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Background:
Systematic Review

Antimicrobial Resistance (AMR) in Italy over the Past Five Years: A Systematic Review

by
Marco Montalti
,
Giorgia Soldà
,
Angelo Capodici
*,
Zeno Di Valerio
,
Giorgia Gribaudo
,
Giusy La Fauci
,
Aurelia Salussolia
,
Francesca Scognamiglio
,
Anna Zannoner
and
Davide Gori
Unit of Hygiene, Department of Biomedical and Neuromotor Sciences, Public Health and Medical Statistics, University of Bologna, 40126 Bologna, Italy
*
Author to whom correspondence should be addressed.
Biologics 2022, 2(2), 151-164; https://doi.org/10.3390/biologics2020012
Submission received: 12 April 2022 / Revised: 9 June 2022 / Accepted: 10 June 2022 / Published: 13 June 2022
Browse Figure
Review Reports Versions Notes

Abstract

:
Antimicrobial Resistance (AMR) has become a global threat to public health systems around the world in recent decades. In 2017, Italy was placed among the worst-performing nations in Europe by the European Centre for Disease Prevention and Control, due to worryingly high levels of AMR in Italian hospitals and regions. The aim of this systematic review was to investigate the state of the art of research on AMR in Italy over the last five years. The PubMed database was searched to identify studies presenting original data. Forty-three of the 9721 records identified were included. Overall, AMR rates ranged from 3% (in a group of sheep farmers) to 78% (in a hospital setting). The methods used to identify the microorganisms, to test their susceptibility and the criteria adopted for the breakpoint were deficient in 7, 7 and 11 studies, respectively. The main findings of our review were that most studies (79.1%) considered hospitalised patients only, 4 studies (9.3%) analysed non-hospitalised populations only. In addition, only 7 studies were multicentric and no scientific literature on the subject was produced in 7 Italian regions. In order to have a solid basis on the topic for the interventions of public health professionals and other stakeholders, studies analysing the phenomenon should be conducted in a methodologically standardised manner, should include all areas of the country and should also focus on out-of-hospital and community-based care and work settings.

1. Introduction

Since the discovery of penicillin in 1928, numerous classes of antibiotics have been researched and used to treat patients, revolutionizing healthcare. However, bacteria and other pathogens have continued to evolve so that they can resist the new drugs. In recent years, the rate of new antibiotic discovery has dropped dramatically while the use of antibiotic therapies has steadily increased. It is also for these reasons that Antimicrobial Resistance (AMR) is considered the major upcoming public health threat with an estimated 10 million AMR victims by the end of 2050 [1].
In 2017, the European Centre for Disease Prevention and Control (ECDC) mission report, Italy was placed among the worst faring nations in Europe in this context, due to worryingly high AMR levels in Italian hospitals and regions [2].
Data collected through the European Antimicrobial Resistance Surveillance Network (EARS-Net) estimates that each year more than 670,000 infections occur in the European Union/European Economic Area (EU/EEA) due to bacteria resistant to antibiotics causing approximately 33,000 deaths as a direct consequence of these infections [3]. In the above-mentioned EU/EEA countries, the estimated cost for health care systems is overall around 1,1 billion euros [4].
If proper blended public health interventions—including antibiotic management programs, the promotion of better hygiene, the use of media campaigns and rapid diagnostic tests—would be implemented, it has been estimated that around 27,000 deaths annually in the EU/EEA could be prevented and around €1.4 billion per year saved [4].
Unfortunately, the ECDC “Surveillance of Antimicrobial Resistance in Europe, 2020 data” showed that during the first year of the COVID-19 pandemic, less engagement in AMR surveillance activities was detected. Besides, it outlines that “in the WHO (World Health Organization) European Region, 20% of countries still reported either having no capacity for generating AMR surveillance data or collecting AMR data only at local level and without a standardized approach” [5].
In 2018, following the adoption of the Global Action Plan on Antimicrobial Resistance (GAP) by the World Health Assembly, which set the goal of having a National Action Plan (NAP) on AMR by 2017, 100 countries had prepared a NAP, and a further 67 had plans in progress [6].
In designing effective strategies to tackle AMR, it is certainly crucial to investigate healthcare workers’ knowledge and attitudes towards antibiotic use. In this regard, a study by the ECDC and Public Health England published in 2021 investigated European healthcare workers’ attitudes towards antibiotic usage, and the overall finding was an immoderate misuse of antibiotics, with a general increasing trend in antibiotic consumption/prescription [7].
Besides, ECDC reports highlighted several times that there is a north-to-south and west-to-east gradient of resistance, with higher rates observed in the southern and eastern parts of the Region. At the same time, efforts to improve antimicrobial consumption in the region remain uneven, and, as per last WHO and ECDC analyses [8] between 2014 and 2018, there were reductions in total antibacterial consumption only in eight out of from 30 EU/EEA countries of the European Surveillance of Antibiotic Consumption network (ESAC-Net), Italy not being one of them. On the other end, for 2020, an overall decrease in community and hospital sector antibiotic consumption in the EU/EEA was reported by ESAC-Net [9].
Improving awareness and knowledge about AMR should be a collective effort since, as stated in the last Surveillance of Antimicrobial Resistance in Europe 2020 data ECDC REPORT, antimicrobial-resistant bacterial microorganisms cannot be contained within borders or regions and therefore there is a need for concerted action to combat AMR throughout the WHO European Region [5].
In the last year we learned how pandemics can disrupt usual workflows and old habits, especially in medicine, while exposing the weaknesses in national health systems as well as in the deficient collaboration and cooperation systems between countries and continents. In this context we remembered how flexibility, resilience and the constant need for updating one’s own knowledge is of the uttermost importance.
At the time of the conception of this study there were no systematic reviews investigating the state of art on AMR in Italy, with this study we aim to analyse how studies were methodologically conducted and to provide a general picture of the phenomenon.

2. Results

A total of 9.721 papers were identified by the initial search and 43 were included in the final analysis (Figure 1).

2.1. Main Characteristics of the Included Studies

Main sample features of the studies included in the analysis are shown in Table 1. All studies were conducted in Italy and 7 (16.3%) were multicentric. Geographically, the Italian regions in which the largest number of studies included in the review were conducted were Lombardy and Lazio (both n = 5, 11.6%), followed by Emilia-Romagna and Campania (n = 4, 9.3%). No studies that met the inclusion criteria of the review were found for 7 (16.3%) Italian regions.
All studies were published between 2016 and 2021 [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52]. However, the studies examined very heterogeneous periods of time between 1988 [32] and 2020 [47,48,49,51,52]. The study that analysed the longest time period described the AMR found between 1988 and 2017 [32], the one with the shortest time period between March and May 2020 [47]. Among the studies included in our review, 30 (70%) were cross-sectional, and 13 (30%) longitudinal.
A sensitivity analysis was performed in a representative sample of the studies selected in order to test heterogeneity in the groups using I2 Statistics. Due to the high heterogeneity that emerged (>50%), the meta-analysis was not conducted.

2.2. Multidrug-Resistant Microorganisms

Overall, the studies included in the review analysed more than 30 different microorganisms. In the study by La Fauci et al. [30] as many as twelve different microorganisms were taken into account, whereas in most studies (n = 23, 53.5%) only one microorganism was analysed [12,13,15,21,23,25,27,29,31,33,34,35,36,37,38,39,40,41,42,44,45,46,50].
The prevailing definition of ‘multidrug-resistant microorganism (MDR)’ adopted was that of non-susceptibility to at least one agent in three or more antimicrobial categories (used in 17 studies, 39.5%). Sixteen studies (37.2%) did not give a definition of multidrug resistant isolate, while five (11.6%) specifically mentioned the criteria of Magiorakos et al. [53].
The main MDR was found to be K. pneumoniae in 9 (20.9%) studies, followed by S. aureus (5, 11.6%) and by P. aeruginosa and M. tuberculosis (both n = 4, 9.3%).
The number of isolates collected and analysed within each study ranged from 3 [36] to 3006 [10]. The percentage of MDR detected in relation to the total number of samples analysed ranged from 3%, found by Mascaro et al. in a group of sheep farmers [27], to 78%, found by Pompilio et al. in a hospital environment [38], excluding studies that only analysed MDR which therefore found a resistance rate of 100% (for K. Pneumoniae [15,17,36,40,43], A. baumanii [12], H. pylori [42], S. aureus [45]).
In terms of the methods used for the identification of microorganisms, 9 (20.9%) studies used culture-based identification methods as well as other 9 (20.9%) studies used the VITEK 2 system (bio-Mérieux, Marcy l’Etoile, France). Seven (16.3%) studies did not show which method was used to identify the microorganisms.
To test the susceptibility of microorganisms, 11 (25.6%) studies used the VITEK 2 system (bio-Mérieux, Marcy l’Etoile, France), which was the most frequently used method, 9 (20.9%) studies used the disk diffusion test, while 7 (16.3%) studies did not mention any method.
Finally, 22 (51.2%) of the studies included in the review adopted breaking points recommended by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [54], 5 (11.6%) studies adopted the Clinical and Laboratory Standards Institute (CLSI) criteria [55], 5 (11.6%) studies adopted both the two most commonly used systems worldwide (EUCAST and CLSI) and 11 (25.6%) studies did not mention any specific criteria.

2.3. Samples and Settings

The human sample sizes of studies included in the review were very heterogeneous: from n = 8 [12] to n = 4672 [23]. In six (14%) studies the sample size studied was not reported. The mean age ranged from 32 [37] to 89 [49] for the samples considering an adult population, 19 (44.2%) studies did not report the demographic characteristics of their study sample, and only one study that considered the paediatric population reported the mean age of the children from whom the samples were taken (with a mean age of 2 years [10]).
The majority of the studies focused on adults and the general population (n = 26, 60.5%), six (14%) studies focused on adults with specific pathological conditions, 4 (9.3%) on paediatric populations, 3 (7%) on farm workers (cows, sheep and pigs), 2 (4.7%) on migrant populations and only one study (2.3%) on athletes.
Most of the studies (n = 34, 79.1%) considered hospitalised patients, and the hospital ward was the setting in which most of the isolates were collected. Five studies (11.6%) used data from samples analysed by microbiology laboratories, thus including both hospitalised and non-hospitalised populations. Only 4 studies (9.3%) analysed non-hospitalised populations only, by sampling on farms [27,33,45] or in public/private gyms [31].

2.4. Quality Assessment

Following the descriptive analysis, we assessed the quality of each study. According to the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE), all the 43 studies classified as observational had a quality level from Poor to Good: 4 (9.3%) studies were of Poor Quality, 14 (32.6%) studies of Intermediate Quality and 25 (58.1%) studies of Good Quality (Table 1).

3. Discussion

Antimicrobial Resistance (AMR) has become one of the most serious threats to public health, accelerated both by the overuse and misuse of antimicrobials in humans and animals and by inadequate infection prevention measures [56].
Among the measures the scientific community has in place to respond to the challenges posed by AMR, in addition to reducing the use of antimicrobials, developing new antimicrobials, improving knowledge of the ecology of resistant bacteria and resistant genes, and increasing stakeholder awareness of the prudent use of antibiotics, the strengthening of the AMR surveillance system in human and animal populations must be considered [57]. Surveillance and epidemiological studies are key tools to prevent the consequences of AMR on public health and the environment.
The AMR situation in Italy has been repeatedly described as worse than in many other European countries and the local data presented during the ECDC Country Visit to Italy also confirmed this trend [2]. As the Country Visit experts pointed out, both robust scientific research in this field and surveillance activities are needed to address the problem. This systematic review provides a snapshot of the current state of research in this field and an understanding of which areas should be further enhanced.
We found that over the past five years only 43 studies on MDR in humans and presenting original data were published in Italy. Excluding studies that focused solely on MDROs (Multi Drug Resistant Organism), resistance rates found in the isolates analysed were ranging between 3%, found by Mascaro et al. in a group of sheep farmers [27], and 78%, found by Pompilio et al. in a hospital environment [38].
Only a few studies were multicentric and in some Italian regions no scientific literature was produced on the topic. AMR is indeed a transregional challenge, especially since the movement of humans (and resistant bacteria) is not limited by regional borders. In a regional framework such as Italy’s, it would be necessary to address the issue of AMR with an action that is as cohesive and standardised as possible.
The quality of the studies analysed according to STROBE, was good/intermediate (with 58% of the papers being of good quality). However, critical issues related to the methodology were present, such as the lack of a reliable, standardized method for micro-organisms identification and susceptibility testing. Furthermore, various authors did not describe what criteria they used for breakpoints, therefore invalidating their scientific contribution, having seen that their studies were not reproducible. This creates a gap in knowledge that cannot be ignored, since public health professionals can’t take policy decisions without rigorous scientific support. Again, for some of the studies included in the review, the demographic characteristics of the human sample taken into consideration, such as mean age, were not present. Knowing the demographic as well as the social characteristics of people with MDR infections could be of great importance to enable AMR and public health professionals to target awareness campaigns on the issue, as recommended by the ECDC [2], and to make antimicrobial stewardship actions more effective [58].
Finally, it should be noted that the vast majority of sample collection settings were hospitals (wards, intensive care units or a transplant centre) while only a small number of studies were conducted in the community (farmers, athletes, etc.). The issue of AMR is certainly more perceived at the hospital level where the means to do research on the subject are also easier to find. However, the scarcity of data on the non-hospitalised population may reflect the little sense of urgency about the current AMR situation and a tendency of many stakeholders to avoid taking charge of the problem, as already highlighted by ECDC experts [2].

3.1. Recommendations

To analyse such a complex topic as AMR, other steps than surveillance should be taken into account such as enhancing infrastructure, conducting antimicrobial stewardship campaigns, and increasing multidisciplinarity. However, as highlighted in our study, surveillance studies analysing AMR should be conducted in a methodologically standardised way and with a global view of the phenomenon. Not only is it necessary to study the phenomenon in areas of the country where little data are available, but it is also essential to bring the focus of public health professionals and other stakeholders to the community and out-of-hospital care and work settings.

3.2. Limitations and Strengths

Our study has a few limitations; first, only one database (PubMed) was searched. Furthermore, it is possible that some terms that deserved to be added to the search string were overlooked. Finally, as some authors may prefer their mother tongue and only English-language studies were included in the review, we may have overlooked some studies. Despite these limitations, with this systematic review we managed to fill the gap pointed out by the ECDC during its country visit in 2017 delineating the current research on AMR in Italy with a methodological point of view.

4. Materials and Methods

We conducted a systematic review, following the Preferred Reporting Items for Systematic Reviews (PRISMA) approach [59], although the study protocol was not registered. The initial search was implemented on July 8, 2021. The search query consisted of terms considered pertinent by the authors. We searched for publications on Pubmed using the following search string: “((Italy OR Italian) AND ((antibio*) OR (antimicr*) OR (drug) AND (resist*))) AND ((“2016/07/08”[Date-Entry]: “2021/07/08”[Date-Entry]))”. We included full-text accessible English-language articles. We excluded studies that did not focus on humans, on the Italian territory, and studies which did not focus on antibiotic resistance. The Prisma Flowchart is displayed in Figure 1.

4.1. Data Extraction

Data was extracted by seven independent reviewers (MM, GS, AC, ZDV, GG, GLF and AZ). Disagreement on extracted data was discussed with an independent arbiter (DG). On the basis of the title and abstract, eligibility for the article was determined, and the full text of the selected papers provided information for the final decision of inclusion or exclusion. A manual search was also performed by reviewing bibliographies of pertaining articles to identify additional studies.
Descriptive variables extracted from each article were: “Title”, “Author”, “Italian Region”, “Publication Month/Year”, “Study Design”, “Study Period”, “Definition of MDR isolate adopted”, “Microorganism Studied”, Main MDR Microorganism”, “N° of isolates”, “MDR %”, “Microorganism Identification Methods”, “Antimicrobial Susceptibility Testing Method”, “Breakpoints used for MIC”, “Sample Features”, “Mean age”, “N° of subjects” and “Setting”.

4.2. Quality Assessment

Eight authors (MM, AC, ZDV, GG, GLF, AS, FS and AZ) independently and blindly assessed the quality of the included studies using the “Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) tool for observational studies” [60]. Any disagreement between the researchers was resolved through discussion. If discussion was not sufficient a blind reviewer (GS) was appealed as a tiebreaker. The STROBE statement is a 22-item tool specifically designed to evaluate observational studies quality. 18 items are the same in the three different checklists and five questions (6-12-14-15) are differently formulated for each study design: (1) Cohort study, (2) Case report study, (3) Cross-sectional study. STROBE does not provide ways to clearly define a score allowing to rate the quality of the study. As a general rule, the higher the score, the higher the quality of the study. We decided to use the cut-offs for three levels of score: 0–14 as poor quality, 15–25 as intermediate quality and 26–33 as good quality of the study.

Author Contributions

Conceptualization and methodology, all authors contributed equally; formal analysis and investigation, M.M., G.S., A.C., Z.D.V., G.G., G.L.F., A.S., F.S. and A.Z.; writing—original draft preparation, M.M., G.S. and A.C.; writing—review and editing, all authors contributed equally; supervision, M.M. and D.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data will be made available upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Biologics 02 00012 g001 550
Figure 1. Flow chart of the study selection process.
Figure 1. Flow chart of the study selection process.
Biologics 02 00012 g001
Table
Table 1. Included studies conducted in Italy focusing on AMR and presenting original data sorted by year of publication.
Table 1. Included studies conducted in Italy focusing on AMR and presenting original data sorted by year of publication.
AuthorItalian RegionYearStudy DesignStudy PeriodDefinition of “Multidrugresistant Isolate” AdoptedMicroorganism StudiedMain MDR MicroorganismN° of IsolatesN° of Multidrug Resistant (N, %)Microorganism Identification MethodsAntimicrobial Susceptibility Testing MethodBreakpoints Used for MICSample FeaturesMean AgeSubjects (N)SettingQuality Assessment (STROBE)
Calzi A
[10]		Liguria2016CSJanuary 2007–December 2014n.a.E.coli; Enterobacteriaceae; PaeruginosaE. coli3006902 (30%)Culture-based BD Phoenix™ Automated Microbiology SystemEUCASTPaediatric patients23364Hospital WardIntermediate
Busani S
[11]		Emilia-Romagna2016CSJanuary 2008–December 2013Non-susceptibility to at least one agent in three or more antimicrobial categoriesStaphylococcus aureus; Enterococcus subspp.; Enterobacteria ceae; Pseudomonas aeruginosa; and AcinetobacterP. aeruginosa11594 (36%)n.a.n.a.n.a.Adult ICU patients with septic shock71381Hospital WardPoor
Bianco A [12]Calabria2016LSMar 2014–May 2014n.a.A. baumanniiA. baumannii88 (100%)MALDI Biotyper® (MBT)VITEK® 2 systemCLSIAdult ICU patients658Hospital WardGood
Del Giudice A [13]Campania2016LS1 January 2008–31 December 2013Resistant to at least isoniazid, H, and rifampin, RM. tuberculosisM. tuberculosis69031 (4.5%)GenoType Mycobacterium CM; BD MGIT TBc Identification assayBD BACTEC™ MGIT™ 960n.a.General population42690Laboratory of Microbiology and VirologyGood
Patriarca F [14]Friuli-Venezia Giulia2016CSJanuary 2013–Junuary 2015Non-susceptibility to at least one agent in three or more antimicrobial categoriesK. Pneumoniae; P. aeruginosa; E. coliK. Pneumoniae24113 (5%)Culture-based n.a.EUCAST; CLSIAdult patients who underwent HSCT56241Transplant CenterGood
Cristina ML [15]Liguria2016CS2013–2014Non-susceptible to imipenem and/or meropenem and/or ertapenem according to the EUCAST breakpointsK. pneumoniaeK. pneumoniae147147 (100%)BD Phoenix™ Automated Microbiology SystemBD Phoenix™ Automated Microbiology SystemEUCASTAdult patients79147Hospital WardGood
Papa V [16]Sicily2016CSMay 2014–October 2014Non-susceptibility to at least one agent in three or more antimicrobial categoriesS. aureus; S. epidermidisCoNS13192(33%)Culture-based Disk diffusion testEUCASTAdult patients72120Hospital wardGood
Giacobbe DR [17]Liguria; Piedmont; Emilia-Romagna2017CSJanuary 2012–March 2014n.a.Staphylococcus spp.; Enterococcus spp.; Enterobacteriaceae; non- fermenting Gram negatives; Candida spp.K. pneumoniae353353 (100%)MALDI Biotyper® (MBT); VITEK® 2 systemVITEK® 2 systemEUCAST; CLSIAdult patients70353Hospital WardIntermediate
Salerno F [18]Multicenter2017LSJanuary 2007–October 2009n.a.GN; GPS. aureus31383 (27%)n.a.n.a.n.a.Adult patientsn.a.203Hospital wardGood
Drago L
[19]		Lombardy2017CSJanuary 2013–June 2015n.a.Staphylococcus spp.; Enterobacteriaceae; propionibacterium acnesStaphylococcus spp.341144 (42%)VITEK® 2 systemVITEK® 2 system; E-TEST® stripsn.a.Adult patients65429Hospital wardGood
Costa E
[20]		Lombardy2017LS2015–2016n.a.S. aureus; extended-spectrum b-lactamase; Enterobacterales; Gram-negative bacteria; EnterococciExtended-spectrum b-lactamase577336 (68%)VITEK® 2 systemVITEK® 2 systemEUCASTPaediatric patients candidates for cardiac surgeryn.a.495Hospital wardGood
Proroga YTR
[21]		Lazio; Campania2017LSJanuary 2013–December 2015Magiorakos et al. criteriaS. entericaS. enterica15090 (60%)n.a.Disk diffusion testCLSIAdult patientsn.a.n.a.Hospital wardGood
Cattaneo C
[22]		Multicenter2018LSMarch 2015–August 2015Non-susceptibility to at least one agent in three or more antimicrobial categoriesVRE; ESBL-P; CarbaRCarbaR2226144 (7%)Culture-based Disk diffusion testEUCASTAdult patients with a haematological neoplasmn.a.144Hospital WardGood
García-Fernández A [23]Multicenter2018LSJanuary 2013–December 2016Non-susceptibility to at least one agent in three or more antimicrobial categoriesCampylobacter spp.C. jejuni17615 (37%)Culture-based s; multiplex PCRDisk diffusion testEUCASTPaediatric and adult patientsn.a.4672Enter-Net ItaliaGood
Forcina A [24]Lombardy2018LSJuly 2012–January 2016Non-susceptibility to at least one agent in three or more antimicrobial categoriesGNBP. aeruginosa547 (16%)Culture-based n.a.n.a.Adult patients undergoing autologous and allogeneic transplantn.a.348Hospital WardPoor
Cama BAV [25]Sicily2018CSJanuary 2016–December 2016n.a.B. melitensisB. melitensis127 (58%)Culture-based n.a.n.a.Adult patientsn.a.24Hospital wardGood
De Angelis G [26]Lazio2018CS2007–2015Non-susceptibility to at least one agent in three or more antimicrobial categoriesE. coli; E. faecium; S. aureus; K. pneumoniae; A. baumannii; P. aeruginosa; Enterobacter spp.E. coli97205336 (54.9%)VITEK 2® system; MALDI Biotyper® (MBT)VITEK® 2 system; MERLIN Diagnostica GmbHEUCASTGeneral populationn.a.n.a.Laboratory of Microbiology and VirologyGood
Mascaro V [27]Calabria2019CSMarch 2017–February 2018Non-susceptibility to at least one agent in three or more antimicrobial categoriesS. aureusS. aureus953 (3%)Gram stain, catalase, and coagulase tests(Pastorextm Staph-plus Bio-Rad), API Staph identification system (bioMérieux)Disk diffusion testEUCASTSheep farm workers46275FarmPoor
Loconsole D [28]Apulia2019CSJanuary 2013–April 2015n.a.Macrolide Resistant M. pneumoniaeMacrolide Resistant M. pneumoniae153 (34%)RT-PCRRT-PCR; MLVAn.a.Adult patients53234Hospital wardGood
Del Prete R [29]Apulia2019CSJanuary 2015–December 2017Non-susceptibility to at least one agent in three or more antimicrobial categoriesK. pneumoniaeK. pneumoniae439439 (58%)VITEK® 2 systemVITEK® 2 systemEUCASTAdult patientsn.a.356Hospital wardGood
La Fauci V [30]Sicily2019LSJune 2017–May 2018Magiorakos et al. criteriaStaphylococcus; Enterobacteria; Pseudomonas; Acinetobacter; Rhizobium; Sphingomonas; Ochrobactrum; Streptococcus spp.; Aerococci; Burkholderia; Roseomonas; Kytococcus.Staphylococcus spp.60847 (15%)VITEK® 2 systemVITEK® 2 systemEUCASTAdult patientsn.a.n.a.Hospital WardPoor
Mascaro V [31]Calabria2019CSMay 2017–March 2018Non-susceptibility to at least one agent in three or more antimicrobial categoriesS. aureusS. aureus10110 (10%)API Staph identification system (bioMérieux)Disk diffusion testEUCASTAthletes23238Public or private gymsintermediate
Grandi G [32]Piedmont2019LS1988–2017Non-susceptibility to at least one agent in three or more antimicrobial categoriesS. aureus; Staphylococcus spp; S. pneumoniae;P. aeruginosa; H. influenzae; Streptococcus spp.S. aureus2898n.a. (8.7%); n.a. (10%); 348 (12%)n.a.Disk diffusion testEUCAST; CLSIAdult patients with ocular infectionn.a.n.a.Hospital WardGood
Pirolo M [33]Calabria2019CSMarch 2018–February 2019Non-susceptible to at least three non β-lactams antimicrobial classesS. aureusS. aureus4919 (9%)Staphytect plus test; PCRVITEK® 2 systemCLSIPig farm workers4688FarmIntermediate
Cannas A [34]Lazio2019CS2011–2016Resistance to isoniazid and rifampicinM. tuberculosisM. tuberculosis92651 (6%)Ziehl Nielsen; hot staining; mRNA testing (E-MTD, TRCReady-80)Proportion dilutionn.a.Adult patients40926Hospital WardIntermediate
Tumbarello M [35]Lazio; Lombardy2020CS1 January 2016–31 December 2017Non-susceptibility to at least one agent in three or more antimicrobial categoriesP. aeruginosaP. aeruginosa24265 (27%)MALDI Biotyper® (MBT)VITEK® 2 system; MERLIN Diagnostica GmbHEUCASTAdult patients71305Hospital WardGood
Papalini C [36]Umbria2020CS2014–2019Magiorakos et al. criteriaK. pneumoniaeK. pneumoniae33 (100%)MALDI Biotyper® (MBT)BD Phoenix™ Automated Microbiology SystemEUCASTGeneral populationn.a.113Laboratory of Microbiology and VirologyIntermediate
Riccardi N [37]Lombardy2020CS1 January 2000–1 Jan 2015n.a.M. tuberculosisM. tuberculosis8603370 (4%)n.a.n.a.n.a.Adult migrant patients32116Hospital WardIntermediate
Pompilio A [38]Lazio2020CS2017–2018Non-susceptibility to at least one agent in three or more antimicrobial categoriesS. maltophiliaS. maltophilia8566 (78%)Thermo Scientific™ Culti-Loops™ API 20NE; VITEK® 2 systemDisk diffusion test; broth microdilution methodCLSIPediatric patientsn.a.n.a.Hospital WardGood
Seminari E [39]Lombardy2020LS1 January 1998–31 December 2017Resistance to isoniazid and rifampicinM. tuberculosisM. tuberculosis91928 (3%)n.a.Culture based identification methods; Mycobacteria Growth Indicator Tube (MGIT)n.a.Adult patients47919Hospital WardIntermediate
Loconsole D [40]Apulia2020CS2014–2016n.a.K. pneumoniaeK. pneumoniae691691 (100%)Cepheid’s GeneXpert® Systemn.a.EUCASTAdult patientsn.a.691Hospital wardGood
Gudiol C [41]Italy2020CS1 January 2006–31 May 2018Non-susceptibility to at least one agent in three or more antimicrobial categoriesP. aeruginosaP. aeruginosa12350 (41%)Culture-based identification methodsn.a.EUCAST; CLSIAdult neutropenic onco-hematological patientsn.a.123Hospital WardGood
Fiorini G [42]Emilia-Romagna2020CS2009–2019n.a.H. pyloriH. pylori294294 (100%)Culture-basedE-TEST® stripsEUCASTAdult migrant patients41294Hospital wardGood
Gentile B [43]Emilia-Romagna2020CS2013–2014n.a.CR-K. pneumoniaeCR-K. pneumoniae2727 (100%)Illumina MiSeq platformVITEK® 2 systemEUCASTAdult patients7226Hospital wardGood
Saracino IM [44]Emilia-Romagna2020CS2016–2019n.a.H. pyloriH. pylori66333%Culture-based E-TEST® stripsEUCASTAdult patients51270Hospital wardIntermediate
Normanno G [45]Veneto2020CS2017–2018n.a.MRSAMRSA44 (100%)Disk diffusion testDisk diffusion testCLSICow farm workersn.a.24FarmIntermediate
Mascellino MT [46]Lazio2020CS2017Resistance to more than one antibioticH. pyloriH. pylori80a) 24 (30%); b) 11 (14%); c) 9 (11%); d) 5 (6%)Pylori Agar; GenoType® HelicoDR testE-TEST® stripsEUCASTAdult patients5980Hospital wardGood
Karruli A [47]Campania2021LS9 March 2020–1 May 2020Magiorakos et al. criteriaK. pneumoniae; A. baumannii; P. aeruginosa; Enterobacter spp.; S. maltophilia; Enterococcus spp.; E. faecium; S. aureusK. pneumoniae3216 (50%)n.a.Thermo Scientific™ Sensititre™n.a.Adults ICU patients with SARS-CoV-2 infection6832Hospital wardIntermediate
Barbadoro P [48]Marche2021LSFebruary 2018–September 2018Magiorakos et al. criteriaK. pneumoniae; E. coliK. pneumoniae247821 (1%)VITEK® 2 systemSensiQuattro Gram-negative SystemEUCASTAdult patientsn.a.2478Hospital wardIntermediate
Gasperini B [49]Marche2021CS(a) Dec 2019Feb 2020; (b) May 2020–July 2020 Non-susceptibility to at least one agent in three or more antimicrobial categoriesE. coli; Klebsiella spp.; Enterococcus spp.; Proteus spp.; Pseudomonas spp.; Enterobacter spp.; Staphylococcus spp.E. colia) 36; b) 47a) 18 (50%); b) 28 (59.6%)Culture-based n.a.EUCASTAdult patientsa) 89; b) 86a) 33; b) 40Hospital wardGood
Magi C
[50]		Marche2021CSOctober 2018–May 2019n.a.K. pneumoniaeK. pneumoniae65018 (3%)MALDI Biotyper® (MBT); VITEK® 2 systemBrilliance™ CRE AgarEUCASTGeneral populationn.a.n.a.Laboratory of Microbiology and VirologyIntermediate
Posteraro B [51]Lazio2021CS1 March 2020–31 May 2020Non-susceptibility to at least one agent in three or more antimicrobial categoriesS. aureus; Enterobacter spp.; E. faecalis; Candida spp.;P. aeruginosaS. aureus6927 (39%)MALDI Biotyper® (MBT)VITEK® 2 system; Sensititre YeastOneEUCAST; CLSIAdults patients with SARS-CoV-2 infection7046Hospital wardGood
Petrillo F [52]Campania2021CS2017–2020n.a.S. aureus; Coagulase-negative staphylococciCoagulase negative staphylococci32296 (61%)Culture-based MicroScan WalkAway 96 PlusEUCASTAdult patientsn.a.322Hospital wardIntermediate
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Montalti, M.; Soldà, G.; Capodici, A.; Di Valerio, Z.; Gribaudo, G.; La Fauci, G.; Salussolia, A.; Scognamiglio, F.; Zannoner, A.; Gori, D. Antimicrobial Resistance (AMR) in Italy over the Past Five Years: A Systematic Review. Biologics 2022, 2, 151-164. https://doi.org/10.3390/biologics2020012

AMA Style

Montalti M, Soldà G, Capodici A, Di Valerio Z, Gribaudo G, La Fauci G, Salussolia A, Scognamiglio F, Zannoner A, Gori D. Antimicrobial Resistance (AMR) in Italy over the Past Five Years: A Systematic Review. Biologics. 2022; 2(2):151-164. https://doi.org/10.3390/biologics2020012

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Montalti, Marco, Giorgia Soldà, Angelo Capodici, Zeno Di Valerio, Giorgia Gribaudo, Giusy La Fauci, Aurelia Salussolia, Francesca Scognamiglio, Anna Zannoner, and Davide Gori. 2022. "Antimicrobial Resistance (AMR) in Italy over the Past Five Years: A Systematic Review" Biologics 2, no. 2: 151-164. https://doi.org/10.3390/biologics2020012

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