the coronavirus disease 2019 outbreak. MedRxiv (2020)
Smeraglia, F., Basso, M.A., Famiglietti, G., Eckersley, R., Bernasconi, A., Balato, G.: Partial wrist denervation versus total wrist denervation: a systematic review of the literature. Hand Surg. Rehabil. 39(6), 487–491 (2020). https://doi.org/10.1016/j.hansur.2020.05.010
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
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
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
Mercorio, F., Mezzanzanica, M., Moscato, V., Picariello, A., Sperlí, G.: DICO: a graph-DB framework for community detection on big scholarly data. IEEE Trans. Emerg. Top. Comput. 9(4), 1987–2003 (2021). https://doi.org/10.1109/TETC.2019.2952765
La Gatta, V., Moscato, V., Pennone, M., Postiglione, M., Sperlí, G.: Music recommendation via hypergraph embedding. IEEE Trans. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3146968
Maniscalco, G.T., et al.: Remission of early persistent cladribine-induced neutropenia after filgrastim therapy in a patient with relapsing-remitting multiple sclerosis. Multiple Sclerosis Relat. Disord. 43 (2020): 102151
Romano, M., et al.: Symbolic dynamics in cardiotocographic monitoring. In: 2013 E-Health and Bioengineering Conference (EHB). IEEE (2013)
Iuppariello, L., et al.: A novel approach to estimate the upper limb reaching movement in three-dimensional space. Inform. Med. Unlocked 15, 100155 (2019)
Balato, G., et al.: Laboratory-based versus qualitative assessment of α-defensin in periprosthetic hip and knee infections: a systematic review and meta-analysis. Arch. Orthop. Trauma Surg. 140(3), 293–301 (2019). https://doi.org/10.1007/s00402-019-03232-5
Balato, G., et al.: Hip and knee section, prevention, surgical technique: proceedings of international consensus on orthopedic infections. J. Arthroplasty 34(2S), S301–S307 (2019)
Ascione, T., et al.: Clinical and microbiological outcomes in haematogenous spondylodiscitis treated conservatively. Eur. Spine J. 26(4), 489–495 (2017). https://doi.org/10.1007/s00586-017-5036-4
Ylenia, C., et al.: A clinical decision support system based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients. Math. Biosci. Eng. 18(3), 2654–2674 (2021)
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)
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). IEEE (2017)
Improta, G., et al.: Management of the diabetic patient in the diagnostic care pathway. In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds.) EMBEC 2020. IP, vol. 80, pp. 784–792. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64610-3_88
Improta, G., et al.: Evaluation of medical training courses satisfaction: Qualitative analysis and analytic hierarchy process. In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds.) EMBEC 2020. IP, vol. 80, pp. 518–526. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64610-3_59
Ponsiglione, A.M., et al.: A hybrid analytic hierarchy process and Likert scale approach for the quality assessment of medical education programs. Mathematics 10(9), 1426 (2022). https://doi.org/10.3390/math10091426
Scala, A., et al.: Regression models to study the total LOS related to valvuloplasty. Int. J. Environ. Res. Public Health 19(5), 3117 (2022)
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)
Cortesi, P.A., et al.: Cost-effectiveness and budget impact of emicizumab prophylaxis in haemophilia a patients with inhibitors. Thromb. Haemost. 120(02), 216–228 (2020)
Improta, G., Simone, T., Bracale, M.: HTA (Health Technology Assessment): a means to reach governance goals and to guide health politics on the topic of clinical Risk management. In: Dössel, O., Schlegel, W.C. (eds.) World Congress on Medical Physics and Biomedical Engineering, pp. 166–169. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03893-8_47
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)
Orabona, G.T., Calvanese, C., Ferri, A., Committeri, U., Improta, G.: A comparison of a SARS-CoV-2 rapid-test and serological-test in a Public Health Hospital GDA. J. Infect. Dev. Countries (2022)
Montella, E., Ferraro, A., Sperlì, G., Triassi, M., Santini, S., Improta, G.: Predictive analysis of healthcare-associated blood stream infections in the neonatal intensive care unit using artificial intelligence: a single center study. Int. J. Environ. Res. Public Health 19(5), 2498 (2022)
Scala, A., Alfano, R., Borrelli, A., Rossi, G., Triassi, M.: Logistic regression to study the change in length of stay in a department of ophthalmology in CoViD-19 era. In: 2021 International Symposium on Biomedical Engineering and Computational Biology, BECB 2021 (2021)
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
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Scala, A. et al. (2023). Use of Statistical Analysis to Evaluate How Covid-19 Has Changed the Management of the Neurosurgery Department of the AORN “A. Cardarelli” in Naples. 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_48
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