Antibiotic resistance is a global health problem in the 21st century [1]. Since penicillin's discovery around the turn of the 20th century, antibiotics have transformed medicine, reducing infectious disease deaths and illnesses. However, the rapid and often irresponsible administration of these drugs has accelerated bacterial development, resulting in resistant strains. Without prompt action, common illnesses and injuries might become deadly due to antibiotic resistance, something the WHO has frequently warned about [2]. Antibiotic-resistant illnesses kill 1.27 million people annually. Antibiotic resistance may rise to 10 million by 2050 if current trends continue [3].

The fact that gram-positive and gram-negative bacteria have diverse resistance pathways complicates antibiotic resistance [4]. Gram-positive bacteria like Staphylococcus aureus and Enterococcus species have acquired resistance mechanisms by altering their DNA and acquiring resistance genes, usually from plasmids. S. aureus MRSA is a leading cause of serious skin, bloodstream, and pneumonia in hospitals and the general public [5]. Critical care units also struggle with vancomycin-resistant enterococci (VRE) due to limited treatment choices.
Gram-negative bacteria, including Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and E. coli, are the actual threat [6]. These species feature efflux pumps and impenetrable cell walls in addition to acquired resistance determinants. CRE and ESBL-producing Enterobacteriaceae are antibiotic-resistant bacteria. Most hospital-acquired infections, especially in immunocompromised patients, surgical wards, and intensive care units, are caused by multidrug-resistant gram-negative bacteria. Bacterial resistance is rising worldwide due to poor infection control, antibiotic stewardship initiatives, over-the-counter medicine availability, and incorrect antibiotic prescribing [7]. In developing nations, poor diagnostic facilities, sanitation, and antibiotic regulation increase the problem. Since these factors accelerate resistance variations, surveillance studies are essential for therapeutic decision-making.

Figure 1 Antibiotic Resistance in Bacteria [8]

Relevance to India and PMCH Context

Antimicrobial resistance plagues several nations, including India. Due to overuse of broad-spectrum antibiotics, weak hospital infection control, high infectious disease rates, and uncontrolled access to drugs, this burden is increased. Studies have found alarmingly high resistance rates in Indian hospitals' most common diseases [9]. Carbapenem-resistant isolates and fluoroquinolone- and third-generation cephalosporin-resistant E. coli and Klebsiella species are becoming more prevalent. The high prevalence of MRSA causes more severe illness, longer hospital stays, and more expensive treatment.

Antibiotic resistance plagues PMCH and other tertiary care institutions [10]. PMCH is a significant teaching hospital in Bihar and serves a diverse patient population [11]. Because peripheral health clinics are sluggish to refer patients to PMCH, many arrive with advanced infections. This increases the likelihood of encountering Multidrug-Resistant Organisms (MROs). PMCH treats many patients who need surgery, critical care, or long-term hospitalisation, who are particularly susceptible to resistant infections. Hospitals can breed antibiotic-resistant germs due to high antibiotic use and restricted patient quarters [12].

Local antibiotic resistance data is crucial since hospital and unit-level antibiotic resistance differed greatly. Despite their utility, national and global surveillance statistics may not correctly reflect PMCH resistance trends. Retrospective microbiology laboratory record analysis is recommended to better understand bacterial infections and resistance patterns in this clinical environment. These results influence hospital antibiotic policies, patient outcomes, and empirical antibiotic administration.

Justification for Study Duration and Sample Size

For this study, 90 clinical samples were retrospectively examined from January to June 2025. Multiple arguments support the chosen timeframe. First, antibiotic resistance trends might vary due to infection seasonality, hospital admission rates, and antibiotic prescribing habits. These dynamics are captured reasonably in a six-month timeframe for an academic assignment. Retrospective investigations allow realistic data analysis during this time period without straining resources by using pre-existing laboratory information. Cases that met the inclusion criteria during the investigation determined the sample size of 90. The current sample size is sufficient for descriptive analysis of bacterial isolates and resistance trends, while larger samples provide better statistical power. Simple and instructive, the dataset helps clinicians detect issues and resistance tendencies. The study's retrospective technique uses real-world data from real patient samples, supporting the results despite the small sample size.
This research's main purpose was to show PMCH resistance throughout the study, not to draw general statistical conclusions about the population. Long-term monitoring programs require location-specific assessments with a deadline. The results can be used as baseline data for future research with larger cohorts and longer periods.

Objective of the study

• To identify bacterial pathogens from clinical isolates.
• To evaluate antibiotic resistance patterns.
• To suggest implications for empirical therapy.

Study Design This observational retrospective study examined bacterial isolate antibiotic susceptibility patterns using microbiology lab data. A retrospective strategy was chosen because it provides systematic data evaluation, which is necessary for antibiotic resistance insights without impacting clinical care. Setting The study was conducted in the Department of Microbiology at PMCH, a tertiary care teaching hospital in Bihar, India, serving rural and urban patients. PMCH's microbiology laboratory evaluates clinical specimens from inpatient and outpatient departments, making it a suitable place to study antibiotic resistance developments in this region. Study Period The analysis covered a six-month duration from January 2025 to June 2025, during which clinical isolates and their corresponding antibiograms were collected and reviewed. Sample Size and Types A total of 90 non-duplicate clinical samples were included in the study. These samples represented a variety of clinical specimens routinely received for culture and sensitivity testing. The distribution included: • Urine samples – collected from patients with suspected urinary tract infections. • Pus/wound swabs – obtained from surgical site infections, abscesses, and wound discharges. • Blood cultures – from patients with suspected septicemia. • Sputum samples – from respiratory tract infections such as pneumonia or bronchitis. • Other body fluids – including cerebrospinal fluid (CSF) and pleural fluid, wherever available. Inclusion and Exclusion Criteria • Inclusion criteria: Only clinical isolates with complete identification and antibiotic susceptibility data were included in the study. • Exclusion criteria: Samples showing contamination, mixed growth, or incomplete antibiogram reports were excluded to maintain the accuracy and reliability of data. Microbiological Methods Researchers processed all samples using standard microbiological methods. Bacteria from clinical specimens cultured on Blood, MacConkey, and Nutrient agar were identified using colony morphology, Gramme stain, and routine biochemical assays like catalase, coagulase, indole, citrate, and urease. We confirmed with automated technologies like VITEK-2 when possible. Antibiotic susceptibility testing was done using the Kirby-Bauer disc diffusion method, per CLSI guidelines from 2025. Drug discs from carbapenems, glycopeptides, penicillins, and cephalosporins were added to Mueller-Hinton agar after inoculation with 0.5 McFarland standard inoculum. The measured zone sizes were classed as Sensitive, Intermediate, or Resistant by CLSI. Data Analysis Data were compiled and entered into Microsoft Excel for analysis. The resistance percentages of bacterial isolates to different classes of antibiotics were calculated. Results were presented using frequency distributions and tables to provide a clear overview of resistance patterns. The ability to withstand the effects of three or more classes of antimicrobials was considered a defining characteristic of MDR. Ethical Considerations The study was based on retrospective analysis of laboratory records and did not involve direct patient intervention. Patient identifiers were anonymized to ensure confidentiality. Approval for conducting the study was sought from the Institutional Ethics Committee of PMCH before data collection.

Demographic Characteristics

Out of the 90 clinical samples analyzed, patient demographics were available for 84 cases. The age distribution ranged from 5 years to 78 years, with the majority belonging to the 21–40 years age group (42.8%), followed by 41–60 years (31.0%). The gender distribution showed a slight male predominance with 52.4% males (n=44) and 47.6% females (n=40). This pattern reflects the general patient admission trend at PMCH.

Distribution of Bacterial Isolates

The study identified several clinically significant bacterial species. Among the isolates, gram-negative bacteria constituted the majority (71.1%), while gram-positive bacteria accounted for 28.9%. The most common isolate was Escherichia coli (34.4%), predominantly from urine samples. Klebsiella pneumoniae (20.0%) and Pseudomonas aeruginosa (11.1%) were frequently identified from pus, blood, and respiratory specimens. Among gram-positive organisms, Staphylococcus aureus (13.3%), including Methicillin-Resistant Strains (MRSA), was the leading pathogen. Other isolates included Enterococcus spp. (7.8%), Acinetobacter baumannii (6.7%), and less frequent organisms like Proteus spp. (6.7%).

Table 1 Distribution of bacterial isolates (n=90)

Antibiotic Resistance Patterns

Resistance analysis showed variable patterns across bacterial isolates.

• Escherichia coli: High resistance to ampicillin (80.6%) and third-generation cephalosporins (71.0%). Resistance to carbapenems was relatively low (12.9%), while aminoglycosides (gentamicin: 32.3%) and nitrofurantoin (19.4%) retained better activity.
• Klebsiella pneumoniae: Showed resistance to cephalosporins (77.8%) and fluoroquinolones (66.7%). Carbapenem resistance was detected in 27.8% isolates.
• Pseudomonas aeruginosa: Displayed high resistance to fluoroquinolones (60.0%) and ceftazidime (70.0%), but retained moderate sensitivity to amikacin (30.0% resistant) and piperacillin-tazobactam (40.0% resistant).
• Staphylococcus aureus: Among 12 isolates, 5 were MRSA (41.6%). Penicillin resistance was universal (100%), while resistance to erythromycin (58.3%) and ciprofloxacin (50.0%) was common. Vancomycin and linezolid showed no resistance.
• Enterococcus spp.: Demonstrated high resistance to ampicillin (71.4%) and erythromycin (57.1%), while all isolates remained sensitive to vancomycin and linezolid.
• Acinetobacter baumannii: Exhibited alarming resistance, with carbapenem resistance at 50.0% and over 70% resistance to cephalosporins and aminoglycosides.
• Proteus spp.: Resistance to cephalosporins (66.7%) and fluoroquinolones (50.0%) was common, but carbapenems remained effective (resistance 16.7%).

Table 2 Antibiotic resistance pattern by organism (%)

Multidrug Resistance (MDR)

Overall, 42.2% (n=38) of isolates were MDR. MDR was most prevalent among Acinetobacter baumannii (83.3%), Klebsiella pneumoniae (55.6%), and MRSA strains of Staphylococcus aureus (41.6%).

Antibiotic resistance is becoming one of the biggest clinical issues, affecting healthcare costs, mortality, and morbidity. This retrospective analysis at PMCH from January to June 2025 shows typical germ resistance patterns that cause major health concerns. Data from 90 samples provide a useful glimpse of the local resistance burden, highlighting critical patterns confirmed by national statistics and reflecting hospital-specific challenges.

Interpretation of Findings

Gram-negative bacteria are known to cause community- and hospital-acquired diseases, and 71.1% of clinical isolates were these bacteria. They included 34.4% Escherichia coli, 20.0% Klebsiella pneumoniae, and 11.1% Pseudomonas aeruginosa. These findings are consistent with epidemiological patterns of urine, bloodstream, and respiratory tract infections involving these bacteria. Penicillins and third-generation cephalosporins are the most resistant (82% and 72%). This trend is concerning because cephalosporins are often the first-line treatment in hospitals. This study found that numerous bacteria, including E. coli and Klebsiella pneumoniae, produce ESBLs. Although carbapenems, often used as "last-line" therapies, showed modest resistance (22% overall), Klebsiella (27.8% of bacteria) and Acinetobacter baumannii (50%) were alarming. Intensive care unit microorganisms with the highest multidrug resistance rates were Acinetobacter (83.3%).

Gram-positive bacteria had 13.3% Staphylococcus aureus isolates, 41.6% of which were methicillin-resistant. Besides penicillin, erythromycin (58.3% resistance) and ciprofloxacin (50%) were resistant. The fact that vancomycin and linezolid still work against methicillin-resistant Staphylococcus aureus is promising. The research population did not reveal vancomycin resistance, despite the rising prevalence of VRE in some places. Enterococcus spp. were ampicillin (71.4%) and erythromycin (57.1%) resistant. Multidrug resistance is severe, since 42.2% of isolates were resistant to at least one agent in three or more antibiotic groups. This has major implications for clinical management due to the diminishing empirical therapeutic options.

Comparison with Previous Studies

This study's results match national and worldwide antibiotic resistance reports; in study 1, several Indian surveillance investigations have identified similar patterns. A 2023 ICMR study 2 found that over 70% of E. coli isolates were resistant to third-generation cephalosporins and over one-third to fluoroquinolones. Klebsiella pneumoniae had worrisome carbapenem resistance and 50% beta-lactam resistance. Our data confirms that 71.0% of E. coli and 77.8% of Klebsiella are cephalosporin-resistant.

Table 3 Comparison with Previous Studies on Antibiotic Resistance

They discovered a 50% resistance rate, which is consistent with a multicenter research study 3 in The Lancet Regional Health - Southeast Asia (2022) that revealed carbapenem-resistant Acinetobacter baumannii growing in Indian intensive care units.
Multidrug-resistant bacteria are increasing worldwide, according to the WHO's Global Antimicrobial Resistance Surveillance System. Some African and Asian countries have over 60% third-generation cephalosporin and E. coli resistance. While strict management has reduced MRSA prevalence in several Western countries, it remains high in many low- and middle-income countries, including India. Our study's 41.6% MRSA rate matches Indian rates of 35–50%. These analogies show that PMCH's resistance patterns are part of a global phenomenon. However, regional variances like Staphylococcus aureus and Enterococcus not having vancomycin resistance underscore the necessity for hospital-specific data to guide empirical treatment decisions.

Strengths and Limitations of the Study

Our investigation of antibiotic resistance at PMCH gives clinicians vital data to guide empirical treatment. Its strengths include using clinical isolates from a variety of specimens and the real-life infection load in healthcare settings. Clinical and Laboratory Standards Institute (CLSI)-compliant susceptibility testing procedures yield more accurate and reproducible results. Another benefit of including gram-positive and gram-negative pathogens is that resistance across the bacterial spectrum is assessed. However, some limits must be considered. The retrospective design's limitations in establishing causative linkages make it impossible to collect detailed clinical data, such as antibiotic exposure, comorbidities, and treatment results. The sample size of 90 isolates is sufficient for descriptive analysis, but not for generalisation. Larger prospective studies are needed for stronger statistical findings. The resistance genes were not studied using cutting-edge molecular approaches like whole-genome sequencing or polymerase chain reaction. Finally, the study's single-center methodology may prevent generalisation to other hospitals in Bihar or other hospitals. Though limited, the analysis provides a solid framework for PMCH's future research and policymaking.

Future Directions

Multiple community and institutional initiatives should be prioritised to combat expanding antibiotic resistance. To detect resistance trends and emerging threats early, PMCH must build continuing surveillance programs and integrate them into national and regional antimicrobial resistance networks. Another, culture and sensitivity-based de-escalation should be prioritised to promote rational antibiotic usage through enhanced antimicrobial stewardship initiatives. To ensure empirical therapy is successful while avoiding broad-spectrum use, a hospital-specific antibiotic policy based on local resistance data is essential. To eliminate MROs in hospital settings, strict infection prevention and management are needed. These include handwashing, sterilisation, seclusion, and cleanliness. To stay current on resistance mechanisms, prescribing procedures, and other issues, healthcare workers must attend regular education and training sessions. Diagnostic support from rapid testing and molecular assays can aid early detection and focused treatment. Finally, politicians should support public health campaigns to prevent antibiotic overuse and control over-the-counter antibiotic sales. These efforts will preserve antibiotic efficacy and fight antibiotic resistance at PMCH and beyond.

This six-month retrospective investigation of 90 clinical isolates at PMCH shows the growing problem of antibiotic resistance in tertiary care facilities. The study found that gram-negative bacteria with high penicillin, cephalosporin, and fluoroquinolone resistance caused most illnesses. Unfortunately, Acinetobacter baumannii exhibited the highest multidrug resistance rates, while Klebsiella and Acinetobacter showed carbapenem resistance. Gram-positive organisms were mixed, but vancomycin and linezolid were extremely efficient against Enterococcus species and MRSA. These findings show that PMCH practitioners have obstacles when administering first-line medicines empirically. Local resistance data should guide antibiotic selection, with culture-based therapy and de-escalation strategies as priorities. Minimise carbapenem and glycopeptide use to preserve effective options against certain gram-negative and resistant gram-positive infections. The study recommends strict antibiotic management, resistance monitoring, and infection control. Without these approaches, the therapeutic toolbox for common disorders is shrinking, threatening patient outcomes.

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