Hospital acquired infections in COVID-19 patients in sub intensive care unit: analysis of two waves of admissions
Keywords:
Covid 19, Hospital Acquired Infection, Subintensive UnitAbstract
Background and aim: The pandemic caused by SARS-COV-2 has increased Semi-Intensive Care Unit (SICU) admission, causing an increase in healthcare-associated infection (HAI). Mostly HAI reveals the same risk factors, but fewer studies have analyzed the possibility of multiple coinfections in these patients. The study aimed was to identify patterns of co-presence of different species describing at the same time the association between such patterns and patient demographics and, finally, comparing the patterns between the two cohorts of COVID-19 patients admitted at Policlinico during the first wave and the second one). Methods: All the patients admitted to SICUs during two COVID-19 waves, from March to June 2020 months and from October to December 2020, were screened following the local infection control surveillance program; whoever manifested fever has undergone on microbiological culture to detect bacterial species. Statistical analysis was performed to observe the existence of microbiological patterns through DBSCAN method. Results: 246 patients were investigated and 83 patients were considered in our study because they presented infection symptoms with a mean age of 67 years and 33.7% of female patients. During the first and second waves were found respectively 10 and 8 bacterial clusters with no difference regarding the most frequent species. Conclusions: The results show the importance of an analysis which considers the risk factors for the possibility of co- and superinfection (such as age and gender) to structure a good prognostic tool to predict which patients will encounter severe coinfections during hospitalization.
References
https://opendatadpc.maps.arcgis.com/apps/dashboards/b0c68bce2cce478eaac82fe38d4138b1 (last view 13.05.2021)
Rivieccio BA, Luconi E, Boracchi P, Pariani E, Romanò L, Salini S, Castaldi S, Biganzoli E, Galli M. Heterogeneity of COVID-19 outbreak in Italy. Acta Biomed 2020; Vol. 91, N. 2: 31-34 DOI: 10.23750/abm.v91i2.9579
De Filippis G, Cavazzana L, Errico M, Olivieri P, Parravicini E, Curci R, De Murtas G, Gimigliano A, Carnevali D, Letzgus M, Visconti A, Castaldi S, Auxilia F. After the COVID 19 outbreak in Italy: What have we learnt? Travel Medicine and Infectious Disease 38 (2020) 101761
Auxilia F, Maraschini A, Bono P, Ungaro R, Luconi E, Biganzoli E, Castaldi S. COVID-19: new scenario old problems. Acta Biomed 2020; Vol. 91, Suplement9:90-91 DOI: 10.23750/abm.v91i2-S.10119
Burriel MS, Keys M, Campillo-Artero C, Agodi A, Barchitta M, Gikas A, Palos C, Lopez-Casasnovas G. Impact of multi-drug resistant bacteria on economic and clinical outcomes of healthcare-associated infections in adults: Systematic review and meta-analysis. PLoS One 2020; 15(1): e0227139 doi: 10.1371/journal.pone.0227139
Maque M, Sartelli M, McKimm J, Bakar MA. Health care associated infections an overview. Infect Drug Resist 2018; 11: 2321e33
Hu LQ, Wang J, Huang A, Wang D, Wang J. COVID-19 and improved prevention of hospital-acquired infection. British Journal of Anaesthesia 2020; e318-e319 doi: 10.1016/j.bja.2020.05.037
Accardi R, Castaldi S, Marzullo A, Ronchi S, Laquintana D, Lusignani M. Prevention of healthcare associated infections: a descriptive study. Ann Ig. 2017 Mar-Apr;29(2):101-115. doi: 10.7416/ai.2017.2137. PMID: 28244579.
Ardoino I, Zangirolami F, Iemmi D, Lanzoni M, Cargnelutti M, Biganzoli E, Castaldi S. Risk factors and epidemiology of Acinetobacter baumannii infections in a university hospital in Northern Italy: A case-control study. Am J Infect Control. 2016 Dec 1;44(12):1600-1605. doi: 10.1016/j.ajic.2016.05.005.
Mellace L, Consonni D, Jacchetti G, Del Medico M, Colombo R, Velati M, Formica S, Cappellini MD, Castaldi S, Fabio G. Epidemiology of Clostridium difficile-associated disease in internal medicine wards in northern Italy. Intern Emerg Med. 2013 Dec;8(8):717-23. doi: 10.1007/s11739-012-0752-6.
Vidal CG, Saniuan G, Moreno-Garcia E, Puerta-Alcade P, Garcia-Pouton N, Chumbita M, Fernandez-Pittol M, Pitart C, Inciarte A, Bodro M, Morata L, Ambrosioni J, Grafia I, Meira F, Manaya I, Cardozo C, Casal C, Tellez A, Castro P, Marco F, Garcia F, Mensa J, Martinez A, Soriano A. Incidence of co-infections and superinfections in hospitalized patients with COVID-19: a retrospective cohort study.Clinical Microbiology and Infection 27 (2021) 83e88 https://doi.org/10.1016/j.cmi.2020.07.041
Castaldi S, Luconi E, Marano G,Auxilia F, Maraschini A, Bono P, Ungaro R, Bandera A, Boracchi P, Biganzoli E. Hospital acquired infections in COVID-19 patients in sub intensive care unit. Acta Biomed 2020; Vol. 91, N. 3: e2020017 DOI: 10.23750/abm.v91i3.10376
Borghesi A, Golemi S, Carapella N et al. Lombardy, Italy: COVID-19 second wave less severe than the first? A preliminary investigation, 03 November 2020, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-101345/v1])
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231
Choi, Seung-Seok, Sung-Hyuk Cha, and Charles C. Tappert. "A survey of binary similarity and distance measures." Journal of systemics, cybernetics and informatics 8.1 (2010): 43-48
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 1-21)
Rousseeuw, Peter J. "Silhouettes: a graphical aid to the interpretation and validation of cluster analysis." Journal of computational and applied mathematics 20 (1987): 53-65)
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/)
Hahsler M, Piekenbrock M, Doran D (2019). “dbscan: Fast Density-Based Clustering with R.” Journal of Statistical Software, 91(1), 1–30. doi: 10.18637/jss.v091.i01
Habashi NM, Camporota L, Gatto LA, Nieman G. Functional pathophysiology of SARS-CoV-2-induced acute lung injury and clinical implications. J Appl Physiol (1985). 2021 Mar 1;130(3):877-891.
McCullers JA. The co-pathogenesis of influenza viruses with bacteria in the lung. Nat Rev Microbiol. 2014 Apr;12(4):252-62.
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020 Feb 15;395(10223):497-506.
Snow TAC, Longobardo A, Brealey D, Down J, Satta G, Singer M, Arulkumaran N. Beneficial ex vivo immunomodulatory and clinical effects of clarithromycin in COVID-19. J Infect Chemother. 2022 Apr 14:S1341-321X(22)00109-X.
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020 Feb 15;395(10223):507-513.
Wilson LA, Rogers Van Katwyk S, Fafard P, Viens AM, Hoffman SJ. Lessons learned from COVID-19 for the post-antibiotic future. Global Health. 2020 Oct 8;16(1):94.
Joseph Loscalzo, Anthony Fauci, Dennis Kasper, Stephen Hauser, Dan Longo, J. Larry Jameson. Harrison's Principles of Internal Medicine 21e. McGraw Hill. 2022
Arentz M, Yim E, Klaff L, Lokhandwala S, Riedo FX, Chong M, et al. Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State. JAMA [Internet] 2020
Barrasa H, Rello J, Tejada S, Martin A, Balziskueta G, Vinuesa C, et al. SARS-Cov-2 in Spanish intensive care: early experience with 15-day survival in Vitoria.
Anaesth Crit Care Pain Med 2020.
Cai Q, Huang D, Ou P, Yu H, Zhu Z, Xia Z et al. COVID-19 in a designated infectious diseases hospital outside Hubei Province, China. Allergy 2020 ((Su,
Fu) School of Medicine, Southern University of Science and Technology,
Shenzhen, Guangdong 518055, China).
Feng Y, Ling Y, Bai T, Xie Y, Huang J, Li J et al. COVID-19 with different severity: a multicenter study of clinical features. Am J Respir Crit Care Med 2020
Wang L, He W, Yu X, Hu D, Bao M, Liu H, Zhou J, Jiang H. Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up. J Infect. 2020 Jun;80(6):639-645.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Pier Mario Perrone, Silvana Castaldi, Ester Luconi, Giuseppe Marano, Francesco Auxilia, Anna Maraschini, Patrizia Bono, Laura Alagna, Emanuele Palomba, Alessandra Bandera, Patrizia Boracchi, Elia Biganzoli
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Transfer of Copyright and Permission to Reproduce Parts of Published Papers.
Authors retain the copyright for their published work. No formal permission will be required to reproduce parts (tables or illustrations) of published papers, provided the source is quoted appropriately and reproduction has no commercial intent. Reproductions with commercial intent will require written permission and payment of royalties.