1. Introduction
The emergence of and subsequent pandemic caused by the COVID-19 virus have required major adjustments to healthcare systems and frameworks. As part of the response, infection control and antimicrobial stewardship programs (ASPs) have had to rapidly adapt in real time in the face of an evolving body of evidence [1].
According to a recent publication by Cassini et al. on attributable deaths and lost disability-adjusted life years caused by infections due to multidrug-resistant (MDR) bacteria in the European Union, Italy has a high estimated burden of antibiotic-resistant infections (201,584 cases, median) [2]. The recent COVID-19 pandemic has caused an increase in hospitalizations, creating a challenging situation in the healthcare system. Severe cases require prolonged hospitalization in intensive care units (ICU), where patients are at high risk for hospital-acquired infections (HAIs) due to the invasive procedures performed [3].
As reported in 2003 for the SARS-CoV epidemic, there was an increased rate of methicillin-resistant Staphylococcus aureus (MRSA) from 3.53% (3.53 cases per 100 admissions) during the pre-SARS period to 25.3% during the SARS period (p < 0.001), with an increased rate of ventilator-acquired pneumonia in ICUs, mostly (47.1%) caused by MRSA [4].
A recent review of the current medical literature showed that rates of bacterial or fungal co-infection reported in patients presenting with coronavirus infections appear to be low [5]. In nine studies reporting on bacterial co-infection in COVID-19 cases, bacterial/fungal co-infection was reported in 8% of cases; other reported incidences vary between 3.6 and 43% [6]. Many authors recognize the importance of superinfection, but definitive data are still lacking [7,8].
Antibiotic misuse coupled with a strained healthcare workforce and a reduced surveillance capacity for antibiotic-resistant organisms may lead to antimicrobial resistance as a lasting consequence of the COVID-19 pandemic [9,10]. Despite its viral nature, initial studies indicate that antibiotics are frequently prescribed to patients with COVID-19, largely due to suspected bacterial co-infections. Systematic reviews have estimated that more than 70% received antibacterial therapy, predominantly broad-spectrum and often empiric [11].
Understanding the patterns and predictors of antibiotic prescribing in COVID-19 can help to identify opportunities for interventions and target antibiotic stewardship strategies to improve the quality and safety of antibiotic use in the setting of the COVID-19 pandemic. Our objective was to compare antibiotic use and incidence of HAIs in the period covering the first through third waves of COVID-19 (1 February 2020–30 March 2021) with the pre-COVID period (1 August 2019–30 January 2020) to outline the changing epidemiology of nosocomial infections during the SARS-CoV2 pandemic.
2. Methods
2.1. Study Design and Objective
This is a retrospective and prospective observational epidemiological study at Molinette Hospital, located in the City of Health and Sciences, a 1200-bed teaching hospital in partnership with the University of Turin that provides various medical, surgical, and intensive care services. The study was approved by the Ethical Committee under protocol number 007831.
The pre-COVID period was defined as 6 months before the outbreak in Italy (1 August 2019–30 January 2020). The mentioned data were obtained retrospectively from hospital records. At the beginning of the COVID-19 pandemic, prospective data were collected as the pandemic evolved. The COVID period covered one year of the pandemic (1 Febuary 2020–30 March 2021) and included three waves.
2.2. Data Collection
Patients admitted to the hospital, including to surgical, medical, and intensive care units, from August 2019 to March 2021 were included if their stool sample was positive for Clostridium difficile (C. difficile) and/or their blood culture, respiratory culture (bronchoalveolar lavage or tracheal aspirate), or rectal swab was positive for MDR.
MDR organisms included K. pneumoniae carbapenemase-producing K. pneumoniae (KPC-Kp), extended-spectrum beta-lactamase Escherichia coli (ESBL-E. coli), methicillin-resistant Staphylococcus aureus (MRSA), and carbapenem-resistant P. aeruginosa and A. baumannii (CR-PA; CR-AB). All positive blood and/or respiratory cultures for the previously mentioned organisms and C. difficile were reported. The absolute frequency and percentage were reported, as well as incidence ratios and rates expressed per 1000 patient-days (PD).
Monthly incidence of antibiotic use was calculated as defined daily dose (DDD). The DDD of carbapenems, vancomycin, fluoroquinolones (FQ), cephalosporins (CS), piperacillin–tazobactam, ceftazidime–avibactam (CAZ-AVI), and colistin were reported. Antibiotic use was obtained through pharmacy database records and normalized to defined daily doses (DDD) per 1000 PD.
2.3. Microbiological Detection Methods
Identification of bacterial isolates and antimicrobial susceptibility testing (AST) were performed on the Microscan Walkaway 96 plus system (Beckman Coulter, Brea, CA, USA) according to the EUCAST breakpoints. MRSA and ESBL phenotypes were detected according to the latest version of the “EUCAST guidelines for detection of resistance mechanisms and specific resistances of clinical and/or epidemiological importance”. Both were detected by broth microdilution tests integrated into the Microscan Walkaway panels. Staphylococcus aureus was considered MRSA by the positivity to the cefoxitin screening test (MIC > 4 mg/L) or if oxacillin MIC was >2 mg/L. ESBL production test was positive if a ≥8-fold reduction was observed in the MIC of cefotaxime, ceftazidime, or cefepime in combination with clavulanic acid (fixed concentration 4 mg/L) in comparison with the MIC of cephalosporins alone [12].
Carbapenemase production was detected by the phenotypic modified Hodge test and confirmed by immunochromatographic lateral flow tests (ICT) for rapid detection and typing of carbapenemases. Species identification was performed by MALDI-TOF MS.
2.4. Statistical Analysis
MDRO ratios and rates during the two time periods, prior to vs. during the COVID-19 pandemic, were compared using chi-squared tests and incidence rate ratios (IRRs), respectively. An interrupted time series analysis on trends in antimicrobial consumption (expressed as monthly DDD/1000 PD) was performed using an ARIMA model. The breakpoint was set on 1 February 2020 to investigate changes in antimicrobial usage prior to vs. during the COVID-19 pandemic. Estimates, standard errors, and p-value were reported. All analyses were performed using SPSS v. 27.0 (SPSS Inc., Armonk, NY, USA), and two-tailed statistical significance was set at <0.05.
3. Results
3.1. Microbiological Trends in Hospital
A significant increase in all MDR infections (p-value < 0.001) was observed comparing the COVID period to the previous period. KPC-KP isolates increased from 278 (14%) to 445 (23%) during COVID. ESBL-E. coli isolates increased from 173 (9%) to 225 (11.5%). CR-AB and CR-PA isolates increased from 26 (1.5%) to 199 (5%) and from 56 (3%) to 71 (4%), respectively. CDI rates increased from 92 (5%) to 130 (7%). The frequencies of HAP, BSI, and rectal swab isolates are reported in Table 1.
Adjusting per 1000 PD, we observed a significant increase in KPC-Kp during COVID from 3.41 to 4.46 isolates/1000 patient-days (IRR = 0.76) (Table 2). Similarly, CR-AB and MRSA isolates significantly increased during the COVID period from 0.32 to 2.00 isolates/1000 patient-days (IRR = 0.19) and 0.75 to 0.97 (IRR = 0.94), respectively. Rectal colonization by KPC-Kp and CR-AB was also higher during the COVID period, with IRRs of 0.97 and 0.2, respectively. On the other hand, the rates of ESBL-E. coli, CR-PA, and CDI were higher in the pre-COVID period, with IRRs of 1.14, 1.18, and 1.06, respectively. Further details are reported in Table 2.
3.2. Antibiotic Use
Overall Antibiotic Use
The use of first-generation cephalosporins and carbapenem saw a sharp decrease at the beginning of the COVID period, with a significant change in level (−539 and −564 DDD/1000 PD; p = 0.001 and p = 0.04, respectively). Although no significant trend was found, the pre-COVID downward trends were not maintained in the COVID period. FQ use showed a significant increasing trend in the pre-COVID period but saw a significant reduction in the COVID period (−204 DDD/1000 PD; p = 0.026). Figure 1 shows the time series analysis of the consumption of antibiotics, and Table 3 shows the estimates of DDD/1000 PD.
The use of fourth- and fifth-generation cephalosporins increased at the beginning of the COVID period, with a significant change in level (344 and 531 DDD/1000 PD; p = 0.02 and 0.002, respectively). Piperacillin–tazobactam use increased with a significant change in level (133 DDD/1000 PD; p = 0.053). No significant trends were identified in the pre-COVID or COVID periods for the use of fourth- and fifth-generation cephalosporins, piperacillin–tazobactam, and vancomycin. Colistin consumption decreased at the beginning of the COVID period (−87 DDD/1000 PD), but it was reversed to increase with a change in trend of 6.9 DDD/1000 PD during the COVID period. However, no significant trends were identified for colistin. Classifying per ward, no significant changes in trends were specifically observed in the medical, surgical, or ICU wards.
4. Discussion
The impact of the COVID-19 pandemic goes beyond the individual level, causing several public health issues, including antimicrobial resistance. One of the major concerns regarding hospitalized patients with COVID-19 are bacterial superinfections, especially in the ICUs and in patients with invasive devices, and outbreaks of MDR bacteria resulting from poor adherence to infection control practices. Earlier reports demonstrated HAIs in COVID-19 patients varying from 10–45% [13,14,15,16] but provided limited evidence about the microbial causality and the effect on outcomes. Some studies have described infections caused by MDR Gram-negative bacteria (Enterobacterales, A. baumannii, P. aeruginosa) in COVID-19 [17]; others have shown an increased number of co-infections due to the most common bacteria, such as Mycoplasma pneumoniae, P. aeruginosa, H. influenzae, and Klebsiella spp. [6].
This study described the incidence of MDR infections and changes in antibiotic use during the pandemic. Our data showed a reduction in MDR infections during the first wave of COVID-19, probably due to the increased use of personal protective equipment (PPE) and stronger adherence to infection control procedures. Moreover, the first COVID-19 wave might have benefitted from the active antimicrobial stewardship program that was ongoing at our institution, which had a positive influence on MDR trends in the last years before the pandemic [18].
Our data show that bacterial infections during the late periods of the pandemic increased; we demonstrated an increase in MDR infections in blood and respiratory cultures, particularly infections with with KPC-Kp, CR-AB, and MRSA, which is consistent with similar reports of patients with COVID-19 [19,20]. During COVID-19, there were important differences in the case mix: there was a greater proportion of clinically severe patients, while non-severe patients were encouraged to stay at home. Moreover, critically ill patients with COVID-19 required longer hospital stays and, consequently, posed a greater risk for acquiring nosocomial infections. This might have contributed to the HAI incidence ratio. In addition, our institute was a regional referral center for ExtraCorporeal Membrane Oxygenation (ECMO) in critically ill patients with COVID-19 acute respiratory distress syndrome (ARDS), and many patients were transferred to our facility from different hospitals. Those facts also might have affected the epidemiology and aided the MDR diffusion.
The high incidence of MDR could also be explained by the increased capacity of the hospital to admit all patients. This called for a great number of transferred healthcare staff who were probably inadequately familiar with the usual infection prevention practices. Another possible explanation is the high frequency of invasive procedures and a reduced focus on source control. Infection control strategies were strongly focused on preventing airborne viral pathogens, and less attention was probably paid to routine infection prevention activities (e.g., practices to prevent CLABSI and VAP, such as periodic evaluation of central lines, spontaneous breathing trials, and sedation interruption) [1,21].
Violations of the infection control protocols prompted by the increased workload and reduced availability of healthcare workers during the pandemic, combined with an institutional inability to sustain prevention and control measures, might have contributed to this finding.
In our study, we observed an increased consumption of antibiotics in the first wave of the COVID-19 pandemic, probably related to the earlier published data suggesting that COVID-19 be treated as community-acquired pneumoniae (CAP), using beta-lactams and azithromycin or FQ.
The lack of robust epidemiologic records in the initial stage of the pandemic, the relatively increased prevalence of bacterial co-infections (11–35%) in previous influenza outbreaks [22], and the involvement of co-infection with higher morbidity and mortality [23] perhaps affected physicians’ choice to start antibiotic therapy. This is further supported by the challenge of preventing a bacterial co-infection since the signs and laboratory and imaging results in COVID-19 overlap with pneumonia [24,25].
Later, the treatment conception was modified to eliminate the necessity of antibiotic coverage in COVID-19 patients except for critically ill patients and for patients with high clinical suspicion for bacterial infections [26,27]. This finding was also confirmed by our data, where antibiotic consumption did not significantly change during the second and third waves of the pandemic. A reduction in antibiotic use was perhaps not significantly achieved because our epidemiology of MDR was always endemic in our setting; therefore, we did not manifest major changes in antibiotic use.
During COVID-19, real-time collaboration between physicians, nurses, pharmacy, and infection control practitioners is demanded for antimicrobial stewardship. Infection control nurses incorporate elements of ASP into their infection control manuals and educational programs. ASP educational materials during the pandemic should include updates on local prescription practices, resistance patterns, and recommended approaches to treating common infections. Front-line nurses can also contribute to stewardship in numerous ways: obtaining blood cultures before antibiotic administration, the timely initiation of antibiotics in septic patients, adjustment and de-escalation of antimicrobials by communicating microbiology results to physicians, monitoring for adverse events related to antibiotics, reviewing orders for antibiotics, performing “time-outs” and assessing the need for ongoing antibiotic use, assessing for transition to oral antibiotics, and providing patient education. Pharmacists’ responsibilities include promoting optimal use through aiding in the appropriate selection, optimal dosing, rapid initiation, and proper monitoring and de-escalation of antimicrobial therapies, as well as the development of restricted antimicrobial use procedures, therapeutic interchange, de-escalation, treatment guidelines, and clinical care plans. Finally, the coordination between Microbiology, Infectious Diseases, and Preventive Medicine and Public Health should guide decisions regarding antibiotic use and the prevention of resistances [28,29].
One of the major strengths of our study include that it reported three waves of the COVID pandemic and evaluated invasive infections and colonization by MDR bacteria. Limitations of our study include the fact that it is a single-center study influenced by local epidemiology. Other limitations include the study’s descriptive nature as an epidemiological report without reporting clinical data, patient history, comorbidities, and therapies. We also did not outline the association between rectal colonization and developing invasive infection in this population, nor did we conduct an a priori sample size calculation.
In conclusion, our results describe the incidence of bacterial infections before and during the COVID-19 period and encourage the rapid re-establishment of antimicrobial stewardship to limit antimicrobial resistance. Despite the infection control practices in place during the COVID-19 pandemic, invasive procedures, prolonged hospital stays, and workload during the pandemic increased the risk of HAIs caused by MDR. Identifying changes in the resistance profile since the onset of the COVID-19 pandemic is an alarm for healthcare institutions and government agencies regarding the need to focus efforts on investigation and control, and to support and maintain a stewardship program and infection control practices.
Conceptualization, N.S., S.C. and F.G.D.R.; methodology, N.S., S.C. and F.G.D.R.; formal analysis, N.S. and C.V.; investigation, N.S.; resources, S.G., D.F., F.C., G.B., C.M.Z. and R.C.; data curation, N.S.; writing—original draft preparation, N.S.; writing—review and editing, S.C. and F.G.D.R.; supervision, F.G.D.R. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Molinette Hospital, City of Health and Sciences on 24 May 2021 (protocol code 007831).
Patient consent was waived due to the retrospective nature of the study and data collection from electronic systems without direct contact with patients.
Due to patient confidentiality, raw data will be available upon request with a compelling reason.
The authors declare no conflict of interest. Preliminary data of this study were presented at ECCMID 2021 as a poster presentation.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. Time series analysis of antibiotic consumption. (a) carbapenem (b) fluoroquinolones (c) piperacillin-tazobactam.
MDR isolates pre- and during COVID.
| MDR | Total N (%) | Pre-COVID N (%) | COVID N (%) | p-Value |
|---|---|---|---|---|
| KPC-Kp | 723 (36.9) | 278 (14.2) | 445 (22.7) | |
| Respiratory | 111 (5.7) | 33 (1.7) | 78 (4) | <0.001 |
| BSI | 74 (3.8) | 33 (1.7) | 41 (2.1) | <0.001 |
| RS | 538 (27.4) | 212 (10.8) | 326 (16.6) | <0.001 |
| ESBL-E. coli | 398 (20.3) | 173 (8.8) | 225 (11.5) | |
| Respiratory | 45 (2.3) | 17 (0.9) | 28 (1.4) | <0.001 |
| BSI | 104 (5.3) | 48 (2.4) | 56 (2.9) | <0.001 |
| RS | 249 (12.7) | 108 (5.5) | 141 (7.2) | <0.001 |
| CR-AB | 225 (11.5) | 26 (1.4) | 199 (4.9) | |
| Respiratory | 80 (4.1) | 7 (0.4) | 73 (3.7) | <0.001 |
| BSI | 27 (1.4) | 4 (0.2) | 23 (1.2) | <0.001 |
| RS | 118 (6) | 15 (0.8) | 103 (5.3) | <0.001 |
| CR-PA | 127 (6.5) | 56 (2.9) | 71 (3.7) | |
| Respiratory | 104 (5.3) | 47 (2.4) | 57 (2.9) | <0.001 |
| BSI | 14 (0.7) | 5 (0.3) | 9 (0.5) | <0.001 |
| RS | 9 (0.5) | 4 (0.2) | 5 (0.3) | <0.001 |
| MRSA | 158 (8) | 1 (3.1) | 97 (5) | |
| Respiratory | 69 (3.5) | 24 (1.2) | 45 (2.3) | <0.001 |
| BSI | 89 (4.5) | 37 (1.9) | 52 (2.7) | <0.001 |
| CDI | 222 (11.3) | 92 (4.7) | 130 (6.6) | <0.001 |
Abbreviations: BSI: blood-stream infections, RS: rectal swab, KPC-Kp: K. pneumoniae carbapenemase-producing K. pneumoniae, ESBL: extended-spectrum beta-lactamase, CR-AB: carbapenem-resistant A. baumanii, CR-PA: carbapenem-resistant P. aeruginosa, MRSA: methicillin-resistant Staphylococcus aureus, CDI: Clostridium difficile infection.
Incidence rate ratios for adjusted isolates per 1000 patient-days.
| MDR | Total/1000 PD | Pre-COVID/1000 PD | COVID/1000 PD | IRR | 95% CI | p-Value |
|---|---|---|---|---|---|---|
| KPC | 3.99 | 3.41 | 4.46 | 0.93 | 0.23–3.83 | 0.95 |
| Respiratory | 0.61 | 0.40 | 0.78 | 0.63 | 0.01–28.34 | 1 |
| BSI | 0.41 | 0.40 | 0.41 | 1.19 | 0.02–92.89 | 1 |
| RS | 2.97 | 2.60 | 3.27 | 0.97 | 0.19–4.95 | 1 |
| ESBL-E. coli | 2.19 | 2.12 | 2.26 | 1.15 | 0.18–7.47 | 1 |
| Respiratory | 0.25 | 0.21 | 0.28 | 0.92 | 0.03–262.60 | 0.97 |
| BSI | 0.57 | 0.59 | 0.56 | 1.29 | 0.03–49.86 | 0.90 |
| RS | 1.37 | 1.32 | 1.41 | 1.14 | 0.10–12.28 | 1 |
| A. baumanni | 1.24 | 0.32 | 2.00 | 0.20 | 0.04–8.161 | 0.60 |
| Respiratory | 0.44 | 0.09 | 0.73 | 0.15 | 0.04–153.1 | 1 |
| BSI | 0.15 | 0.05 | 0.23 | 0.27 | 0.06–4209 | 0.77 |
| RS | 0.65 | 0.18 | 1.03 | 0.21 | 0.02–31.91 | 1 |
| P. aeruginosa | 0.70 | 0.69 | 0.71 | 1.18 | 0.04–32.62 | 0.90 |
| Respiratory | 0.57 | 0.58 | 0.57 | 1.24 | 0.03–48.10 | 0.90 |
| BSI | 0.08 | 0.06 | 0.09 | 0.81 | 0.02–2494 | 0.96 |
| RS | 0.05 | 0.05 | 0.05 | 1.22 | 0.05–295,300 | 0.97 |
| MRSA | 0.87 | 0.75 | 0.97 | 0.94 | 0.05–19.24 | 19 |
| Respiratory | 0.38 | 0.29 | 0.45 | 0.79 | 0.07–83.78 | 1 |
| BSI | 0.49 | 0.45 | 0.52 | 1.05 | 0.02–57.18 | 1 |
| CDI | 1.22 | 1.13 | 1.30 | 1.06 | 0.09–13.21 | 1 |
Overall antibiotic trends in interrupted time series analysis in hospital.
| Ward | Estimate | SE | p-Value |
|---|---|---|---|
| All wards | |||
| 1st generation cephalosporins | |||
| Pre-COVID trend | −2.154 | 24.073 | 0.930 |
| Change in level | −539.527 | 132.812 | 0.001 |
| Change in trend | 12.972 | 24.777 | 0.609 |
| 2nd generation cephalosporins | |||
| Pre-COVID trend | −0.778 | 0.476 | 0.126 |
| Change in level | −2.537 | 2.710 | 0.366 |
| Change in trend | 0.797 | 0.495 | 0.131 |
| 3rd generation cephalosporins | |||
| Pre-COVID trend | −173.114 | 173.102 | 0.336 |
| Change in level | −690.663 | 945.501 | 0.478 |
| Change in trend | 159.432 | 176.906 | 0.384 |
| 4th generation cephalosporins | |||
| Pre-COVID trend | −19.712 | 21.995 | 0.386 |
| Change in level | 344.480 | 131.317 | 0.021 |
| Change in trend | 15.061 | 23.222 | 0.528 |
| 5th generation cephalosporins | |||
| Pre-COVID trend | 21.778 | 23.732 | 0.376 |
| Change in level | 531.828 | 134.128 | 0.002 |
| Change in trend | −46.451 | 24.593 | 0.081 |
| Piperacillin–tazobactam | |||
| Pre-COVID trend | 5.576 | 11.369 | 0.632 |
| Change in level | 133.944 | 62.834 | 0.053 |
| Change in trend | −7.214 | 11.619 | 0.545 |
| Fluoroquinolones | |||
| Pre-COVID trend | 173.061 | 77.478 | 0.044 |
| Change in level | 893.653 | 449.492 | 0.068 |
| Change in trend | −204.120 | 81.122 | 0.026 |
| Vancomycin | |||
| Pre-COVID trend | −2.472 | 9.394 | 0.797 |
| Change in level | −50.358 | 52.584 | 0.356 |
| Change in trend | 1.013 | 9.720 | 0.919 |
| Carbapenems | |||
| Pre-COVID trend | −19.560 | 44.567 | 0.668 |
| Change in level | −564.805 | 247.041 | 0.040 |
| Change in trend | 15.009 | 45.425 | 0.746 |
| Caz-avi | |||
| Pre-COVID trend | 2.472 | 9.394 | 0.797 |
| Change in level | 50.358 | 52.584 | 0.356 |
| Change in trend | −1.013 | 9.720 | 0.919 |
| Colistin | |||
| Pre-COVID trend | 3.023 | 11.170 | 0.791 |
| Change in level | −87.292 | 67.925 | 0.221 |
| Change in trend | 6.923 | 11.923 | 0.571 |
References
1. Shbaklo, N.; Lupia, T.; De Rosa, F.G.; Corcione, S. Infection Control in the Era of COVID-19: A Narrative Review. Antibiotics; 2021; 10, 1244. [DOI: https://dx.doi.org/10.3390/antibiotics10101244] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34680824]
2. Cassini, A.; Högberg, L.D.; Plachouras, D.; Quattrocchi, A.; Hoxha, A.; Simonsen, G.S.; Colomb-Cotinat, M.; Kretzschmar, M.E.; Devleesschauwer, B.; Cecchini, M. et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: A population-level modelling analysis. Lancet Infect. Dis.; 2019; 19, pp. 56-66. [DOI: https://dx.doi.org/10.1016/S1473-3099(18)30605-4]
3. De Rosa, F.; Palazzo, A.; Rosso, T.; Shbaklo, N.; Mussa, M.; Boglione, L.; Borgogno, E.; Rossati, A.; Pinna, S.M.; Scabini, S. et al. Risk Factors for Mortality in COVID-19 Hospitalized Patients in Piedmont, Italy: Results from the Multicenter, Regional, CORACLE Registry. J. Clin. Med.; 2021; 10, 1951. [DOI: https://dx.doi.org/10.3390/jcm10091951] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34062864]
4. Yap, F.H.Y.; Gomersall, C.; Fung, K.S.C.; Ho, P.L.; Ho, O.-M.; Lam, P.K.N.; Lam, D.T.C.; Lyon, D.J.; Joynt, G. Increase in Methicillin-Resistant Staphylococcus aureus Acquisition Rate and Change in Pathogen Pattern Associated with an Outbreak of Severe Acute Respiratory Syndrome. Clin. Infect. Dis.; 2004; 39, pp. 511-516. [DOI: https://dx.doi.org/10.1086/422641]
5. Langford, B.J.; So, M.; Raybardhan, S.; Leung, V.; Soucy, J.-P.R.; Westwood, D.; Daneman, N.; MacFadden, D.R. Antibiotic prescribing in patients with COVID-19: Rapid review and meta-analysis. Clin. Microbiol. Infect.; 2021; 27, pp. 520-531. [DOI: https://dx.doi.org/10.1016/j.cmi.2020.12.018]
6. Lansbury, L.; Lim, B.; Baskaran, V.; Lim, W.S. Co-infections in people with COVID-19: A systematic review and meta-analysis. J. Infect.; 2020; 81, pp. 266-275. [DOI: https://dx.doi.org/10.1016/j.jinf.2020.05.046]
7. Zhou, P.; Liu, Z.; Chen, Y.; Xiao, Y.; Huang, X.; Fan, X.-G. Bacterial and fungal infections in COVID-19 patients: A matter of concern. Infect. Control Hosp. Epidemiol.; 2020; 41, pp. 1124-1125. [DOI: https://dx.doi.org/10.1017/ice.2020.156]
8. Rawson, T.M.; Moore, L.S.P.; Zhu, N.; Ranganathan, N.; Skolimowska, K.; Gilchrist, M.; Satta, G.; Cooke, G.; Holmes, A.H. Bacterial and Fungal Coinfection in Individuals With Coronavirus: A Rapid Review To Support COVID-19 Antimicrobial Prescribing. Clin. Infect. Dis.; 2020; 71, pp. 2459-2468. [DOI: https://dx.doi.org/10.1093/cid/ciaa530]
9. Nieuwlaat, R.; Mbuagbaw, L.; Mertz, D.; Burrows, L.L.; E Bowdish, D.M.; Moja, L.; Wright, G.D.; Schünemann, H.J. Coronavirus Disease 2019 and Antimicrobial Resistance: Parallel and Interacting Health Emergencies. Clin. Infect. Dis.; 2021; 72, pp. 1657-1659. [DOI: https://dx.doi.org/10.1093/cid/ciaa773]
10. van Duin, D.; Barlow, G.; Nathwani, D. The impact of the COVID-19 pandemic on antimicrobial resistance: A debate. JAC-Antimicrob. Resist.; 2020; 2, dlaa053. [DOI: https://dx.doi.org/10.1093/jacamr/dlaa053]
11. Abu-Rub, L.; Abdelrahman, H.; Johar, A.-R.; Alhussain, H.; Hadi, H.; Eltai, N. Antibiotics Prescribing in Intensive Care Settings during the COVID-19 Era: A Systematic Review. Antibiotics; 2021; 10, 935. [DOI: https://dx.doi.org/10.3390/antibiotics10080935] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34438985]
12. EUCAST_Detection_of_Resistance_Mechanisms_170711.pdf. Available online: https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Resistance_mechanisms/EUCAST_detection_of_resistance_mechanisms_170711.pdf (accessed on 16 May 2022).
13. Goyal, P.; Choi, J.J.; Pinheiro, L.C.; Schenck, E.J.; Chen, R.; Jabri, A.; Satlin, M.J.; Campion, T.R., Jr.; Nahid, M.; Ringel, J.B. et al. Clinical Characteristics of Covid-19 in New York City. N. Engl. J. Med.; 2020; 382, pp. 2372-2374. [DOI: https://dx.doi.org/10.1056/NEJMc2010419] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32302078]
14. Chen, N.; Zhou, M.; Dong, X.; Qu, J.; Gong, F.; Han, Y.; Qiu, Y.; Wang, J.; Liu, Y.; Wei, Y. et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet; 2020; 395, pp. 507-513. [DOI: https://dx.doi.org/10.1016/S0140-6736(20)30211-7]
15. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X. et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet; 2020; 395, pp. 1054-1062. [DOI: https://dx.doi.org/10.1016/S0140-6736(20)30566-3]
16. Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet; 2020; 395, pp. 497-506. [DOI: https://dx.doi.org/10.1016/S0140-6736(20)30183-5]
17. Langford, B.J.; So, M.; Raybardhan, S.; Leung, V.; Westwood, D.; MacFadden, D.R.; Soucy, J.-P.R.; Daneman, N. Bacterial co-infection and secondary infection in patients with COVID-19: A living rapid review and meta-analysis. Clin. Microbiol. Infect.; 2020; 26, pp. 1622-1629. [DOI: https://dx.doi.org/10.1016/j.cmi.2020.07.016]
18. Corcione, S.; Shbaklo, N.; Vicentini, C.; Corradi, A.; Scabini, S.; Pinna, S.M.; Tarozzo, A.; Curtoni, A.; Cattel, F.; Cavallo, R. et al. Impact of an empiric antimicrobial therapy manual on antimicrobial usage and multidrug resistant organism trends in a large Italian teaching hospital. Infect. Prev. Pract.; 2021; 100187. [DOI: https://dx.doi.org/10.1016/j.infpip.2021.100187]
19. Garcia-Vidal, C.; Sanjuan, G.; Moreno-García, E.; Puerta-Alcalde, P.; Garcia-Pouton, N.; Chumbita, M.; Fernandez-Pittol, M.; Pitart, C.; Inciarte, A.; Bodro, M. et al. Incidence of co-infections and superinfections in hospitalized patients with COVID-19: A retrospective cohort study. Clin. Microbiol. Infect.; 2021; 27, pp. 83-88. [DOI: https://dx.doi.org/10.1016/j.cmi.2020.07.041]
20. Dudoignon, E.; Caméléna, F.; Deniau, B.; Habay, A.; Coutrot, M.; Ressaire, Q.; Plaud, B.; Berçot, B.; Dépret, F. Bacterial Pneumonia in COVID-19 Critically Ill Patients: A Case Series. Clin. Infect. Dis.; 2021; 72, pp. 905-906. [DOI: https://dx.doi.org/10.1093/cid/ciaa762]
21. Stevens, M.P.; Doll, M.; Pryor, R.; Godbout, E.; Cooper, K.; Bearman, G. Impact of COVID-19 on traditional healthcare-associated infection prevention efforts. Infect. Control Hosp. Epidemiol.; 2020; 41, pp. 946-947. [DOI: https://dx.doi.org/10.1017/ice.2020.141]
22. Klein, E.Y.; Monteforte, B.; Gupta, A.; Jiang, W.; May, L.; Hsieh, Y.; Dugas, A. The frequency of influenza and bacterial coinfection: A systematic review and meta-analysis. Influ. Other Respir. Viruses; 2016; 10, pp. 394-403. [DOI: https://dx.doi.org/10.1111/irv.12398] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27232677]
23. Palacios, G.; Hornig, M.; Cisterna, D.; Savji, N.; Bussetti, A.V.; Kapoor, V.; Hui, J.; Tokarz, R.; Briese, T.; Baumeister, E. et al. Streptococcus pneumoniae Coinfection Is Correlated with the Severity of H1N1 Pandemic Influenza. PLoS ONE; 2009; 4, e8540. [DOI: https://dx.doi.org/10.1371/journal.pone.0008540] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20046873]
24. Rodriguez-Morales, A.J.; Cardona-Ospina, J.A.; Gutiérrez-Ocampo, E.; Villamizar-Peña, R.; Holguin-Rivera, Y.; Escalera-Antezana, J.P.; Alvarado-Arnez, L.E.; Bonilla-Aldana, D.K.; Franco-Paredes, C.; Henao-Martinez, A.F. et al. Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med. Infect. Dis.; 2020; 34, 101623. [DOI: https://dx.doi.org/10.1016/j.tmaid.2020.101623]
25. Huttner, B.; Catho, G.; Pano-Pardo, J.; Pulcini, C.; Schouten, J. COVID-19: Don’t neglect antimicrobial stewardship principles!. Clin. Microbiol. Infect.; 2020; 26, pp. 808-810. [DOI: https://dx.doi.org/10.1016/j.cmi.2020.04.024] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32360446]
26. Bassetti, M.; Giacobbe, D.R.; Bruzzi, P.; Barisione, E.; Centanni, S.; Castaldo, N.; Corcione, S.; De Rosa, F.G.; Di Marco, F.; Gori, A. et al. Clinical Management of Adult Patients with COVID-19 Outside Intensive Care Units: Guidelines from the Italian Society of Anti-Infective Therapy (SITA) and the Italian Society of Pulmonology (SIP). Infect. Dis. Ther.; 2021; 10, pp. 1837-1885. [DOI: https://dx.doi.org/10.1007/s40121-021-00487-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34328629]
27. Montrucchio, G.; Corcione, S.; Sales, G.; Curtoni, A.; De Rosa, F.; Brazzi, L. Carbapenem-resistant Klebsiella pneumoniae in ICU-admitted COVID-19 patients: Keep an eye on the ball. J. Glob. Antimicrob. Resist.; 2020; 23, pp. 398-400. [DOI: https://dx.doi.org/10.1016/j.jgar.2020.11.004] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33242674]
28. Courtenay, M.; Burnett, E.; Castro-Sánchez, E.; Du Toit, B.; Figueiredo, R.; Gallagher, R.; Gotterson, F.; Kennedy, H.; Manias, E.; McEwen, J. et al. Preparing nurses for COVID-19 response efforts through involvement in antimicrobial stewardship programmes. J. Hosp. Infect.; 2020; 106, pp. 176-178. [DOI: https://dx.doi.org/10.1016/j.jhin.2020.06.011] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32531230]
29. Elbeddini, A.; Prabaharan, T.; Almasalkhi, S.; Tran, C. Pharmacists and COVID-19. J. Pharm. Policy Pract.; 2020; 13, pp. 1-4. [DOI: https://dx.doi.org/10.1186/s40545-020-00241-3]
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; Corcione, Silvia 2 ; Vicentini, Costanza 3
; Giordano, Susanna 4 ; Fiorentino, Denise 4 ; Bianco, Gabriele 5 ; Cattel, Francesco 4 ; Cavallo, Rossana 5 ; Zotti, Carla Maria 3 ; De Rosa, Francesco Giuseppe 1
1 Department of Medical Sciences, Infectious Diseases, University of Turin, 10124 Turin, Italy;
2 Department of Medical Sciences, Infectious Diseases, University of Turin, 10124 Turin, Italy;
3 Department of Public Health and Paediatrics, University of Turin, 10124 Turin, Italy;
4 S.C. Farmacia Ospedaliera, A.O.U. Città Della Salute e Della Scienza di Torino, 10126 Turin, Italy;
5 Microbiology and Virology Unit, Turin University, 10124 Turin, Italy;