Pseudomonas aeruginosa antibiotic susceptibility profiles, genomic epidemiology and resistance mechanisms: a nation-wide five-year time lapse analysis

Abstract

Background: Pseudomonas aeruginosa healthcare-associated infections are one of the top antimicrobial resistance threats world-wide. In order to analyze the current trends, we performed a Spanish nation-wide high-resolution analysis of the susceptibility profiles, the genomic epidemiology and the resistome of P. aeruginosa over a five-year time lapse.

Methods: A total of 3.180 nonduplicated P. aeruginosa clinical isolates from two Spanish nation-wide surveys performed in October 2017 and 2022 were analyzed. MICs of 13 antipseudomonals were determined by ISO-EUCAST. Multidrug resistance (MDR)/extensively drug resistance (XDR)/difficult to treat resistance (DTR)/pandrug resistance (PDR) profiles were defined following established criteria. All XDR/DTR isolates were subjected to whole genome sequencing (WGS).

Findings: A decrease in resistance to all tested antibiotics, including older and newer antimicrobials, was observed in 2022 vs 2017. Likewise, a major reduction of XDR (15.2% vs 5.9%) and DTR (4.2 vs 2.1%) profiles was evidenced, and even more patent among ICU isolates [XDR (26.0% vs 6.0%) and DTR (8.9% vs 2.6%)] (p < 0.001). The prevalence of Extended-spectrum β-lactamase/carbapenemase production was slightly lower in 2022 (2.1%. vs 3.1%, p = 0.064). However, there was a significant increase in the proportion of carbapenemase production among carbapenem-resistant strains (29.4% vs 18.1%, p = 0.0246). While ST175 was still the most frequent clone among XDR, a slight reduction in its prevalence was noted (35.9% vs 45.5%, p = 0.106) as opposed to ST235 which increased significantly (24.3% vs 12.3%, p = 0.0062).

Interpretation: While the generalized decrease in P. aeruginosa resistance, linked to a major reduction in the prevalence of XDR strains, is encouraging, the negative counterpart is the increase in the proportion of XDR strains producing carbapenemases, associated to the significant advance of the concerning world-wide disseminated hypervirulent high-risk clone ST235. Continued high-resolution surveillance, integrating phenotypic and genomic data, is necessary for understanding resistance trends and analyzing the impact of national plans on antimicrobial resistance.

Funding: MSD and the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación and Unión Europea-NextGenerationEU.

Keywords: COVID-19; Electronic health records; PASC; Post-acute sequelae of SARS-CoV-2; SARS-CoV-2.

Fig. 1

a. Comparative analysis of the prevalence of resistance to 13 antipseudomonal agents in 2017 and 2022 Spanish nation-wide studies. b. Comparative analysis of the prevalence of resistance among ICU and nonICU isolates from the 2022 study. c. Comparative analysis of the prevalence of resistance in ICU isolates from 2017 to 2022 studies. In the case of meropenem, “I” (susceptible increased exposure) isolates were also represented (in a lighter tone and with numbers between brackets) in addition to resistant isolates for reference since these isolates show low level resistance. Statistical significance (Chi-square, X²) indicated (∗∗∗p < 0.0001; ∗∗p < 0.01; ∗p < 0.05).

Fig. 2

a. Comparative analysis of the distribution of MDR XDR and DTR profiles in 2017 and 2022 Spanish nation-wide studies. As described in the material and methods section, XDR isolates are a fraction of MDR isolates and DTR isolates a fraction of XDR isolates. Statistical significance (Chi-square, X²) indicated (∗∗∗p < 0.0001; ∗∗p < 0.01; ∗p < 0.05). b. Comparative analysis of the distribution of XDR profiles in the different Spanish region in 2017 and 2022 studies.

Fig. 3

Distribution of isolates and resistance profiles according to sample type (a) and hospital wards (b) in 2017 and 2022 Spanish nation-wide studies. ER, emergency room. Statistical significance (Chi-square, X²) comparing both studies (∗∗∗p < 0.0001; ∗∗p < 0.01; ∗p < 0.05) and between hospital wards in 2022 (^###p < 0.0001; ^##p < 0.01; ^#p < 0.05) are indicated.

Fig. 4

Percentage of hospitals showing lower (green), equal (orange) or higher (red) resistance rates in 2022 than in 2017. Only the 51 hospitals participating in both studies were included in the analysis. Statistical significance (Chi-square, X²) indicated (∗∗∗p < 0.0001; ∗∗p < 0.01; ∗p < 0.05).

Fig. 5

a. Prevalence of ESBLs and carbapenemases for the complete collection of P. aeruginosa isolates from 2017 to 2022 studies. b. Prevalence of ESBLs and carbapenemases (carbapenemases specifically indicated in parenthesis) among XDR isolates. c. Prevalence of carbapenemases among meropenem resistant isolates. Statistical significance (Chi-square, X²) indicated (∗p < 0.05).

Fig. 6

Distribution of sequence types among XDR P. aeruginosa isolates recovered from the 2017 and 2022 studies. Statistical significance (Chi-square, X²) for the 2017 vs 2022 comparison indicated (∗∗p < 0.01; ∗p < 0.05). STs accounting for ≥2% of the XDR isolates in any of the two studies are shown individually, while all those representing <2% of isolates are included in “Others”.

Fig. 7

Core genome phylogenetic reconstruction of the 2017 and 2022 XDR ST175 (a) and ST235 (b)P. aeruginosa isolates. Year column labeled in gray and white corresponds with 2017 and 2022 isolates, respectively. Following columns correspond to β-lactamases and other acquired enzymes. Code description of changes in most commonly mutated genes (oprD, mexZ, mexR, ampR, ampC, parS, PBP3, parC and gyrA) is represented on hand-side. Each colour of each column corresponds to a single mutation.