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BackgroundA multitude of definitions determining multidrug resistance (MDR) of Gram-negative organisms exist worldwide. The definitions differ depending on their purpose and on the issueing country or organization. The MDR definitions of the European Centre for Disease Prevention and Control (ECDC) were primarily chosen to harmonize epidemiological surveillance. The German Commission of Hospital Hygiene and Infection Prevention (KRINKO) issued a national guideline which is mainly used to guide infection prevention and control (IPC) measures. The Swiss University Hospital Zurich (UHZ) – in absentia of national guidelines – developed its own definition for IPC purposes. In this study we aimed to determine the effects of different definitions of multidrug-resistance on rates of Gram-negative multidrug-resistant organisms (GN-MDRO).MethodsMDR definitions of the ECDC, the German KRINKO and the Swiss University Hospital Zurich were applied on a dataset comprising isolates of Escherichia coli, Klebsiella pneumoniae, Enterobacter sp., Pseudomonas aeruginosa, and Acinetobacter baumannii complex. Rates of GN-MDRO were compared and the percentage of patients with a GN-MDRO was calculated.ResultsIn total 11′407 isolates from a 35 month period were included. For Enterobacterales and P. aeruginosa, highest MDR-rates resulted from applying the ‘ECDC-MDR’ definition. ‘ECDC-MDR’ rates were up to four times higher compared to ‘KRINKO-3/4MRGN’ rates, and up to six times higher compared to UHZ rates. Lowest rates were observed when applying the ‘KRINKO-4MRGN’ definitions. Comparing the ‘KRINKO-3/4MRGN’ with the UHZ definitions did not show uniform trends, but yielded higher rates for E. coli and lower rates for P. aeruginosa. On the patient level, the percentages of GN-MDRO carriers were 2.1, 5.5, 6.6, and 18.2% when applying the ‘KRINKO-4MRGN’, ‘UHZ-MDR’, ‘KRINKO-3/4MRGN’, and the ‘ECDC-MDR’ definition, respectively.ConclusionsDifferent MDR-definitions lead to considerable variation in rates of GN-MDRO. Differences arise from the number of antibiotic categories required to be resistant, the categories and drugs considered relevant, and the antibiotic panel tested. MDR definitions should be chosen carefully depending on their purpose and local resistance rates, as definitions guiding isolation precautions have direct effects on costs and patient care.

Background: Interpretative reading of antimicrobial susceptibility test (AST) results allows inferring biochemical resistance mechanisms from resistance phenotypes. For aminoglycosides, however, correlations between resistance pathways inferred on the basis of the European Committee on Antimicrobial Susceptibility Testing (EUCAST) clinical breakpoints and expert rules versus genotypes are generally poor. This study aimed at developing and validating a decision tree based on resistance phenotypes determined by disc diffusion and based on epidemiological cutoffs (ECOFFs) to infer the corresponding resistance mechanisms in Escherichia coli. Methods: Phenotypic antibiotic susceptibility of thirty wild-type and 458 aminoglycoside-resistant E. coli clinical isolates was determined by disc diffusion and the genomes were sequenced. Based on well-defined cutoffs , we developed a phenotype-based algorithm (Aminoglycoside Resistance Mechanism Inference Algorithm-ARMIA) to infer the biochemical mechanisms responsible for the corresponding aminoglycoside resistance phenotypes. The mechanisms inferred from susceptibility to kanamycin, tobramycin and gentamicin were analysed using ARMIA-or EUCAST-based AST interpretation and validated by whole genome sequencing (WGS) of the host bacteria. Findings: ARMIA-based inference of resistance mechanisms and WGS data were congruent in 441/458 isolates (96•3%). In contrast, there was a poor correlation between resistance mechanisms inferred using EUCAST CBPs/expert rules and WGS data (418/488, 85•6%). Based on the assumption that resistance mechanisms can result in therapeutic failure, EUCAST produced 63 (12•9%) very major errors (vME), compared to only 2 (0•4%) vME with ARMIA. When used for detection and identification of resistance mechanisms, ARMIA resolved N95% vMEs generated by EUCAST-based AST interpretation. Interpretation: This study demonstrates that ECOFF-based analysis of AST data of only four aminoglycosides provides accurate information on the resistance mechanisms in E. coli. Since aminoglycoside resistance mechanisms, despite having in certain cases a minimal effect on the minimal inhibitory concentration, may compromise the bactericidal activity of aminoglycosides, prompt detection of resistance mechanisms is crucial for therapy. Using ARMIA as an interpretative rule set for editing AST results allows for better predictions of in vivo activity of this drug class.

Antibiotic resistance poses a major threat to health and incurs high economic costs worldwide. Rapid detection of resistance mechanisms can contribute to improving patient care and preventing the dissemination of antimicrobial resistance.

Objectives: We quantified the percentage of multidrug-resistant organism (MDRO) carriers among repatriated patients. We identified factors associated with MDRO carriage, and we evaluated the yield of MDRO detection per screened body site. Design: Retrospective cohort study. Setting: A tertiary-care center in Switzerland. Patients: Adult patients after a stay in a healthcare institution abroad. Methods: Patients were screened for MDRO carriage. Standard sites, including nose and throat, groins, and (since mid-2018) rectum, and risk-based sites (wounds, urine, tracheal secretion) were sampled. MDROs were defined as methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), extended-spectrum β-lactamase (ESBL)– and carbapenemase-producing Enterobacterales (CPE), multidrug-resistant (MDR) Enterobacterales, and MDR nonfermenting gram-negative rods. Risk factors for MDRO carriage were assessed using multivariate logistic regression. Results: Between May 2017 and April 2019, 438 patients were screened and 107 (24.4%) tested positive for an MDRO, predominantly ESBL-producing and MDR Enterobacterales. Risk factors for MDRO colonization were the length of stay in hospital abroad, antibiotic treatment with ‘Watch’ and ‘Reserve’ antibiotics, and region of hospitalization abroad. Rectal swabs had the highest yield for detecting patients with MDR intestinal bacteria, but nose/throat and groins, or wound samples were more sensitive for MRSA or nonfermenting gram-negative organisms, respectively. Conclusions: We identified risk factors for MDRO carriage and body sites with the highest yield for a specific MDRO, which might help to target screening and isolation and reduce screening costs.