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. 2026 Mar 6;26:344. doi: 10.1186/s12866-026-04833-y

Antimicrobial resistance patterns and carbapenemase gene distribution in pediatric Pseudomonas aeruginosa isolates: molecular and epidemiological insights from an Iranian referral center

Erfaneh Jafari 1, Babak Pourakbari 1, Mohammad Reza Asadi Karam 2, Reza Azizian 1, Maryam Sotoudeh Anvari 3, Setareh Mamishi 1,✉
PMCID: PMC13078054  PMID: 41792596

Abstract

Background

Antimicrobial resistance in Pseudomonas aeruginosa represents a major challenge in pediatric healthcare, yet molecular epidemiological data from children in the Middle East are limited. This study aimed to characterize antimicrobial resistance patterns, carbapenemase gene profiles, and transmission dynamics in a tertiary pediatric hospital in Iran.

Methods

We analyzed 110 P. aeruginosa isolates from pediatric patients (December 2023-August 2024) using disk diffusion susceptibility testing, PCR detection of carbapenemase genes (blaIMP, blaKPC, blaNDM, blaOXA, blaSIM, blaSPM, and blaVIM), and RAPD-PCR genotyping. Multivariate logistic regression analysis was used to identify predictors of resistance.

Results

Carbapenem resistance (CR) affected 40.9% of isolates, with 37.3% multidrug-resistant (MDR) and 10.0% extensively drug-resistant. Among CR isolates, blaVIM (68.9%) and blaNDM (55.6%) predominated, with 49.1% harboring multiple carbapenemase genes. Age was a significant predictor of antimicrobial resistance (p < 0.05 for most antibiotics). Children < 5 years demonstrated significantly lower resistance compared to those > 10 years, with the strongest associations observed for fluoroquinolones (ciprofloxacin: AOR = 0.046 (CI: 0.010–0.212), p < 0.001; norfloxacin: AOR = 0.061 (CI: 0.013–0.283), p = 0.002) and some β-lactams (meropenem: AOR = 0.196 (CI: 0.062–0.623), p = 0.021). Gender showed no significant association with resistance across all antibiotics tested (p > 0.05). Gene coexistence was a significant predictor for β-lactams (imipenem: AOR = 1.968 (CI: 1.314–2.946), p = 0.001). RAPD-PCR revealed 23 genetic clusters, with ward-specific clustering patterns suggesting nosocomial transmission, particularly in intensive care units (ICUs).

Conclusion

This study demonstrates an alarming burden of carbapenemase-producing P. aeruginosa among Iranian pediatric patients, with age-dependent antibiotic resistance, frequent co-existence of carbapenemase genes suggesting horizontal gene transfer, and ward-specific genetic clustering consistent with nosocomial transmission. These observations underscore the necessity for age-focused therapeutic strategies, intensified ICU surveillance, and targeted antimicrobial stewardship.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-026-04833-y.

Keywords: Pseudomonas aeruginosa, Multi-drug resistance, Carbapenem resistance, Genotyping, Children

Background

Antimicrobial resistance represents one of the greatest threats to global health, with the World Health Organization (WHO) identifying Pseudomonas aeruginosa (P. aeruginosa) as a high-priority pathogen in 2024 [1]. This Gram-negative opportunistic pathogen causes severe infections in healthcare settings, particularly among immunocompromised patients, and has demonstrated the capacity to develop resistance to all available antibiotics [2, 3]. The emergence of multidrug-resistant (MDR) and carbapenem-resistant P. aeruginosa (CRPA) poses substantial therapeutic challenges globally. Carbapenem resistance (CR) is frequently mediated by class B β-lactamases (metallo-β-lactamases), with genes such as blaVIM, blaNDM, blaIMP, and blaKPC increasingly reported worldwide [4, 5].

Pediatric populations face unique vulnerabilities to P. aeruginosa infections due to immature immunity, chronic conditions, invasive devices, and immunosuppression, with bacteremia mortality reaching 18–61% in critically ill children. Treatment is particularly challenging because many anti-pseudomonal antibiotics have pediatric contraindications or lack adequate safety data, while age-related pharmacokinetic differences complicate dosing. Understanding age-specific resistance patterns and molecular epidemiology is therefore essential for developing appropriate pediatric treatment guidelines [6–8].

Despite the clinical importance of P. aeruginosa in pediatric populations, molecular epidemiology data from Iran and the Middle East remain limited, with most studies focusing on adults or lacking comprehensive resistance gene profiling combined with genetic typing [9]. Random Amplified Polymorphic DNA PCR (RAPD-PCR) provides a cost-effective, rapid approach for investigating genetic diversity and transmission events in resource-limited settings, though with recognized reproducibility limitations compared to higher-resolution methods such as multi-locus sequence typing (MLST) or whole-genome sequencing (WGS) [10–13]. Understanding ward-specific distribution, age-related resistance patterns, and genetic clustering can inform targeted infection control interventions and antimicrobial stewardship strategies tailored to local epidemiology.

Therefore, this study aimed to comprehensively characterize antimicrobial resistance patterns, carbapenemase-encoding gene distribution, and genetic diversity of P. aeruginosa isolates from pediatric patients at a major referral hospital in Tehran, Iran. Specifically, we sought to identify independent predictors of resistance, assess carbapenemase gene prevalence and co-existence, and examine genetic relationships to inform infection control and treatment strategies for this vulnerable population.

Materials and methods

Sampling and processing

Between December 2023 and August 2024, all P. aeruginosa isolates recovered from pediatric patients (aged 0–15 years) at Children's Medical Center, a referral hospital in Tehran, Iran, were consecutively enrolled without pre-selection criteria. Both hospitalized and outpatient children were included. Clinical specimens comprised urine, blood, tracheal aspirates, bronchoalveolar lavage (BAL), wound swabs, eye discharge, and other body fluids. To ensure independence, d

uplicate isolates from the same patient within 30 days were excluded, with only the first isolate retained for analysis. Identification was performed using conventional microbiological and biochemical tests, including oxidase activity, motility, citrate utilization, triple sugar iron agar reactions, oxidative-fermentative glucose metabolism, and other standard biochemical assays, following established protocols [14]. No automated identification system was applied, as the API system is based on the same biochemical reactions, carbohydrate metabolism, enzyme activity, and substrate utilization, as conventional tests, differing only in standardization and format. Confirmed P. aeruginosa isolates were preserved in tryptic soy broth (Merck) containing 10% glycerol at − 80 °C until further analysis.

Antimicrobial susceptibility testing

The susceptibility of all isolates to antibiotics was assessed using the Kirby-Bauer disc diffusion method on Mueller–Hinton agar (Merck), following the standards and breakpoints set by the Clinical and Laboratory Standards Institute (CLSI) [15]. The antibiotic discs (Mast Group Ltd., UK) utilized in the testing included amikacin (30 µg), aztreonam (30 µg), ceftazidime (30 µg), cefepime (30 µg), ciprofloxacin (5 µg), imipenem (10 µg), norfloxacin (10 µg), meropenem (10 µg), and piperacillin-tazobactam (100/10 µg). Quality control was conducted using P. aeruginosa ATCC 27853. The classification of MDR patterns was based on resistance to at least one antibiotic from three or more different antimicrobial categories [16]. Additionally, isolates meeting the resistance criteria for any carbapenem (imipenem or meropenem) were classified as CRPA.

Phenotypic detection of metallo-β-lactamase

To assess Metallo-β-lactamase (MBL) production in CRPA cultures, the combined double-disk synergy test (CDDST) was employed. A 0.5 McFarland suspension of each isolate was spread onto Mueller–Hinton agar plates. Two discs of imipenem (10 µg) and ceftazidime (30 µg) were placed 2 cm apart on the agar surface. For one of the discs for each β-lactam antibiotic, 5 µL of 0.5 M EDTA solution (pH 8.0) was added directly. After incubating overnight at 37 °C, an increase in the inhibition zone of 8 mm or more around the combined disc compared to the individual antimicrobial disc was interpreted as a positive result for MBL production [17].

Detection of carbapenemase-producing genes

For detection of carbapenemase-encoding genes, PCR was carried out on all isolates using the primer sequences shown in Table 1 [18–21]. PCR conditions included an initial denaturation at 94 °C for 10 min, followed by 40 cycles of 94 °C for 30 s, 50–55 °C for 45 s (primer-specific; Table 1), and 72 °C for 50 s, with a final extension at 72 °C for 7 min. The DNA templates were prepared using the phenol–chloroform extraction method. The PCR amplification was done in a 25 µL reaction mixture as previously detailed [22]. Nuclease-free water was used as a negative control, and previously characterized clinical isolates carrying the target carbapenemase genes were included as positive controls in each PCR run to validate amplification conditions and primer performance. Amplicon specificity was assessed in silico using NCBI Primer-BLAST and confirmed by comparing observed band sizes with the expected product sizes (Table 1) following agarose gel electrophoresis.

Table 1.

Primer sequences and PCR products size

Target Gene Primer Sequence (5′ to 3′) Annealing Tm Amplicon Size (bp) Reference
blaIMP F: GAAGGCGTTTATGTTCATAC 50°C 587 [20]
R: GTACGTTTCAAGAGTGATGC
blaKPC F: ATGTCACTGTATCGCCGTCTA 55°C 888 [21]
R: TTACTGCCCGTTGACGCCCAA
blaNDM F: GGTTTGGCGATCTGGTTTTC 52°C 621 [18]
R: CGGAATGGCTCATCACGATC
blaOXA-48 F: GCGTGGTTAAGGATGAACAC 52°C 426 [18]
R: CATCAAGTTCAACCCAACCG
blaSIM F: TACAAGGGATTCGGCATCG 52°C 571 [19]
R: TAATGGCCTGTTCCCATGTG
blaSPM F: AAAATCTGGGTACGCAAACG 52°C 271 [18]
R: ACATTATCCGCTGGAACAGG
blaVIM F: GATGGTGTTTGGTCGCATA 52°C 390 [18]
R: CGAATGCGCAGCACCAG
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Molecular typing using RAPD-PCR

Using primer 272 (5ʹ-AGCGGGCCAA-3ʹ), we performed RAPD-PCR to type all P. aeruginosa isolates obtained from diverse nosocomial infections. Amplification was carried out in a 25 µL volume using 12 µL of 2 × Taq DNA Polymerase Master Mix RED (Amplicon), 10 µL of DNase/RNase-free water, 2 µL of DNA template (20–30 ng), and 1 µL of primer (0.4 mM), with cycling conditions of 95 °C for 5 min, 30 cycles at 94/36/72 °C (1/1/5 min), and a final extension at 72 °C for 15 min [23]. Technical variability was controlled by preparing all reactions with a single master mix and running them on one thermal cycler, using standardized DNA templates (20–30 ng/µL) and duplicate reactions of P. aeruginosa ATCC 27853 as an internal control. However, complete duplicate testing of all isolates was not performed. The amplified products were analyzed compared with a 100 bp DNA ladder (DM2300 ExcelBand™, SMOBIO), using 1.5% agarose gel (Sigma), and visualized through staining with DNA Gel Stain (Pishgam). RAPD-PCR banding patterns were compared using GelCompar II, version 6.5 (Applied Maths), and clustering was performed using the unweighted pair group method with arithmetic mean (UPGMA) algorithm. Then the distance matrix obtained from GelCompar II was used within Python to generate a circular dendrogram. A 30% distance cut-off (70% similarity) was applied for cluster definition.

Data analysis

Categorical data were summarized as frequencies and percentages. Comparisons of resistance proportions between CRPA and non-CRPA isolates were assessed using Pearson’s chi-square and Fisher's exact test. Cramér's V was used to measure the strength of association between two categorical variables. Correlations between gene coexistence and MDR or CRPA phenotypes were assessed using Spearman's rank correlation. Multivariable binary logistic regression was performed to identify independent predictors of antibiotic resistance while controlling for potential confounders, including age group, gender, sample type, and number of coexisting carbapenemase genes. All variables were retained in the final models to provide adjusted estimates. Model discrimination was evaluated using classification tables (sensitivity, specificity, and overall p-value). Results are reported as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). All analyses were conducted using IBM SPSS version 25.0 and Excel (Microsoft 365 suite), with p ≤ 0.05 considered statistically significant.

Results

Sample characteristics

A total of 110 P. aeruginosa isolates were identified from pediatric patients, comprising 67 males (60.9%) and 43 females (39.1%), with a mean age of 4.9 ± 5.3 years. Of all isolates, 62.7% (n = 69) were from children aged < 5 years, 16.4% (n = 18) from those aged 5–10 years, and 20.9% (n = 23) from children > 10 years. Among patients grouped by resistance phenotype, CRPA isolates showed higher prevalence in the > 10 years age group (35.6% vs. 53.3% in < 5 years), while MDR isolates demonstrated a similar trend (39% vs. 51.2% in < 5 years) (Table 2). Clinical samples originated from diverse anatomical sites, reflecting the organism's versatility in pediatric infections. Urine samples were most common (36 isolates, 32.7%), followed by BAL (19, 17.3%), blood (12, 10.9%), wound swabs (12, 10.9%), tracheal tubes (11, 10%), eye discharge (9, 8.2%), and other sources (9, 8.2%) (Fig. 1A). Hospital ward distribution revealed the highest prevalence in Emergency (17.3%), Surgery (13.6%), and Infant-ICU (10.0%), with the remaining wards contributing 0.9–6.4% each (Fig. 2).

Table 2.

Demographic characteristic of patients

Variables Total (n = 110) CRPA (n = 45) MDR (n = 41)
Gender No. (%) Male 67 (60.9) 25 (55.6) 24 (58.5)
Female 43 (39.1) 20 (44.4) 17 (41.5)
Age No. (%)  < 5 year 69 (62.7) 24 (53.3) 21 (51.2)
5–10 year 18 (16.4) 5 (11.1) 4 (9.8)
 > 10 year 23 (20.9) 16 (35.6) 16 (39)
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Variables are represented by No. (%). CRPA: carbapenem-resistant P. aeruginosa; MDR: multidrug resistant

Fig. 1.

Fig. 1

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Distribution of clinical samples and pigments among P. aeruginosa isolates. A The frequency of clinical specimens, with urine and BAL as the most common sources, B Pigment production, dominated by Pyoverdine, followed by Pyocyanin

Fig. 2.

Fig. 2

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Distribution of cases across hospital departments. Emergency and Surgery recorded the highest proportions, followed by Infant-ICU, EICU, and CICU. This pattern underscores critical care units as major contributors to case burden, reflecting higher infection risk in vulnerable patients. (OPD: outpatient department; ICU: intensive care unit; EICU: Emergency ICU; CICU: Coronary ICU; ICU-OH: ICU-Open Heart; PICU: Pediatric ICU; NICU: Neonatal ICU)

Pigment production analysis revealed 57 isolates (51.8%) producing yellow-green pyoverdine, 21 (19.1%) producing blue-green pyocyanin, 14 (12.7%) producing brown-black pyomelanin, 2 (1.8%) producing reddish-brown pyorubin, and 43 (30.1%) non-pigment producers (Fig. 1B). Notably, pyoverdine-producing isolates showed a significant negative association with MDR (χ2 = 10.588, p = 0.001) and CRPA (χ2 = 10.422, p = 0.001) phenotypes, whereas pyocyanin and pyomelanin production showed no significant associations with resistance phenotypes.

Antibiotic resistance profile

Among the 110 P. aeruginosa isolates, 12 (10.9%) were susceptible to all nine antibiotics tested, while 98 (89.1%) displayed non-susceptibility to at least one antibiotic. Of these, 45 isolates (40.9%) met criteria for CRPA, and 41 (37.3%) for the MDR phenotype. Resistance rates varied substantially across the antibiotics tested. Aztreonam showed the highest resistance (59 isolates, 53.6%), followed by imipenem (43, 39.1%), cefepime (39, 35.5%), and piperacillin-tazobactam (35, 31.8%). Lower resistance rates were observed for ceftazidime (32, 29.1%), meropenem (30, 27.3%), amikacin (24, 21.8%), and norfloxacin (17, 15.5%). The lowest resistance was to ciprofloxacin (21, 19.1%). Notably, susceptibility exceeded 74% for norfloxacin (81.8%), ciprofloxacin (75.5%), and amikacin (74.5%) and was particularly high in urinary isolates, with 94.4% and 86.1% remaining susceptible to amikacin and norfloxacin, respectively. Twenty-four isolates (21.8%) tested positive for MBL production using CDDST. Differential analysis between CR and non-CR isolates revealed significantly elevated resistance rates in CRPA for cefepime (75.6% vs. 7.7%), ceftazidime (62.2% vs. 6.2%), piperacillin-tazobactam (66.7% vs. 7.7%), and ciprofloxacin (42.2% vs. 3.1%), all with p < 0.001 (Table 3). Among 41 MDR isolates, resistance distribution was 3 antibiotic classes (10, 24.4%), 4 classes (11, 26.8%), 5 classes (7, 17.1%), and 6 + classes (13, 31.7%). Eleven MDR isolates (26.8%) demonstrated resistance to all 9 tested antibiotics. While these findings do not meet formal criteria for extensively drug-resistant (XDR) due to incomplete class coverage, such extensive resistance across commonly used antipseudomonal agents markedly restricts treatment choices and underscores the potential for poor clinical outcomes.

Table 3.

Antimicrobial susceptibility patterns of P. aeruginosa isolates

Antibiotic Total (n = 110) Non-CRPA (n = 65) CRPA (n = 45) P-value
R I S R I S R I S
Amikacin 24 (21.8) 4 (3.6) 82 (74.5) 1 (1.5) 1 (1.5) 63 (96.9) 23 (51.1) 3 (6.7) 19 (42.2)  < 0.001
Aztreonam 59 (53.6) 31 (28.2) 20 (18.2) 22(33.8) 23 (35.4) 20 (30.8) 37 (82.2) 8 (17.8) - 0.021
Ceftazidime 32 (29.1) 1 (0.9) 77 (70) 4 (6.2) 0 61 (93.8) 28 (62.2) 1 (2.2) 16 (35.6)  < 0.001
Cefepime 39 (35.5) 4 (3.6) 67 (60.9) 5 (7.7) 1 (1.5) 59 (90.8) 34 (75.6) 3 (6.7) 8 (17.8)  < 0.001
Ciprofloxacin 21 (19.1) 6 (5.5) 83 (75.5) 2 (3.1) 1 (1.5) 62 (95.4) 19 (42.2) 5 (11.1) 21 (46.7)  < 0.001
Imipenem 43 (39.1) 4 (3.6) 63 (57.3) - 3 (4.6) 62 (95.4) 43 (95.6) 1 (2.2) 1 (2.2)  < 0.001
Meropenem 30 (27.3) 7 (6.4) 73 (66.4) - 2 (3.1) 63 (96.9) 30 (66.7) 5 (11.1) 10 (22.2)  < 0.001
Norfloxacin 17 (15.5) 3 (2.7) 90 (81.8) - 1 (1.5) 64 (98.5) 17 (37.8) 2 (4.4) 26 (57.8)  < 0.001
Piperacillin-Tazobactam 35 (31.8) 14 (12.7) 61 (55.5) 5 (7.7) 8 (12.3) 52 (80) 30 (66.7) 6 (13.3) 9 (20)  < 0.001
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Variables are represented by No. (%). R: Resistant; I: Intermediate; S: Sensitive; Non-CRPA: Non-carbapenem-resistant P. aeruginosa; CRPA: carbapenem-resistant P. aeruginosa; Zone diameter breakpoints specific to P. aeruginosa: amikacin (S ≥ 17 mm, R ≤ 14 mm), aztreonam (S ≥ 22 mm, R ≤ 15 mm), ceftazidime (S ≥ 18 mm, R ≤ 14 mm), cefepime (S ≥ 18 mm, R ≤ 14 mm), ciprofloxacin (S ≥ 21 mm, R ≤ 15 mm), imipenem (S ≥ 19 mm, R ≤ 15 mm), meropenem (S ≥ 19 mm, R ≤ 15 mm), norfloxacin (S ≥ 17 mm, R ≤ 12 mm), and piperacillin-tazobactam (S ≥ 22 mm, R ≤ 17 mm)

Figure 3 displays the age- and sex-stratified proportions of P. aeruginosa isolates resistant to each antibiotic across the three predefined age categories. Multivariate binary logistic regression identified independent predictors of resistance for each antibiotic (Supplementary Table S1). Age emerged as a consistent protective factor: children < 5 years showed significantly lower odds of resistance to aztreonam (AOR = 0.292, CI: 0.085–1.008), ceftazidime (AOR = 0.283, CI: 0.100–0.806), ciprofloxacin (AOR = 0.046, CI: 0.010–0.212), meropenem (AOR = 0.196, CI: 0.062–0.623), norfloxacin (AOR = 0.061, CI: 0.013–0.283), and piperacillin-tazobactam (AOR = 0.046, CI: 0.003–0.631) compared to children > 10 years. This age-protective effect suggests either different pathogenic mechanisms in younger children, differential exposure to resistant strains, or pharmacokinetic differences affecting antimicrobial effectiveness. Gender showed no significant association with resistance across all 9 antibiotics tested (all p > 0.05).

Fig. 3.

Fig. 3

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Age‑ and sex‑stratified proportions of antimicrobial resistance among Pseudomonas aeruginosa isolates. Line plots show the percentage of resistant isolates to each tested antibiotic across three age categories (< 5 years, 5–10 years, > 10 years), stratified by sex (solid line = males; dashed line = females). Each point represents the observed proportion of resistant isolates within a given age-sex stratum; lines are drawn only to aid visual comparison and do not indicate fitted trends. No formal statistical tests for trend were applied to these plots; inferential statistics for age‑ and sex‑related differences in resistance are provided in Supplementary Material 1

Sample type independently predicted resistance for amikacin, imipenem, and piperacillin-tazobactam. Blood/wound samples showed 82% lower odds of amikacin resistance compared to urine samples (AOR = 0.118, CI: 0.020–0.701), but paradoxically, 8.7-fold higher odds of piperacillin-tazobactam resistance (AOR = 8.698, CI: 1.481–51.086), suggesting that resistance mechanisms in systemic infections differ from urinary tract infections.

Carbapenem resistance genes

PCR screening of 110 isolates for seven carbapenemase-encoding genes revealed that 95 (86.3%) harbored at least one carbapenemase gene. Gene prevalence in the total population was blaVIM (55.5%, n = 61), blaSPM (34.5%, n = 38), blaNDM (30.0%, n = 33), and blaKPC (27.3%, n = 30). The genes blaIMP (5.5%), blaOXA-48 (3.6%), and blaSIM (1.8%) were detected at lower frequencies. Among CRPA isolates specifically (n = 45), the distribution was markedly different: blaVIM (68.9%), blaNDM (55.6%), blaKPC (53.3%), and blaSPM (44.4%), indicating strong enrichment of these genes in CR strains.

Gene coexistence correlated significantly with the MDR phenotype (Spearman's ρ = 0.351, p < 0.001), indicating that accumulation of multiple carbapenemase genes drives MDR. Among CRPA isolates, 35.5% harbored exactly 3 genes, 20% harbored 2 genes, 17.7% harbored 4 genes, and 15.5% harbored 1 gene, with up to 5 genes detected in single isolates. The blaKPC/blaNDM/blaSPM combination appeared in 8.8% of CRPA isolates, and blaKPC/blaNDM/blaVIM in 6.6%, suggesting hotspots of horizontal gene transfer or clonal expansion of multiply resistant strains.

Carbapenemase gene coexistence independently predicted resistance to aztreonam (AOR = 1.744, CI: 1.207–2.519, p = 0.003), cefepime (AOR = 1.608, CI: 1.127–2.294, p = 0.009), imipenem (AOR = 1.968, CI: 1.314–2.946, p = 0.001), and piperacillin-tazobactam (AOR = 1.968, CI: 1.322–2.928, p < 0.001). Each additional gene increased the odds of resistance by 68–97%, highlighting the clinical significance of gene accumulation.

Molecular typing

RAPD-PCR analysis using primer 272 produced 1–9 DNA fragments in the 100–3000 bp range. The dendrogram analysis identified 23 distinct genetic clusters with varying cluster sizes (Fig. 4). There were 9 clusters with 2 isolates, 7 with 3 isolates, 2 with 5 isolates, 2 with 7 isolates, and single clusters each with 4, 12, and 15 isolates. Additionally, 16 isolates (14.5%) were singletons not clustering with any other isolates.

Fig. 4.

Fig. 4

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Circular dendrogram of Pseudomonas aeruginosa isolates based on RAPD‑PCR profiles. The tree was generated using the UPGMA clustering, clusters were defined at a ≤ 30% distance threshold (≥ 70% similarity), highlighted in distinct colors, with isolate IDs displayed on the outer ring

The dendrogram annotations displayed each isolate's specimen type, hospital ward, and carbapenemase gene profile (Supplementary Figure S1), revealing that isolates with close genetic profiles often originated from the same ward, particularly in intensive care unit (ICU) settings. Sixteen isolates (14.5%) did not cluster with any others at the 70% similarity threshold, indicating genetically divergent strains. These ungrouped isolates were distributed across multiple wards, suggesting diverse exogenous sources rather than nosocomial transmission.

Members from 11 clusters (47.8%) exhibited > 90% genetic similarity, suggesting highly conserved clonal lineages. Chi-square analysis revealed a significant association between RAPD cluster assignment and hospital ward (χ2 = 47.389, p = 0.012), with ICUs showing enrichment of specific clusters (Cramer's V = 0.328, p = 0.012), consistent with nosocomial transmission. Two isolates from CICU displayed nearly complete genetic identity (> 99% similarity), indicating probable direct clonal transmission from a single source. No statistically significant associations emerged between RAPD cluster assignment and specimen type (p = 0.603). Gene coexistence patterns did not significantly vary by ward or specimen type in univariate analyses (all p > 0.05).

Discussion

The present study provides an updated molecular and epidemiological profile of pediatric P. aeruginosa isolates from a tertiary children's hospital in Tehran, emphasizing MDR, CR, carbapenemase gene carriage, and in-hospital genetic relatedness. Resistance burden was high: 89.1% of isolates showed non-susceptibility to ≥ 1 agent, 40.9% qualified as CRPA, 37.3% as MDR, and 10% as highly resistant, highlighting a constrained therapeutic landscape for common antipseudomonal options in pediatric settings. These rates are consistent with regional evidence showing that MDR/CR P. aeruginosa is increasingly concentrated in acute-care and ICU settings, where antimicrobial selection pressure and transmission opportunities are highest. Our findings align with reports from the Middle East and North Africa (MENA), which consistently identify MDR P. aeruginosa, including MBL-mediated resistance, as a major hospital threat, particularly in critical-care settings [7–9, 24].

Most isolates in this study were recovered from children younger than five years. In addition, urine and respiratory specimens (BAL/tracheal) accounted for a large proportion of the isolates, consistent with the organism’s role in urinary and respiratory infections among hospitalized children. The observed ward distribution was highest in emergency, surgery, and ICU-associated units. This pattern is epidemiologically plausible, as these settings concentrate acutely ill patients, invasive procedures, and broad-spectrum antibiotic exposure, collectively increasing selection and transmission pressure. Recent Iranian hospital-based work has similarly emphasized the role of high-acuity wards in concentrating CR isolates, supporting the plausibility of these ward-level patterns [25, 26].

Pigment production, particularly pyoverdine and pyocyanin, has been linked to P. aeruginosa pathogenicity through iron acquisition, oxidative stress, and immune evasion. The observed inverse association between pyoverdine production and MDR/CRPA phenotypes may indicate possible fitness trade-offs between iron uptake systems and CR mechanisms or differing selective pressures across clinical niches that favor either high virulence or high resistance but not both [27–29]. However, these pigment-resistance associations should be interpreted as exploratory observations requiring validation in larger, hypothesis-driven studies with mechanistic investigation, as the causal relationships remain unclear.

Aztreonam showed the highest resistance proportion (53.6%), and resistance to key anti-pseudomonal β-lactams/carbapenems was also high (e.g., imipenem 39.1% resistant; cefepime 35.5% resistant; piperacillin-tazobactam 31.8% resistant), underscoring that multiple commonly used antipseudomonal options may be compromised locally. The significantly higher resistance among CRPA versus non-CRPA isolates across most agents supports the clinical expectation that CR often co-segregates with broader MDR mechanisms and/or co-carriage of additional resistance determinants. Contemporary reviews of P. aeruginosa resistance note that combined mechanisms (β-lactamases, porin loss, and efflux upregulation) commonly interact to produce multidrug phenotypes and reduce the efficacy of multiple β-lactam classes [20, 30–32].

Our CRPA rate (40.9%) substantially exceeds rates reported from the same hospital in concurrent pediatric studies: 15% among general pediatric patients and 18% among cystic fibrosis patients with high antimicrobial sensitivity [23, 26]. Regional pediatric data from Saudi Arabia show MDR prevalence of 7.4%, markedly lower than our findings (37.3%) [30]. The frequency of CRPA isolates and the MDR isolates in this study represents a significant clinical challenge, especially given the limited therapeutic options available for treating resistant isolates in pediatric patients. These findings corroborate global reports of rising CR in pediatric populations, particularly in ICUs [33].

The multivariable modeling showed age-dependent resistance after adjustment for sex, specimen group, and gene co-existence. Isolates from children < 5 years had markedly lower odds of fluoroquinolone resistance (ciprofloxacin AOR = 0.046, CI: 0.010–0.212; norfloxacin AOR = 0.061, CI: 0.013–0.283), with additional age effects seen for meropenem and several other agents in the regression outputs. This age signal likely reflects a combination of pediatric prescribing practices (e.g., comparatively constrained fluoroquinolone usage in younger children) and different prior antibiotic exposure histories across age strata, which together can change local selection pressure and resistance ecology [34].

A high proportion of isolates carried at least one screened carbapenemase gene (86.3%), and blaVIM was the most prevalent (55.5%), followed by blaSPM, blaNDM, and blaKPC, which align with previous studies indicating that these genes are commonly associated with CR isolates in pediatric settings [19, 20, 31, 35]. Among CRPA isolates, blaVIM (68.9%) and blaNDM (55.6%) predominated, reflecting regional trends, although a concurrent study from Tehran reported a higher prevalence of blaNDM (30%) than blaVIM (5%) [26]. The predominance of blaVIM is consistent with the MENA regional evidence base, where blaVIM is commonly reported as the most frequent MBL among MDR P. aeruginosa, including ICU-associated isolates. The enrichment of several genes among CRPA (including higher proportions of blaKPC, blaSPM, and blaNDM in CRPA) supports the concept that CR in this setting frequently occurs in backgrounds with substantial β-lactamase gene burden [9].

Importantly, multivariable analysis results support that increasing gene co-existence is not only descriptive but also functionally associated with resistance outcomes in several β-lactams (e.g., aztreonam AOR = 1.744, p = 0.003; cefepime AOR = 1.608, p = 0.009; imipenem AOR = 1.968, p = 0.001). In addition, gene co-existence correlated with MDR (Pearson r = 0.326, p = 0.001), supporting the interpretation that accumulation of carbapenemase determinants tracks with broader multidrug phenotypes in this population. This pattern is compatible with regional and global observations that CR in P. aeruginosa often arises through combined processes (horizontal gene transfer of β-lactamases plus chromosomal changes such as oprD disruption), rather than a single mechanism operating in isolation [32, 36].

RAPD-PCR revealed high genetic diversity (23 clusters plus singletons at the selected similarity threshold), suggesting that resistance in our hospital is not driven solely by one dominant clone. Nevertheless, the presence of ward-specific clustering, particularly among ICU-associated isolates, and very high similarity among some isolate pairs is consistent with probable localized transmission events superimposed on a diverse background population. Similar ICU-focused RAPD patterns previously have been described in Tehran ICU settings, supporting the interpretation that intermittent clonal spread is plausible and actionable for infection control [13, 23, 37, 38].

This study has several limitations. The single-center tertiary referral hospital design may limit generalizability to other pediatric settings or regions. Although no automated confirmatory identification system was used, bacterial identification was based on well-established conventional biochemical methods that interrogate the same core metabolic and enzymatic reactions as standardized commercial platforms such as API, thereby minimizing the risk of misidentification. The antimicrobial susceptibility panel was restricted to nine routinely available antipseudomonal agents based on local laboratory capacity and national CLSI guideline implementation. Important agents such as colistin, levofloxacin, tobramycin, and a new combination agent ceftolozane-tazobactam were not tested due to resource and availability constraints, which limits our ability to fully characterize therapeutic options. Reliance on disk diffusion without minimum inhibitory concentration (MIC) determination may misclassify isolates with borderline CR. We did not collect clinical outcome data (treatment response, mortality, length of stay), precluding correlation of resistance profiles with patient outcomes. RAPD-PCR, while cost-effective, has lower reproducibility and discriminatory power compared to MLST or whole-genome sequencing, limiting definitive transmission chain mapping. The moderate sample size (n = 110) may reduce statistical power for detecting associations in stratified analyses. The cross-sectional design precludes assessment of temporal trends or intervention effectiveness. Future multi-center prospective studies with MIC testing, higher-resolution typing methods, and clinical outcome data would address these limitations.

In conclusion, given the combined picture of high resistance burden, gene co-existence, and ward-linked clustering, targeted interventions should prioritize ICU-focused surveillance, reinforcement of device-associated infection prevention bundles, and antibiotic stewardship strategies that reduce unnecessary broad-spectrum exposure while preserving antipseudomonal activity for severe infections. Where feasible, incorporating higher-resolution typing (e.g., MLST or WGS) for clustered isolates would strengthen transmission inference, help identify reservoirs, and allow more definitive outbreak confirmation compared with RAPD alone.

Supplementary Information

Supplementary Material 1. (18.8KB, docx)
Supplementary Material 2. (103.1KB, docx)

Acknowledgements

We thank the personnel of PIDRC and the Microbiology Lab at Children's Medical Center Hospital for their assistance in this project.

Abbreviations

AORs

Adjusted odds ratios

BAL

Bronchoalveolar lavage

CDDST

Combined double-disk synergy test

Cis

Confidence intervals

CLSI

Clinical and Laboratory Standards Institute

CR

Carbapenem-resistant

CRPA

Carbapenem-resistant P. aeruginosa

ICU

Intensive care unit

MBL

Metallo-β-lactamase

MDR

Multi-drug resistant

MENA

Middle East and North Africa

MIC

Minimum inhibitory concentration

MLST

Multi-locus sequence typing

OPD

Outpatient department

P. aeruginosa

Pseudomonas aeruginosa

PCR

Polymerase chain reaction

RAPD-PCR

Random amplified polymorphic DNA-PCR

UPGMA

Unweighted pair group method with arithmetic mean

WGS

Whole genome sequencing

WHO

World Health Organization

Authors’ contributions

EJ contributed to conceptualization, methodology, investigation, data curation, analysis, visualization and writing the original draft; BP and SM contributed to the study design, validation, supervision, and review & editing; MRAK contributed to investigation, data curation and review & editing; RA and MSA were responsible for resources and data collection. All authors read and approved the final manuscript.

Funding

This work was financially supported by Tehran University of Medical Sciences as part of Erfaneh Jafari’s PhD dissertation. The funders had no role in study design and execution, decision to publish, or preparation of the manuscript.

Data availability

The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The study was conducted in accordance with the principles of the Declaration of Helsinki. The Ethics Committee of Tehran University of Medical Sciences approved the sampling protocols (IR.TUMS.CHMC.REC.1402.136), and written informed consent was secured from the parents or guardians of the patients before the data collection began.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

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Supplementary Materials

Supplementary Material 1. (18.8KB, docx)
Supplementary Material 2. (103.1KB, docx)

Data Availability Statement

The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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