Original article / research
| Year :
2026 |
Month :
January
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Volume :
15 |
Issue :
1 |
Page :
MO01 - MO07 |
Full Version
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Bacteriological Profile, Characterisation of MDR and XDR Bacteria in Pyogenic Infections among Patients: A Descriptive Retrospective Study from a Tertiary Care Hospital, Ujjain, Madhya Pradesh, India
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Sonali Waske, Abha Ekka, Pushpendra Pathak, Yogyata Marothi
1. Associate Professor, Department of Microbiology, R.D. Gardi Medical College, Ujjain, Madhya Pradesh, India.
2. Associate Professor, Department of Community Medicine, R.S.D.K.S GMC, Ambikapur, Chhattisgarh, India.
3. Demonstrator (PhD), Department of Microbiology, R.D. Gardi Medical College, Ujjain, Madhya Pradesh, India.
4. Professor and Head, Department of Microbiology, R.D. Gardi Medical College, Ujjain, Madhya Pradesh, India.
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Correspondence
Address :
Dr. Sonali Waske,
Agar Road, F9, Charak Residency, Ujjain-456006, Madhya Pradesh, India.
E-mail: SonaliWaske04@gmail.com
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| | | ABSTRACT |  | : Introduction: Pyogenic infections are prevalent in India and can manifest in diverse body parts, including the skin and soft-tissues, respiratory tract, and visceral organs. The pathogenesis of pyogenic infections is typically attributed to microorganisms such as Staphylococcus aureus, Klebsiella pneumoniae, Escherichia coli, and Pseudomonas aeruginosa. The emergence of Multidrug-Resistant (MDR) and Extensively Drug-Resistant (XDR) organisms complicates their management. A comprehensive understanding of aetiology, predisposing factors, and therapeutic modalities of pyogenic infections is essential for effective management.
Aim: To identify MDR and XDR isolates in pus samples and to determine their antimicrobial susceptibility patterns.
Materials and Methods: This hospital-based descriptive retrospective study was conducted in the Microbiology Laboratory at CRGH Hospital and Ruxmaniben Deepchand Gardi Medical College, Ujjain, Madhya Pradesh, India, from March 2024 to April 2025. The study included all pus samples, regardless of age and gender, that yielded growth of pathogenic bacteria. A total of 342 pus samples were received in sterile containers and were immediately processed for microscopic examination and culture on routine Blood Agar and Mac Conkey Agar, following standard guidelines. After identifying bacteria using various biochemical tests, their Antimicrobial Susceptibility Testing (AST) was performed according to established standards. Isolates were classified as MDR or XDR based on standard definitions. For statistical analysis, the Chi-square test was applied to find the association of risk factors and co-morbidities with pyogenic infections, with a p-value <0.05 considered statistically significant.
Results: Among the 342 pus samples, 174 were male (51%) and 168 were female (49%). The majority of samples from males were received from the 51 to 60-year age group (51 samples, 29.4%), followed by the 41 to 50-year age group (42 samples, 24.1%). A total of 164 organisms (48%) were isolated. Co-morbid conditions included diabetes mellitus (62 cases, 18%) and addictive behaviours such as smoking (121 cases, 35%) and alcoholism (108 cases, 32%). The associations of co-morbidities (such as diabetes mellitus, hypertension, their co-existence, trauma, and tuberculosis) and addictive behaviours (such as smoking, alcoholism, and their co-existence) with pyogenic infections were analysed, with a p-value <0.05 considered statistically significant. The predominant Gram-Negative Bacteria (GNB) isolated from the Enterobacterales were E. coli (44 isolates, 26.8%), followed by Klebsiella pneumoniae (31 isolates, 18.9%). Among the non fermenting GNB, Pseudomonas aeruginosa was isolated in 10 cases (6.1%) and Acinetobacter spp. in 3 cases (1.8%). The Gram-Positive Cocci (GPC) isolated included Staphylococcus aureus (41 cases, 25%). GNB were mainly susceptible to the carbapenem group (Meropenem and Ertapenem), and all S. aureus isolates were susceptible to vancomycin, linezolid, and teicoplanin. The prevalence of MDR and XDR among GNB was 37 (40.2%) and 31 (33.6%), respectively. The percentages of MDR and XDR in E. coli were 24 (54.5%) and 10 (22.7%), respectively, while in K. pneumoniae, both MDR and XDR were found in 11 cases (35.4%). The prevalence of MDR among S. aureus was 34 (82.9%), with methicillin-resistant S. aureus found in 24 cases (59%).
Conclusion: This study identified diabetes mellitus, smoking, and alcoholism as frequently observed risk factors. Antimicrobial Resistance (AMR) is rising significantly in MDR and XDR strains among both GNB and GPC. It is crucial to initiate the development and implementation of effective antimicrobial stewardship programs. |
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| Keywords
: Alcoholism, Diabetes mellitus, Extensive drug resistant, Multidrug resistant, Smoking |
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| DOI and Others
: DOI: 10.7860/NJLM/2026/81420.2948
Date of Submission: Jun 19, 2025
Date of Peer Review: Aug 08, 2025
Date of Acceptance: Oct 10, 2025
Date of Publishing: Jan 01, 2026
AUTHOR DECLARATION:
• Financial or Other Competing Interests: None
• Was Ethics Committee Approval obtained for this study? Yes
• Was informed consent obtained from the subjects involved in the study? No
• For any images presented appropriate consent has been obtained from the subjects. NA
PLAGIARISM CHECKING METHODS:
• Plagiarism X-checker: Jul 16, 2025
• Manual Googling: Oct 03, 2025
• iThenticate Software: Oct 09, 2025 (9%)
ETYMOLOGY: Author Origin
EMENDATIONS: 7
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INTRODUCTION |
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The term pyogenic infections refers to infections that produce pus. These are characterised by local inflammation, typically resulting from the infiltration and multiplication of pyogenic organisms within the human body through various routes, leading to the accumulation of dead leukocytes and infectious agents (1). The body’s immune system acts against the invading microorganisms, and the accumulation of dead leukocytes results in the formation of a thick whitish or yellow liquid known as pus (2).
Pyogenic infections can occur either endogenously or exogenously, with bacteria, fungi, viruses, or parasites being the main causative agents. Impetigo, cellulitis, furuncles, abscesses, blepharitis, and necrotising fasciitis are examples of skin and soft-tissue infections (SSTIs) (3). Bone infections include osteomyelitis, septic arthritis, and spondylodiscitis, while ear infections include otitis media, and urinary tract infections include cystitis (1). Pyogenic granulomas, lesions of the skin and mucous membranes, may appear on the face, mouth, lips, tongue, nose, fingers, and toes. Meningitis and surgical site infections are also common forms of pyogenic infections. Human SSTIs often occur during or after trauma, burn injuries, or surgical procedures (1),(3).
Several factors influence the development of pyogenic infections, including wound condition, microbial load, and host defense mechanisms. Advanced age, diabetes mellitus, malignancies, postsurgical complications, heavy exercise, muscular injuries, infancy (under two years of age), smoking, alcoholism, and chronic conditions such as chronic renal and chronic liver diseases are recognised risk factors that vary depending on the type of infection (4),(5). In developing countries, about 40% of reported cases are classified as suppurative pyogenic infections (6).
The global rise of MDR, XDR, and pan-drug-resistant bacteria has made the management of such infections increasingly difficult (7). MDR and XDR GNB strains- including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii- and the GPC Methicillin-Resistant Staphylococcus aureus (MRSA) have become increasingly associated with pyogenic infections in hospital settings over recent decades (8),(9),(10). Studies from India have reported the prevalence of MDR-GNB ranging from 26% to 34%, with 12.1% being XDR, whereas a study from Pakistan reported a prevalence as high as 64% (11),(12),(13). The rising prevalence of these MDR and XDR organisms poses a serious public health concern. Continuous surveillance of these pathogens is essential for developing appropriate treatment regimens and early therapeutic strategies, as no single antibiotic is effective against all of them.
This study aims to determine the burden, risk factors, Aetiological agents, and antibiogram of MDR and XDR organisms isolated from pus samples at a tertiary care hospital, with the goal of aiding empirical therapy formulation and implementing infection control strategies to prevent MDR.
The research involves isolating and identifying common pyogenic infection-causing pathogens such as Staphylococcus aureus, along with emerging drug-resistant strains like Acinetobacter baumannii and Klebsiella pneumoniae, from patient pus samples to determine their specific AST patterns. Concurrently, patient data are analysed to identify risk factors, including underlying health conditions such as diabetes mellitus and hypertension, as well as lifestyle factors such as smoking and alcoholism.
The ultimate goal is to establish a clinicomicrobiological correlation, linking patient risk factors to the type of bacteria and its resistance profile. This local data is crucial for developing tailored antibiotic stewardship programs and treatment guidelines to effectively combat the growing threat of MDR infections within a specific hospital setting.
The primary and secondary objectives of the study are to analyse the AST patterns of isolates from pyogenic infections and to identify associated risk factors and co-morbid conditions.
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Material and Methods |
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The present study was a hospital-based descriptive retrospective study conducted in the Department of Microbiology at CRGH and Ruxmaniben Deepchand Gardi Medical College, Ujjain, Madhya Pradesh, India, from March 2024 to April 2025. The study commenced after obtaining permission from the Institutional Ethics Committee (IEC), with approval letter number IEC/06/25, dated 18/04/2025. Retrospective data collection was performed by reviewing patients’ case files (secondary data) from April 2025 to May 2025, and data analysis was carried out in June 2025.
Inclusion criteria: Data from patients’ medical records with confirmed pyogenic infections were included in the study. The selected case files contained culture and susceptibility reports of pus samples showing the growth of pathogenic bacteria.
Exclusion criteria: Case files of patients whose pus culture reports showed commensal or contaminant growth (e.g., unusual colony morphology or growth on negative control plates) and repetitive samples were excluded from the study.
Sample size calculation: The sample size was calculated based on the study by Devi KD et al., assuming a prevalence (p) of 30.9% (14). The formula used for calculation was: n=z2* P* (100-P)/e2,
Where,
Z=1.96 (at 95% confidence interval)
P=30.9% (assumed prevalence)
e=5% (absolute error)
n=(1.96^2) \times 30.9 \times (100-30.9)/5^2=342
Thus, the calculated sample size was N=342.
Study Procedure
Medical records of patients were reviewed to obtain demographic information, clinical profiles, associated risk factors, and details of pathogen isolates along with their antimicrobial susceptibility patterns for analysis. All pus samples received in sterile containers were subjected to culture on blood agar and MacConkey agar, followed by microscopic examination (Gram staining and Ziehl-Neelsen staining). Plates were incubated aerobically at 37°C for 24 hours. Organisms were identified using standard laboratory procedures (1),(15). AST of bacterial isolates was performed on Mueller-Hinton Agar (MHA) using the modified Kirby-Bauer disk diffusion method as per Clinical and Laboratory Standards Institute (CLSI) guidelines (16). Reference strains of Staphylococcus aureus (ATCC 25923), Escherichia coli (ATCC 25922), and Pseudomonas aeruginosa (ATCC 27853) were used as quality controls for susceptibility testing in accordance with CLSI standards (16).
All bacterial isolates were classified as MDR or XDR based on standard definitions (7):
MDR: An isolate not susceptible to at least one agent in three or more antimicrobial classes tested.
XDR: An MDR isolate not susceptible to at least one agent in all but two or fewer antimicrobial classes.
Statistical Analysis
All statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) version 21.0 (SPSS Inc., Chicago, IL, USA) for Microsoft Windows. Data were described in terms of percentages. The Chi-square test was applied to determine the association between risk factors (smoking and alcohol consumption) and co-morbid conditions (such as diabetes mellitus and hypertension) with the type of pyogenic infection. A p-value <0.05 was considered statistically significant.
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Results |
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Of the 342 pus samples received, 164 (48%) showed bacterial growth, with a slight predominance of GNB-92 (56%)- over GPC- 72 (44%). Three samples showed mixed growth (included in the total bacterial isolates, n=164). The study found a nearly equal distribution of samples between male (174; 51%) and female (168; 49%) patients. The highest number of male samples were from the 51-60-year age group (51; 29.4%), while female samples were most frequent in the 41-50-year age group (42; 25%). Most samples were received from the Surgery department (102; 30%), followed by Orthopaedics (85; 25%), Obstetrics and Gynaecology (65; 19%), ENT (31; 9%), Pulmonary Medicine (21; 6%), Medicine and Dermatology (14; 4%) each, and Paediatrics (10; 3%). The most common infection source was infected wounds (102; 30%), followed by non healing ulcers (85; 25%), pyothorax (47; 14%), and abscesses (40; 12%). Other infections included surgical site infections (16; 5%), discharging sinuses (16; 5%), Chronic Suppurative Otitis Media (CSOM) and burn wounds (13; 4%) each, and others such as Fournier’s gangrene and necrotising fasciitis (7; 2%), and urethral discharge (3; 1%) (Table/Fig 1). A clinical evaluation of patients revealed that diabetes mellitus was the most common co-morbidity, present in 62 (18%) cases. The co-existence of diabetes and hypertension was also highly prevalent, affecting 55 (16%) patients, while hypertension alone was observed in 15 (4%), trauma in 14 (4%), and Mycobacterium tuberculosis infection in 10 (3%). Lifestyle analysis showed that smoking was the most common addiction (121; 35%), followed by alcoholism (108; 32%). A considerable number of patients (87; 25.4%) had both habits. Associations between co-morbidities (diabetes mellitus, hypertension, their co-existence, trauma, and tuberculosis) and lifestyle factors (smoking, alcoholism, and their co-existence) with pyogenic infections were analysed, with p-value <0.05 considered statistically significant (Table/Fig 1). Among patients with infected wounds, the co-existence of diabetes and hypertension was the most common comorbidity (39; 38.2%), while smoking was the predominant addiction (56; 54.9%) (Table/Fig 1).
Out of 164 bacterial isolates, Enterobacterales were the most prevalent GNB group (79; 48%), with Escherichia coli being the most frequently isolated organism (44; 27%), followed by Klebsiella pneumoniae (31; 19%). Other non fermenting GNB included Pseudomonas aeruginosa (10; 6%) and Acinetobacter spp. (3; 2%). Among GPC, the most common isolate was Staphylococcus aureus (41; 25%), followed by Coagulase-Negative Staphylococci (CONS) (31; 19%). Overall, E. coli and S. aureus were the two most prevalent pathogens (Table/Fig 2).
Of all isolates, the highest number (48; 29.2%) were recovered from infected wounds. The most common organism in these wounds was E. coli (16; 33.3%), followed by S. aureus (13; 27%) and K. pneumoniae (7; 14.5%). Less frequent isolates included Klebsiella aerogenes (2; 4.1%), Acinetobacter spp. (2; 4.1%), and P. aeruginosa (1; 2%) (Table/Fig 3).
The study found a statistically significant association (p-value <0.05) for E. coli, S. aureus, and CONS, indicating a strong correlation between these bacterial species and specific types of infections (Table/Fig 3).
The distribution of pathogens varied by department. The highest number of isolates (59; 36%) were obtained from the Surgery department, where Klebsiella pneumoniae was the most prevalent pathogen (18; 31%), followed by E. coli (13; 22%) and S. aureus (10; 17%). Less common isolates included P. aeruginosa (3; 5%) and Acinetobacter spp. (2; 3%). This highlights the significant presence of K. pneumoniae and other GNB in surgical settings.
In samples from ICUs (7; 4.2%), Klebsiella pneumoniae was the predominant causative pathogen, which may be attributed to the fact that ICU patients are typically critically ill and immunocompromised (Table/Fig 4).
A significant proportion of GNB exhibited MDR (37; 40.2%) and XDR (31; 33.6%). In contrast, GPC showed a similar rate of MDR (34; 47.2%), but no XDR strains were detected. Among specific bacteria, Staphylococcus aureus isolates showed an alarming MDR rate of 34 (82.9%), with no XDR strains detected. Among GNB, the highest percentage of MDR was observed in Escherichia coli (24; 54.5%), while the highest percentage of XDR was found in Klebsiella pneumoniae (11; 35%) (Table/Fig 5).
This finding highlights a serious concern regarding drug resistance, particularly in GNB and specific strains such as S. aureus and E. coli. These isolates showed the highest susceptibility to minocycline, meropenem, and ertapenem-41 (93%) and 25 (81%) each-followed by cefoperazone-sulbactam (40; 91%). K. pneumoniae demonstrated the highest susceptibility to minocycline, meropenem, and ertapenem (25; 81%) each, followed by piperacillin-tazobactam, cefoperazone- sulbactam, imipenem, amikacin, and tobramycin (22; 70%) each. Pseudomonas aeruginosa showed the highest susceptibility to meropenem (8; 80%), followed by piperacillin–tazobactam (7; 70%) and tobramycin (7; 70%) (Table/Fig 6).
Among S. aureus isolates, 24 (59%) were identified as MRSA. The highest antimicrobial susceptibility was observed with vancomycin, linezolid, and levofloxacin (24; 100%) each, followed by gentamicin (17; 71%). Inducible Clindamycin Resistance (ICR) was detected in 5 (21%) S. aureus isolates (Table/Fig 7).
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Discussion |
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In the present study, culture positivity was 164 (48%), which correlates with findings by Devi KD et al., (30.9%) and Wajid M et al., (43.3%) (14),(17). However, some studies have reported higher culture positivity rates (6),(18). In this study, the isolation rate of GNB (92; 56%) was higher than that of GPC (72; 44%), consistent with earlier reports (6),(18). The majority of samples were obtained from male patients (174; 51%), compared to female patients (168; 49%), findings that are similar to previous studies (18),(19). The highest culture positivity was observed in the Surgery Department (59; 36%), followed by Orthopaedics (32; 20%), Obstetrics and Gynaecology (28; 17%), ENT (20; 12%), Dermatology (10; 6%), and ICU (7; 4.2%). Comparable results were reported by Bankar N et al., (20).
In the present study, the most common co-morbidity was Diabetes Mellitus (DM) (62; 18%), followed by the co-existence of DM with Hypertension (55; 16%), and Hypertension alone (15; 4.3%). Similar findings were reported by Kar M et al., who identified DM (25; 35.67%) as the predominant comorbidity (21). Pre-existing DM was associated with poorer clinical outcomes. It is estimated that 693 million people will have DM by 2045, making it one of the most significant global health challenges of the 21st century (22). This poses a major concern in infectious diseases, as DM increases susceptibility to various infections (4),(23).
In this study, the most common addiction was smoking (121; 35.53%), followed by alcohol consumption (108; 32%), and combined smoking and alcohol use (87; 25.4%), which aligns with findings from other studies (23). Previous studies have also reported smoking as a risk factor for pyogenic infections (21),(24). The associations between co-morbidities and addictions with pyogenic infections are presented in (Table/Fig 1). Among patients with infected wounds, the co-existence of DM and Hypertension was the most frequent co-morbidity (39; 38.2%), followed by DM alone (17; 16.6%), Hypertension (4; 3.9%), Mycobacterium tuberculosis (3; 2.9%), and Trauma (2; 1.9%). The most common addiction was smoking (56; 54.9%), followed by combined smoking and alcohol use (45; 44.1%), and alcohol alone (44; 43.1%). Associations of co-morbidities (DM, hypertension, trauma, tuberculosis) and addictions (smoking, alcoholism, and their co-existence) with pyogenic infections were statistically significant (p-value <0.05). Similar associations of DM, tuberculosis, and smoking were reported by Pathak A et al., (25).
In this study, the most frequently isolated organism was E. coli (44; 26.8%), followed by S. aureus (41; 25%), K. pneumoniae (31; 18.9%), P. aeruginosa (10; 6%), Acinetobacter spp. (3; 2%), K. aerogenes (2; 1%), and Proteus mirabilis (2; 1%), consistent with earlier studies (9),(21). Among GPC, S. aureus (41; 25%) was the most frequent isolate, followed by Coagulase-Negative Staphylococci (CONS) (31; 19%), similar to findings reported in previous studies (6),(18). Mixed growth was observed in 3 (0.87%) samples in the present study, which is consistent with previous reports (14). Several studies have associated mixed bacterial growth with poor wound care, increased microbial persistence, ineffective antimicrobial therapy, and unfavourable patient outcomes (14).
A study from Mysuru, Karnataka reported 30.07% MDR GNB among pyogenic infections (26). In the present study, the prevalence of MDR and XDR among GNB was 37 (40.2%) and 31 (33.6%), respectively. However, a study by Ingale H, reported higher rates of MDR (68.8%) and XDR (47.2%) in GNB isolates (27).
Multidrug-Resistant (MDR) Gram-Negative Bacilli (GNB): E. coli was the most predominant MDR GNB, accounting for 24 (54.5%) of isolates, followed by K. pneumoniae (11; 35.4%), and both Acinetobacter spp. (1; 33.3%) and P. aeruginosa (1; 10%).
Extensively Drug-Resistant (XDR) Gram-Negative Bacilli (GNB): Among XDR GNB, Klebsiella aerogenes 2 (100%) and Proteus mirabilis 2 (100%) showed complete resistance, followed by Acinetobacter spp. 2 (66.6%), P. aeruginosa 4 (40%), K. pneumoniae 11 (35.4%), and E. coli 10 (22.7%). A study by Devi KD et al., and Kavya V et al., reported higher MDR rates in Acinetobacter baumannii, at 2 (66.7%) and 27 (16.2%) respectively. The findings of the present study align with these reports (14),(26).
Ingale H et al., reported extremely high MDR (63; 91.3%) and XDR (43; 62.3%) rates among E. coli isolates (27). Similarly, other studies also reported higher MDR E. coli isolation rates, 33.3% and 43 (25.9%) by Chakraborty A et al., and Kavya V et al., respectively (18),(26). Previous research consistently shows high levels of drug resistance in K. pneumoniae. Kavya V et al., reported nearly half (48.12%) of isolates as MDR. Ingale H et al., found high rates of both MDR (69%) and XDR (42.8%), while Kalita JM et al., reported an even higher MDR rate of 74.79% (26),(27),(28).
Ingale H, also found significant resistance in P. aeruginosa, reporting MDR rates of 42.5% and XDR rates of 33.7%. In contrast, other studies have shown variable MDR rates ranging from 9.6% to 61.9% (Kavya V et al., 16 [9.6%]; Devi KD et al., 5 [16.7%]; and Soni M et al., 61.9%, respectively) (14),(26),(29). In contrast to the study by Ingale H, which reported MDR and XDR rates of 67% and 11.3% for S. aureus (27), the present study found a significantly higher MDR rate of 82.9% but no XDR strains.
The high prevalence of MDR and XDR bacteria in pyogenic infections represents a critical public health concern, driven by multiple factors. The inappropriate and excessive use of antibiotics exerts strong selective pressure, enabling resistant strains to proliferate. Pyogenic pathogens such as P. aeruginosa, Acinetobacter baumannii, and MRSA readily acquire and transfer resistance genes. Additionally, their capacity to form biofilms provides physical protection from both antibiotics and host immune responses, complicating treatment and promoting resistance. Healthcare environments are major reservouirs for MDR and XDR pathogens. Previous hospitalisation, use of medical devices, and inadequate infection control practices facilitate the transmission of resistant bacteria among patients.
E. coli strains demonstrated high susceptibility to carbapenems-Meropenem and Ertapenem 41 (93%) each- and Minocycline 41 (93%), followed by Cefoperazone-Sulbactam 40 (91%). However, susceptibility markedly decreased for commonly used Third-Generation Cephalosporins (TGCs) such as Ceftriaxone 13 (30%) and Cefotaxime 14 (32%). These findings align with a study from Odisha, which reported high susceptibility to the carbapenem group (Imipenem and Meropenem, 92%) and Cefoperazone-Sulbactam (80%), but reduced susceptibility to Ceftriaxone (36%). Previous studies have also reported higher susceptibility to carbapenems [21,30] and lower susceptibility to routinely used TGCs (21).
The antimicrobial susceptibility pattern of Klebsiella pneumoniae showed high sensitivity to Meropenem, Ertapenem, and Minocycline (25; 81% each). For TGCs- Ceftriaxone and Cefotaxime—the susceptibility was 9 (30%) each, while Ciprofloxacin showed the lowest susceptibility at 6 (19%). Similar susceptibility patterns for carbapenems were reported from Odisha, with Meropenem (80%); however, susceptibility to Ceftriaxone (65%) and Ciprofloxacin (70%) was higher than that found in the present study (30). Conversely, another study reported lower susceptibility patterns than those observed here (21).
Antimicrobial Susceptibility Pattern of P. aeruginosa
The antimicrobial susceptibility pattern of P. aeruginosa showed significantly reduced sensitivity to carbapenems- Meropenem 8 (80%) and Imipenem 6 (60%)- and markedly decreased susceptibility to anti-pseudomonal cephalosporins, including Ceftazidime 4 (40%) and Cefepime 3 (30%). Sensitivity to Piperacillin-Tazobactam and Tobramycin was 7 (70%) each. A previous study reported higher susceptibility to Meropenem (94%) and lower susceptibility to Imipenem (53%), Piperacillin-Tazobactam (67%), and Ceftazidime (47%), findings that correlate with the present study (30). Compared to the current findings, another study reported a lower susceptibility pattern (21).
Acinetobacter spp.
Acinetobacter spp. isolates demonstrated low susceptibility to carbapenems- Imipenem and Meropenem 1 (33%) each- but showed better sensitivity to Minocycline and Tobramycin 2 (67%) each. Previous studies have reported higher susceptibility to Imipenem and Meropenem (93% each) (30).
The high resistance observed to TGCs and fluoroquinolones in E. coli and K. pneumoniae is a significant concern and is likely due to widespread ESBL production. This pattern aligns with the continued high susceptibility to carbapenems and Piperacillin-Tazobactam. A notable observation is the much higher susceptibility to Minocycline compared to Tetracycline, suggesting a specific resistance mechanism less effective against Minocycline. Overall, the data indicate a high burden of MDR organisms, particularly ESBL producers, though ESBL production was not directly detected in this study.
Methicillin-Resistant Staphylococcus aureus (MRSA)
In this study, MRSA accounted for 24 (59%) isolates, while MSSA comprised 17 (41%). These findings are consistent with a previous study reporting MRSA prevalence at 51.86% (21). However, other studies have reported lower MRSA rates-151 (13.26%) by Kalita JM et al., 37.3% by ICMR AMRSN, and 40% as reported by the Indian Network for Surveillance of Antimicrobial Resistance (INSAR) group (28),(31),(32). In India, the prevalence of MRSA ranges from 40% to 70% among S. aureus isolates, with variations observed between hospital and community settings (33).
All MRSA isolates in this study were susceptible to Vancomycin, Teicoplanin, Linezolid, and Levofloxacin 24 (100%) each. Inducible Clindamycin Resistance (ICR) was detected in 5 (21%) S. aureus isolates. S. aureus is a common commensal organism found on the skin and mucous membranes, and MRSA can colonise these sites, increasing the risk of infection. MRSA produces toxins and enzymes that facilitate tissue invasion and damage. Its multidrug resistance poses a challenge in clinical management. Healthcare workers can serve as carriers, contributing to the spread of MDR MRSA within hospital settings.
Clinical Implications and Antibiogram Significance
The study provides an invaluable “antibiogram”- a snapshot of local antimicrobial resistance patterns. This information is crucial in clinical settings. The observed high resistance to TGCs, once considered reliable antibiotics, indicates that standard empirical treatment protocols may now be ineffective. This resistance can lead to adverse outcomes such as prolonged illness and increased mortality.
These findings necessitate changes in clinical practice. Physicians managing severe pyogenic infections should consider using more potent, last-resort agents such as carbapenems from the outset or include them as part of combination therapy. Furthermore, identifying patient risk factors- such as underlying co-morbidities and lifestyle habits- can enable a more targeted and cautious approach to treatment in high-risk individuals.
Limitation(s)
The present study was conducted at a single centre and, therefore, reflects data from a limited geographical area regarding the distribution of aetiological agents, co-morbid conditions, and predisposing factors associated with pyogenic infections. A multicentre study would provide more comprehensive insights and aid in formulating effective antimicrobial stewardship policies for better management of such infections.
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Conclusion |
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This study found that pyogenic infections are predominantly caused by GNB, with E. coli being the most common pathogen. A significant finding is the alarming rise in antimicrobial resistance, including MDR and XDR strains among GNB. The study also highlights the highest burden of MDR among S. aureus isolates and the emergence of highly resistant pathogens such as Acinetobacter spp. and K. pneumoniae, which pose major therapeutic challenges. These findings are critical for clinicians, providing essential guidance for revising empirical treatment strategies to improve patient outcomes. To address this growing threat, the study recommends regular updates to local empirical treatment protocols, implementation of robust Antimicrobial Stewardship Programs (ASP) with continuous surveillance and mandatory culture testing, and strengthening infection control measures. Future research should focus on the molecular characterisation of resistance genes, identification of community sources of resistant bacteria, and evaluation of the impact of ASPs on resistance trends and patient outcomes.
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| | 1. | Koneman WK, Allen SD, Janda WM, Schreckenberger PC, Propcop GW, Woods GL, et al. Color Atlas and Textbook of Diagnostic Microbiology, 7th ed. Lippincott-Raven Publisher. 2020; 624-62.
[ Google Scholar] | | 2. | Baltimore RS. Pseudomonas. In: Nelson Textbook of Pediatrics. 2000;862-864.
[ Google Scholar] | | 3. | Cogen AL, Nizet V, Gallo RL. Skin microbiota: A source of disease or defense? Br J Dermatol. 2008;158(3):442-55.
[ Google Scholar] [ CrossRef] [ PubMed] | | 4. | Milosevic B, Cevik M, Urosevic A, Nikolic N, Poluga J, Jovanovic M, et al. Risk factors associated with poor clinical outcome in pyogenic spinal infections: 5-years’ intensive care experience. J Infect Dev Ctries. 2020;14:36-41. Doi: 10.3855/jidc.12260.
[ Google Scholar] [ CrossRef] [ PubMed] | | 5. | Ramakrishnan K, Salinas RC, Agudelo Higuita NI. Skin and soft tissue infections. Am Fam Physician. 2015;92(6):474-83. PMID: 26371732.
[ Google Scholar] | | 6. | Bajare B, Dhangar A, Tankhiwale S, Shrikhande S. Bacteriological profile of pyogenic infections and their antimicrobial susceptibility in a tertiary care hospital in central India. Med Lab J. 2024;18(2):16-18.
[ Google Scholar] [ CrossRef] | | 7. | Magiorakos AP, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: An international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18(3):268-81. Available from: https:// www.ncbi.nlm.nih.gov/pubmed/21793988.
[ Google Scholar] [ CrossRef] [ PubMed] | | 8. | Sajjanar V, Premalatha DE, Siddesh KC, Prakash N. Study of bacteriological profile and antibiotic susceptibility pattern of pus isolates in tertiary care hospital. IP Int J Med Microbiol Trop Dis. 2023;9(4):253-57.
[ Google Scholar] [ CrossRef] | | 9. | Trojan R, Razdan L, Singh N. Antibiotic susceptibility patterns of bacterial isolates from pus samples in a tertiary care hospital of Punjab, India. Int J Microbiol. 2016;2016:9302692. Doi: 10.1155/2016/9302692. Epub 2016 Oct 30. PMID: 27872643; PMCID: PMC5107258.
[ Google Scholar] [ CrossRef] [ PubMed] | | 10. | Wadekar MD, Sathish JV, Jayashree, Pooja C. Bacteriological profile of pus samples and their antibiotic susceptibility pattern. Indian J Microbiol Res. 2020;7(1):43-47.
[ Google Scholar] [ CrossRef] | | 11. | Nagvekar V, Sawant S, Amey S. Prevalence of multidrug-resistant Gram-negative bacteria cases at admission in a multispeciality hospital. J Glob Antimicrob Resist. 2020;22:457-61. Doi: 10.1016/j.jgar.2020.08.016.
[ Google Scholar] [ CrossRef] [ PubMed] | | 12. | Alkofide H, Alhammad AM, Alruwaili A, Aldemerdash A, Almangour TA, Alsuwayegh A, et al. Multidrug-resistant and extensively drug-resistant enterobacteriaceae: Prevalence, treatments, and outcomes- A retrospective cohort study. Infect Drug Resist. 2020;13:4653-62. Doi: 10.2147/IDR.S281223.
[ Google Scholar] [ CrossRef] [ PubMed] | | 13. | Abbas S, Sabir AU, Khalid N, Sabir S, Khalid S, Haseeb S, et al. Frequency of extensively drug-resistant gram-negative pathogens in a tertiary care hospital in Pakistan. Cureus. 2020;12(12): e11914. Doi: 10.7759/cureus.11914.
[ Google Scholar] [ CrossRef] | | 14. | Devi KD, Khandait M, Sharma M, Sardar M, Singh A, Saha R, et al. Prevalence of multidrug-resistant Gram-negative bacteria causing pyogenic infections at a tertiary care hospital in Haryana. MGM J Med Sci. 2023;10:517-23.
[ Google Scholar] [ CrossRef] | | 15. | Collee JG, Marimon BP, Frazer AG, Simmons A. Makie and McCartney Practical Medical Microbiology. 14th ed. Elsevier; 1996:361-368.
[ Google Scholar] | | 16. | CLSI. Performance standards for antimicrobial susceptibility testing; 35th ed. CLSI supplement M100. Clinical and Laboratory Standards Institute; 2025.
[ Google Scholar] | | 17. | Wajid M, Naaz S, Hashmiya SS. Evaluation of bacteriological profile of pyogenic infections and their susceptibilty profiles at a teritary care hospital. Int J Health Sci. 2022;6(S3):5237-46. Available from: https://doi.org/10.53730/ijhs.v6nS3.7079.
[ Google Scholar] [ CrossRef] | | 18. | Chakraborty A, Chanda DD, Choudhury N, Manjula NG, Shilpa BM. A retrospective study on the pyogenic pathogens and their antibiotic susceptibility patterns along with the ESβL production. Adv Microbiol. 2021;11:317-26. Doi: 10.4236/aim.2021.116024.
[ Google Scholar] [ CrossRef] | | 19. | Vyas A, Vij A, Singh D. Bacteriological profile and antimicrobial susceptibility pattern of isolates from pus samples in a tertiary care hospital. JAMU. 2022;2(1):08-15.
[ Google Scholar] [ CrossRef] | | 20. | Bankar N, Wankhade A, Bramhane RB, Hathiwala R, Chandi DH. Bacteriological profile of pus/wound swab andantimicrobial susceptibility of Staphylococcus aureus isolated from of pus & wound swab of indoor patients of tertiary care hospital in Durg, Chhattisgarh, India. Int J Innovative Res Med Sci. 2018;3(4):1976-80.
[ Google Scholar] | | 21. | Kar M, Dubey A, Siddiqui T, Sahu C. Evaluation of clinico-epidemiology profile and susceptibility pattern of bacterial isolates of purulent samples in a University Hospital, India. J Adv Microbiol. 2023;23(1):18-25. Doi: 10.9734/jamb/2023/ v23i1701.
[ Google Scholar] [ CrossRef] | | 22. | Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, et al. IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271-81.
[ Google Scholar] [ CrossRef] [ PubMed] | | 23. | Bhagat TS, Kumar L, Garg P, Goel A, Aggarwal A, Gupta S. To study the clinical profile and management of cellulitis of lower limb in Northern India. Int J Low Extrem Wounds. 2023;22(1):44-47. Doi: 10.1177/1534734620986679. Epub 2021 Mar 20. PMID: 33745348.
[ Google Scholar] [ CrossRef] [ PubMed] | | 24. | Bagaitkar J, Demuth DR, Scott DA. Tobacco use increases susceptibility to bacterial infection. Tob Induc Dis. 2008;4(1):12. Doi: 10.1186/1617-9625-4-12. PMID: 19094204; PMCID: PMC2628337.
[ Google Scholar] [ CrossRef] [ PubMed] | | 25. | Pathak A, Saliba EA, Sharma S, Mahadik VK, Shah H, Lundborg CS. Incidence and factors associated with surgical site infections in a teaching hospital in Ujjain, India. Am J Infect Control. 2014;42(1):e11-e15. Doi: 10.1016/j.ajic.2013.06.013. Epub 2013 Oct 23. PMID: 24268969.
[ Google Scholar] [ CrossRef] [ PubMed] | | 26. | Kavya V, Mahale RP, Deepashree R, Jamal AN. Burden of multidrug-resistant gram-negative bacterial infections in a tertiary care hospital. J Pure Appl Microbiol. 2024;18(3):1729-40. Doi: 10.22207/JPAM.18.3.22.
[ Google Scholar] [ CrossRef] | | 27. | Ingale H. Multidrug resistance in bacterial isolates from pus samples: A hospital-based descriptive cross-sectional study. Natl J Lab Med. 2024. Doi: 10.7860/ NJLM/2024/69555.2872.
[ Google Scholar] [ CrossRef] | | 28. | Kalita JM, Nag VL, Kombade S, Yedale K. Multidrug resistant superbugs in pyogenic infections: A study from Western Rajasthan, India. Pan Afr Med J. 2021;38:409. Available from: https://www.panafrican-med-journal.com/content/ article/38/409/full/.
[ Google Scholar] | | 29. | Soni M, Kapoor G, Perumal N, Chaurasia D. Emergence of multidrug-resistant non-fermenting gram-negative bacilli in a tertiary care teaching hospital of Central India: Is colistin resistance still a distant threat? Cureus. 2023;15(5):e39243. Doi: 10.7759/cureus.39243.
[ Google Scholar] [ CrossRef] | | 30. | Mukherjee S, Mishra S, Tiwary S. Microbial profile and antibiogram of pus isolate in a tertiary care hospital of Western Odisha. J Evolution Med Dent Sci. 2020;9(16):1325-30. Doi: 10.14260/jemds/2020/289.
[ Google Scholar] [ CrossRef] | | 31. | Rajkumar S, Sistla S, Manoharan M, Sugumar M, Nagasundaram N, Parija SC, et al. Prevalence and genetic mechanisms of antimicrobial resistance in Staphylococcus species: A multicentre report of the Indian council of medical research antimicrobial resistance surveillance network. Indian J Med Microbiol. 2017;35(1):53-60.
[ Google Scholar] [ CrossRef] [ PubMed] | | 32. | Indian Network for Surveillance of Antimicrobial Resistance (INSAR) group, India. Methicillin resistant Staphylococcus aureus (MRSA) in India: Prevalence & susceptibility pattern. Indian J Med Res. 2013;137(2):363-69.
[ Google Scholar] | | 33. | Singhal T, Rodrigues C, Soman R, Wattal C, Swaminathan S, Nambi S, et al. Treatment of MRSA infections in India: Clinical insights from a Delphi analysis. Indian J Med Microbiol. 2022;40(1):35-45. Doi: 10.1016/j.ijmmb.2021.11.005. [ Google Scholar] [ CrossRef] [ PubMed]
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