Antimicrobial resistance (AMR) has become a major global health issue, threatening the effective management of infectious diseases. The increasing inability of antibiotics to treat infections leads to prolonged illness, higher mortality rates, and increased healthcare expenditure. Reports from international health agencies highlight AMR as one of the leading challenges of this century. The situation is particularly concerning in developing countries, including India, where factors such as irrational antibiotic usage, easy over-the-counter availability, and inadequate infection control practices accelerate the emergence of resistant organisms. In tertiary care hospitals, these issues are further intensified due to the high burden of critically ill patients, frequent use of invasive procedures, and widespread administration of broad-spectrum antibiotics. Gram-negative organisms such as Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa are commonly associated with hospital-acquired infections and are increasingly exhibiting resistance mechanisms like ESBL production and carbapenem resistance. Similarly, Gram-positive organisms, particularly Staphylococcus aureus, have developed resistance patterns such as methicillin resistance, complicating treatment strategies. Empirical antibiotic therapy, though necessary in many clinical situations, may contribute to resistance when not guided by local susceptibility data. In this context, hospital antibiograms play a vital role by providing cumulative resistance patterns that assist clinicians in selecting appropriate therapy. Furthermore, antimicrobial stewardship programs have been recommended to optimize antibiotic use and reduce resistance. These programs depend heavily on local microbiological data to ensure effective implementation. Given the increasing prevalence of AMR and the need for region-specific data, this study was undertaken to analyze the antimicrobial resistance patterns of bacterial isolates in a tertiary care hospital and to support evidence-based clinical decision-making.

Study Design and Setting This was a cross-sectional descriptive study conducted in the Department of Microbiology at Navodaya Medical College Hospital & Research Centre, Raichur, a tertiary care teaching hospital catering to both urban and rural populations. Study Duration The study was carried out over a period of three months from January to March 2023. Study Population and Sample Size All bacterial isolates obtained from various clinical specimens during the study period were included. A total of 179 non-duplicate bacterial isolates were analyzed. Only the first isolate from each patient was considered to avoid duplication and bias. Inclusion Criteria • All clinically significant bacterial isolates obtained from patient samples • Isolates from both inpatient and outpatient departments • Non-duplicate isolates (only one isolate per patient) Exclusion Criteria • Duplicate isolates from the same patient • Contaminants and non-significant growth • Fungal isolates and non-bacterial pathogens Sample Collection and Processing Clinical specimens including urine, blood, pus, sputum, and other body fluids were collected under aseptic conditions and transported promptly to the microbiology laboratory. Samples were processed according to standard microbiological procedures. Isolation and Identification of Organisms Bacterial isolates were identified based on colony morphology, Gram staining, and standard biochemical tests. Identification protocols were followed as per conventional microbiological methods. Antimicrobial Susceptibility Testing (AST) Antimicrobial susceptibility testing was performed using the Kirby–Bauer disc diffusion method on Mueller–Hinton agar. The results were interpreted according to the guidelines of the Clinical and Laboratory Standards Institute. A panel of antibiotics tested included: • Beta-lactams: Ampicillin, Amoxicillin-clavulanate • Cephalosporins: Ceftriaxone, Cefotaxime, Ceftazidime • Carbapenems: Imipenem, Meropenem • Aminoglycosides: Amikacin, Gentamicin • Fluoroquinolones: Ciprofloxacin, Levofloxacin • Others: Cotrimoxazole, Nitrofurantoin, Doxycycline, Linezolid, Vancomycin Zone diameters were measured and categorized as Sensitive, Intermediate, or Resistant as per CLSI standards. Derivation of Resistance Patterns Antibiotic resistance percentages were derived from susceptibility data using the formula: Resistance (%) = 100 − Sensitivity (%). Intermediate results were not included in resistance calculations and were interpreted separately where applicable. Data Collection and Analysis Antibiotic susceptibility data were compiled, and results were expressed as percentage sensitivity for each organism-antibiotic combination. The antibiogram was constructed based on cumulative data. Sensitivity patterns were interpreted as: • >80% sensitivity: Highly effective • 60–80% sensitivity: Moderately effective • <60% sensitivity: Less effective Organisms with fewer than 30 isolates were interpreted with caution due to limited statistical reliability. Data were analyzed using descriptive statistics, including frequencies and percentages, and presented in tabular form. Ethical Considerations: Institutional approval was obtained prior to the study

A total of 179 bacterial isolates were analyzed, with a predominance of Gram-negative organisms. The majority of isolates were Gram-negative bacteria, with Klebsiella pneumoniae (22.9%) being the most common, followed by Staphylococcus aureus (21.2%) among Gram-positive organisms. CoNS also contributed significantly (17.3%). (Table 1) Table 1: Distribution of Bacterial Isolates Organism Number (n) Percentage (%) Klebsiella pneumoniae 41 22.9 Escherichia coli 26 14.5 Pseudomonas aeruginosa 16 8.9 Citrobacter spp. 9 5.0 Proteus spp. 2 1.1 Enterobacter spp. 2 1.1 Staphylococcus aureus 38 21.2 CoNS 31 17.3 Enterococcus spp. 3 1.7 Beta-hemolytic streptococci 11 6.1 Total 179 100 Gram-negative organisms showed complete resistance to ampicillin (0%) and low sensitivity to cephalosporins (35–50%) and fluoroquinolones (20–47%). Higher sensitivity was observed with piperacillin–tazobactam (82–94%), carbapenems (75–90%), and amikacin (80–88%), indicating their continued effectiveness. (Table 2) Table 2: Antibiotic Sensitivity Pattern of Gram-negative Isolates (%) Organism AMP AMC PTZ CTR CTX CIP COT IPM MRP AK E. coli 0 32 84 35 35 20 30 85 88 80 Klebsiella pneumoniae 0 50 82 45 42 25 67 75 74 80 Proteus spp. 0 40 88 84 86 75 54 85 90 84 Pseudomonas aeruginosa 0 IR 94 IR IR 78 30 85 86 88 Citrobacter spp. 0 25 90 50 50 82 50 86 84 84 Enterobacter spp. 0 35 94 40 45 80 54 82 85 82 Gram-positive organisms showed low sensitivity to fluoroquinolones (25–33%) and moderate sensitivity to cotrimoxazole (55–68%). However, high sensitivity was observed for doxycycline/tetracycline (>85%), and linezolid and vancomycin remained effective (~76–87%), indicating their importance in treatment. (Table 3) Table 3: Antibiotic Sensitivity Pattern of Gram-positive Isolates (%) Organism AMC PTZ CIP COT DOX TET VAN LZ Staphylococcus aureus 85 78 25 55 85 88 78 76 CoNS 88 80 33 68 94 94 87 85 Enterococcus spp. 50 80 45 65 90 85 85 80 Beta-hemolytic streptococci 85 96 80 48 88 88 85 88 : Most first-line antibiotics showed reduced effectiveness (<60%), whereas reserve drugs demonstrated high sensitivity (>80%), highlighting the emergence of multidrug resistance and the need for antibiotic stewardship. (Table 4) Table 4: Interpretation of Antibiotic Sensitivity Sensitivity Range Interpretation >80% Highly sensitive 60–80% Moderately sensitive <60% Poor sensitivity Gram-negative organisms demonstrated very high resistance to commonly used antibiotics, particularly ampicillin (100% resistance across all isolates), cephalosporins (50–65%), and fluoroquinolones (53–80%). Moderate resistance was seen with cotrimoxazole. In contrast, lower resistance rates were observed with piperacillin–tazobactam, carbapenems, and amikacin, although emerging resistance to carbapenems (10–25%) is concerning. (Table 5) Table 5: Antibiotic Resistance Pattern of Gram-negative Isolates (%) Organism AMP AMC PTZ CTR CTX CIP COT IPM MRP AK E. coli 100 68 16 65 65 80 70 15 12 20 Klebsiella pneumoniae 100 50 18 55 58 75 33 25 26 20 Proteus spp. 100 60 12 16 14 25 46 15 10 16 Pseudomonas aeruginosa 100 — 6 — — 22 70 15 14 12 Citrobacter spp. 100 75 10 50 50 18 50 14 16 16 Enterobacter spp. 100 65 6 60 55 20 46 18 15 18 Among Gram-positive isolates, high resistance to fluoroquinolones (67–75%) and moderate resistance to cotrimoxazole were observed. However, low resistance to doxycycline, tetracycline, vancomycin, and linezolid indicates their continued effectiveness. (Table 6) Table 6: Antibiotic Resistance Pattern of Gram-positive Isolates (%) Organism AMC PTZ CIP COT DOX TET VAN LZ Staphylococcus aureus 15 22 75 45 15 12 22 24 CoNS 12 20 67 32 6 6 13 15 Enterococcus spp. 50 20 55 35 10 15 15 20 Beta-hemolytic streptococci 15 4 20

The present study highlights a significant burden of antimicrobial resistance among bacterial isolates in a tertiary care hospital, with a predominance of Gram-negative organisms. This finding is consistent with earlier studies that identify Gram-negative bacteria as major contributors to healthcare-associated infections due to their ability to rapidly acquire and disseminate resistance mechanisms (9). Among the isolates, Klebsiella pneumoniae and Escherichia coli were the most frequently encountered Gram-negative pathogens. The resistance pattern analysis revealed complete resistance to ampicillin (100%) across all Gram-negative isolates, rendering it ineffective for empirical therapy. Similar findings have been reported in Indian studies, where ampicillin resistance among Enterobacteriaceae is nearly universal (10,11). The observed high resistance to cephalosporins (50–65%) and fluoroquinolones (53–80%) suggests widespread production of beta-lactamases, particularly extended-spectrum beta-lactamases (ESBLs). ESBL-producing organisms are known to confer resistance to third-generation cephalosporins and are increasingly reported in hospital settings (12,13). Additionally, rising fluoroquinolone resistance has been attributed to overuse and selective pressure in both community and hospital environments (14,15). Cotrimoxazole also demonstrated moderate to high resistance (30–70%), limiting its empirical use. Although piperacillin–tazobactam, carbapenems, and amikacin showed relatively lower resistance rates, the emerging resistance to carbapenems (10–26%) is particularly concerning. This finding may indicate the early emergence of carbapenem-resistant Enterobacteriaceae (CRE), which are associated with high mortality rates and limited therapeutic options (16). The global spread of carbapenemase-producing organisms has been recognized as a major public health threat (17). Pseudomonas aeruginosa exhibited comparatively lower resistance to piperacillin–tazobactam and carbapenems but retained resistance to several other antibiotic classes. This is consistent with its known intrinsic resistance mechanisms and ability to develop resistance during therapy, making it a challenging pathogen in hospital settings (9). Among Gram-positive organisms, Staphylococcus aureus and coagulase-negative staphylococci (CoNS) were predominant. The resistance pattern demonstrated high resistance to fluoroquinolones (67–75%), indicating reduced clinical utility of these agents. Similar trends have been reported in previous studies, reflecting increasing resistance among Gram-positive pathogens due to antibiotic pressure (18). However, low resistance to doxycycline, tetracycline, vancomycin, and linezolid suggests that these antibiotics remain effective therapeutic options. The continued effectiveness of vancomycin and linezolid is particularly important in the management of resistant Gram-positive infections, including methicillin-resistant Staphylococcus aureus (MRSA) (19,20). The findings of this study also indicate a decline in the effectiveness of first-line antibiotics, with many showing resistance rates exceeding 60%. This trend reflects the consequences of inappropriate empirical therapy and highlights the need for evidence-based antibiotic selection. In contrast, reserve antibiotics demonstrated better activity; however, their indiscriminate use may accelerate the emergence of resistance to these critical drugs (21,22). Overall, these findings emphasize the importance of continuous antimicrobial surveillance and institution-specific antibiograms, which are essential for guiding empirical therapy and improving patient outcomes. Furthermore, the implementation of antimicrobial stewardship programs is critical to optimize antibiotic use and reduce the spread of resistance (21,22).

The study demonstrates a substantial burden of antimicrobial resistance, particularly among Gram-negative pathogens, with reduced effectiveness of commonly used antibiotics. The resistance observed across multiple antibiotic classes suggests a probable high prevalence of multidrug-resistant organisms. Although reserve antibiotics remain relatively effective, emerging resistance to these agents is concerning. Continuous antimicrobial surveillance, strict infection control practices, and rational antibiotic prescribing through antimicrobial stewardship programs are essential to curb the progression of resistance and improve clinical outcomes.

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