Next Article in Journal
Infection Frequency and Allelic Variants of Toxoplasma gondii in Wildlife from the Panama Canal Zone
Previous Article in Journal
Genotypes and Mutations Associated with Macrolide Resistance of Chlamydia trachomatis in Urine Samples in School-Going, Urban Adolescents 14–19 Years from Panama: A Cross-Sectional Descriptive Study
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prevalence and Antimicrobial Susceptibility Patterns of Wound and Pus Bacterial Pathogens at a Tertiary Care Hospital in Central Riyadh, Saudi Arabia

1
Microbiology Department, Medical Diagnostic Laboratories, Dr. Sulaiman Al Habib Medical Group, Riyadh 12333, Saudi Arabia
2
Pathology Department, College of Medicine, King Saud University, Riyadh 11451, Saudi Arabia
3
Laboratory Department, Chief Medical Officer (CMO) Office, Dr. Sulaiman Al Habib Medical Group, Riyadh 12214, Saudi Arabia
*
Author to whom correspondence should be addressed.
Microbiol. Res. 2024, 15(4), 2015-2034; https://doi.org/10.3390/microbiolres15040135
Submission received: 15 August 2024 / Revised: 29 September 2024 / Accepted: 30 September 2024 / Published: 2 October 2024

Abstract

:
The long history and extensive use of antibiotics have caused resistant bacterial pathogens to emerge, increasing mortality and morbidity. The current study was designed to see the prevalence of aerobic bacterial isolates with their antimicrobial resistance pattern from out- and inpatients requested for wound or pus culture. Retrospective study conducted at a tertiary care hospital in central Riyadh from January 2023 to December 2023. Samples were collected and inoculated onto the appropriate media following standard guidelines. Bacterial pathogens were identified by the Vitek2 compact system. Antimicrobial susceptibility was tested using the Kirby–Bauer disk diffusion method as well as by MIC determination through the Vitek2 compact. A total of 1186 subjects were included in the study with a bacterial isolation rate of 691 (58.3%). Out of these, 155 positive cultures had incomplete information or anaerobic or fungal growth and were excluded from the study. With a slight female predominance (54.9%), the majority of subjects (72.2%) were outpatients, and over half of the isolates (55.2%) were Gram-positive. The most common isolate was Staphylococcus spp. (44.4%), followed by E. coli (13.6%) and P. aeruginosa (12.9%). The highest resistance was reported against penicillin followed by fusidic acid against Gram-positive bacteria. Methicillin-resistant Staphylococcus aureus (MRSA) was detected in 40.5% of Staphylococcus aureus (S. aureus) isolates. Amikacin was the most susceptible antibiotic against all Gram-negative isolates. MDR Gram-negative bacteria accounted for 51.9% of wound infection isolates (95% CI: 45.95 to 58.33) while 6.3% (95% CI: 4.39 to 8.86) were XDR (nonsusceptibility to at least one agent in all but two or fewer antimicrobial categories). A high prevalence of bacterial isolates, with S. aureus as the predominant pathogen, showed high rates of multidrug resistance. This highlights the importance of monitoring antibiotic choices for prophylaxis and treatment in the study area.

1. Introduction

Skin is the body’s largest organ that plays a crucial role in safeguarding internal organs and harboring colonizing microbiota. This normal flora prevents pathogenic bacteria from attaching to the healthy skin surface but, on the other hand, can become pathogenic if they gain entry into the body in the form of cuts or injuries [1]. Wounds create a favorable environment for microbial colonization and infection due to their moist, warm, and nutritive nature. While the human body has defense mechanisms that prevent many harmless bacteria from causing disease, any breach in the skin, such as trauma or surgery, can lead to bacterial infections [2]. The occurrence of pus within the wound site represents a classic sign of infection, along with systemic symptoms like fever, localized tenderness, and swelling [3]. Patients with high-risk wounds, often associated with factors like old age or poor nutritional status, are more susceptible to pathogenic bacterial infiltration and infection [4]. Bacterial infections in wounds can significantly impede the healing process by prolonging inflammation and producing virulence factors that promote bacterial growth and tissue destruction [5].
Wounds can be classified into various types based on different criteria such as etiology, morphology, and healing process. Common types of wounds include diabetic foot ulcers, pressure sores, surgical site infections, and burns [6,7,8]. Trauma, whether accidental or intentional, is the main cause of wounds. Hospital-acquired wounds, including surgical and device-related ones, are a distinct subgroup and pressure sores from immobility are another concern [9]. Wound infection evolution is a complex process influenced by multiple factors. These factors include skin conditions, bacterial load and virulence, the nature of the surgical procedures, prior exposure to antibiotics, and the immune status of the affected individual [10]. Infections in wounds can stem from a wide array of pathogens, including bacteria, fungi, protozoa, and viruses. Among the common bacterial culprits associated with wound infections are notorious species such as Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Proteus species, as well as different Enterococcus and Streptococcus species [11].
Annually, a significant number of individuals around the globe endure the pain and trauma of wounds, struggle with wounds that are slow to heal, or face the challenge of acute wounds that are further complicated due to infections. Significant progress has been made in infection control strategies, yet eradicating drug-resistant pathogens remains a challenge due to the overuse of antibiotics. These resistant strains contribute to higher morbidity and mortality, especially in wound infections. Healthcare settings are sources of these antibiotic-resistant pathogens like MRSA and VRE, leading to difficult-to-treat hospital-acquired wound infections [12].
Wound infections are a significant concern in healthcare settings, including hospitals in Saudi Arabia. They pose a significant concern for healthcare practitioners due to the potential for escalating trauma to the patient, as well as the considerable impact they can have on both financial resources and the overall efficiency of the healthcare system [13]. The prevalence of wound infections in the country is influenced by various factors such as the complexity of intensive care unit (ICU) environments, the increased number of patients with serious diseases, widespread gastrointestinal colonization, and extensive use of antimicrobial drugs [14]. Effectively managing wound infections is essential not only for the well-being of the patient but also for the sustainability of healthcare services. Understanding the various causative agents responsible for wound infections plays a crucial role in guiding treatment decisions, implementing infection control protocols in healthcare facilities, and establishing effective antibiotic policies.
The high prevalence of multidrug-resistant bacteria accounts for a significant health issue in the region as inadequately managed wounds contribute significantly to heightened rates of patient suffering and prolonged hospitalization [15]. This survey was conducted in central Riyadh at a tertiary care hospital to identify aerobic bacterial isolates from a group of patients with different types of wound infections. Furthermore, the study was designed to detect antimicrobial and MDR profiles of various bacterial isolates.
Consistent monitoring of evolving wound infection trends, including sensitivity and multidrug resistance profiles, is essential for effective treatment. Health providers can adapt therapies to combat antimicrobial resistance, improving patient care. This approach helps manage infections by adjusting treatments promptly and minimizing risks.

2. Material and Methods

2.1. Study Design and Setting

The current study was designed as a retrospective design carried out from January 2023 to December 2023. All culture requests under the label of wound or pus culture received during the defined study period were included. The data were collected for a tertiary care hospital of 450 beds in central Riyadh, Saudi Arabia.

2.2. Demographic Data of Study Participants and Sample Size

A wound culture is requested if there is a suspected infection when the wound is not healing or exudating pus. The study included both males and females of all age groups who attended the hospital as outpatients or inpatients during the study period. A total of 1186 specimens were requested for wound culture from various types of wounds like surgical wounds, diabetic wounds, and deep and superficial wounds. Patient data were taken from the medical health records of the hospital and microbial growth and antimicrobial susceptibility reports were taken from the microbiology lab of the hospital. Patients with inadequate demography or missing antimicrobial susceptibility data were excluded from the study.

2.3. Identification of Bacterial Isolates

Upon the physician’s request, the specimen was collected from the patient by a nurse or doctor inside the clinic or ward following the standard protocol for wound culture. The samples were transported immediately to the microbiology lab for processing. The wound culture was inoculated and incubated aerobically and anaerobically according to the standard guidelines of the hospital on different enriched and selective media. Primary identification was made by colony morphology and growth characteristics on different media and by Gram stain. Final identification was made by the Vitek2 compact system following the manufacturer’s guidelines.

2.4. Antimicrobial Susceptibility Testing

Antimicrobial susceptibility testing was performed by both Kirby–Bauer disk diffusion and micro broth dilution methods for minimal inhibitory concentration (MIC) determination by the Vitek2 compact system. Interpretation of disk zone sizes and MIC values was made according to Clinical Laboratory Standard Institute (CLSI) 2023 guidelines. Antimicrobial agents were selected as per CLSI guidelines for various bacterial species.

2.5. Statistical Analysis

Statistical analysis was performed by SPSS (IBM Co., Armonk, NY, USA). Categorical data were presented as the frequency and percentage and analyzed using the chi-square test or exact test, as appropriate. Univariate logistic regression analysis was performed to assess the association between the gender of patients and the probability of testing positive for each isolate. A two-tailed p value < 0.05 was considered statistically significant.

2.6. Ethical Consideration

The study was approved by the Research Committee of Dr. Sulaiman Al Habib Medical Group Research Center, Riyadh, Kingdom of Saudi Arabia (IRB study number RC24.05.15).

3. Results

3.1. Demographic Characteristics of the Study Participants

A total of 1186 specimens were requested during the study period under pus and wound culture. Microbial growth was obvious in 691 (58.3%) of the samples, whereas 495 cultures (41.7%) were sterile. The total number of positive cultures included in the study was 536, while the remaining 155 positive cultures had incomplete information or anaerobic or fungal growth. Among the total of 536 patients with infected wounds, 42.4% and 28.9% were respectively in the 12 to 40 years age group and the older than 60 years group, with a slight female predominance (54.9%). The majority of subjects (72.2%) were outpatients as shown in Table 1.

3.2. Prevalence of Bacterial Isolates among Different Types of Wound Infections

Out of 536 samples, monomicrobial infection was evident in 285 (53%) cases, while the remaining 251 (47%) cases were reported as polymicrobial infections. Over half of the isolates (55.2%) were Gram-positive and overall the most common isolate was Staphylococcus spp. (44.4%), followed by E. coli (13.6%), P. aeruginosa (12.9%), E. faecalis (10.8%), K. pneumonia (8.4%), Proteus spp. (3.7%), Enterobacter spp. (3%), Serratia spp. (1.3%), Citrobacter spp. (1.1%), and Acinetobacter spp. (0.7%). The frequencies of bacterial isolates from different types of wound infection are shown in Figure 1. Table 2 shows the characteristics of bacterial isolates from inpatients and outpatients.

3.3. Antimicrobial Susceptibility of Gram-Positive Isolates

The highest resistance was reported against penicillin followed by fusidic acid in both S. aureus and CoNS. MRSA was detected in 40.5% of S. aureus isolates. The detailed percentage of resistance of these isolates is shown in Figure 2.
E. faecalis isolates showed the highest resistance against tetracycline, followed by ampicillin and penicillin. Vancomycin resistance was reported in four E. faecalis isolates (8.6%); however, no resistance was seen against linezolid. The detail is given in Figure 3.

3.4. Antimicrobial Susceptibility of Gram-Negative Isolates

Data in Figure 4 and Figure 5 represent the antimicrobial resistance to various tested antibiotics against Enterobacteriaceae and P. aeruginosa, respectively. More than 50% of the Enterobacteriaceae isolates were found resistant to augmentin, cefalothin, cefuroxime, cefixime, and cefpodoxime. Amikacin was the most susceptible antibiotic as 99.4% of Enterobacteriaceae isolates were susceptible to it. P. aeruginosa also showed the highest susceptibility to amikacin (98.6%), followed by gentamicin, ceftazidime, and cefepime (97.1% each). Carbapenem resistance was noticed in 8.7% of P. aeruginosa isolates.

3.5. Antimicrobial Resistance Profile Correlation with Gender

A statistically significant relation was detected between the gender of patients and resistance of Gram-positive isolates to ampicillin (p = 0.022), showing a significantly higher proportion of resistant isolates among males than females (Table 3).
There was a significant relation between gender of patients and resistance of Gram-negative isolates to imipenem (p = 0.010), meropenem (p < 0.001), levofloxacin (p = 0.014), ofloxacin (p = 0.020), gentamicin (p = 0.047), and piperacillin/tazobactam (p = 0.006), with significantly higher proportions of resistant isolates among males than females (Table 4).

3.6. Antimicrobial Resistance Profile Correlation with Different Age Groups

There was a statistically significant relation between the age of patients and resistance of Gram-positive isolates to ampicillin (p = 0.006), penicillin (p < 0.001), erythromycin (p = 0.013), and tetracycline (p < 0.001) (Table 5).
Age of patients was significantly associated with the resistance of Gram-negative isolates to imipenem (p = 0.003), meropenem (p = 0.006), ciprofloxacin (p = 0.020), levofloxacin (p = 0.003), ofloxacin (p = 0.006), gentamicin (p = 0.029), piperacillin/tazobactam (p = 0.006), cefpodoxime (p = 0.034), and aztreonam (p = 0.029) (Table 6).

3.7. Antimicrobial Resistance Profile Correlation with the Patient Settings

Among bacterial isolates from wound swab cultures, the proportions of those obtained from inpatients that showed resistance to imipenem (p = 0.006), meropenem (p = 0.040), ceftriaxone (p = 0.011), piperacillin/tazobactam (p = 0.028), ampicillin (p = 0.025), vancomycin (p = 0.004), and erythromycin (p = 0.041) were significantly higher than those obtained from outpatients (Table 7).

3.8. Frequency of Multidrug and Other Resistant Profiles

Among the total infected wound cultures, 9.5% (n = 51) of cases were because of ESBL-producing Enterobacteriaceae, whereas 0.9% (n = 5) of cases were due to carbapenemase-producing bacteria. As depicted in Table 8, MDR bacteria accounted for 51.9% of wound infection isolates (95% CI: 45.95 to 58.33) while 6.3% (95% CI: 4.39 to 8.86) were XDR.

4. Discussion

This study evaluates the prevalence of different bacteria isolated from various types of wound infections among patients from a tertiary care hospital in central Riyadh. Out of 1186 specimens requested during the study period under pus and wound culture, 691 were positive for microbial growth with an isolation rate of 58.3%. This prevalence rate was similar to the findings of Alharbi, who reported a 56.1% isolation rate of bacterial growth from wound infections in Jeddah [16].
The slight predominance of monomicrobial infection was evident in our study (53%) whereas 47% were polymicrobial infections. These findings were in line with those of Hassan et al., who reported 60% monomicrobial wound infection [17]. Likewise, Alharbi also found a predominance of monomicrobial infections of 90.6% [16]. The ratio of polymicrobial wound infections in this study is much higher as compared to those of Alharbi and Maharjan et al., who reported 9.4% and 2.5% of wound cultures yielding more than one type of bacteria, respectively [16,18]. This difference could be due to many factors, including wound characteristics and environmental conditions, that interact in complex ways to shape bacterial communities in wounds, impacting their composition, behavior, evolution, and response to treatment [19].
Our survey revealed a slight predominance of Gram-positive bacteria (55.2%) over Gram-negative bacteria (44.8%). These findings are in contrast to other studies where Gram-negative predominance was reported [16,17,19,20]. Variations in participants’ demographic traits, reflecting diverse age, gender, and cultural backgrounds, could lead to these contrasting outcomes.
S. aureus was the most commonly isolated wound pathogen, followed by E. coli and P. aeruginosa. This was in parallel with the findings of El-Saed et al. [21]. Another report by Puca et al. also supports our findings [20]. Various studies confirm the predominance of S. aureus in wound cultures, up to 65% reported by Mulu et al. [22]. This predominance is not surprising as S. aureus is normally present on human skin. Among Gram-negative isolates, E. coli (13.6%) was the predominant isolate in our setting followed by P. aeruginosa (12.9%). Various studies performed previously on wound infections reported E. coli and P. aeruginosa as the second most common cause of wound infection after S. aureus [16,18,22,23].
Staphylococci showed the highest resistance to penicillin with the resistance rate reaching up to 96.6%. Our results showed 40.5% MRSA strains among all S. aureus isolates. The prevalence of MRSA in Saudi Arabia has been the subject of several studies. A review of the literature reveals varying prevalence rates across different regions and periods. El Amin et al. conducted a retrospective survey on 186 S. aureus isolates and reported almost the same rate (39.5%) of MRSA isolation [24]. However, a remarkable increase in the prevalence of MRSA was revealed in our findings compared to a recent survey that was conducted by Alharbi et al. who reported a 29% resistance rate to oxacillin against S. aureus [16]. MRSA prevalence was reported to be 83% by Saba et al. [25]. Reduced susceptibility to vancomycin has been reported in Saudi Arabia in previous studies by Al-Obeid et al. and Alzolibani et al. [26,27], although we found S. aureus 100% susceptible to vancomycin and linezolid in our setting. However, vancomycin-resistant enterococci were observed at 8.6% in our study. This rate of resistance is also remarkably high in comparison to that of Somily et al., who reported vancomycin-resistant enterococci at 4.5% [28]. These variations could be associated with the different patient settings and bacterial identification methods.
Regarding Enterobacteriaceae in Gram-negative sensitivity, amikacin was the most effective antibiotic tested with a resistance rate of 0.6%. High rates of resistance were noticed in all Enterobacteriaceae isolates against beta-lactam antibiotics except for carbapenems, imipenem (10.8%), and meropenem (3%). Resistance to meropenem was due to carbapenem-resistant K. pneumoniae (3%). However, Proteus spp. showed a resistance to imipenem but all these isolates were susceptible to meropenem. The rest of the Enterobacteriaceae members were 100% susceptible to these tested carbapenems in our survey. In the past decade, carbapenem-resistant Enterobacteriaceae (CRE), particularly carbapenem-resistant K. pneumoniae with the New Delhi metallo-β-lactamase gene, has emerged as a global threat, spreading in regions like Asia, Europe, the UK, and the Arab countries [29]. An abrupt increase in the prevalence of CRE was reported in a recent study from western Saudi Arabia from 8% in 2017 to 61% in 2019 [30]. However, our low percentage could be due to the site of infection included in our study in comparison to the surveillance study for screening CRE only.
Likewise, amikacin was the most effective antibiotic against P. aeruginosa as well. The highest resistance was noticed against aztreonam (15.9%), followed by quinolones (13–14.5%). Carbapenem-resistant P. aeruginosa was found at 8.7% in our survey. Our results are not in agreement with those of Momenah et al., who reported a high resistance to cefepime (42.7%), followed by ciprofloxacin (34.3%), imipenem (30.5%), ceftazidime (29.2%), gentamicin (26%), and piperacillin/tazobactam (24.2%) [31]. These variations in antibiotic susceptibility patterns stem from factors like number of isolates included in the study, screening duration, antibiotic use, selective pressure, and study conditions, reflecting diverse settings and environmental influences [32].
Antimicrobial resistance is a significant global health concern affected by demographic changes, especially the increasing elderly population. Understanding the impact of demographic changes is crucial due to a lack of knowledge in this area. The relationship between age, gender, infections, and antibiotic resistance is complex, requiring a nuanced understanding to develop effective strategies against drug-resistant infections. In our study, the overall sample of male patients showed higher levels of antimicrobial resistance with statistically significant differences noticed in ampicillin, carbapenem, quinolones, and piperacillin/tazobactam. This male predominance for antimicrobial resistance was supported by Akhavizadegan et al. [33]. Overall, antimicrobial resistance predominance was noticed in the age group of more than 60 years. Older patients with multiple comorbidities receive care in various healthcare settings and are more vulnerable to antibiotic-resistant pathogens, making careful antibiotic use crucial for effective infection management. The significance of age as a factor influencing the variation in antimicrobial resistance has not only been visually delineated for Europe in previous studies [34] but has also been thoroughly investigated in studies focused on specific regions [35,36]. However, to our knowledge, this is the first report from the region highlighting the impact of gender and age on antimicrobial resistance from wound infections.
Our study highlights the urgent need for better infection control in healthcare settings due to high antibiotic resistance among inpatients, emphasizing monitoring, targeted interventions, and collaboration to combat resistance effectively. All five cases of vancomycin-resistant enterococci were inpatient. Two cases of colistin resistance were also isolated from inpatient samples. Carbapenem resistance was also significantly high among inpatients, indicating the overuse of these life-saving, last-resort drugs in our hospital setting. The prevalence of MDR Gram-negative bacteria was 51.9% in our study, which is supported by various other surveys [37,38]. However, our MDR prevalence is remarkably high as compared to that of Alharbi et al., who recorded a 22% isolation rate of MDR bacteria from wound infections [16]. Variations in isolated pathogens, study population, medication access, treatment adherence, infection control, and antimicrobial use may contribute to these discrepancies.
As a retrospective study, in-depth details about patient profiles were not available, which was one of our study limitations. Missing data on pathologies (missing wound site, etc.) and limited numbers of bacterial isolates in this single-center study were other constraints. We could not retrieve data for anaerobic bacterial isolates and streptococci were also excluded from our survey as we were unable to have their antimicrobial susceptibility pattern recorded by the laboratory.
In conclusion, analysis of wound samples showed prevalent monomicrobial infections, with S. aureus, E. coli, and P. aeruginosa as the main pathogens. High MRSA incidence among S. aureus emphasized antibiotic resistance challenges. Vancomycin and linezolid were effective against S. aureus, while amikacin worked best against Gram-negative isolates. Many isolates were multidrug-resistant, escalating antibiotic resistance concerns and hindering treatment options. Urgent action is needed to develop new treatments and improve antimicrobial practices in healthcare settings. This knowledge enables local healthcare professionals to make informed choices regarding empirical therapy, enhances the effectiveness of infection prevention strategies within medical settings, and contributes to the development of rational approaches toward antibiotic usage. By staying abreast of the causative factors behind wound infections, healthcare providers can optimize patient care, minimize the risks associated with infections, and work towards achieving cost-effective and sustainable healthcare management strategies in our setting.

Author Contributions

F.K., conceptualization, analysis and interpretation of results, draft manuscript writing and editing; C.P. and D.F.M.F., Data collection; A.E. and A.M., Methodology and Results interpretation; O.T.K., Supervision and revision of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was approved by the Research Committee of Dr. Sulaiman Al Habib Medical Group Research Center, Riyadh, Kingdom of Saudi Arabia (IRB study number RC24.05.15).

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Yang, Y.; Huang, J.; Zeng, A.; Long, X.; Yu, N.; Wang, X. The role of the skin microbiome in wound healing. Burn. Trauma 2024, 12, tkad059. [Google Scholar] [CrossRef] [PubMed]
  2. Li, J.; Liu, X.; Tan, L.; Cui, Z.; Yang, X.; Liang, Y.; Li, Z.; Zhu, S.; Zheng, Y.; Yeung, K.W.K.; et al. Zinc-Doped Prussian Blue Enhances Photothermal Clearance of Staphylococcus aureus and Promotes Tissue Repair in Infected Wounds. Nat. Commun. 2019, 10, 4490. [Google Scholar] [CrossRef] [PubMed]
  3. Priscilla, R.; Ghosh, A. Bacterial Isolates and their Antibiotic Susceptibility Pattern among Patients with Pus and/or Wound discharge at Gouri Devi Institute of Medical Sciences and Hospital. Int. J. Curr. Microbiol. Appl. Sci. 2019, 8, 2468–2474. [Google Scholar] [CrossRef]
  4. Zimmermann, C.; Troeltzsch, D.; Giménez-Rivera, V.A.; Galli, S.J.; Metz, M.; Maurer, M.; Siebenhaar, F. Mast Cells Are Critical for Controlling the Bacterial Burden and the Healing of Infected Wounds. Proc. Natl. Acad. Sci. USA 2019, 116, 20500–20504. [Google Scholar] [CrossRef]
  5. Zhou, L.; Zheng, H.; Liu, Z.; Wang, S.; Liu, Z.; Chen, F.; Zhang, H.; Kong, J.; Zhou, F.; Zhang, Q. Conductive Antibacterial Hemostatic Multifunctional Scaffolds Based on Ti3C2Tx MXene Nanosheets for Promoting Multidrug-Resistant Bacteria-Infected Wound Healing. ACS Nano 2021, 15, 2468–2480. [Google Scholar] [CrossRef] [PubMed]
  6. Liu, Z.; Agu, E.; Pedersen, P.J.; Lindsay, C.; Tulu, B.; Strong, D.M. Chronic Wound Image Augmentation and Assessment Using Semi-Supervised Progressive Multi-Granularity EfficientNet. IEEE Open J. Eng. Med. Biol. 2024, 5, 404–420. [Google Scholar] [CrossRef]
  7. Lo, Z.J.; Mak, M.H.W.; Liang, S.; Chan, Y.M.; Goh, C.C.; Lai, T.; Tan, A.; Thng, P.; Rodriguez, J.; Weyde, T.; et al. Development of an Explainable Artificial Intelligence Model for Asian Vascular Wound Images. Int. Wound J. 2023, 21, e14565. [Google Scholar] [CrossRef]
  8. Frykberg, R.G.; Banks, J. Challenges in the Treatment of Chronic Wounds. Adv. Wound Care 2015, 4, 560–582. [Google Scholar] [CrossRef] [PubMed]
  9. Giacometti, A.; Cirioni, O.; Schimizzi, A.M.; Del Prete, M.S.; Barchiesi, F.; D’Errico, M.M.; Petrelli, E.; Scalise, G. Epidemiology and microbiology of surgical wound infections. J. Clin. Microbiol. 2000, 38, 918–922. [Google Scholar] [CrossRef]
  10. Sule, A.M.; Thanni, L.O.A.; Odu, O.A.S.; Olusanya, O. Bacterial pathogens associated with infected wounds in Ogun State University Teaching Hospital, Sagamu, Nigeria. Afr. J. Clin. Exp. Microbiol. 2002, 3, 13–16. [Google Scholar] [CrossRef]
  11. Zhao, G.; Hochwalt, P.C.; Usui, M.L.; Underwood, R.A.; Singh, P.K.; James, G.A.; Stewart, P.S.; Fleckman, P.; Olerud, J.E. Delayed wound healing in diabetic (db/db) mice with Pseudomonas aeruginosa biofilm challenge: A model for the study of chronic wounds. Wound Repair Regen. 2010, 18, 467–477. [Google Scholar] [CrossRef] [PubMed]
  12. Sani, R.A.; Garba, S.A.; Oyewole, O.A. Antibiotic Resistance Profile of Gram Negative Bacteria Isolated from Surgical Wounds in Minna, Bida, Kontagora and Suleja Areas of Niger State. Am. J. Med. Med. Sci. 2012, 2, 20–24. [Google Scholar]
  13. Bereket, W.; Hemalatha, K.; Getenet, B.; Wondwossen, T.; Solomon, A.; Zeynudin, A.; Kannan, S. Update on bacterial nosocomial infections. Eur. Rev. Med. Pharmacol. Sci. 2012, 16, 1039–1044. [Google Scholar]
  14. Ibrahim, M.T. Prevalence of Acinetobacter Baumannii in Saudi Arabia: Risk Factors, Antimicrobial Resistance Patterns and Mechanisms of Carbapenem Resistance. Ann. Clin. Microbiol. Antimicrob. 2019, 18, 1. [Google Scholar] [CrossRef]
  15. Banawas, S.S.; Alobaidi, A.S.; Dawoud, T.M.; AlDehaimi, A.; Alsubaie, F.M.; Abdel-Hadi, A.; Manikandan, P. Prevalence of Multidrug-Resistant Bacteria in Healthcare-Associated Bloodstream Infections at Hospitals in Riyadh, Saudi Arabia. Pathogens 2023, 12, 1075. [Google Scholar] [CrossRef]
  16. Alharbi, A.S. Bacteriological profile of wound swab and their antibiogram pattern in a tertiary care hospital, Saudi Arabia. Saudi Med. J. 2022, 43, 1373–1382. [Google Scholar] [CrossRef]
  17. Hassan, M.A.; Abd El-Aziz, S.; Elbadry, H.M.; El-Aassar, S.A.; Tamer, T.M. Prevalence, antimicrobial resistance profile, and characterization of multi-drug resistant bacteria from various infected wounds in North Egypt. Saudi J. Biol. Sci. 2022, 29, 2978–2988. [Google Scholar] [CrossRef] [PubMed]
  18. Maharjan, N.; Mahawal, B.S. Bacteriological Profile of Wound Infection and Antibiotic Susceptibility Pattern of Various Isolates in a Tertiary Care Center. J. Lumbini Med. Coll. 2020, 8, 218–224. [Google Scholar]
  19. Upreti, N.; Rayamajhee, B.; Sherchan, S.P.; Choudhari, M.K.; Banjara, M.R. Prevalence of methicillin resistant Staphylococcus aureus, multidrug resistant and extended spectrum β-lactamase producing gram negative bacilli causing wound infections at a tertiary care hospital of Nepal. Antimicrob. Resist. Infect. Control 2018, 7, 121. [Google Scholar] [CrossRef]
  20. Puca, V.; Marulli, R.Z.; Grande, R.; Vitale, I.; Niro, A.; Molinaro, G.; Prezioso, S.; Muraro, R.; Di Giovanni, P. Microbial Species Isolated from Infected Wounds and Antimicrobial Resistance Analysis: Data Emerging from a Three-Years Retrospective Study. Antibiotics 2021, 10, 1162. [Google Scholar] [CrossRef]
  21. El-Saed, A.; Balkhy, H.H.; Alshamrani, M.M.; Aljohani, S.; Alsaedi, A.; Al Nasser, W.; El Gammal, A.; Almohrij, S.A.; Alyousef, Z.; Almunif, S.; et al. High contribution and impact of resistant gram negative pathogens causing surgical site infections at a multi-hospital healthcare system in Saudi Arabia, 2007–2016. BMC Infect. Dis. 2020, 20, 275. [Google Scholar] [CrossRef]
  22. Mulu, W.; Kibru, G.; Beyene, G.; Damtie, M. Postoperative Nosocomial Infections and Antimicrobial Resistance Pattern of Bacteria Isolates among Patients Admitted at Felege Hiwot Referral Hospital, Bahirdar, Ethiopia. Ethiop. J. Health Sci. 2012, 22, 7–18. [Google Scholar] [PubMed]
  23. Shimekaw, M.; Tigabu, A.; Tessema, B. Bacterial Profile, Antimicrobial Susceptibility Pattern, and Associated Risk Factors among Patients with Wound Infections at Debre Markos Referral Hospital, Northwest, Ethiopia. Int. J. Low. Extrem. Wounds 2022, 21, 182–192. [Google Scholar] [CrossRef] [PubMed]
  24. El Amin, N.M.; Faidah, H.S. Methicillin-resistant Staphylococcus aureus in the western region of Saudi Arabia: Prevalence and antibiotic susceptibility pattern. Ann. Saudi Med. 2012, 32, 513–516. [Google Scholar] [CrossRef]
  25. Saba, C.K.S.; Amenyona, J.K.; Kpordze, S.W. Prevalence and pattern of antibiotic resistance of Staphylococcus aureus isolated from door handles and other points of contact in public hospitals in Ghana. Antimicrob. Resist. Infect. Control 2017, 6, 44. [Google Scholar] [CrossRef] [PubMed]
  26. Alzolibani, A.A.; Al Robaee, A.A.; Al Shobaili, H.A.; Bilal, J.A.; Issa Ahmad, M.; Bin Saif, G. Documentation of vancomycin-resistant Staphylococcus aureus (VRSA) among children with atopic dermatitis in the Qassim region, Saudi Arabia. Acta Dermatovenerol. Alp. Pannonica Adriat. 2012, 21, 51–53. [Google Scholar]
  27. Al-Obeid, S.; Haddad, Q.; Cherkaoui, A.; Schrenzel, J.; François, P. First Detection of an Invasive Staphylococcus aureus Strain (D958) with Reduced Susceptibility to Glycopeptides in Saudi Arabia. J. Clin. Microbiol. 2010, 48, 2199–2204. [Google Scholar] [CrossRef]
  28. Somily, A.M.; Al-Mohizea, M.M.; Absar, M.M.; Fatani, A.J.; Ridha, A.M.; Al-Ahdal, M.N.; Senok, A.C.; Al-Qahtani, A.A. Molecular epidemiology of vancomycin resistant enterococci in a tertiary care hospital in Saudi Arabia. Microb. Pathog. 2016, 97, 79–83. [Google Scholar] [CrossRef]
  29. Alqahtani, M.; Tickler, I.A.; Al Deesi, Z.; AlFouzan, W.; Al Jabri, A.; Al Jindan, R.; Al Johani, S.; Alkahtani, S.A.; Al Kharusi, A.; Mokaddas, E.; et al. Molecular detection of carbapenem resistance genes in rectal swabs from patients in Gulf Cooperation Council hospitals. J. Hosp. Infect. 2021, 112, 96–103. [Google Scholar] [CrossRef]
  30. Taha, R.; Mowallad, A.; Mufti, A.; Althaqafi, A.; Jiman-Fatani, A.A.; El-Hossary, D.; Ossenkopp, J.; AlhajHussein, B.; Kaaki, M.; Jawi, N.; et al. Prevalence of Carbapenem-Resistant Enterobacteriaceae in Western Saudi Arabia and Increasing Trends in the Antimicrobial Resistance of Enterobacteriaceae. Cureus 2023, 15, e35050. [Google Scholar] [CrossRef]
  31. Momenah, A.M.; Bakri, R.A.; Jalal, N.A.; Ashgar, S.S.; Felemban, R.F.; Bantun, F.; Hariri, S.H.; Barhameen, A.A.; Faidah, H.; AL-Said, H.M. Antimicrobial Resistance Pattern of Pseudomonas aeruginosa: An 11-Year Experience in a Tertiary Care Hospital in Makkah, Saudi Arabia. Infect. Drug Resist. 2023, 16, 4113–4122. [Google Scholar] [CrossRef] [PubMed]
  32. Khan, M.A.; Faiz, A. Antimicrobial resistance patterns of Pseudomonas aeruginosa in tertiary care hospitals of Makkah and Jeddah. Ann. Saudi Med. 2016, 36, 23–28. [Google Scholar] [CrossRef] [PubMed]
  33. Akhavizadegan, H.; Hosamirudsar, H.; Pirroti, H.; Akbarpour, S. Antibiotic resistance: A comparison between inpatient and outpatient uropathogens. East. Mediterr. Health J. 2021, 27, 124–130. [Google Scholar] [CrossRef] [PubMed]
  34. OECD. Stemming the Superbug Tide; OECD Publishing: Paris, France, 2018; Available online: https://www.oecd.org/en/publications/2018/11/stemming-the-superbug-tide_g1g98de5.html (accessed on 25 September 2024).
  35. Swami, S.K.; Banerjee, R. Comparison of hospital-wide and age and location—Stratified antibiograms of S. aureus, E. coli, and S. pneumoniae: Age- and location-stratified antibiograms. SpringerPlus 2013, 2, 63. [Google Scholar] [CrossRef] [PubMed]
  36. Weber, S.G.; Miller, R.R.; Perencevich, E.N.; Tolentino, J.; Meltzer, D.; Pitrak, D.; McGregor, J.C.; Sachs, G.A.; Harris, A.D.; Furuno, J.P. Prevalence of antimicrobial-resistant bacteria isolated from older versus younger hospitalized adults: Results of a two-centre study. J. Antimicrob. Chemother. 2009, 64, 1291–1298. [Google Scholar] [CrossRef]
  37. Sleem, A.; Melake, N.; Eissa, N.; Keshk, T. Prevalence of multidrug-resistant bacteria isolated from patients with burn infection. Menoufia Med. J. 2015, 28, 677–684. [Google Scholar]
  38. Ahmed, E.F.; Rasmi, A.H.; Darwish, A.M.A.; Gad, G.F.M. Prevalence and resistance profile of bacteria isolated from wound infections among a group of patients in upper Egypt: A descriptive cross-sectional study. BMC Res. Notes 2023, 16, 106. [Google Scholar] [CrossRef]
Figure 1. Prevalence of bacterial isolates from different types of wound infections. Percentage was calculated out of the number of samples for each type of wound. Drainage * (from ear/bile).
Figure 1. Prevalence of bacterial isolates from different types of wound infections. Percentage was calculated out of the number of samples for each type of wound. Drainage * (from ear/bile).
Microbiolres 15 00135 g001
Figure 2. Antimicrobial resistance of S. aureus (n = 205) and CoNS (n = 33). P: penicillin, OX: oxacillin, E: erythromycin, DA: clindamycin, CN: gentamicin, TS: co-trimoxazole, CIP: ciprofloxacin, LEV: levofloxacin, TE: tetracycline, VA: vancomycin, LZD: linezolid, FD: fusidic acid.
Figure 2. Antimicrobial resistance of S. aureus (n = 205) and CoNS (n = 33). P: penicillin, OX: oxacillin, E: erythromycin, DA: clindamycin, CN: gentamicin, TS: co-trimoxazole, CIP: ciprofloxacin, LEV: levofloxacin, TE: tetracycline, VA: vancomycin, LZD: linezolid, FD: fusidic acid.
Microbiolres 15 00135 g002
Figure 3. Antimicrobial resistance of E. faecalis (n = 58). AMP: ampicillin, P: penicillin, LZD: linezolid, TE: tetracycline, VA: vancomycin.
Figure 3. Antimicrobial resistance of E. faecalis (n = 58). AMP: ampicillin, P: penicillin, LZD: linezolid, TE: tetracycline, VA: vancomycin.
Microbiolres 15 00135 g003
Figure 4. Antimicrobial resistance of Enterobacteriaceae (n = 167). AMC: augmentin, CEFAL: cefalothin, CXM: cefuroxime, CFM: cefixime, CPD: cefpodoxime, CRO: ceftriaxone, FEP: cefepime, TS: co-trimoxazole, CIP: ciprofloxacin, LEV: levofloxacin, OFX: ofloxacin, TZP: piperacillin–tazobactam, IMP: imipenem, MEM: meropenem, AK: amikacin, CN: gentamicin.
Figure 4. Antimicrobial resistance of Enterobacteriaceae (n = 167). AMC: augmentin, CEFAL: cefalothin, CXM: cefuroxime, CFM: cefixime, CPD: cefpodoxime, CRO: ceftriaxone, FEP: cefepime, TS: co-trimoxazole, CIP: ciprofloxacin, LEV: levofloxacin, OFX: ofloxacin, TZP: piperacillin–tazobactam, IMP: imipenem, MEM: meropenem, AK: amikacin, CN: gentamicin.
Microbiolres 15 00135 g004
Figure 5. Antimicrobial resistance of Pseudomonas (n = 69). CAZ: ceftazidime, FEP: cefepime, CIP: ciprofloxacin, LEV: levofloxacin, OFX: ofloxacin, TZP: piperacillin–tazobactam, IMP: imipenem, MEM: meropenem, AK: amikacin, CN: gentamicin, ATM: aztreonam.
Figure 5. Antimicrobial resistance of Pseudomonas (n = 69). CAZ: ceftazidime, FEP: cefepime, CIP: ciprofloxacin, LEV: levofloxacin, OFX: ofloxacin, TZP: piperacillin–tazobactam, IMP: imipenem, MEM: meropenem, AK: amikacin, CN: gentamicin, ATM: aztreonam.
Microbiolres 15 00135 g005
Table 1. Characteristics of patients with wound cultures included in the study.
Table 1. Characteristics of patients with wound cultures included in the study.
CharacteristicFrequency (n)Percentage (%)
Age group (in years)
  <125810.8
  12 to 4022742.4
  41 to 609617.9
  >6015528.9
Gender
  Male24245.1
  Female29454.9
Patient setting
  Inpatient14927.8
  Outpatient38772.2
n = number.
Table 2. Characteristics of bacterial isolates of wound infection among inpatients and outpatients.
Table 2. Characteristics of bacterial isolates of wound infection among inpatients and outpatients.
CharacteristicInpatient n (%)Outpatient n (%)Total n (%)
Gram reaction
  Negative85 (35.4)155 (64.6)240 (44.8)
  Positive64 (21.6)232 (78.4)296 (55.2)
Type of infection
  Monomicrobial59 (20.7)226 (79.3)285 (53.2)
  Polymicrobial90 (35.9)161 (64.1)251 (46.8)
Type of bacteria
  Staphylococcus35 (14.7)203 (85.3)238 (44.4)
  S. aureus33 (16.1)172 (83.9)205 (38.2)
  S. epidermidis0 (0)1 (100)1 (0.2)
  S. lugdunensis2 (6.3)30 (93.7)32 (6)
  E. faecalis29 (50)29 (50)58 (10.8)
  E. coli18 (24.7)55 (75.3)73 (13.6)
  K. pneumoniae20 (44.4)25 (55.6)45 (8.4)
  P. aeruginosa24 (34.8)45 (65.2)69 (12.9)
  E. cloacae4 (25)12 (75)16 (3)
  Proteus spp.11 (55)9 (45)20 (3.7)
  S. marcescens4 (57.1)3 (42.9)7 (1.3)
  Citrobacter spp.1 (16.7)5 (83.3)6 (1.1)
  Acinetobacter spp.3 (75)1 (25)4 (0.7)
n = number.
Table 3. Comparison between males and females of antimicrobial-resistant patterns of Gram-positive isolates.
Table 3. Comparison between males and females of antimicrobial-resistant patterns of Gram-positive isolates.
AntibioticSusceptibility Profile p Value
SensitiveResistant
Gentamicin
Male117 (94.4%)7 (6.1%)0.871
Female107 (93.9%)7 (5.6%)
Co-trimoxazole
Male106 (85.5%)18 (14.5%)0.647
Female95 (83.3%)19 (16.7%)
Ciprofloxacin
Male106 (85.5%)18 (14.5%)0.169
Female104 (91.2%)10 (8.8%)
Levofloxacin
Male105 (84.7%)19 (15.3%)0.123
Female104 (91.2%)10 (8.8%)
Oxacillin
Male81 (65.3%)43 (34.7%)0.366
Female68 (59.6%)46 (40.4%)
Fusidic acid
Male56 (45.2%)68 (54.8%)0.454
Female46 (40.4%)68 (59.6%)
Clindamycin
Male108 (87.1%)16 (12.9%)0.413
Female95 (83.3%)19 (16.7%)
Ampicillin
Male16 (61.5%)10 (38.5%)0.022
Female28 (87.5%)4 (12.5%)
Penicillin
Male23 (15.3%)127 (84.7%)0.110
Female33 (22.6%)113 (77.4%)
Vancomycin
Male146 (97.3%)4 (2.7%)0.371
Female145 (99.3%)1 (0.7%)
Linezolid
Male150 (100%)0 (0%)---
Female146 (100%)0 (0%)
Erythromycin
Male88 (59.5%)60 (40.5%)0.250
Female76 (52.8%)68 (47.2%)
Tetracycline
Male126 (84%)24 (16%)0.659
Female119 (82.1%)26 (17.9%)
Data are presented as frequency (%), statistical significance at p value < 0.0003 (after Bonferroni correction).
Table 4. Comparison between males and females of antimicrobial-resistant patterns of Gram-negative isolates.
Table 4. Comparison between males and females of antimicrobial-resistant patterns of Gram-negative isolates.
AntibioticSusceptibility Profile p Value
SensitiveResistant
Ampicillin–sulbactam
Male0 (0%)2 (100%)0.333
Female2 (100%)0 (0%)
Colistin
Male1 (50%)1 (50%)>0.999
Female0 (0%)2 (100%)
Tigecycline
Male2 (100%)0 (0%)---
Female2 (100%)0 (0%)
Amikacin
Male92 (100%)0 (0%)0.288
Female145 (98%)3 (2%)
Cefepime
Male75 (81.5%)17 (18.5%)0.734
Female118 (79.7%)30 (20.3%)
Imipenem
Male77 (83.7%)15 (16.3%)0.010
Female139 (93.9%)9 (6.1%)
Meropenem
Male81 (88%)11 (12%)<0.001
Female148 (100%)0 (0%)
Ciprofloxacin
Male59 (64.1%)33 (35.9%)0.380
Female103 (69.6%)45 (30.4%)
Levofloxacin
Male61 (66.3%)31 (33.7%)0.014
Female119 (80.4%)29 (19.6%)
Ofloxacin
Male61 (66.3%)31 (33.7%)0.020
Female118 (79.7%)30 (20.3%)
Gentamicin
Male85 (92.4%)7 (7.6%)0.047
Female145 (98%)3 (2%)
Ceftriaxone
Male35 (61.4%)22 (38.6%)>0.999
Female70 (61.4%)44 (38.6%)
Co-trimoxazole
Male39 (68.4%)18 (31.6%)0.723
Female81 (71.1%)33 (28.9%)
Ceftazidime
Male34 (91.9%)3 (8.1%)0.240
Female36 (100%)0 (0%)
Piperacillin/tazobactam
Male80 (88.9%)10 (11.1%)0.006
Female143 (97.9%)3 (2.1%)
Ampicillin
Male6 (26.1%)17 (73.9%)0.188
Female29 (41.4%)41 (58.6%)
Amoxicillin/clavulanic acid
Male16 (29.1%)39 (70.9%)0.197
Female44 (39.3%)68 (60.7%)
Cephalothin
Male14 (25.5%)41 (74.5%)0.078
Female44 (39.3%)68 (60.7%)
Cefuroxime
Male19 (34.5%)36 (65.5%)0.303
Female48 (42.9%)64 (57.1%)
Cefixime
Male22 (40%)33 (60%)0.498
Female51 (45.5%)61 (54.5%)
Cefpodoxime
Male22 (40%)33 (60%)0.371
Female53 (47.3%)59 (52.7%)
Aztreonam
Male28 (80%)7 (20%)0.307
Female30 (90.9%)3 (9.1%)
Data are presented as frequency (%), statistical significance at p value < 0.002 (after Bonferroni correction).
Table 5. Comparison of age groups in antimicrobial-resistant patterns of Gram-positive isolates.
Table 5. Comparison of age groups in antimicrobial-resistant patterns of Gram-positive isolates.
AntibioticAge Groupp Value
<12 yrs12–40 yrs41–60 yrs>60 yrs
Gentamicin
Sensitive49 (94.2%)95 (95%)43 (89.6%)37 (97.4%)0.466
Resistant3 (5.8%)5 (5%)5 (10.4%)1 (2.6%)
Co-trimoxazole
Sensitive40 (76.9%)88 (88%)42 (87.5%)31 (81.6%)0.286
Resistant12 (23.1%)12 (12%)6 (12.5%)7 (18.4%)
Ciprofloxacin
Sensitive43 (82.7%)87 (87%)45 (93.8%)35 (92.1%)0.303
Resistant9 (17.3%)13 (13%)3 (6.3%)3 (7.9%)
Levofloxacin
Sensitive43 (82.7%)86 (86%)45 (93.8%)35 (92.1%)0.282
Resistant9 (17.3%)14 (14%)3 (6.3%)3 (7.9%)
Oxacillin
Sensitive29 (55.8%)60 (60%)32 (66.7%)28 (73.7%)0.301
Resistant23 (44.2%)40 (40%)16 (33.3%)10 (26.3%)
Fusidic acid
Sensitive22 (42.3%)47 (47%)16 (33.3%)17 (44.7%)0.468
Resistant30 (57.7%)53 (53%)32 (66.7%)21 (55.3%)
Clindamycin
Sensitive45 (86.5%)87 (87%)38 (79.2%)33 (86.8%)0.614
Resistant7 (13.5%)13 (13%)10 (20.8%)5 (13.2%)
Ampicillin
Sensitive---23 (92%)5 (100%)16 (57.1%)0.006
Resistant---2 (8%)0 (0%)12 (42.9%)
Penicillin
Sensitive0 (0%)29 (23.2%)8 (15.1%)19 (28.8%)<0.001
Resistant52 (100%)96 (76.8%)45 (84.9%)47 (71.2%)
Vancomycin
Sensitive52 (100%)124 (99.2%)52 (98.1%)63 (95.5%)0.176
Resistant0 (0%)1 (0.8%)1 (1.9%)3 (4.5%)
Linezolid
Sensitive52 (100%)125 (100%)53 (100%)66 (100%)---
Resistant0 (0%)0 (0%)0 (0%)0 (0%)
Erythromycin
Sensitive34 (65.4%)69 (55.6%)35 (67.3%)26 (40.6%)0.013
Resistant18 (34.6%)55 (44.4%)17 (32.7%)38 (59.4%)
Tetracycline
Sensitive47 (90.4%)107 (85.6%)49 (94.2%)42 (63.6%)<0.001
Resistant5 (9.6%)18 (14.4%)3 (5.8%)24 (36.4%)
Data are presented as frequency (%), statistical significance at p value < 0.0003 (after Bonferroni correction).
Table 6. Comparison of age groups in antimicrobial-resistant patterns of Gram-negative isolates.
Table 6. Comparison of age groups in antimicrobial-resistant patterns of Gram-negative isolates.
AntibioticAge Groupp Value
<12 yrs12–40 yrs41–60 yrs>60 yrs
Ampicillin–sulbactam
Sensitive------1 (33.3%)1 (100%)>0.999
Resistant------2 (66.7%)0 (0%)
Colistin
Sensitive------1 (33.3%)0 (0%)>0.999
Resistant------2 (66.7%)1 (100%)
Tigecycline
Sensitive------3 (100%)1 (100%)---
Resistant------0 (0%)0 (0%)
Amikacin
Sensitive6 (100%)102 (100%)42 (97.7%)87 (97.8%)0.354
Resistant0 (0%)0 (0%)1 (2.3%)2 (2.2%)
Cefepime
Sensitive5 (83.3%)82 (80.4%)38 (88.4%)68 (76.4%)0.449
Resistant1 (16.7%)20 (19.6%)5 (11.6%)21 (23.6%)
Imipenem
Sensitive6 (100%)100 (98%)34 (79.1%)76 (85.4%)0.003
Resistant0 (0%)2 (2%)9 (20.9%)13 (14.6%)
Meropenem
Sensitive6 (100%)102 (100%)42 (97.7%)79 (88.8%)0.006
Resistant0 (0%)0 (0%)1 (2.3%)10 (11.2%)
Ciprofloxacin
Sensitive6 (100%)77 (75.5%)27 (62.8%)52 (58.4%)0.020
Resistant0 (0%)25 (24.5%)16 (37.2%)37 (41.6%)
Levofloxacin
Sensitive6 (100%)86 (84.3%)32 (74.4%)56 (62.9%)0.003
Resistant0 (0%)16 (15.7%)11 (25.6%)33 (37.1%)
Ofloxacin
Sensitive6 (100%)85 (83.3%)32 (74.4%)56 (62.9%)0.006
Resistant0 (0%)17 (16.7%)11 (25.6%)33 (37.1%)
Gentamicin
Sensitive5 (83.3%)102 (100%)39 (90.7%)84 (94.4%)0.029
Resistant1 (16.7%)0 (0%)4 (9.3%)5 (5.6%)
Ceftriaxone
Sensitive1 (50%)54 (67.5%)23 (67.6%)27 (49.1%)0.135
Resistant1 (50%)26 (32.5%)11 (32.4%)28 (50.9%)
Co-trimoxazole
Sensitive2 (100%)61 (76.3%)23 (67.6%)34 (61.8%)0.239
Resistant0 (0%)19 (23.8%)11 (32.4%)21 (38.2%)
Ceftazidime
Sensitive4 (100%)22 (100%)11 (91.7%)33 (94.3%)0.641
Resistant0 (0%)0 (0%)1 (8.3%)2 (5.7%)
Piperacillin/
tazobactam
Sensitive6 (100%)101 (99%)41 (97.6%)75 (87.2%)0.006
Resistant0 (0%)1 (1%)1 (2.4%)11 (12.8%)
Ampicillin
Sensitive0 (0%)21 (44.7%)9 (45%)5 (20%)0.127
Resistant1 (100%)26 (55.3%)11 (55%)20 (80%)
Amoxicillin/clavulanic acid
Sensitive0 (0%)32 (40%)15 (48.4%)13 (24.1%)0.057
Resistant2 (100%)48 (60%)16 (51.6%)41 (75.9%)
Cephalothin
Sensitive0 (0%)33 (41.3%)13 (41.9%)12 (22.2%)0.064
Resistant2 (100%)47 (58.8%)18 (58.1%)42 (77.8%)
Cefuroxime
Sensitive0 (0%)36 (45%)16 (51.6%)15 (27.8%)0.051
Resistant2 (100%)44 (55%)15 (48.4%)39 (72.2%)
Cefixime
Sensitive0 (0%)39 (48.8%)17 (54.8%)17 (31.5%)0.052
Resistant2 (100%)41 (51.3%)14 (45.2%)37 (68.5%)
Cefpodoxime
Sensitive0 (0%)41 (51.3%)17 (54.8%)17 (31.5%)0.034
Resistant2 (100%)39 (48.8%)14 (45.2%)37 (68.5%)
Aztreonam
Sensitive4 (100%)22 (100%)8 (88.9%)24 (72.7%)0.029
Resistant0 (0%)0 (0%)1 (11.1%)9 (27.3%)
Data are presented as frequency (%), statistical significance at p value < 0.002 (after Bonferroni correction).
Table 7. Comparison between inpatients and outpatients of antimicrobial resistance of bacterial isolates from wound swab cultures.
Table 7. Comparison between inpatients and outpatients of antimicrobial resistance of bacterial isolates from wound swab cultures.
AntibioticInpatientOutpatientp Value
Ampicillin–sulbactam
Sensitive2 (66.7%)------
Resistant1 (33.3%)---
Colistin
Sensitive1 (33.3%)------
Resistant2 (66.7%)---
Tigecycline
Sensitive3 (100%)------
Resistant0 (0%)---
Amikacin
Sensitive63 (96.9%)109 (99.1%)0.556
Resistant2 (3.1%)1 (0.9%)
Cefepime
Sensitive48 (73.8%)91 (82.7%)0.160
Resistant17 (26.2%)19 (17.3%)
Imipenem
Sensitive52 (80%)103 (93.6%)0.006
Resistant13 (20%)7 (6.4%)
Meropenem
Sensitive58 (89.2%)107 (97.3%)0.040
Resistant7 (10.8%)3 (2.7%)
Ofloxacin
Sensitive43 (66.2%)84 (76.4%)0.144
Resistant22 (33.8%)26 (23.6%)
Gentamicin
Sensitive83 (94.3%)176 (97.8%)0.159
Resistant5 (5.7%)4 (2.2%)
Ciprofloxacin
Sensitive62 (70.5%)136 (75.6%)0.372
Resistant26 (29.5%)44 (24.4%)
Levofloxacin
Sensitive64 (72.7%)144 (80%)0.180
Resistant24 (27.3%)36 (20%)
Ceftriaxone
Sensitive20 (44.4%)53 (67.9%)0.011
Resistant25 (55.6%)25 (32.1%)
Ceftazidime
Sensitive20 (87%)32 (100%)0.068
Resistant3 (13%)0 (0%)
Co-trimoxazole
Sensitive50 (73.5%)115 (77.7%)0.502
Resistant18 (26.5%)33 (22.3%)
Piperacillin/tazobactam
Sensitive53 (86.9%)106 (96.4%)0.028
Resistant8 (13.1%)4 (3.6%)
Ampicillin
Sensitive17 (39.5%)44 (61.1%)0.025
Resistant26 (60.5%)28 (38.9%)
Amoxicillin/clavulanic acid
Sensitive13 (31%)32 (41%)0.277
Resistant29 (69%)46 (59%)
Cephalothin
Sensitive11 (26.2%)31 (39.7%)0.138
Resistant31 (73.8%)47 (60.3%)
Cefuroxime
Sensitive13 (31%)36 (46.2%)0.106
Resistant29 (69%)42 (53.8%)
Cefixime
Sensitive14 (33.3%)38 (48.7%)0.105
Resistant28 (66.7%)40 (51.3%)
Cefpodoxime
Sensitive14 (33.3%)38 (48.7%)0.105
Resistant28 (66.7%)40 (51.3%)
Aztreonam
Sensitive14 (70%)27 (87.1%)0.163
Resistant6 (30%)4 (12.9%)
Penicillin
Sensitive17 (36.2%)23 (25.6%)0.195
Resistant30 (63.8%)67 (74.4%)
Tetracycline
Sensitive30 (63.8%)71 (78.9%)0.057
Resistant17 (36.2%)19 (21.1%)
Vancomycin
Sensitive42 (89.4%)90 (100%)0.004
Resistant5 (10.6%)0 (0%)
Linezolid
Sensitive47 (100%)90 (100%)---
Resistant0 (0%)0 (0%)
Oxacillin
Sensitive16 (69.6%)53 (75.7%)0.559
Resistant7 (30.4%)17 (24.3%)
Clindamycin
Sensitive21 (91.3%)55 (78.6%)0.224
Resistant2 (8.7%)15 (21.4%)
Fusidic acid
Sensitive7 (30.4%)28 (40%)0.411
Resistant16 (69.6%)42 (60%)
Erythromycin
Sensitive15 (32.6%)45 (51.1%)0.041
Resistant31 (67.4%)43 (48.9%)
Data are presented as frequency (%), statistical significance at p value < 0.001 (after Bonferroni correction).
Table 8. MDR, XDR, and PDR profile of wound infection isolates (n = 536).
Table 8. MDR, XDR, and PDR profile of wound infection isolates (n = 536).
PhenotypeFrequency (n)Percentage (%)
MDR27851.9
XDR346.3
PDR00.0
CAR50.9
ESBL519.5
MRSA8415.7
VRE40.7
MDR: Multidrug-resistant, XDR: Extensively drug-resistant, PDR: Pan drug-resistant, CAR: Carbapenamase producer, ESBL: Extended-spectrum beta-lactamase producer, MRSA: Methicillin-resistant Staphylococcus aureus, VRE: Vancomycin-resistant enterococci.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Khalid, F.; Poulose, C.; Farah, D.F.M.; Mahmood, A.; Elsheikh, A.; Khojah, O.T. Prevalence and Antimicrobial Susceptibility Patterns of Wound and Pus Bacterial Pathogens at a Tertiary Care Hospital in Central Riyadh, Saudi Arabia. Microbiol. Res. 2024, 15, 2015-2034. https://doi.org/10.3390/microbiolres15040135

AMA Style

Khalid F, Poulose C, Farah DFM, Mahmood A, Elsheikh A, Khojah OT. Prevalence and Antimicrobial Susceptibility Patterns of Wound and Pus Bacterial Pathogens at a Tertiary Care Hospital in Central Riyadh, Saudi Arabia. Microbiology Research. 2024; 15(4):2015-2034. https://doi.org/10.3390/microbiolres15040135

Chicago/Turabian Style

Khalid, Fizza, Christy Poulose, Dalal Farah Mousa Farah, Abid Mahmood, Azza Elsheikh, and Osamah T. Khojah. 2024. "Prevalence and Antimicrobial Susceptibility Patterns of Wound and Pus Bacterial Pathogens at a Tertiary Care Hospital in Central Riyadh, Saudi Arabia" Microbiology Research 15, no. 4: 2015-2034. https://doi.org/10.3390/microbiolres15040135

APA Style

Khalid, F., Poulose, C., Farah, D. F. M., Mahmood, A., Elsheikh, A., & Khojah, O. T. (2024). Prevalence and Antimicrobial Susceptibility Patterns of Wound and Pus Bacterial Pathogens at a Tertiary Care Hospital in Central Riyadh, Saudi Arabia. Microbiology Research, 15(4), 2015-2034. https://doi.org/10.3390/microbiolres15040135

Article Metrics

Back to TopTop