ESKAPE pathogen alert: resistance trends in Pseudomonas aeruginosa from clinical isolates in Johannesburg (2019–2023)

Introduction

Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen known for causing nosocomial infections in healthcare settings, especially in intensive care units (ICUs).

It is the third most common Gram-negative bacillus isolated from ICUs globally (Vidal-Cortés et al., 2025). Immunocompromised individuals, such as patients with cystic fibrosis (CF), chronic obstructive pulmonary disease (COPD), and bronchiectasis, are particularly susceptible to infection by this pathogen.

P. aeruginosa possesses the ability to adapt to the harsh environment of the lungs through the use of virulence factors such as quorum sensing activation and biofilm formation, thereby causing persistent infections that are difficult to treat (Garcia-Clemente et al., 2020). Multidrug-resistant (MDR) P. aeruginosa is known to cause recurrent infections in patients with CF, as these patients may harbour the pathogen in their lungs.

Due to the pathogen’s ability to thrive in moist environments, P. aeruginosa can colonise hospital equipment, namely, mechanical ventilators (invasive and non-invasive), as it possesses resistance to certain antiseptics. Ventilator-associated pneumonia (VAP), a common hospital-associated infection (HAI) caused by this organism, is a leading cause of mortality in critically ill patients (Moursi et al., 2025). Hence, one of the major infections caused by this bacterium is lower respiratory infections, where the organism can adapt to the pulmonary environment due to the numerous virulence factors it possesses.

P. aeruginosa is known to cause a wide range of skin and soft tissue infections, including folliculitis, green nail syndrome, hot hand–foot syndrome, uncomplicated acute otitis externa, subcutaneous nodules, malignant otitis externa, ecthyma gangrenosum, and necrotising infections (Hartmann et al., 2026). This pathogen is the third most common cause of urinary tract infections (UTIs) worldwide, where relapse infections occur due to biofilm formation and increasing antibiotic resistance (Martin-Duval et al., 2025).

P. aeruginosa uses its lipopolysaccharide chain (LPS) for attachment to host cells and its flagellum for motility. The organism excretes numerous toxins and secretion factors that aid in causing infection by countering the host’s immune response. Exotoxins produced disrupt host cell functions and cause tissue damage, whereas enzymes produced aid in tissue invasion and suppression of the immune system (Moursi et al., 2025). These virulence factors, including the production of a biofilm, are the root cause of persistence of chronic infections caused by P. aeruginosa.

The leading causes of treatment difficulties in P. aeruginosa infections include efflux pumps that remove antibiotics from the bacterial cell and enzymes that inactivate antibiotics, rendering them ineffective (Vidal-Cortés et al., 2025). P. aeruginosa may acquire antibiotic resistance by means of horizontal gene transfer or mutations. MDR P. aeruginosa is defined as a strain that is resistant to three or more categories of antipseudomonal agents, whereas extensively drug-resistant (XDR) strains of P. aeruginosa are susceptible to only two or fewer categories of antipseudomonal agents (Cosentino et al., 2023). The term difficult-to-treat (DTR) resistance was introduced in 2018 and refers to strains resistant to nearly all antipseudomonal agents, including all first-line agents (Karruli et al., 2023). MDR P. aeruginosa may arise from the overuse and misuse of treatment drugs. These strains are a concern in healthcare settings because treatment options are limited, and the few options available are often costly and are accompanied by adverse side effects.

The World Health Organisation (WHO) and the Centers for Disease Control and Prevention (CDC) have classified P. aeruginosa as a ‘critical’ pathogen included in the ESKAPE group of pathogens. This group includes Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. These pathogens pose a serious threat in nosocomial infections, as isolates are often resistant to multiple antimicrobials, and the development of new antimicrobials to treat these MDR strains is a priority. Surveillance of the antimicrobial susceptibility patterns of ESKAPE pathogens, such as P. aeruginosa, is essential to understand the extent of resistance in South Africa. This information supports programmes that create treatment guidelines, which may reduce the development of MDR strains and aid in the prevention of the spread of MDR P. aeruginosa.

A study in Cape Town focused on P. aeruginosa bloodstream infections in children and discovered that the use of appropriate empiric therapy may lower the mortality rate of P. aeruginosa-associated bloodstream infections (Dame et al., 2020). Another study in Cape Town compared the antibiotic resistance and virulence profiles of Enterococcus faecium, Klebsiella pneumoniae, and Pseudomonas aeruginosa (Denissen et al., 2024). This study, conducted in both environmental and clinical settings, concluded that P. aeruginosa poses a public health risk to individuals in low- and middle-income households that rely on environmental water sources for daily needs.

This study aimed to determine and describe the antimicrobial susceptibility patterns of P. aeruginosa isolates from various clinical samples in Johannesburg and to assess variations in the susceptibility from 2019 to 2023. Furthermore, the study sought to identify shifts and trends in antimicrobial resistance. Considering that MDR P. aeruginosa is a WHO priority pathogen, results of this study will contribute to the global antimicrobial resistance (AMR) discourse by improving understanding of the progression of antibiotic resistance in this pathogen and supporting decision-making regarding treatment regimens for P. aeruginosa infections. This will assist in guiding the future development of new antipseudomonal antibiotics.

Materials and methods

Study design and setting

This study was conducted using clinical data from a private laboratory in Johannesburg between 1 January 2019 and 31 December 2023. The healthcare setting in South Africa is separated into private and public sectors. Public hospitals are serviced by government-run laboratories, whereas private hospitals are serviced by private laboratories. The laboratory chosen for this study is the main hub of a private laboratory group situated in Johannesburg, which has 250 laboratories located throughout the province of Gauteng. It services hospitals across the entire Gauteng region, hence providing representation of the resistance patterns in the province for the private healthcare sector.

Study population

Data used for the study included laboratory results from patients with positive culture results for P. aeruginosa (n = 16,167), together with susceptibility testing results, collected between 1 January 2019 and 31 December 2023. Information regarding the hospital unit from which patients originated was not included in the study as the data collected were clinical laboratory results that did not include clinical records of the patients. The study focused only on determining trends in resistance patterns from a laboratory perspective. Samples included urine, ear swabs, respiratory samples, pus swabs, tissue samples, pus fluid samples, and blood cultures from patients of any age or sex.

Data collection

Secondary data were requested from the laboratory. These datasets were analysed for routine clinical results and stored prior to the study timeframe. The data were requested from the laboratory database upon ethical approval on 4 February 2025. Once permission was granted, the data were recorded in an Excel spreadsheet. A total of 16,167 datasets positive for P. aeruginosa isolates from patients with no specific age, sex, or race restrictions formed the core of the study. Data collected before 1 January 2019 and after 31 December 2023 were excluded from the sample set. Data included 16,167 patient datasets positive for P. aeruginosa isolates, and these sets excluded repeated isolates.

Laboratory testing

Laboratory samples were processed and plated on various culture media according to the type of sample at their respective benches. P. aeruginosa isolates were identified from cultures using the Gram staining method, colony morphology, rapid bench tests, and the MALDI-TOF mass spectrometry instrument (Bruker Daltonics, Germany, Microflex LT/SH). Antimicrobial susceptibility testing was then conducted using the Kirby–Bauer method and the automated susceptibility testing instrument VITEK-2 system (BioMérieux Inc., Marcy l’Étoile, France). When performing automated susceptibility testing, single colonies from a pure culture were used to ensure a pure suspension for accurate/true results. All antimicrobial zone of inhibition diameters and breakpoints were reported according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. Antibiotics were classified as susceptible, intermediate, or resistant according to CLSI inhibition diameter and breakpoint guidelines. The antibiotics included in the study were P. aeruginosa antibiotics recommended under the CLSI tiered reporting strategy. The CLSI editions used for the breakpoint guidelines in this study were M100-Ed29 (2019), M100-Ed30 (2020), M100-Ed31 (2021), M100-Ed32 (2022), and M100-Ed33 (2023). The laboratory updates its zone diameter breakpoints annually according to the revised edition of the CLSI guidelines. The breakpoints used between 2019 and 2023 are presented in Table 1. In 2023, CLSI ceased to recommend gentamicin as a treatment option for P. aeruginosa. The laboratory stopped reporting susceptibility testing results for gentamicin in 2023.

Table 1. This table provides the zone diameter breakpoints used from the CLSI guideline editions between 2019 to 2023.

The laboratory performed antimicrobial susceptibility testing on P. aeruginosa isolates from routine samples and interpreted the result with the use of the following guidelines. All breakpoints remained the same in all editions used with exception of changes made to gentamicin, piperacillin/tazobactam, and tobramycin in M100-ed33 (2023).

Data analysis

All data were de-identified when received from the laboratory. No patient names or identification numbers were included; therefore, data from the same patient cannot be identified. Each requisition number was treated as an individual sample, and it was not possible to determine whether different requisition numbers belonged to the same patient. The analysis involved inserting the data into frequency cross-tabulations and using the chi-square test of independence in IBM SPSS Statistics software. The chi-square test of independence was used to determine whether a significant relationship existed between two categorical variables, with significance assessed using the p-value. The laboratory classified the antibiotic results obtained according to the CLSI guidelines, as mentioned, and these classifications were used directly in the analyses. An isolate was classified as MDR if it was resistant to three or more categories of antipseudomonal agents. During analyses, isolates were categorised as MDR if they were resistant to four or more antibiotics from the panel of antibiotics tested against P. aeruginosa. Isolates were categorised as sensitive if three or fewer antibiotic results were resistant from the testing panel. This was to ensure that three or more categories of antipseudomonal agents were included. The percentages of MDR versus sensitive strains were compared from 2019 to 2023 across the different sample types. The percentages of susceptible and resistant results for each antibiotic were compared from 2019 to 2023. The data were visualised using graphs and tables, which aided in identifying trends in the data.

Ethical considerations

Ethical approval was obtained from the Research Ethics Committee (UJ-REC) of the University of Johannesburg on 4 February 2025 under clearance number REC−3188−2025. Data were requested from the laboratory after ethical approval from the Lancet Publications Committee (LPC) of Lancet Laboratories on 19 February 2025.

Data were collected retrospectively, with all data de-identified to ensure compliance with the Protection of Personal Information Act (POPIA). Due to the use of secondary clinical data, a waiver of informed consent was obtained, as no contact with patients occurred, and informed consent from the patients was not required.

Results

Demographic data

The predominant age group amongst the 16,167 clinical samples was patients aged over 60 years (50.8%), with a mean age of 74 years. The age group 11–20 years presented with the fewest P. aeruginosa isolates. The data suggest that the majority of P. aeruginosa isolates in this study was obtained from elderly patients (Figure 1).

Isolation of P. aeruginosa amongst different sample types

Among the 16,167 P. aeruginosa isolates collected from 2019 to 2023, P. aeruginosa was predominantly isolated from urine samples (30.7%), respiratory samples (28.7%), and tissue samples (21.1%) (Table 2), whereas ear swabs (0.9%) and fluids (2.4%) had the fewest P. aeruginosa isolates within the 5-year period.

Table 2. Total percentage (%) of P. aeruginosa isolates from each clinical sample over the 5-year period.

The average percentage of P. aeruginosa isolates were calculated for each clinical sample type for each year (2019 to 2023) of the data set collected. This identified any trends of P. aeruginosa isolates in different clinical samples for each year included in the study, where the highest percentage of isolates for each clinical sample are highlighted from 2019 to 2023. Urine samples showed the highest average of P. aeruginosa isolates overall.

Bold values indicate the overall average percentage of isolates per clinical sample.

Trends of multidrug-resistant P. aeruginosa

The total percentage of sensitive isolates versus MDR isolates was calculated for each clinical sample from 2019 to 2023. When comparing the antimicrobial susceptibility across each year from the study, the chi-square result yielded a value of 184.704 with a p-value of <0.001, indicating a high level of statistical significance over time. The fluctuations in antimicrobial susceptibility were not random; the year was associated with changes in antimicrobial susceptibility. This supports the observation of increasing antimicrobial resistance over the years of the study period. The cumulative increase in MDR P. aeruginosa isolates refers to the strains of P. aeruginosa obtaining resistance through acquired and adaptive resistance mechanisms, resulting in strains becoming resistant to newer generations of antibiotics more frequently within the chosen timeframe. This revealed a 2.9% rise in MDR P. aeruginosa isolates from 2019 to 2023, with a surge from 2019 to 2021 (Figure 2).

Further analysis demonstrated that blood culture samples had the highest proportion of MDR isolates in 2019, 2020, and 2023. Between 2019 and 2022, the percentage of MDR P. aeruginosa in blood cultures increased by 7.3%, followed by a 6.3% decrease in multidrug resistance in 2023 (Table 3). The clinical specimen type with the highest proportion of MDR P. aeruginosa isolates in 2021 and 2022 was pus fluid samples. Respiratory samples revealed a 6% surge in MDR isolates from 2020 to 2021. Respiratory samples presented with an overall 5.5% increase in MDR P. aeruginosa isolates within the 5-year period. The overall percentage of MDR P. aeruginosa isolates increased from 2019 to 2023.

Table 3. Percentage (%) of sensitive isolates (Sens) and multidrug resistant (MDR) isolates for each clinical specimen from 2019 to 2023.

The percentage of MDR isolates and sensitive isolates were calculated for each clinical sample for each year. This highlighted the change of MDR isolates for each clinical sample over the 5-year period chosen for the study. Trends of high MDR isolate percentage in blood culture and fluid samples are highlighted.

Identifying shifts and trends in antibiotics

There is a highly statistically significant association between antibiotics and collection year, as supported by a p-value of <0.001. The antibiotics were associated with time (year), indicating an increase in resistance over time. A total of three antibiotics (ciprofloxacin, imipenem, and meropenem) showed resistance above 20% in 2019 and 2020, as shown in Table 4. The year 2021 had five antibiotics with resistance over 20%, namely, ciprofloxacin, imipenem, meropenem, doripenem, and ceftazidime-avibactam. In 2022, six antibiotics showed resistance over 20%, namely, cefepime, imipenem, meropenem, doripenem, ceftazidime-avibactam, and ceftolozane/tazobactam. Lastly, the 2023 antimicrobial susceptibility testing results showed several antibiotics with resistance over 20%, namely, ciprofloxacin, imipenem, meropenem, doripenem, ceftazidime-avibactam, ceftolozane/tazobactam, and aztreonam.

Table 4. Frequency of resistance (%) for each antibiotic from 2019 to 2023.

The total percentage of isolates resistant to each antibiotic was calculated for each year of the study. Both sensitive and MDR isolates were included in this calculation. This table highlighted trends in resistance for each antibiotic. The following antimicrobials were only tested against MDR isolates: ceftazidime-avibactam, ceftolozane/tazobactam, colistin, and aztreonam.

Newer antipseudomonal drugs such as ceftazidime-avibactam (n = 666 MDR isolates tested, of which 454 isolates showed resistance), ceftolozane/tazobactam (n = 559 MDR isolates tested, of which 379 isolates showed resistance), and aztreonam (n = 14 MDR isolates tested, of which 5 MDR isolates showed resistance) were tested against MDR isolates only and were not included in routine susceptibility testing. These antibiotics had limited susceptibility results due to their recent introduction during the chosen 5-year period and demonstrated relatively high resistance rates. However, because susceptibility testing for these agents was largely restricted to MDR isolates, the generalisability of these findings is limited to the broader isolate population.

The percentage of gentamicin resistance decreased by 4.5% from 2019 to 2023. This could be due to the private laboratory not capturing the results for this antibiotic for P. aeruginosa over the years on most benches, due to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) discouraging the reporting of this antibiotic in 2020 and updates to the CLSI guidelines in 2023 (M100-ed33), where gentamicin is not recommended as a treatment option for this pathogen. Between 2019 and 2023, there was an overall increase in resistance for all other antibiotics. Resistance to amikacin increased by 2.4% and to tobramycin by 0.9% from 2019 to 2023. Resistance to beta-lactams such as ceftazidime increased by 3.8%, cefepime increased by 3.3%, and piperacillin/tazobactam increased by 3.9%. Carbapenems such as imipenem increased by 3.6%, meropenem increased by 2.3%, and doripenem increased by 6.1%. Carbapenems presented with a continuously high percentage of resistance in each year. Ciprofloxacin, the only fluoroquinolone tested against P. aeruginosa, presented a percentage of resistance greater than 20% in most years. However, ciprofloxacin resistance declined by 4.7% within the 5-year period. Colistin (also known as polymyxin B) minimum inhibitory concentrations were performed exclusively for MDR P. aeruginosa isolates. The percentage of resistance increased by 7.8% from 2019 to 2020; however, there was a 10% decline from 2020 to 2021.

Trends of resistance in sample types

Respiratory, urine, and tissue samples presented with the highest levels of antibiotic resistance within each antibiotic category overall (Figure 3). Respiratory samples showed the highest prevalence of resistance to carbapenems (42.1%) and beta-lactams (37.7%). Urine samples showed the second-highest prevalence of resistance to carbapenems and beta-lactam drugs, with total percentages of 23.8% and 28.9%, respectively. Tissue samples presented with the third-highest prevalence of resistance to carbapenems and beta-lactams, with total percentages of 14.1% and 14.6%, respectively. The clinical sample type that showed the highest prevalence of resistance to aminoglycosides was urine samples, with a percentage of 38.8%. The second-highest prevalence of resistance to aminoglycosides was observed in respiratory samples (30.6%), whereas the third-highest prevalence was observed in tissue samples (12.8%).

Discussion

This study aimed to identify antimicrobial resistance trends in the ESKAPE pathogen P. aeruginosa using 16,167 clinical samples positive for P. aeruginosa isolates in Johannesburg. Multidrug resistance occurring in this group of pathogens is of concern in healthcare settings due to the treatment difficulties it presents.

The demographic data suggest that P. aeruginosa is more prevalent in the elderly, as more than half (50.8%) of the clinical samples used were determined to be from the age group 60 years and older. In 2023, a study in Saudi Arabia reported that the age group most affected by MDR P. aeruginosa is 50years and older (Alshammari et al., 2023).

The clinical specimens from which P. aeruginosa was most commonly isolated were urine (30.7%), respiratory (28.7%), and tissue samples (21.1%). One study determined that ESKAPE pathogens were commonly isolated from urine and blood culture samples, whilst another found that sputum samples (30.3%) and wound swabs (21.4%) had the highest number of P. aeruginosa isolates (Moursi et al., 2025; Orhan et al., 2024). In contrast, a study in Kuwait reported similar results to the present study, stating that P. aeruginosa was more frequently isolated from respiratory (42.5%) and urine (20.3%) samples (Althaferi et al., 2025).

Blood culture samples had the highest percentage of MDR P. aeruginosa isolates in 2019 (32.3%), 2020 (31.5%), and 2023 (33.3%). A study in Germany on P. aeruginosa bloodstream infections in haematological patients described that 20% of isolates were MDR and 34% showed resistance to carbapenems, where prior exposure to antibiotics was associated with resistance (Kessel et al., 2025). The increase of 7.3% in MDR P. aeruginosa in blood culture samples between 2019 and 2022 could be due to prolonged stays in the ICU. South Africa experienced the height of its COVID-19 waves during 2020–2021, wherein steroids such as glucocorticoids were used to treat severe cases of COVID-19. A study conducted in Japan suggested that steroids are independently associated with the development of bacteraemia (Nakagawara et al., 2023). In 2021, a study in South Africa concluded that of the positive blood cultures submitted, 14.6% of blood culture bottles flagged positive, and of those positive cultures, 34.3% were probable contaminants (Opperman and Samodien, 2021). Empirical therapy refers to the administration of broad-spectrum antibiotics to a patient whilst awaiting culture results from the laboratory. Once the pathogen has been identified, the clinician will change the antibiotic therapy to a narrow-spectrum antibiotic that targets the pathogen causing the infection. The study suggested that empirical antibiotic therapy may have been administered for positive blood cultures before identifying the organism from a culture and before determining whether it is the cause of infection or contamination due to poor sample collection. Empirical therapy should be withheld if there is no compelling evidence that the patient has a bacterial infection. The use of empirical antibiotic therapy can greatly contribute to the development of antibiotic resistance amongst bacteria, in addition to poor patient outcomes (Engelbrecht et al., 2022).

The second-highest clinical sample type with MDR P. aeruginosa isolates was respiratory samples. Respiratory samples presented with an overall increase of 5.5% in MDR isolates from 2019 to 2023 and an increase of 6% in MDR P. aeruginosa isolates from respiratory samples in 2020 and 2021. The increase from 2020 to 2021 may have been affected by the COVID-19 viral outbreak occurring during this time. Infected patients may have been given antibiotics for treatment in cases where the virus was not properly diagnosed, and diagnosis was based on presenting upper respiratory tract symptoms. The risk of misdiagnosing a patient may lead to the administration of antibiotics that do not target the cause of the infection (Al-Daghistani et al., 2025). A study in 2024 concluded that the COVID-19 pandemic influenced the resistance patterns of P. aeruginosa, as critical antibiotics were associated with resistance over the 19-year study period (Li et al., 2024). The prescription of incorrect antibiotics may lead to the development of multidrug-resistant organisms in addition to worsening patient outcomes. Another study conducted in 2024 stated that 70% of patients admitted to hospital with respiratory infections who had positive PCR tests for viruses and no evidence of bacterial infection were administered antibiotic therapy (Hovind et al., 2024). Viral and bacterial upper respiratory infections may be mistreated due to incorrect tests being performed or negligence, which may have contributed to the growing antimicrobial resistance seen in P. aeruginosa. Patients infected with respiratory viruses had difficulty breathing, which often led to admission to the intensive care unit (ICU), with invasive mechanical ventilation for long periods of time in severe cases (Lalla et al., 2021). According to a study in Saudi Arabia, patients admitted to the ICU were more likely to be on mechanical ventilation, where elderly patients are at risk of prolonged ICU admission and ventilator-associated pneumonia (VAP) (Alshamrani et al., 2024).

Carbapenems presented with the highest resistance rates overall, which peaked in 2021, with imipenem and meropenem resistance over 20% in P. aeruginosa for all five years included in the study, whereas doripenem showed resistance in over 20% of isolates from 2021 to 2023. Antibiotic resistance amongst organisms is increasing rapidly worldwide and is becoming a threat to public health (Elshamy and Aboshanab, 2020). Respiratory samples presented with the highest percentage of carbapenem resistance (42.1%), and urine samples presented with the second-highest percentage of carbapenem resistance (23.8%). Studies have shown that the COVID-19 pandemic may have accelerated the rise of carbapenem resistance in Gram-negative bacteria due to a large number of the population acquiring respiratory infections and the intense use of antibiotics (Sakagianni et al., 2025; Shi et al., 2025; Langford et al., 2023).

The beta-lactam group showed that respiratory samples had the highest percentage of resistance (37.7%), with urine samples showing the second-highest percentage of resistance (28.9%). A study in Iran showed that tobramycin had a resistance rate of 45% and amikacin had a resistance rate of 43% amongst aminoglycoside-resistant P. aeruginosa (Saeli et al., 2024). This correlates with the current study in that aminoglycoside resistance results identified urine samples as having the highest percentage of resistance (38.8%), whereas respiratory samples showed the second-highest percentage of resistance (30.6%). P. aeruginosa additionally acquires resistance to antibiotics via mutations and horizontal gene transfer from more resistant isolates of the pathogen (Elfadadny et al., 2024). As isolates became resistant due to increased use of antibiotics, drug-resistant strains were able to easily spread and thrive in hospital settings during the pandemic, hence the high MDR percentages in 2020–2021.

Fluoroquinolones such as ciprofloxacin showed resistance over 20% in 2019 (24.5%), 2020 (23.6%), 2021 (20.6%), and 2023 (20.4%). The decrease in the use of ciprofloxacin could have contributed to the decrease in resistance observed from 2019 to 2023. A study published in 2025 described the possibility of a reversal of ciprofloxacin resistance in P. aeruginosa due to antibiotic-free environments, where there is no selective pressure for mutations in the nfxB gene that cause the overexpression of efflux pumps (Hernando-Amado et al., 2025).

The presence of colistin resistance in P. aeruginosa is a concern for critically ill patients, as this is a last-line treatment option in cases of XDR P. aeruginosa. This resistance may be due to chromosomal mutations or plasma-mediated spread of the mcr gene (Narimisa et al., 2024). The study showed high percentages of resistance to ceftazidime-avibactam and ceftolozane/tazobactam; however, these newer antibiotics were tested on only a small number of MDR P. aeruginosa isolates from the 16,167 datasets used. Therefore, the results do not reflect the true level of population-level resistance to these two antibiotics.

Recent studies on AMR in Gauteng demonstrated the incidence rate of ESKAPE pathogens in bloodstream infections and determined that the most common ESKAPE pathogens isolated are Acinetobacter baumannii and Staphylococcus aureus (Mthombeni et al., 2024). A study conducted on AMR in urine samples from elderly patients in Gauteng determined that higher AMR rates in elderly patients may be due to empirical UTI treatment in long-term care facilities (Labuschagne et al., 2026). Another study in Gauteng concluded that the prevalence of high AMR in Listeria monocytogenes in beef products may lead to difficulties in treatment for human consumers (Gana et al., 2024). Difficulties in treatment options can lead to costly, last-line antibiotics becoming the only available option, with these last-line therapies often being associated with adverse effects. AMR can be combated through antimicrobial stewardship, which aims to reduce the misuse and overuse of antibiotics to preserve their effectiveness for future use. Antimicrobial stewardship promotes the use of narrow-spectrum antibiotics and discourages empirical therapy, which can accelerate AMR through the use of broad-spectrum antibiotics.

Good infection control practices, according to the WHO, include good hand hygiene, limiting physical contact with patients (wearing gloves and gowns), isolating patients, disinfection of the environment, and surveillance. The implementation of these practices in hospital settings, particularly in ICUs, which are prone to nosocomial infections, could help inhibit the spread of MDR P. aeruginosa.

Increasing surveillance of P. aeruginosa will aid in identifying strains that are MDR, in addition to help isolate affected patients to limit the spread of these resistant strains to other immunocompromised patients (Spagnolo et al., 2021).

Study limitations and strengths

Retrospective clinical data from one private laboratory in Johannesburg were used; the data may therefore be incomplete or biased, as they were collected for clinical diagnosis rather than research purposes. Although this laboratory services the private sector throughout the Gauteng region and receives a large number of clinical samples from the province, it can represent only the local epidemiology of the private sector and not that of the public sector. The data focused on antimicrobial susceptibility testing for patient treatment and did not include additional testing for research purposes. Variation in susceptibility methods may be a limitation, as the bench susceptibility is open to human error. Patient clinical data were not included in this study, leading to a lack of knowledge regarding previous antibiotics used for patient treatment, which may have influenced resistance patterns. The data collected did not include information on the hospital unit/ward from which the patient originated, as the data were collected from a clinical laboratory and were de-identified without patient clinical information. Such information may have assisted in determining the importance of the isolate and whether P. aeruginosa was isolated due to colonisation or active infection. Repeated isolates from the same patient may have been used due to de-identified datasets collected, making it impossible to determine whether multiple datasets belonged to the same patient, especially in ICU patients who may have submitted multiple samples over a period of time under different requisition numbers. The emergence of new antibiotics also limited the analysis, as these antibiotics were registered in South Africa towards the end of the study period (2021–2022).

Conclusion

This study provided insight into the antimicrobial patterns of the WHO ESKAPE pathogen P. aeruginosa in Johannesburg, South Africa. The rise in MDR P. aeruginosa within the chosen 5-year study period has been highlighted, in addition to the rise in resistance within all antipseudomonal treatment drug categories. Antibiotic stewardship in addition to infection control, plays a role in preventing the development and spread of these resistant strains. Greater focus must be placed on these practices to ensure the prevention of antimicrobial resistance. Due to resistance developing in newer antipseudomonal drugs, there is a demand for new drugs and further research into novel treatment options.

Further investigations should be carried out to expand knowledge of the patterns of P. aeruginosa and other ESKAPE pathogens in the clinical setting. This will deepen the understanding of how this pathogen’s susceptibility changes and further contribute to the global AMR discourse on ESKAPE pathogens, supporting decision-making regarding treatment options for the future.

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Footnotes

Edited by: Márió Gajdács, University of Szeged, Hungary

Reviewed by: Evita Mayasari, Universitas Sumatera Utara, Indonesia

Federico Giovagnorio, Azienda Ospedaliera Santa Maria degli Angeli Pordenone, Italy

Footnotes

AMR, antimicrobial resistance; CRE, carbapenem-resistant enterobacterales; CDC, Centers for Disease Control and Prevention; CLSI, Clinical and Laboratory Standards Institute; COPD, chronic obstructive pulmonary disease; CF, cystic fibrosis; DTR, difficult-to-treat; ESBL, extended-spectrum β-lactamase; ESKAPE, Enterococcus spp., Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp; EUCAST, European Committee on Antimicrobial Susceptibility Testing; XDR, extensively drug-resistant; HAI, hospital-associated infection; ICU, intensive care unit; LPS, lipopolysaccharide chain; MDR, multidrug-resistant; UTI, urinary tract infection; VAP, ventilator-associated pneumonia; WHO, World Health Organisation.