Abstract
Background
This study provides the first prospective, genomically validated dataset integrated into the global MultiDrug-Resistant Organism (MDRO) Network from the Middle East and North Africa region. To address the substantial gap in antimicrobial resistance (AMR) data, the American University of Beirut Medical Center (AUBMC) investigated carbapenem-resistant Enterobacterales (CRE), Acinetobacter baumannii (CRAB), and Pseudomonas aeruginosa (CRPA).
Methods
Consecutive, hospitalized patients whose clinical cultures were positive for CRE, CRAB, or CRPA were prospectively enrolled between June 2018 and November 2019. Data on demographics, hospitalization, outcomes, and antimicrobial susceptibility were collected. Whole-genome sequencing (WGS) characterized the isolates. The primary outcome was the Desirability of Outcome Ranking (DOOR).
Results
Ninety-four patients were included: 35 with CRE, 19 with CRAB, and 40 with CRPA. Overall, 30% of patients survived without adverse events, whereas 33% experienced two or more adverse events. Patients with CRAB had the poorest prognosis, with only 5% surviving without complications, and a significantly different DOOR distribution versus CRE or CRPA (p = 0.023). Adjusted DOOR probabilities confirmed the poorer outcomes associated with CRAB: when compared with CRE or CRPA, a randomly selected CRAB patient had only a 37.7% (95% CI, 24.9–52.6%) and 38.8% (95% CI, 26.5–52.7%) probability of achieving a more desirable outcome, respectively. WGS identified blaNDM−5 (24%) and blaOXA−48 (24%) as the most common genes in CRE, blaOXA−23 in all CRAB strains, and blaVIM−2 in 88% of CRPA strains. The dominant clones were CG383 K. pneumoniae (CRE), CG2 (CRAB), and CG111 (CRPA).
Conclusions
MDRO infections in Lebanon have distinct molecular patterns compared to the US, Chinese, South American, and Australian cohorts, as well as poor outcomes, especially with CRAB. Ongoing surveillance, strengthening regional collaboration, and future clinical trials are essential to guide diagnostic and therapeutic strategies in settings with increasing AMR.
Trial Registry
ClinicalTrials.gov.
Trial Registration number
Date of registration
15-08-2018.
Keywords: Antimicrobial resistance, Carbapenemase genes, Genomic epidemiology, Molecular surveillance, Multidrug-resistant pathogens, Infection outcomes
Background
Over the past decade, antimicrobial resistance (AMR) among Gram-negative bacteria (GNB) has increased worldwide, with particular attention given to multidrug-resistant organisms (MDROs). The World Health Organization (WHO) recently identified carbapenem-resistant (CR) Acinetobacter baumannii (CRAB) and CR Enterobacterales (CRE) as critical priority pathogens and CR Pseudomonas aeruginosa (CRPA) as high priority in their global priority list for research and development [1]. These pathogens were selected because of their severe clinical and economic consequences, the shortage of existing treatment options, and the limited number of new active agents in the pipeline [2]. It is projected that by 2050, MDROs will result in 10 million deaths annually [3]. Furthermore, the COVID-19 pandemic has been associated with an increase in AMR [4, 5], potentially worsening the situation.
The global burden of AMR has long been recognized, prompting initiatives such as the WHO’s 2015 global action plan [6]. Despite these efforts, substantial gaps in addressing AMR remain, particularly in middle- and low-income countries. In the Middle East and North Africa (MENA) region, data on MDROs are limited, but collaborative efforts are underway to address this growing challenge [7]. A review of AMR in Arab League countries revealed high rates of MDROs throughout the region [8]. Investigating this issue in MENA countries is important because the region has diverse populations, is a crossroad for travel and human mobilization, and has high human and zoonotic antibiotic consumption [9]. Since 2004, many Arab countries, including Lebanon, have recorded significant rates of antimicrobial use and misuse [10]. Local reports indicate increasing rates of CR pathogens in Lebanon, especially in tertiary care centers such as the American University of Beirut Medical Center (AUBMC). The first β-lactamase-mediated CRE was reported in Lebanon in 2008, when a Klebsiella pneumoniae strain carrying the blaOXA−48 gene was identified [2]. In North Lebanon, a study on 6,103 GNB reported that the rate of CRE increased from 1.71% in 2016 to 5.19% in 2017 [11]. Various Lebanese hospitals also reported increasing rates of CRAB and CRPA between 2011 and 2013 [12]. However, Lebanese national data on AMR rates have been variable due to the limited number of studies from a few academic hospitals and uncontrolled antimicrobial use [12].
To provide both clinical and epidemiological observational data on MDRO infections, AUBMC is one of only two sites from the MENA region in the MDRO Network, which includes 57 international sites from 11 countries (ClinicalTrials.gov Identifier: NCT03646227). The MDRO Network is an initiative by the Antibacterial Resistance Leadership Group (ARLG), which aims to advance the understanding of AMR through clinical research and provide an infrastructure for future clinical and translational research. The MDRO Network contains 3 study cohorts: the Consortium on Resistance Against Carbapenems in Klebsiella and Other Enterobacterales (CRACKLE-II) [13], the Prospective Observational Pseudomonas Study (POP) [14], and the Study Network of Acinetobacter as a Carbapenem-Resistant Pathogen (SNAP) [15]. This study provides regionally distinct genomic and clinical insights that differ markedly from other global MDRO Network sites. Data from AUBMC are presented herein to describe the clinical characteristics of patients infected with CR-GNB.
Methods
Patient population and study setting
This is an observational and prospective cohort study. Data were collected between June 2018 and November 2019 from consecutive hospitalized patients with at least one positive culture for the following MDROs: CRE, CRAB, and CRPA, which were isolated from different anatomical sites (blood, respiratory, urine, wound, intraabdominal, and other samples) during their hospitalization at AUBMC. Patient demographics, course of hospitalization, treatment, and outcomes were included at enrollment. The primary outcome for this analysis was the Desirability of Outcome Ranking (DOOR).
All patients eligible for inclusion were hospitalized, including pediatric patients, who had at least one isolate of CRE, CRAB, or CRPA from a clinical culture. Outpatients were excluded to ensure adequate collection of isolates and clinical data. Patients with cystic fibrosis and a positive CRPA culture were also excluded. Both infected and colonized patients were included, as described in previous MDRO Network studies [16].
Bacterial isolates and their identification
The bacterial isolates were recovered and characterized at the Clinical Microbiology Laboratory (CML), Department of Pathology and Laboratory Medicine, at AUBMC. The isolates were identified via matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) (Bruker Daltonik, GmbH, Bremen, Germany). Local phenotypic antimicrobial susceptibility testing (AST) was performed with the disk diffusion (DD, disks purchased from BBL, Becton Dickinson, USA) and Etest strips minimal inhibitory concentration (MICs, strips purchased from Liofilchem, Scozia, Italy) methods and interpreted according to the Clinical and Laboratory Standards Institute (CLSI) guidelines [17]. For screening suspected carbapenemase producers among Enterobacterales, ertapenem disks (10 µg/mL) were used. Those isolates showing a < 25 mm zone of inhibition were then confirmed via MIC Etests against ertapenem, imipenem and meropenem. Carbapenem resistance in CRAB and CRPA was assessed using imipenem and meropenem disk diffusion and Etest MICs. When applicable, ceftazidime-avibactam (CZA) and/or ceftalozane-tazobactam (CT) Etest MIC strips were also tested.
Molecular characterization
For microbiologic and molecular examination, the isolated MDRO samples underwent further confirmatory AST at a central laboratory (Centre for Clinical Research, University of Queensland, Brisbane, Australia), assessment of antibiotic resistance mechanisms, and genomic analysis via whole-genome sequencing (WGS). Draft genomes were assembled via SPAdes v3.13.0 [18]. The multilocus sequence types (MLSTs) were determined with mlst v2.22. Carbapenemase genes were identified via AMRFinderPlus v3.10.20 [19].
Data analysis was performed using SAS software version 9.4. Differences in continuous variables among the demographic data from the 3 cohorts (CRACKLE-II, SNAP, and POP) were analyzed using the Kruskal‒Wallis test, whereas Fisher’s Exact test was used for the categorical variables. The primary outcome was DOOR at 30 days, which categorizes patients into four ordinal outcome groups: (1) survival without unfavorable events, (2) survival with one event, (3) survival with two or three events, and (4) mortality. Unfavorable events include no clinical response at 30 days with either persistent symptoms or recurrent infection, unsuccessful discharge or readmission within 30 days, and adverse events such as renal failure or Clostridioides difficile infection. The 95% confidence intervals (CIs) were calculated, and p-values < 0.05 were considered significant. Unadjusted and inverse probability weighting (IPW)-adjusted DOOR probabilities of a favorable outcome at 30 days, along with their 95% CIs, were estimated to compare the outcomes of patients with CRE, CRAB, and CRPA. IPW was applied using logistic regression to adjust for baseline characteristics, including the age-adjusted Charlson Comorbidity Index score, prehospitalization origin (home vs. not home), intensive care unit (ICU) admission, Pitt Bacteremia Score, and anatomical source of infection.
Definitions
In the remainder of the manuscript, the term MDRO is used to indicate any Enterobacterales, A. baumannii, or P. aeruginosa displaying in vitro nonsusceptibility to any of the carbapenems as per the CLSI guidelines [17]. Infected patients are defined as having a CRE, CRAB, or CRPA isolated from blood or other sterile sites, and respiratory cases are deemed infected after clinical adjudication [16]. Colonized patients are defined as those with a positive culture from a non-sterile site (e.g., respiratory tract, urine, wound) without clinical signs or symptoms meeting the criteria for active infection, as adjudicated by the study investigators in accordance with the MDRO Network protocol [16]. Respiratory isolates were classified per the guidelines of the American Thoracic Society and the Infectious Diseases Society of America [20, 21], while positive urine and surgical wound cultures were evaluated according to the CDC National Healthcare Safety Network definitions [22]. Eventually, only the first indexed culture from each patient was included in the final analyses. The severity of acute illness was calculated through the components of the Pitt Bacteremia Score, which has been shown to predict mortality in both bacteremic and non-bacteremic patients [23, 24], as well as in colonized patients [25].
This study was conducted under a waiver of informed consent to ensure the inclusion of all consecutive patients. The study was reviewed and approved by the Institutional Review Board at AUBMC.
Results
Patient demographics
A total of 94 patients with CRE (n = 35, 37%), CRAB (n = 19, 20%), and CRPA (n = 40, 43%) were included in the analysis. Among them, 66 (70%) were infections and 28 (30%) were colonizations. The distribution of infection versus colonization differed across pathogen groups: among CRE patients, 22 (63%) had infections and 13 (37%) were colonized; among CRAB patients, 14 (74%) had infections and 5 (26%) were colonized; and among CRPA patients, 30 (75%) had infections and 10 (25%) were colonized. The median age of the enrolled patients was 63 years (interquartile range [IQR] 47–72), most of whom were males (63%). The anatomical source of the isolates was variable, as detailed in Table 1.
Table 1.
Baseline characteristics
| Patient Characteristics | CRE n (%) (N = 35) |
CRAB n (%) (N = 19) |
CRPA n (%) (N = 40) |
Total n (%) (N = 94) |
P Value |
|---|---|---|---|---|---|
| Age at FPC, median years (Q1, Q3) | 56 (28, 75) | 67 (56, 73) | 64 (53, 72) | 63 (47, 72) | 0.316 a |
| Sex, male | 23 (66) | 11 (58) | 25 (63) | 59 (63) | 0.856 b |
| Comorbidity | |||||
| Diabetes mellitus | 6 (17) | 6 (32) | 11 (28) | 23 (24) | 0.309 b |
| Coronary artery disease | 15 (43) | 7 (37) | 12 (30) | 34 (36) | 0.529 b |
| Congestive heart failure | 7 (21) | 6 (32) | 7 (18) | 20 (22) | 0.572 b |
| Chronic kidney diseasec | 2 (6) | 2 (11) | 3 (8) | 7 (7) | 0.779 b |
| Chronic obstructive pulmonary disease | 3 (9) | 3 (16) | 3 (8) | 9 (10) | 0.595 b |
| Malignancy | 14 (40) | 6 (32) | 11 (28) | 31 (33) | 0.535 b |
| Immunocompromised | 8 (23) | 1 (5) | 10 (25) | 19 (20) | 0.186 b |
| Recent or current chemotherapy | 7 (20) | 1 (5) | 8 (20) | 16 (17) | 0.348 b |
| Charlson Comorbidity Index score, median (Q1, Q3) | 2 (1, 4) | 2 (1, 3) | 2 (0, 3) | 2 (0, 3) | 0.413 a |
| Origin at admission | |||||
| Home | 29 (83) | 14 (74) | 32 (80) | 75 (80) | 0.915 b |
| Transferred from another hospital | 5 (14) | 4 (21) | 5 (13) | 14 (15) | |
| Transferred from a foreign country | 1 (3) | 1 (5) | 2 (5) | 4 (4) | |
| ICU admission prior to FPC | 21 (60) | 14 (74) | 29 (73) | 64 (68) | 0.426 b |
| Days from admission to FPC, median (Q1, Q3) | 6 (0, 21) | 12 (2, 38) | 19 (5, 51) | 14 (1, 34) | 0.081 a |
| Location at FPC | 0.001 b | ||||
| Emergency department | 9 (26) | 0 (0) | 0 (0) | 9 (10) | |
| ICU | 12 (34) | 11 (58) | 16 (40) | 39 (41) | |
| Wards and other | 14 (40) | 8 (42) | 24 (60) | 46 (49) | |
| Pitt bacteremia score ≥ 4 | 5 (14) | 11 (58) | 14 (35) | 30 (32) | 0.004 b |
| Culture Anatomical Source | 0.025 b | ||||
| Blood: infection | 12 (34) | 5 (26) | 7 (18) | 24 (26) | |
| Non-wound abdominal: infection | 2 (6) | 0 (0) | 0 (0) | 2 (2) | |
| Respiratory: infection | 1 (3) | 8 (42) | 16 (40) | 25 (27) | |
| Respiratory: colonization | 5 (14) | 2 (11) | 6 (15) | 13 (14) | |
| Urine: infection | 7 (20) | 1 (5) | 5 (13) | 13 (14) | |
| Urine: colonization | 4 (11) | 3 (16) | 1 (3) | 8 (9) | |
| Wound: infection | 0 (0) | 0 (0) | 2 (5) | 2 (2) | |
| Wound: colonization | 4 (11) | 0 (0) | 3 (8) | 7 (7) | |
Legend: Comparison of patient characteristics, hospitalization, and culture sources among patients with CRE, CRAB, and CRPA infections
CRAB, carbapenem-resistant Acinetobacter baumannii; CRE, carbapenem-resistant Enterobacterales; CRPA, carbapenem-resistant Pseudomonas aeruginosa; FPC, first positive culture; ICU, intensive care unit; Q1, first quartile; Q3, third quartile
a Kruskal‒Wallis test
b Fisher’s Exact test
c Not severe
The most frequent comorbidities in the total cohort were coronary artery disease (36%) and malignancy (33%), with no statistically significant differences among the groups. 25% of CRPA patients and 23% of CRE patients were immunocompromised, whereas only 5% of CRAB patients were immunocompromised (p = 0.186). Diabetes mellitus was present in 24% of patients, whereas nonsevere chronic kidney disease and chronic obstructive pulmonary disease were present in 7% and 10%, respectively. The median Charlson Comorbidity Index score was 2 (IQR, 0–3), which was consistent across cohorts (p = 0.413) (Table 1).
Hospitalization and healthcare-related risk factors
80% of the enrolled patients were admitted from home, whereas 15% were transferred from another hospital or healthcare facility. Only 4 patients were transferred from a neighboring country and thought to have acquired the infection prior to their transfer to AUBMC (Table 1).
The overall median number of days from admission to the day of the first positive culture (FPC) was 14 days (IQR, 1–34 days; p = 0.081). The median days from admission to FPC were 6 for CRE (IQR, 0–21), 19 days for CRPA (IQR, 5–51), and 12 days (IQR, 2–38) for CRAB.
68% of the patients had a stay in the ICU prior to the day of the FPC, and 41% of the patients were admitted to the ICU on the day of the FPC. The remainder were distributed between the medical/surgical wards and hematology/oncology wards (48%), followed by the emergency department (10%) (Table 1).
Among the 94 patients included in the study, the median length of hospital stay from admission to discharge or death was 41.5 days (IQR 16–93). However, significant variability was observed across the cohorts (p = 0.028), with CRAB patients having the longest stays (47 days, IQR 22–96) and CRE patients experiencing shorter stays (25 days; IQR, 8–47) (Table 2).
Table 2.
Desirability of outcome ranking among patients with CRE, CRAB, and CRPA infections
| CRE n (%) (N = 35) |
CRAB n (%) (N = 19) |
CRPA n (%) (N = 40) |
Total n (%) (N = 94) |
P Value | |
|---|---|---|---|---|---|
| Admission to discharge/death, median days (Q1, Q3) | 25 (8, 47) | 47 (22, 96) | 52.5 (20, 99) | 41.5 (16, 93) | |
| Disposition after discharge | 0.280 b | ||||
| Home | 26 (74) | 9 (47) | 22 (55) | 57 (61) | |
| Death | 7 (20) | 9 (47) | 12 (30) | 28 (30) | |
| Remain in hospital | 1 (3) | 0 (0) | 3 (8) | 4 (4) | |
| Transferred | 1 (3) | 1 (5) | 3 (8) | 5 (5) | |
| 30-day mortality | 5 (14) | 5 (26) | 7 (18) | 17 (18) | 0.505 b |
| 90-day mortality | 7 (20) | 8 (42) | 11 (28) | 26 (28) | 0.234 b |
| Four category DOOR | 0.023 a | ||||
| Alive without events c | 13 (37) | 1 (5) | 14 (35) | 28 (30) | |
| Alive with 1 event | 11 (31) | 4 (21) | 3 (8) | 18 (19) | |
| Alive with 2 or 3 events | 6 (17) | 9 (47) | 16 (40) | 31 (33) | |
| Dead | 5 (14) | 5 (26) | 7 (18) | 17 (18) | |
Legend: Outcomes of patients across the 3 cohorts include length of hospital stay and discharge dispositions, including 30-day mortality and 90-day mortality. The four ordinal DOOR categories were calculated at 30 days and were based on the number of unfavorable events experienced by patients during that period
CRAB, carbapenem-resistant Acinetobacter baumannii; CRE, carbapenem-resistant Enterobacterales; CRPA, carbapenem-resistant Pseudomonas aeruginosa;; DOOR, Desirability of Outcome Ranking; Q1, first quartile; Q3, third quartile
aKruskal‒Wallis test
bFisher’s Exact test
cUnfavorable events include: no clinical response at 30 days with either persistent symptoms or recurrent infection, unsuccessful discharge or readmission within 30 days, and adverse events such as renal failure or Clostridioides difficile infection
Outcomes
The DOOR analysis revealed that 30% of the patients survived without any adverse events, whereas 33% experienced 2 or more events (Table 2). Notably, patients with CRAB had the poorest outcome, with only 5% surviving without complications, 47% experiencing 2 + events, and 26% not surviving. The DOOR distribution for CRAB patients was significantly different from that for patients with CRE or CRPA (p = 0.023) (Table 2). DOOR probability estimates confirmed the substantially worse outcomes associated with CRAB. The DOOR probability represents the likelihood that a randomly selected patient from the first group achieves a more desirable 30-day outcome than a randomly selected patient from the second group, and a value below 50% indicates that the first group faces a worse outcome. The adjusted DOOR probability for CRAB vs. CRE was 37.7% (95% CI, 24.9–52.6%), indicating that a randomly selected CRAB patient had only a 37.7% chance of achieving a more desirable outcome than a randomly selected CRE patient (Table 3). Similarly, the adjusted DOOR probability for CRAB vs. CRPA was 38.8% (95% CI, 26.5–52.7%), again demonstrating that CRAB patients were less likely to achieve a more desirable outcome than CRPA patients. Together, these estimates confirm that CRAB was associated with the poorest 30-day outcomes across all three groups. In contrast, the adjusted DOOR probability for CRPA vs. CRE was 50.6% (95% CI, 38.2–62.9%), indicating comparable outcomes between these two groups.
Table 3.
Unadjusted and IPW-adjusted probabilities of DOOR outcomes
| DOOR Probability Estimate (95% CI) | ||
|---|---|---|
| CRAB vs. CRE (n = 54) | Unadjusted DOOR | 26.9% (16.1%, 41.4%) |
| Adjusted DOOR | 37.7% (24.9%, 52.6%) | |
| CRPA vs. CRE (n = 75) | Unadjusted DOOR | 42.4% (30.6%, 55.2%) |
| Adjusted DOOR | 50.6% (38.2%, 62.9%) | |
| CRAB vs. CRPA (n = 59) | Unadjusted DOOR | 37.3% (25.1%, 51.4%) |
| Adjusted DOOR | 38.8% (26.5%, 52.7%) | |
Legend: The probability of 30-day DOOR outcomes was estimated for patients on the basis of their infecting organism and presented with Halperin 95% CIs. IPW was used to adjust the estimates for key differences in patient baseline characteristics via logistic regression. The variables included in the adjustment were age-adjusted Charlson Comorbidity Index score, prehospitalization origin (home vs. not home), ICU admission, Pitt Bacteremia Score, and anatomical source of infection
CRAB, carbapenem-resistant Acinetobacter baumannii; CRE, carbapenem-resistant Enterobacterales; CRPA, carbapenem-resistant Pseudomonas aeruginosa; CI, confidence interval; DOOR, Desirability of Outcome Ranking; ICU, intensive care unit; IPW, inverse probability weighting
The majority (n = 57, 61%) of patients were discharged home, while 28 patients (30%) died in the hospital. Disposition outcomes did not differ significantly between the groups (p = 0.280). The 30-day mortality rate was 18% (n = 17/94) overall, with 14% for CRE (n = 5/35), 26% for CRAB (n = 5/19), and 18% for CRPA (n = 7/40) (p = 0.505) (Table 2).
Microbiologic and molecular analysis
Antibiotic Susceptibility Profiles
A total of 57 isolates underwent central AST, which included 1 CRE, 17 CRAB, and 39 CRPA. For CREs, the AST was limited to K. pneumoniae only, which was tested only to confirm carbapenem resistance in isolates lacking a carbapenemase gene. All 17 CRAB isolates demonstrated resistance to most antibiotics, whereas the CRPA isolates presented a more varying pattern of resistance. Among the novel β-lactam/β-lactamase inhibitor combinations, CZA resistance was reported in 27 CRPA isolates (69%), whereas CT resistance was reported in 20 CRPA isolates (51%) (Table 4).
Table 4.
Antibiotic susceptibility profiles of CRKP, CRAB, and CRPA
| Antibiotic | CRKP a, b Resistant n (%) (N = 1) |
CRAB b Resistant n (%) (N = 17) |
CRPA c Resistant n (%) (N = 39) |
|---|---|---|---|
| Amikacin | - | 17 (100) | 22 (56) |
| Cefepime | - | 17 (100) | 28 (72) |
| Ciprofloxacin | - | 17 (100) | 27 (69) |
| Meropenem | 1 (100) | 17 (100) | 36 (92) |
| Piperacillin-Tazobactam | - | 17 (100) | 19 (49) |
| Colistin | - | 5 (29) | 10 (26) |
| Ceftazidime-Avibactam | - | - | 27 (69) |
| Ceftolozane-Tazobactam | - | - | 20 (51) |
Legend: Antibiotic susceptibility testing was performed in a central laboratory to confirm resistance to carbapenems
CRAB, carbapenem-resistant Acinetobacter baumannii; CRKP, carbapenem-resistant Klebsiella pneumoniae; CRPA, carbapenem-resistant Pseudomonas aeruginosa
aAmong 7 CRKP isolates included in the study, 6 of them carried the carbapenemase gene and did not undergo antibiotic susceptibility testing
bAll testing was done using the broth microdilution method
cAll testing was done using agar dilution, except for Meropenem, where broth microdilution was used
Whole-Genome Sequencing
WGS of 78 MDRO isolates (83%), including 25 CRE, 15 CRAB, and 38 CRPA isolates, revealed distinct genetic characteristics across species. Among the CRE genes, blaNDM-5 (24%, n = 6) and blaOXA-48 (24%, n = 6) were the most common carbapenemase-encoding genes and were mutually exclusive, whereas all CRAB genes harbored blaOXA-23. In the CRPA, blaVIM-2 (88%) was the predominant carbapenemase-encoding gene (Table 5).
Table 5.
Carbapenemase genes and clonal groups in CRE, CRPA, and CRAB isolates
| Organism (N) | Carbapenemase Presence N (%) | Carbapenemase Genes (N, %) | Clonal Groups (N, %) | K Locus (N, %) | O Locus (N, %) | Outer Membrane Porin (N, %) | OC Locus (N, %) | OprD Mutation |
|---|---|---|---|---|---|---|---|---|
| CRE (25) | 22 (88%) | blaNDM-5 (6, 24%) | - | - | - | - | - | - |
| blaOXA-48 (6, 24%) | ||||||||
| blaNDM-1 (3, 12%) | ||||||||
| blaNDM-4 (3, 12%) | ||||||||
| blaOXA-181 (2, 8%) | ||||||||
| blaNDM-5;blaOXA-14 (2, 8%) | ||||||||
| blaNDM-7 (1, 4%) | ||||||||
| blaOXA-244 (1, 4%) | ||||||||
| • Escherichia coli / Shigella spp. (N = 18, 72%) | - | - | - | - | - | - | - | - |
| • Klebsiella spp. (N = 7, 28%) | - | blaNDM-1 (1, 14%) | CG258_11 (1, 14%), CG383 (2, 29%) | KL24 (1, 14%) | O1v2 (3, 43%) | OmpK35 mutation (3, 43%) | - | - |
| blaNDM-4 (1, 14%) | CG15 (1, 14%) | O2v1 (1, 14%) | ||||||
| blaNDM-5;blaOXA-48 (2, 29%) | Other (3, 43%) | O2v2 (1, 14%) | ||||||
| blaNDM-7 (1, 14%) | Others (2, 28%) | |||||||
| blaOXA-48 (1, 14%) | ||||||||
| CRAB (15) | 15 (100%) | blaOXA-23 (15, 100%) | CG2 (15, 100%) | KL234 (2, 13%) | - | - | OCL1 (15, 100%) | - |
| KL3 (2, 13%) | ||||||||
| KL9 (11, 73%) | ||||||||
| CRPA (38) | 17 (45%) | VIM-2 (15, 88%) | CG111 (12, 32%), CG357 (5, 13%) | - | - | - | - | 23 (61%) |
| IMP-15 (2, 12%) | CG235 (1, 3%) | |||||||
| Other (20, 53%) |
Legend: Whole-genome sequencing results show the variability of carbapenemase-encoding genes found in the isolates of infected patients. The predominant genes were blaNDM-5 and blaOXA-48 among CREs, blaOXA-23 in CRAB, and blaVIM-2 in CRPA
CRAB, carbapenem-resistant Acinetobacter baumannii; CRE, carbapenem-resistant Enterobacterales; CRPA, carbapenem-resistant Pseudomonas aeruginosa
CR K. pneumoniae (CRKP) displayed clade heterogeneity, with the majority being CG383 (29%), followed by CG25811 (14%) and CG15 (14%). The CRAB isolates were all CG2, while CRPA showed a broader distribution, with CG111 (32%) being the most prevalent (Table 5).
In Klebsiella spp., KL24 (14%) was the most common K locus, and O1v2 (43%) was the most common O locus. Among CRAB, OCL1 (100%) was universally present. OmpK35 mutations were noted in 3 CRKP isolates (43%), whereas OprD mutations were detected in 23 CRPA isolates (61%), which may increase carbapenem resistance (Table 5).
Discussion
This study provides epidemiological and clinical insights into MDROs from a tertiary care center in Lebanon. Aligning with global and regional trends, our data revealed distinct molecular profiles of carbapenemase-producing bacteria whose infections were associated with poor clinical outcomes. Notably, the carbapanemase distribution in Lebanon contrasts with patterns reported in the US, China, South America, and Australia, emphasizing the unique molecular profile of the country.
Multiple studies from tertiary medical centers in Lebanon have described an increasing trend in MDRO, raising a national health concern. According to a retrospective analysis of Lebanese surveillance data from 2010 to 2018 involving 15,908 isolates, CR was the most reported CR for A. baumannii (81.7%), followed by K. pneumoniae (6.5%) and E. coli (3.3%) [26]. Another study from Lebanon reported a notable increase from 3% in 2010 to 32% in 2018 in CR among GNB [27]. In an analysis of AMR in the Arab world in 2018, the prevalence of CRE was estimated to be 1–4% in Lebanon, with K. pneumoniae accounting for 3% and E. coli accounting for 1% of the CRE isolates [8]. CRPA represented 28% of 3,920 isolates, whereas CRAB comprised up to 82% of 3,552 isolates from various sources [8]. In another compilation-based surveillance study from a network of 13 Lebanese hospitals reflecting the national situation from 2015 to 2016, different levels of CR ranging from 1 to 5% for E. coli and 1–10% for Klebsiella spp. were reported [28]. Other nationwide studies reported that the prevalence of CRPA in Lebanon since 2011 ranged between 27% and 30%, whereas CRAB cases had rates between 74% and 82% [2, 29].
In line with these alarming resistance trends, our study identified patient and hospitalization factors linked to infections caused by CR GNB. Our results revealed that most patients acquired these infections prior to or during their current intensive care unit (ICU) stay, a known risk factor for CR acquisition [30]. The median days to FPC from admission for CRE was comparable to that of the US cohort of the MDRO Network (2 days [IQR, 0–16]) [13], whereas for CRPA, the median days to FPC were longer than those of the US (4 days [IQR, 0–21]), China (10 days [IQR, 2–25]), and Singapore and Australia (13 days [IQR, 1–25]), but similar to those of South America (20 days [IQR, 2–43]) [14]. The Lebanese CRAB cohort had a time to positivity similar to that of Singapore and Australia’s (12 days [IQR, 3–26]) but very different from that of the US (2 days [1, 7]), China (7 days [2, 15]) and South-Central America (16 days [9, 31]) [15].
The overall 30-day mortality for CRE was 14%, which was lower than that of the MDRO Network cohort of the US (25%), although our sample size was much smaller [13]. However, national studies have reported that morality rates due to CRE are closer to 26% [31] and have reached 44% [32]. The highest mortality rate was reported in patients with CRAB (26%), which was very similar to that reported in the overall global cohort of the MDRO Network (24%, n = 128/536) [15]. This finding is similar to other studies, where CRAB had higher mortality rates than did CRE [33] and CRPA [34]. This could be attributed to CRAB being present in more invasive settings [33], especially when it causes bloodstream infections [35, 36]. CRAB is also associated with comorbid risk factors, such as chronic liver and renal diseases [37]. A previous study of 128 patients at AUBMC reported that the mortality rate for multidrug-resistant (MDR) A. baumannii infections, including CRAB, ranged between 52% and 66% [38]. For CRPA, the 30-day mortality rate in this cohort was 18%. In AUBMC, a previous study reported that all-cause mortality from P. aeruginosa infection was 19.5%, where 10% of the isolates were reported to be MDR [39].
The molecular analysis of our isolates revealed blaNDM−5 and blaOXA−48 as the predominant carbapenemase-encoding genes in CRE, which is consistent with other local reports [40, 41]. blaOXA−48 was previously reported to account for 48% of all CRE isolates at AUBMC [42]. This finding suggests stable genomic patterns of carbapenemase dissemination. However, this is a unique finding compared with other regions from the MDRO Network, as the most common carbapenemase genes for CRE were blaKPC−2 (51%) and blaKPC−3 (41%) in the US [13], whereas specifically for CRKP, the most common gene was also blaKPC−2 in China (94%) and South America (39%) [43]. The frequent occurrence of blaOXA−48 in Lebanon may be due to a combination of factors related to its geographical proximity to other countries with similar genetic characteristics [44, 45], as well as mass migratory trends [41]. On the other hand, blaOXA−23 was the universally detected gene in our CRAB samples, and its predominance is consistent with other national studies [46, 47]. All samples had a CG2 clonal group, similar to other cohorts from the MDRO Network with a majority of CG2, except for South-Central America, where CG1 and CG25 were predominant (28% for both) [15]. The majority of CRPA carbapenemase-encoding genes were blaVIM−2 (88%), whereas China and South Central America had more blaKPC genes (23% and 32%, respectively), and Australia and Singapore had both blaIMP and blaNDM genes (21% for both) [14].
Our study has several strengths, including its prospective, observational design and the use of advanced molecular techniques to reveal the genetic profiles and resistance mechanisms of our MDRO isolates. Participation in the MDRO Network allowed comparison of our local data with international cohorts, which contextualizes our findings within a global framework.
This study is limited by a small sample size, which may affect the statistical power and ability to detect significant differences among the cohorts. Although AUBMC is the largest medical institution in the country, reporting from a single center may hinder generalizability. Furthermore, our small sample size restricts individual assessment of each infection type and pathogen. The inclusion of both infected and colonized patients warrants additional consideration. Colonized patients, by definition, lack active infection and may have more favorable clinical trajectories, potentially influencing outcome comparisons across pathogen groups. In our cohort, colonization rates varied across the three groups (CRE: 37%; CRAB: 26%; CRPA: 25%), which may have differentially affected the observed DOOR distributions. Notably, CRAB patients had the poorest outcomes despite potentially including colonized cases, suggesting that the true burden of CRAB disease may be even greater. This approach was consistent with the methodology of the MDRO Network studies [13–15], in which the inclusion of colonized patients was intended to capture the full clinical spectrum of MDRO carriage and its downstream consequences. Future analyses with a larger sample size could stratify outcomes by infection status to better quantify this effect. Additionally, isolates were collected between 2018 and 2019, and the molecular epidemiology and antimicrobial susceptibility patterns of CR organisms in Lebanon may have evolved since this period, particularly in the context of the COVID-19 pandemic and the recent wars. Our data should therefore be interpreted as a baseline snapshot, and future prospective studies incorporating more recent isolates are warranted to capture longitudinal resistance trends.
Conclusions
Our study contributes to the global understanding of AMR through the MDRO Network initiative. Our institutional data reflect a distinct molecular profile among MDRO isolates and show that infections with these organisms are associated with poor clinical outcomes. These results align with regional and global trends, emphasizing the need for antimicrobial stewardship programs and enhanced infection control practices. Future research should focus on broader, continuous surveillance of MDROs for longitudinal analysis of AMR trends. Owing to the high prevalence of CR-GNB in Lebanon and genetic diversity with unique molecular signatures, future clinical trials of novel therapeutic agents should consider including patients from this country to ensure adequate regional representation.
Acknowledgements
The investigators thank all the patients and their families, and all contributing research fellows and clinical microbiological laboratory personnel. The investigators also thank Chip Chambers and Sara Cosgrove for their detailed review of an earlier version of this article.
Abbreviations
- AMR
Antimicrobial resistance
- ARLG
Antibacterial Resistance Leadership Group
- AUBMC
American University of Beirut Medical Center
- AST
Antibiotic susceptibility testing
- CRAB
Carbapenem-resistant Acinetobacter baumannii
- CRACKLE-II
Consortium on Resistance Against Carbapenems in Klebsiella and Other Enterobacterales
- CRE
Carbapenem-resistant Enterobacterales
- CRKP
Carbapenem-resistant Klebsiella pneumoniae
- CRPA
Carbapenem-resistant Pseudomonas aeruginosa
- CT
Ceftolozane-tazobactam
- CZA
Ceftazidime-avibactam
- DOOR
Desirability of Outcome Ranking
- FPC
First positive culture
- GNB
Gram-negative bacteria
- ICU
Intensive care unit
- IPW
Inverse probability weighting
- IQR
Interquartile range
- MDRO
Multidrug-resistant organism
- MENA
Middle East and North Africa
- MIC
Minimal inhibitory concentration
- POP
Prospective Observational Pseudomonas Study
- SNAP
Study Network of Acinetobacter as a Carbapenem-Resistant Pathogen
- WGS
Whole-genome sequencing
- WHO
World Health Organization
Author contributions
All authors have contributed to this study as follows: JFJ was responsible for the acquisition of laboratory and clinical data, data analysis, and drafting and revision of the article. HK, EJ, JRH, SFH, ZAK, NKZ, were involved in the drafting and revision of the manuscript. LK and YL performed data analysis and drafted and revised the article. JJ, AGA, GFA, and GMM acquired laboratory data and drafter and revised the article. YD, VGF, DLP, MJS, DVD, and SSK were involved in the conception and design of the study, data analysis, and drafting and revising the article. All authors have approved the final version of the manuscript.
Funding
This work was supported by the National Institutes for Allergy and Infectious Diseases at the National Institutes of Health under Award Number UM1AI104681 (April 2016-April 2019, Y5-Y8). The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.
Data availability
The datasets generated and/or analysed during the current study are available in the BioProject repository, under the following accession numbers: PRJNA658369, PRJNA824880, and PRJNA906166.
Ethics approval and consent to participate
This study has been approved by the institutional review board at the American University of Beirut under identification number BIO-2017-0479 with a waiver of consent. This research is adherent to all principles stated by the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors of this manuscript have conflicts of interest to disclose as described BioMed Central Infectious Diseases. All authors report funding support from the ARLG of the National Institutes of Health (NIH) and the National Institute of Allergy and Infectious Diseases (NIAID; UM1AI104681) during the conduct of this study. JFJ, HK, EJ, JRH, SFH, ZAK, NKZ, AGA, LK, YL, JJ, GFA, and GMM have nothing to declare other than ARLG grant paid to their institution. YD reports grants paid to his institutions by Japan Agency for Medical Research and Development, Japan Society for the Promotion of Science, honorarium paid to him by Shionogi, and serving on advisory boards for GSK, Shionogi, and Meiji Seika Pharma. VGF reports grants to his institution from Merck, Contrafect, Karius, Basilea, Janssen, AstraZeneca, EDE (Exponential Deep Examination); royalties from UpToDate; personal fees from Akagera, AstraZeneca, Armata, Basilea, Debiopharm, GSK, Integrated Biotherapeutics, Karius; pending patent for a host gene expression signature diagnostic for sepsis; and stock options with Valanbio. DLP serves on the Scientific Advisory Board of the Antibiotic Action Fund, has grant funding from Shionogi, Pfizer and Merck and serves on advisory boards or has given lectures for Pfizer, Merck, bioMerieux, Zuellig, Entasis, QPex and VenatoRx. MJS reports grants/contracts to his institution from Melinta, Merck, Selux Diagnostics, SNIPRBiome, and bioMérieux; and compensation for participation on a DSMB from AbbVie paid directly to him. DVD reports grants and contracts from the NIH, Merck, and Shinogi; consultancy for Actavis, Tetraphase, Sanofi-Pasteur, MedImmune, Astellas, Merck, Allergan, T2Biosystems, Roche, Achaogen, Neumedicine, Shionogi, Pfizer, Entasis, Qpex, Wellspring, Karius, Teva, and Utility paid directly to him; honoraria from Pfizer; and an editor’s stipend from the British Society for Antimicrobial Chemotherapy (BSAC). SKS received speaker honoraria from MSD, Pfizer, Gilead, Menarini, and Hikma.
Footnotes
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analysed during the current study are available in the BioProject repository, under the following accession numbers: PRJNA658369, PRJNA824880, and PRJNA906166.