Abstract
Coronavirus disease has spread throughout the world rapidly and has changed the world health scenario. Each hospital department was faced with an emergency and then reorganized services. The aim of the present work is to assess the impact of the Covid-19 epidemic on the activity of the transplant center in the A.O.R.N. “Antonio Cardarelli” of Naples (Italy). This study was conducted considering all patients undergoing skin transplantation in the years 2019 (in the absence of Covid-19) and 2020 (in the pandemic emergency). In the work, the logistical regression was used to analyze the tie among hospitalization year (as a dependent variable) and the following independent variables: gender, age, Length of stay (LOS), relative weight DRG, discharge mode and admission procedure.
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References
Zhu, N., et al.: A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382(8), 727–733 (2020)
Lancet, T.: COVID-19: learning from experience. Lancet 395(10229), 1011 (2020). https://doi.org/10.1016/S0140-6736(20)30686-3
Uyaroğlu, O.A., et al.: Evaluation of the effect of COVID-19 pandemic on anxiety severity of physicians working in the internal medicine department of a tertiary care hospital: a cross-sectional survey. Int. Med. J. 50, 1350–1358 (2020). https://doi.org/10.1111/imj.14981
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. JAMA Neurol. (2020). https://doi.org/10.1001/jamaneurol.2020.1127. Published online ahead of print 10 April 2020
Houghton, A., Bowling, A., Jones, I., Clarke, K.: Appropriateness of admission and the last 24 hours of hospital care in medical wards in an east London teaching group hospital. Int. J. Qual. Health Care: J. Int. Soc. Qual. Health Care 8(6), 543–553 (1996). https://doi.org/10.1093/intqhc/8.6.543
Platt, J.L.: New directions for organ transplantation. Nature 392(6679 Suppl.), 11–17 (1998)
Ricordi, C., Strom, T.B.: Clinical islet transplantation: advances and immunological challenges. Nat. Rev. Immunol. 4(4), 259–268 (2004)
Vindenes, H.: Hudtransplantasjon [Skin transplantation]. Tidsskr Nor Laegeforen. 119(27), 4050-3 (1999). PMID: 10613096
Kinner, M.A., Daly, W.L.: Skin transplantation. Crit. Care Nurs. Clin. North Am. 4(2), 173–178 (1992). PMID: 1599640
Belle, A., Thiagarajan, R., Soroushmehr, S.M., Navidi, F., Beard, D.A., Najarian, K.: Big data analytics in healthcare. BioMed Res. Int. (2015)
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 (2006)
Bonavolontà, P., et al.: Postoperative complications after removal of pleomorphic adenoma from the parotid gland: a long-term follow up of 297 patients from 2002 to 2016 and a review of publications. Br. J. Oral Maxillofacial Surg. 57(10), 998–1002 (2019). https://doi.org/10.1016/j.bjoms.2019.08.008. ISSN 0266-4356
Solari, D., et al.: Skull base reconstruction after endoscopic endonasal surgery: new strategies for raising the dam. In: 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), pp. 28–32 (2019). https://doi.org/10.1109/METROI4.2019.8792878
Maniscalco, G.T., et al.: Early neutropenia with thrombocytopenia following alemtuzumab treatment for multiple sclerosis: case report and review of literature. Clin. Neurol. Neurosurg. 175, 134–136 (2018)
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. Disorders 43, 102151 (2020)
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)
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)
Ferraro, A., et al.: Implementation of lean practices to reduce healthcare associated infections. Int. J. Healthcare Technol. Manag. 8(1–2), 51–72 (2020)
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
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
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
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
De Santo, A., Galli, A., Gravina, M., Moscato, V., Sperlì, G.: Deep learning for HDD health assessment: an application based on LSTM. IEEE Trans. Comput. 71(1), 69–80 (2020). https://doi.org/10.1109/TC.2020.3042053
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
Provenzano, F., D’Arrigo, G., Zoccali, C., Tripepi, G.: La regressione logistica nella ricerca clinica. CNR-IBIM, Unità di Ricerca di Epidemiologia Clinica e Fisiopatologia delle Malattie Renali e dell’Ipertensione Arteriosa, Reggio Calabria
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
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)
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)
Cesarelli, M., et al.: Prognostic decision support using symbolic dynamics in CTG monitoring. EFMI-STC 186, 140–144 (2013)
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
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
Rosa, D., Balato, G., Ciaramella, G., Soscia, E., Improta, G., Triassi, M.: Long-term clinical results and MRI changes after autologous chondrocyte implantation in the knee of young and active middle aged patients. J. Orthop. Traumatol. 17(1), 55–62 (2015). https://doi.org/10.1007/s10195-015-0383-6
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
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). https://doi.org/10.3934/mbe.2021135
Iuppariello, L., et al.: A novel approach to estimate the upper limb reaching movement in three-dimensional space. Inform. Med. Unlocked 15, 100155 (2019). https://doi.org/10.1016/j.imu.2019.01.005
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
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)
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Montella, E. et al. (2023). Analysis of the Reorganisation of Skin Transplantation Surgeries During the COVID-19 Pandemic. 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_45
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