Assessment of Antibiotic Utilization Using Who’s AWaRe Framework in a South Indian Tertiary Care Hospital

Assessment of Antibiotic Utilization Using Who’s AWaRe Framework in a South Indian Tertiary Care Hospital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Antibiotic Utilization Using Who’s AWaRe Framework in a South Indian Tertiary Care Hospital Pavan Kumar Yanamadala, Prasanna Sai Sri Vallabhareddy, Riton Shil, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6734591/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Introduction: The issue of antimicrobial resistance presents a significant obstacle to global health, largely driven by the inappropriate use of antibiotics, especially in tertiary care settings. In response, the WHO has introduced the AWaRe framework to promote the rational use of antibiotics; however, further investigation is required to effectively implement this framework within Indian hospital settings. Aim: This research intends to assess the patterns of antibiotic prescriptions within a south-Indian tertiary care hospital, employing the WHO's AWaRe framework combined with a range of prescribing indicators. Methods: This observational, prospective study was carried out at a Tertiary Care Hospital in Kakinada from September 2024 to February 2025. Data was collected and analysed from 136 patient records receiving systemic antibiotics via EHR, and clinical outcomes were analysed using descriptive statistics and the Chi-Square test. Results: Our study showed a significant prevalence of Watch group antibiotics, accounting for 63.9% of prescriptions, in contrast to the mere 13.2% for Access group antibiotics. More common empirical prescribing raises concerns regarding the potential escalation of antibiotic resistance. It is crucial to recognise that the patterns of antibiotic prescribing have a significant influence on patient outcomes. Conclusion: The antibiotic prescribing practices in this study suggest a significant need for improvement, particularly in the use of Watch group antibiotics. This offers an opportunity to align better with WHO AWaRe guidelines. Targeted antimicrobial stewardship initiatives are crucial for promoting responsible antibiotic use and enhancing patient outcomes. Future research should evaluate the effectiveness of these interventions and their long-term impact. Anti-bacterial agents Drug Prescriptions Drug Resistance Antimicrobial Stewardship Clinical Outcomes Tertiary hospital Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Antimicrobial resistance (AMR) represents an increasing global health emergency that weakens the effectiveness of treating prevalent infections, increases healthcare costs, resulting in compounded rates of illness and death. In 2019, AMR was linked to around 1.27 million fatalities globally, and predictions indicate that this number could surge to 10 million deaths each year by 2050 if not decisive actions are implemented [ 1 ] . This issue is particularly alarming in the low- and middle-income countries where the inadequacies in the healthcare infrastructure and regulatory frameworks frequently hinder the effective measures against the inappropriate prescribing of antibiotics [ 2 ] . Currently, India is facing a significant challenge in the form of Antimicrobial Resistance, as it carries one of the highest burdens of infectious diseases globally. In the year of 2019, it was reported that 297,000 fatalities were directly linked to AMR, and an additional 1.04 million deaths were attributed to the infections caused by the resistant pathogens [ 3 ] . There is an alarming increase in resistance among significant bacterial pathogens, including Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii, particularly within hospital environments where the use of antibiotics is both inappropriate and frequently based on empirical prescribing [ 4 ] . In India, hospitals play a major role in the emergence and spread of antibiotic-resistant bacteria. Tertiary care centres, in particular, face major challenges such as high patient numbers, more frequent ICU admissions, widespread use of broad-spectrum antibiotics, and a lack of microbiology-guided treatment. A study conducted at a tertiary care facility in Kerala found a significant prevalence of multidrug-resistant organisms (MDROs) in ICUs, which was linked to the inappropriate use and prolonged courses of antimicrobial treatments [ 5 ] . Another study in 2023, carried out by Hegde et al. in Karnataka, uncovered notable variations in the way antibiotics are prescribed across different medical wards. These differences have further complicated the efforts related to antibiotic stewardship (ASP) initiatives, highlighting the need for improved practices and guidelines in the management of antibiotic use [ 6 ] . In light of the growing concerns surrounding antibiotic resistance, the World Health Organisation framed the AWaRe classification in 2017, which sorts antibiotics into three groups: Access, Watch, and Reserve, according to their therapeutic efficacy and potential for resistance development [ 7 ] . This framework has since become a fundamental element of international antimicrobial stewardship initiatives, providing a systematic practice for enhancing the selection, prescription, and oversight of antibiotic use. Despite this, the application of AWaRe-based stewardship approaches in Indian tertiary care settings continues to be uneven. The Global Antimicrobial Resistance and Use Surveillance System (GLASS) report from 2022 indicates that Patients in India are experiencing elevated resistance to carbapenems, fluoroquinolones, and third-generation cephalosporins, which are frequently prescribed and are classified under the Watch and Reserve categories [ 8 ] . Several factors contribute to this issue, including the availability of over-the-counter antibiotics, the practice of prescribing medications empirically without confirming diagnoses, and a lack of awareness or adherence to the prescribing guidelines established by the World Health Organisation [ 9 ] . Furthermore, a significant number of Indian tertiary care hospitals do not have effective antimicrobial stewardship programs, and in the facilities where such programs do exist, they are frequently undermined by poor enforcement and a lack of supportive infrastructure. The absence or ineffective implementation of periodic prescription audits, culture sensitivity testing, and prescriber awareness contributes to an environment that favours the irrational use of antibiotics, especially those classified as Watch and Reserve group drugs, thereby exacerbating the development of resistance patterns [ 10 ] . Despite various global and national initiatives encouraging the rational use of antibiotics, there is a significant lack of data from India regarding AWaRe-based prescribing practices within tertiary care settings. Few Studies [ 11 , 12 , 13 ] have systematically assessed the antibiotic usage patterns in Indian hospitals using the WHO AWaRe framework along with WHO prescribing indicators. Additionally, the effects of empirical antibiotic therapy compared to definitive culture-based therapy on patient outcomes in Indian healthcare settings have not been adequately investigated. Due to the inconsistent applications of WHO prescribing tools and the lack of relevant studies in Indian healthcare settings, there is a significant need for more comprehensive assessments. Acknowledging this significant gap in the research, the current study was initiated to produce organisation-specific, evidence-based insights regarding the rational use of antibiotics. This study focuses on prescribing patterns within a tertiary care facility in South India, using the WHO AWaRe classification in combination with essential prescribing indicators to assess the rationality of antimicrobial usage. Besides, the study delineates between the clinical outcomes of empirical therapy and culture-definitive treatment, thus providing practical data to enhance antimicrobial stewardship practices and ensure compliance with global prescribing standards. MATERIALS AND METHODS Study Design and Setting This research was a prospective, observational study based on existing hospital records, conducted at Trust Multispeciality Hospitals in Kakinada, which is a tertiary care facility offering a diverse range of specialties such as General Medicine, Intensive Care, Surgery, Cardiology, Orthopedics, and Nephrology, which provided an appropriate setting for assessing the antibiotic prescribing practices among inpatients. Enables a thorough examination of prescribing trends, patterns, and outcomes as they manifest in everyday clinical practice, thereby ensuring a high degree of authenticity. The duration of the study spanned six months, from September 2024 to March 2025. Study Population and Eligibility Criteria During the study period, all inpatients of all age groups and genders who were prescribed at least one systemic antibiotic for either a confirmed or suspected bacterial infection were evaluated for inclusion in this study to ensure a comprehensive representation of antibiotic use in the inpatient settings. However, the patients were excluded from consideration if their prescribed antibiotics were solely for prophylactic purposes, those who were treated exclusively with antifungal or antiviral medications without the concomitant use of antibiotics, or for whom the records lacked comprehensive information regarding the antibiotic treatment provided, to specifically focus on the therapeutic usage of antibiotics for bacterial infections. Sample Size Determination The determination of the sample size was performed using Cochran's formula [ 14 ] for proportions, with the assumption of maximum variability in order to provide the most conservative estimate possible. $$\:n=\:\frac{{\text{z}}^{2}\times\:p\:\left(1-p\right)}{{\text{e}}^{2}\:}$$ n = initial sample size Z = Z-value for 95% confidence level = 1.96 p = estimated prevalence = 0.5 (assumed for maximum variability, because there was no prior data available on antibiotic prescribing prevalence in tertiary care settings) e = acceptable margin of error = 0.05 Substituting the above values in the Cochran’s Formula, $$\:n=\:\frac{\left(1.96\right)²\:\times\:0.5\:(1-0.5)}{\left(0.05\right)²\:}$$ $$\:n=\:\frac{\:3.8416\times\:0.5\:\left(0.5\right)}{0.0025}$$ $$\:n=\:\frac{\:0.9604}{0.0025}$$ $$\:n=384.16$$ The n value is calculated, and the initial sample was rounded off to 384 . Given that the total number of eligible patients in the hospital over six months is reported as 210, the sample size is modified by applying the finite population correction formula [ 15 ] , to adjust the sample size for a smaller defined population. $$\:n\:\left(adj\right)=\frac{n0\text{}}{1+\frac{n0\text{}-1}{N}\:}$$ $$\:n\:\left(adj\right)=\frac{384\text{}}{1+\frac{384-1}{210}\:}$$ $$\:n\:\left(adj\right)=\frac{384\text{}}{1+1.8293\:}$$ $$\:n\:\left(adj\right)=\frac{384\text{}}{2.8293\:}$$ $$\:n\:\left(adj\right)\approx\:135.8$$ It is rounded off to 136 to ensure the minimum required observations for the statistical validity, reflecting a 95% confidence level and a margin of error of 5%. Data Collection and Study Procedure Data related to the study were systematically gathered from Electronic Health Records (EHRs) and case sheet documentation through a structured electronic data collection form. The variables analysed comprised demographic information, diagnoses, antibiotic prescriptions (including generic name, dosage, administration route, frequency, and duration), the rationale for use (whether empirical or definitive), classification according to the WHO AWaRe framework, culture sensitivity results, and patient outcomes. The process of data collection followed a standardised protocol that was divided into three definite phases: the first phase involved preparation, which included obtaining ethical approvals, developing the necessary study tools, and conducting pilot testing with a sample size of twelve participants. The pilot testing helped in refining the data collection form, ensuring the clarity of the study variables, and the data extraction procedures. The second phase focused on data retrieval, involving a systematic review of patient records. The final phase was data validation, where weekly audits were conducted to ensure consistency in data extraction, verifying the reliability of study variables, thus minimising the potential data entry errors. There was no direct interaction with patients, as all data were anonymised before the result analysis. Statistical Analysis Data were gathered using Microsoft Excel 2019 and subsequently analysed through both descriptive and inferential statistical methods, like chi-square tests. For categorical variables, frequencies and proportions were recorded. To assess the association/correlation among variables, including AWaRe classification, type of therapy, sensitivity-based prescriptions, and treatment outcomes, Chi-square tests were employed. A p-value of less than 0.05 was deemed statistically significant. Ethical Approval The Institutional Ethics Committee of Aditya Pharmacy College (A) in Surampalem approved the study protocol (Ref: APC-IEC/2024-2025/02) before its commencement. Given that this study included anonymised patient records and did not involve direct interaction with patients, the necessity for informed consent was waived. RESULTS Table 1 Demographics, Antibiotic Usage, and Duration of Therapy (n = 136) Variable Category Males (n = 90) Females (n = 46) Total (n = 136) Age Distribution 0–18 years 2 (2.2%) 0 2 (1.47%) 19–40 years 19 (21.1%) 13 (28.2%) 32 (23.5%) 41–60 years 45 (50%) 25 (54.3%) 70 (51.4%) > 60 years 24 (26.6%) 8 (17.3%) 32 (23.5%) Antibiotics per Patient Single Antibiotic 36 (40%) 17 (37%) 53 (39%) Two Antibiotics 31 (34.4%) 19 (41%) 50 (37%) Three or More Antibiotics 23 (25.5%) 10 (22%) 33 (24%) Duration of Therapy 1–3 Days — — 44 (32.3%) 4–6 Days — — 54 (39.7%) > 6 Days — — 38 (27.9%) Table 2 Prescribing Appropriateness and Administration Based on WHO AWaRe Classification (n = 136) AWaRe Group of Antibiotics Appropriate Partially Appropriate Inappropriate Total (n = 136) Access 8 (44.4%) 7 (38.8%) 3 (16.7%) 18 (13,23%) Watch 60 (69%) 17 (19.5%) 10 (11.5%) 87 (63.9%) Reserve 21 (67.7%) 4 (12.9%) 6 (19.4%) 31 (22.7%) Table 3 Analysis of Antibiotic Utilisation Patterns using WHIO Prescribing Indicators Framework Prescribing Indicator Results Average Number of Antibiotics per Prescription The average antibiotics per patient was 2.1; single therapy was 39%, Dual treatment was 37%, and Triple or more was 24%. Percentage of Prescriptions Containing Antibiotics 100% (All 136 patients received at least one antibiotic). Percentage of Antibiotics Prescribed by Generic Name 71% of the Total Prescriptions in our Study, that is, 97 prescriptions. Percentage of Antibiotics Prescribed from AWaRe List Access: 13.2%, Watch: 63.9%, Reserve: 22.8%. Duration of Antibiotic Therapy 1–3 days: 32.3%, 4–6 days: 39.7%, > 6 days: 27.9%. Route of Administration of Antibiotics IV Therapy: 83%, Oral Therapy: 17% (primarily Watch group). Percentage of Culture-Sensitivity Guided Prescriptions Antibiotics changed post-culture in 12.5% of cases; sensitivity-based prescribing improved outcomes significantly (p < 0.00000005). Appropriateness of Prescriptions Appropriate: 65.4%, Partially Appropriate: 20.6%, Inappropriate: 13.9%. Type of Antibiotic Therapy (Empirical/Definitive) Empirical: 50%, Definitive: 44.9%, Prophylactic: 5.1%. Clinical Outcome of Antibiotic Use Cure/Improvement: 70.6%, No Change: 25%, Worsening: 4.4%; Significant association with AWaRe category (p = 0.0039). Table 4 Summary of Chi-Square Analyses for Factors Influencing Clinical Outcomes S. No. Comparative Factor χ² Value df p-value Significance (α = 0.05) 1 WHO AWaRe Classification vs. Clinical Outcomes 15.398 4 0.0039 Significant 2 Sensitivity Pattern vs. Clinical Outcomes 39.400 4 4.758 x 10 − 8 Highly Significant 3 Therapy Type (Monotherapy vs. Combination) vs. Clinical Outcomes 14.840 4 0.0050 Significant 4 Prescribing Type (Empirical vs. Definitive) vs. Clinical Outcomes 216.042 4 1.3328 x 10 − 44 Extremely Significant DISCUSSION This research evaluated the use of antibiotics through the lens of the WHO AWaRe framework, concentrating specifically on usage patterns, appropriateness, and clinical outcomes. Patient Demographics and Usage Patterns (Table 1) Table 1 demonstrates that the largest percentage of patients fell within the age range of 41–60 years (51.4%), with a notable predominance of males (66%) over females. This observation is consistent with the research conducted by Chizimu et al. in 2024 [16 ] , which indicated that middle-aged and older patients, especially men, exhibited a higher tendency for hospitalisation as a result of age-related immunosuppression and comorbid conditions. Conversely, Mugada Vinodkumar et al. in 2021 [17 ] reported that younger patients (19–44 years) were more prevalent in outpatient environments, likely due to earlier access to healthcare services and variations in the severity of infections. Concerning the administration of antibiotics, Table 1 indicates that 39% of patients were treated with a single antibiotic, 37% with two antibiotics, and 24% with three or more. These results align closely with those presented by Gagliotti et al. in 2024 [18 ] , who noted that 40.6% of prescriptions in European hospital environments were for single antibiotics. Nevertheless, the comparatively elevated proportions of dual or triple therapy observed in our research may be attributed to the empiric management of severe infections, a phenomenon also highlighted by Chizimu et al. in 2024 [16 ] . This underscores the necessity for more precise therapeutic approaches informed by diagnostic evaluations. AWaRe Classification and Appropriateness (Table 2, Figure 1) As shown in Table 2 , the predominant category of antibiotics prescribed was the Watch group, comprising 63.9% of the total, whereas Reserve antibiotics represented 22.8%, and Access antibiotics accounted for a mere 13.2%. This pattern is further depicted in Figure 1, which illustrates the clinical outcomes associated with these AWaRe categories. The Access group exhibited the highest cure rates at 94.4%, in contrast to the Watch group, which had a significantly lower cure rate of 59.8%. These results align with the findings of Pappalardo et al. in 2024 [19 ] and Gagliotti et al. in 2024 [18 ] , both of whom also noted the predominance of the Watch group in hospital environments. The insufficient use of Access group antibiotics, in contrast to the WHO's target of at least 60%, may be attributed to prescribers favouring broad-spectrum agents, particularly in empirical treatment. Potential corrective measures could involve formulary management, the reinforcement of guidelines, and education regarding stewardship principles. The appropriateness of prescribing in our study was found to be relatively favourable, with 65.4% of prescriptions deemed appropriate, as indicated in Table 2. This figure surpasses the 54.9% reported by Sajjad et al. in 2024 [20 ] in a similar South Asian context, which may be attributed to the involvement of clinical pharmacists and strict adherence to hospital protocols. Impact of Key Variables on Clinical Outcomes (Table 3, Figures 1–4) Table 3 illustrates the outcomes of chi-square analyses, indicating that AWaRe classification, sensitivity patterns, therapy type, and prescribing type all had a significant impact on patient outcomes. These results are visually represented in Figures 1–4, respectively. Figure 1 (AWaRe vs. Clinical Outcomes) demonstrates a statistically significant correlation (p = 0.0039), indicating improved outcomes for patients treated with Access antibiotics. This finding reinforces the recommendations set forth by the WHO and underscores the necessity for stewardship initiatives aimed at prioritising these medications. Figure 2 (Sensitivity Patterns vs. Outcomes) illustrates that the highest cure rates were observed in patients administered sensitive antibiotics (93.9%), while those receiving treatment for resistant infections exhibited elevated rates of no change or deteriorating outcomes. This observation is supported by the chi-square p-value of 5.758 × 10⁻⁸ presented in Table 3, and is consistent with the findings of Tadesse et al. in 2022 [21 ] and Rahman et al. in 2022 [22 ] , who similarly reported that culture-guided therapy significantly enhanced outcomes. Nevertheless, only 12.5% of prescriptions in our investigation were informed by sensitivity testing, highlighting the necessity to enhance microbiological services. Figure 3 (Monotherapy vs. Combination Therapy) illustrates that monotherapy yielded superior outcomes compared to combination therapy (p = 0.0050), as indicated in Table 3. Despite the higher prevalence of combination therapy (56.6%), it is typically utilised for resistant or complex infections. These results are consistent with the research conducted by Massawe et al. in 2023 [23 ] and underscore the significance of de-escalating therapy once culture results are obtained. Figure 4 (Prescribing Type vs. Outcomes) illustrates a particularly notable discovery: a highly significant correlation (p = 1.3328 × 10⁻⁴⁴) between the type of prescribing and the resulting outcomes. Although empirical therapy constituted 50% of all prescriptions, definitive therapy produced more favourable outcomes when sensitivity patterns were established. This observation aligns with the findings of Demoz et al. in 2020 [24 ] and Chizimu et al. in 2024 [16 ] , who cautioned against the long-term dangers associated with empirical prescribing. Enhancing investment in rapid diagnostics and antibiograms would facilitate a shift towards more definitive prescribing. Prescribing Indicators and System-Wide Assessment (Table 4) Table 4 assesses our study concerning the prescribing indicators established by the WHO. The mean number of antibiotics prescribed per patient was 2.1, which surpasses the WHO-recommended range of 1.6 to 1.8. This indicates a possible inclination towards overprescribing, which may be influenced by the practices associated with empirical therapy. The rate of generic prescriptions stood at 71%, reflecting moderate adherence to guidelines, yet it remains below the 96.5% compliance reported by Mudenda et al. in 2024 [25 ] . Implementing policies that encourage generic prescribing could contribute to cost reduction and enhance standardisation. Moreover, 83% of antibiotics were administered through the intravenous route, indicating the seriousness of cases requiring hospitalisation. Nevertheless, the adoption of IV-to-oral switch protocols, when clinically suitable, has the potential to enhance cost-effectiveness and shorten the length of hospital stays. Based on the study findings and comparisons with previous literature, several key corrective actions are essential to improve antibiotic utilisation practices. There is an urgent need to encourage the use of Access antibiotics through targeted prescriber education, formulary restrictions, and regular audit-feedback mechanisms. Simultaneously, efforts must be made to expand culture-guided prescribing by enhancing microbiology infrastructure and ensuring timely sensitivity reports, which can significantly improve clinical outcomes. Clinicians should be encouraged to promote rational monotherapy wherever appropriate, as it has shown better success rates compared to combinations in less severe cases. Moreover, the tendency toward broad-spectrum and empirical prescribing must be addressed by limiting empirical therapy and promoting empirical-to-definitive switch protocols once diagnostic results are available. Institutional policies should also focus on boosting generic prescribing through clinician training and strict enforcement of guidelines. Lastly, the adoption of IV-to-oral transition protocols should be prioritised to reduce treatment costs and hospitalisation duration while maintaining therapeutic efficacy. CONCLUSION This study, through the assessment of antibiotic usage trends in a tertiary care hospital located in South India, employs the WHO AWaRe framework alongside various prescribing indicators to reveal critical opportunities for enhancing antibiotic prescribing practices. The notable prevalence of antibiotics categorised as Watch group and the significant amount of empirical therapy, despite substantial evidence indicating improved outcomes with Access group antibiotics and definitive treatment, highlight the pressing necessity for more judicious prescribing. Furthermore, the strong correlations identified between AWaRe classification, sensitivity patterns, therapy type, and prescribing type with clinical outcomes accentuate the importance of strategic interventions aimed at optimising antibiotic utilisation and addressing the escalating challenge of antimicrobial resistance. Although Antimicrobial Stewardship Programs are currently implemented in certain healthcare facilities across India, there exists an urgent requirement to enhance and broaden these initiatives by integrating real-time prescription audits, clinical decision support systems, and the automated incorporation of microbiological data into prescribing processes. It is crucial to focus on diagnostic stewardship, which guarantees the timely and suitable application of culture tests, facilitating a transition from empirical to definitive therapy. Furthermore, multidisciplinary stewardship teams—comprising clinical pharmacists, microbiologists, and specialists in infectious diseases—should be empowered to spearhead educational programs and offer feedback at the prescriber level. This cohesive, data-informed strategy is essential for enhancing clinical outcomes and creating a sustainable framework for antibiotic governance within tertiary care hospitals. Abbreviations AMR Antimicrobial Resistance AWaRe Access, Watch, Reserve (WHO antibiotic classification framework) ASP Antimicrobial Stewardship Program df Degrees of Freedom EHR Electronic Health Record GLASS Global Antimicrobial Resistance and Use Surveillance System ICU Intensive Care Unit IV Intravenous MDRO Multidrug-Resistant Organisms WHO World Health Organisation χ² Chi-Square Declarations Ethical Approval and Consent to Participate : This study obtained approval from the Institutional Ethics Committee of Aditya Pharmacy College (A), located in Surampalem (Ref: APC-IEC/2024-2025/02). The research was carried out in alignment with the ethical standards specified in the Declaration of Helsinki and its later amendments. Since the investigation entailed the examination of anonymised patient records without any direct engagement with participants, the Ethics Committee provided a waiver for the necessity of informed consent. Consent for Publication: Not relevant. This research did not encompass any individual-level information (such as images or personal details) that would necessitate consent for publication. Availability of data and materials: The datasets that were utilised and/or analysed throughout the present study can be obtained from the corresponding author upon a reasonable request. Competing Interests: The authors declare that they have no competing interests. Funding: This research received no specific grant from any funding agency in the public, commercial, and not-for-profit sectors. Author Contributions: Dr. Pavan Kumar Yanamadala was responsible for conceptualising the study, providing academic oversight, and facilitating the critical revision of the manuscript. Riton Shil, Divya Merugu, and Sara Aiswarya Bommeti undertook the research as part of their academic project, playing a significant role in data collection, statistical analysis, interpretation of findings, and drafting the manuscript. Dr. Praveen Sana guided clinically, providing supervision and support in the coordination of hospital-based data collection. Prasanna Sai Sree Vallabhareddy contributed to the literature review and assisted with the formatting of the manuscript. All authors have reviewed and approved the final version of the manuscript and accept responsibility for its content and integrity. Acknowledgements: The authors express their heartfelt gratitude to the healthcare professionals of Trust Multispeciality Hospitals in Kakinada for their unwavering support during the duration of this study. Special acknowledgement is extended to Miss Hepzibah Rani Gidla, Pharm. D Intern of Aditya Pharmacy College (A), Surampalem, for her essential contributions to the statistical analysis, which significantly enhanced the accuracy and clarity of the research results. References Murray CJL, Ikuta KS, Sharara F, Swetschinski L, Aguilar GR, Gray A et al. 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Available from: https://doi.org/10.1093/jacamr/dlae110 Pappalardo F, Garaffo E, D’agata MA. Assessment of in-hospital antibiotics consumption pattern according to the WHO AWaRe classification in a local health authority. Eur J Hosp Pharm. 2024; 31(Suppl 1): A58. Available from: https://doi.org/10.1136/ejhpharm-2024-eahp.119 Sajjad U, Afzal N, Asif M, Rehman MB, Afridi AU, Kazmi T. Evaluation of antibiotic prescription patterns using WHO AWaRe classification. East Mediterr Health J. 2024; 30(2):156–162. Available from: https://doi.org/10.26719/emhj.24.031 Tadesse TY, Molla M, Yimer YS, Kefale B. Evaluation of Antibiotic Prescribing Patterns among Inpatients Using World Health Organisation Prescribing Indicators. SAGE Open Med. 2022; 10:20503121221127390. Available from: https://doi.org/10.1177/20503121221127390 Rahman MR, Chowdhury F, Rahman MM, Arefin Z, Chakrabarty S, Islam MA et al. Point prevalence survey of antibiotic use in tertiary and secondary level hospitals in Bangladesh using the WHO AWaRe classification. Antibiotics (Basel). 2022; 11(810):1–14. Available from: https://doi.org/10.3390/antibiotics11060810 Massawe CS, Kivuyo AB, Mganga KT, Nyawawa SS. Antibiotic Prescribing Patterns and Utilisation in Tertiary Hospitals in Dar es Salaam, Tanzania. JAC Antimicrob Resist. 2023; 5(4): dlad093. Available from: https://doi.org/10.1093/jacamr/dlad093 Demoz GT, Gebreslassie G, Hagazy K, Woldu G, Wahdey S, Tadesse DB et al. Prescribing pattern of antibiotics using WHO prescribing indicators among inpatients in Ethiopia: a need for an antibiotic stewardship program. Infect Drug Resist. 2020; 13:2783–94. Available from: https://doi.org/10.2147/IDR.S262104 Mudenda S, Chilimboyi R, Matafwali SK et al. Hospital prescribing patterns of antibiotics in Zambia using the WHO prescribing indicators post-COVID-19 pandemic: Findings and implications. JAC Antimicrob Resist. 2024; 6(1): dlae023. Available from: https://doi.org/10.1093/jacamr/dlae023 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 27 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6734591","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462353054,"identity":"b3a4a2ed-6037-45fe-be4e-a87e86677cb4","order_by":0,"name":"Pavan Kumar Yanamadala","email":"data:image/png;base64,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","orcid":"","institution":"Aditya Pharmacy College (A)","correspondingAuthor":true,"prefix":"","firstName":"Pavan","middleName":"Kumar","lastName":"Yanamadala","suffix":""},{"id":462353055,"identity":"8d4f5e79-5bab-4576-837f-ddf8738f1814","order_by":1,"name":"Prasanna Sai Sri Vallabhareddy","email":"","orcid":"","institution":"Aditya Pharmacy College (A)","correspondingAuthor":false,"prefix":"","firstName":"Prasanna","middleName":"Sai Sri","lastName":"Vallabhareddy","suffix":""},{"id":462353056,"identity":"b8e57851-026d-471b-9606-2e95db8faba6","order_by":2,"name":"Riton Shil","email":"","orcid":"","institution":"Aditya Pharmacy College (A)","correspondingAuthor":false,"prefix":"","firstName":"Riton","middleName":"","lastName":"Shil","suffix":""},{"id":462353057,"identity":"fd2ee27d-19b0-4ca2-9d5d-c6d525a6ee07","order_by":3,"name":"Sara Aiswarya Bommeti","email":"","orcid":"","institution":"Aditya Pharmacy College (A)","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"Aiswarya","lastName":"Bommeti","suffix":""},{"id":462353058,"identity":"dfc2ab5d-2b57-461c-a055-de100a4842a8","order_by":4,"name":"Divya Merugu","email":"","orcid":"","institution":"Aditya Pharmacy College (A)","correspondingAuthor":false,"prefix":"","firstName":"Divya","middleName":"","lastName":"Merugu","suffix":""},{"id":462353059,"identity":"0e7c3b60-c4bf-4e02-9996-dd254d665396","order_by":5,"name":"Praveen Sana","email":"","orcid":"","institution":"Trust Multispeciality Hospitals","correspondingAuthor":false,"prefix":"","firstName":"Praveen","middleName":"","lastName":"Sana","suffix":""}],"badges":[],"createdAt":"2025-05-23 16:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6734591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6734591/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83601049,"identity":"1f9d9063-65a6-426b-8e68-23acfb22c4dc","added_by":"auto","created_at":"2025-05-29 09:12:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Antibiotics based on WHO’s AWaRe Classification Vs. Clinical Outcomes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6734591/v1/ccd4f7d25867849adea64e3e.png"},{"id":83601306,"identity":"1ca627ec-d637-4fe1-a399-bdfac8f706c6","added_by":"auto","created_at":"2025-05-29 09:20:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36278,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical Outcomes based on Sensitivity Patterns of Antibiotics\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6734591/v1/2e2591283470fef3d1bcc71a.png"},{"id":83601307,"identity":"71f9ef87-46c9-4ce3-b8c0-b0270353ba29","added_by":"auto","created_at":"2025-05-29 09:20:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Clinical Outcomes in Monotherapy Vs. Combination Therapy\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6734591/v1/d4cb2de696a85217b41e7a1a.png"},{"id":83601047,"identity":"4e2fc351-3cc7-429a-bb95-8de12b9edc54","added_by":"auto","created_at":"2025-05-29 09:12:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical Outcomes based on Antibiotic Prescribing Type\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6734591/v1/2a71fb1f8d05325d24c9c271.png"},{"id":83601979,"identity":"81f9f15c-6dfb-462f-a467-30457d13685a","added_by":"auto","created_at":"2025-05-29 09:28:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1393592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6734591/v1/d3ce2683-b8a3-448a-ad8c-b6ccc895c49e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssessment of Antibiotic Utilization Using Who’s AWaRe Framework in a South Indian Tertiary Care Hospital\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAntimicrobial resistance (AMR) represents an increasing global health emergency that weakens the effectiveness of treating prevalent infections, increases healthcare costs, resulting in compounded rates of illness and death. In 2019, AMR was linked to around 1.27\u0026nbsp;million fatalities globally, and predictions indicate that this number could surge to 10\u0026nbsp;million deaths each year by 2050 if not decisive actions are implemented \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This issue is particularly alarming in the low- and middle-income countries where the inadequacies in the healthcare infrastructure and regulatory frameworks frequently hinder the effective measures against the inappropriate prescribing of antibiotics \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, India is facing a significant challenge in the form of Antimicrobial Resistance, as it carries one of the highest burdens of infectious diseases globally. In the year of 2019, it was reported that 297,000 fatalities were directly linked to AMR, and an additional 1.04\u0026nbsp;million deaths were attributed to the infections caused by the resistant pathogens \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. There is an alarming increase in resistance among significant bacterial pathogens, including Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii, particularly within hospital environments where the use of antibiotics is both inappropriate and frequently based on empirical prescribing \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn India, hospitals play a major role in the emergence and spread of antibiotic-resistant bacteria. Tertiary care centres, in particular, face major challenges such as high patient numbers, more frequent ICU admissions, widespread use of broad-spectrum antibiotics, and a lack of microbiology-guided treatment.\u003c/p\u003e \u003cp\u003eA study conducted at a tertiary care facility in Kerala found a significant prevalence of multidrug-resistant organisms (MDROs) in ICUs, which was linked to the inappropriate use and prolonged courses of antimicrobial treatments \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Another study in 2023, carried out by Hegde et al. in Karnataka, uncovered notable variations in the way antibiotics are prescribed across different medical wards. These differences have further complicated the efforts related to antibiotic stewardship (ASP) initiatives, highlighting the need for improved practices and guidelines in the management of antibiotic use \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn light of the growing concerns surrounding antibiotic resistance, the World Health Organisation framed the AWaRe classification in 2017, which sorts antibiotics into three groups: Access, Watch, and Reserve, according to their therapeutic efficacy and potential for resistance development \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. This framework has since become a fundamental element of international antimicrobial stewardship initiatives, providing a systematic practice for enhancing the selection, prescription, and oversight of antibiotic use.\u003c/p\u003e \u003cp\u003eDespite this, the application of AWaRe-based stewardship approaches in Indian tertiary care settings continues to be uneven. The Global Antimicrobial Resistance and Use Surveillance System (GLASS) report from 2022 indicates that Patients in India are experiencing elevated resistance to carbapenems, fluoroquinolones, and third-generation cephalosporins, which are frequently prescribed and are classified under the Watch and Reserve categories \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Several factors contribute to this issue, including the availability of over-the-counter antibiotics, the practice of prescribing medications empirically without confirming diagnoses, and a lack of awareness or adherence to the prescribing guidelines established by the World Health Organisation \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, a significant number of Indian tertiary care hospitals do not have effective antimicrobial stewardship programs, and in the facilities where such programs do exist, they are frequently undermined by poor enforcement and a lack of supportive infrastructure. The absence or ineffective implementation of periodic prescription audits, culture sensitivity testing, and prescriber awareness contributes to an environment that favours the irrational use of antibiotics, especially those classified as Watch and Reserve group drugs, thereby exacerbating the development of resistance patterns \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite various global and national initiatives encouraging the rational use of antibiotics, there is a significant lack of data from India regarding AWaRe-based prescribing practices within tertiary care settings. Few Studies \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e have systematically assessed the antibiotic usage patterns in Indian hospitals using the WHO AWaRe framework along with WHO prescribing indicators. Additionally, the effects of empirical antibiotic therapy compared to definitive culture-based therapy on patient outcomes in Indian healthcare settings have not been adequately investigated.\u003c/p\u003e \u003cp\u003eDue to the inconsistent applications of WHO prescribing tools and the lack of relevant studies in Indian healthcare settings, there is a significant need for more comprehensive assessments. Acknowledging this significant gap in the research, the current study was initiated to produce organisation-specific, evidence-based insights regarding the rational use of antibiotics. This study focuses on prescribing patterns within a tertiary care facility in South India, using the WHO AWaRe classification in combination with essential prescribing indicators to assess the rationality of antimicrobial usage. Besides, the study delineates between the clinical outcomes of empirical therapy and culture-definitive treatment, thus providing practical data to enhance antimicrobial stewardship practices and ensure compliance with global prescribing standards.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\n\u003cp\u003eThis research was a prospective, observational study based on existing hospital records, conducted at Trust Multispeciality Hospitals in Kakinada, which is a tertiary care facility offering a diverse range of specialties such as General Medicine, Intensive Care, Surgery, Cardiology, Orthopedics, and Nephrology, which provided an appropriate setting for assessing the antibiotic prescribing practices among inpatients. Enables a thorough examination of prescribing trends, patterns, and outcomes as they manifest in everyday clinical practice, thereby ensuring a high degree of authenticity. The duration of the study spanned six months, from September 2024 to March 2025.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy Population and Eligibility Criteria\u003c/h3\u003e\n\u003cp\u003eDuring the study period, all inpatients of all age groups and genders who were prescribed at least one systemic antibiotic for either a confirmed or suspected bacterial infection were evaluated for inclusion in this study to ensure a comprehensive representation of antibiotic use in the inpatient settings. However, the patients were excluded from consideration if their prescribed antibiotics were solely for prophylactic purposes, those who were treated exclusively with antifungal or antiviral medications without the concomitant use of antibiotics, or for whom the records lacked comprehensive information regarding the antibiotic treatment provided, to specifically focus on the therapeutic usage of antibiotics for bacterial infections.\u003c/p\u003e\n\u003ch3\u003eSample Size Determination\u003c/h3\u003e\n\u003cp\u003eThe determination of the sample size was performed using Cochran's formula \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e for proportions, with the assumption of maximum variability in order to provide the most conservative estimate possible.\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:n=\\:\\frac{{\\text{z}}^{2}\\times\\:p\\:\\left(1-p\\right)}{{\\text{e}}^{2}\\:}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;initial sample size\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Z-value for 95% confidence level\u0026thinsp;=\u0026thinsp;\u003cstrong\u003e1.96\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;estimated prevalence\u0026thinsp;=\u0026thinsp;\u003cstrong\u003e0.5\u003c/strong\u003e (assumed for maximum variability, because there was no prior data available on antibiotic prescribing prevalence in tertiary care settings)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cem\u003ee\u003c/em\u003e\u0026thinsp;=\u0026thinsp;acceptable margin of error\u0026thinsp;=\u0026thinsp;\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSubstituting the above values in the Cochran\u0026rsquo;s Formula,\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$$\\:n=\\:\\frac{\\left(1.96\\right)\u0026sup2;\\:\\times\\:0.5\\:(1-0.5)}{\\left(0.05\\right)\u0026sup2;\\:}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equc\" class=\"mathdisplay\"\u003e$$\\:n=\\:\\frac{\\:3.8416\\times\\:0.5\\:\\left(0.5\\right)}{0.0025}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equd\" class=\"mathdisplay\"\u003e$$\\:n=\\:\\frac{\\:0.9604}{0.0025}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Eque\" class=\"mathdisplay\"\u003e$$\\:n=384.16$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe n value is calculated, and the initial sample was rounded off to \u003cstrong\u003e384\u003c/strong\u003e. Given that the total number of eligible patients in the hospital over six months is reported as 210, the sample size is modified by applying the finite population correction formula \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, to adjust the sample size for a smaller defined population.\u003c/p\u003e\n\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equf\" class=\"mathdisplay\"\u003e$$\\:n\\:\\left(adj\\right)=\\frac{n0\\text{}}{1+\\frac{n0\\text{}-1}{N}\\:}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equg\" class=\"mathdisplay\"\u003e$$\\:n\\:\\left(adj\\right)=\\frac{384\\text{}}{1+\\frac{384-1}{210}\\:}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equh\" class=\"mathdisplay\"\u003e$$\\:n\\:\\left(adj\\right)=\\frac{384\\text{}}{1+1.8293\\:}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equi\" class=\"mathdisplay\"\u003e$$\\:n\\:\\left(adj\\right)=\\frac{384\\text{}}{2.8293\\:}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equj\" class=\"mathdisplay\"\u003e$$\\:n\\:\\left(adj\\right)\\approx\\:135.8$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIt is rounded off to \u003cstrong\u003e136\u003c/strong\u003e to ensure the minimum required observations for the statistical validity, reflecting a 95% confidence level and a margin of error of 5%.\u003c/p\u003e\n\u003ch3\u003eData Collection and Study Procedure\u003c/h3\u003e\n\u003cp\u003eData related to the study were systematically gathered from Electronic Health Records (EHRs) and case sheet documentation through a structured electronic data collection form. The variables analysed comprised demographic information, diagnoses, antibiotic prescriptions (including generic name, dosage, administration route, frequency, and duration), the rationale for use (whether empirical or definitive), classification according to the WHO AWaRe framework, culture sensitivity results, and patient outcomes.\u003c/p\u003e\n\u003cp\u003eThe process of data collection followed a standardised protocol that was divided into three definite phases: \u003cstrong\u003ethe first phase\u003c/strong\u003e involved preparation, which included obtaining ethical approvals, developing the necessary study tools, and conducting pilot testing with a sample size of twelve participants. The pilot testing helped in refining the data collection form, ensuring the clarity of the study variables, and the data extraction procedures. \u003cstrong\u003eThe second phase\u003c/strong\u003e focused on data retrieval, involving a systematic review of patient records. \u003cstrong\u003eThe final phase\u003c/strong\u003e was data validation, where weekly audits were conducted to ensure consistency in data extraction, verifying the reliability of study variables, thus minimising the potential data entry errors. There was no direct interaction with patients, as all data were anonymised before the result analysis.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eData were gathered using Microsoft Excel 2019 and subsequently analysed through both descriptive and inferential statistical methods, like chi-square tests. For categorical variables, frequencies and proportions were recorded. To assess the association/correlation among variables, including AWaRe classification, type of therapy, sensitivity-based prescriptions, and treatment outcomes, Chi-square tests were employed. A p-value of less than 0.05 was deemed statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Ethics Committee of Aditya Pharmacy College (A) in Surampalem approved the study protocol (Ref: APC-IEC/2024-2025/02) before its commencement. Given that this study included anonymised patient records and did not involve direct interaction with patients, the necessity for informed consent was waived.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eDemographics, Antibiotic Usage, and Duration of Therapy (n\u0026thinsp;=\u0026thinsp;136)\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eVariable\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCategory\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eMales (n\u0026thinsp;=\u0026thinsp;90)\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eFemales (n\u0026thinsp;=\u0026thinsp;46)\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eTotal (n\u0026thinsp;=\u0026thinsp;136)\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAge Distribution\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0\u0026ndash;18 years\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2 (2.2%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2 (1.47%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e19\u0026ndash;40 years\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e19 (21.1%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e13 (28.2%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e32 (23.5%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e41\u0026ndash;60 years\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e45 (50%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e25 (54.3%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e70 (51.4%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;60 years\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e24 (26.6%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e8 (17.3%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e32 (23.5%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAntibiotics per Patient\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSingle Antibiotic\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e36 (40%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e17 (37%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e53 (39%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eTwo Antibiotics\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e31 (34.4%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e19 (41%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e50 (37%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eThree or More Antibiotics\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e23 (25.5%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e10 (22%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e33 (24%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eDuration of Therapy\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1\u0026ndash;3 Days\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e44 (32.3%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4\u0026ndash;6 Days\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e54 (39.7%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;6 Days\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e38 (27.9%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ePrescribing Appropriateness and Administration Based on WHO AWaRe Classification (n\u0026thinsp;=\u0026thinsp;136)\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAWaRe Group of Antibiotics\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAppropriate\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ePartially Appropriate\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eInappropriate\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eTotal (n\u0026thinsp;=\u0026thinsp;136)\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAccess\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e8 (44.4%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e7 (38.8%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3 (16.7%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e18 (13,23%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eWatch\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e60 (69%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e17 (19.5%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e10 (11.5%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e87 (63.9%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eReserve\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e21 (67.7%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4 (12.9%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e6 (19.4%)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e31 (22.7%)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u003cbr\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAnalysis of Antibiotic Utilisation Patterns using WHIO Prescribing Indicators Framework\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ePrescribing Indicator\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eResults\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAverage Number of Antibiotics per Prescription\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eThe average antibiotics per patient was 2.1; single therapy was 39%, Dual treatment was 37%, and Triple or more was 24%.\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003ePercentage of Prescriptions Containing Antibiotics\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e100% (All 136 patients received at least one antibiotic).\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003ePercentage of Antibiotics Prescribed by Generic Name\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e71% of the Total Prescriptions in our Study, that is, 97 prescriptions.\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003ePercentage of Antibiotics Prescribed from AWaRe List\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAccess: 13.2%, Watch: 63.9%, Reserve: 22.8%.\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eDuration of Antibiotic Therapy\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1\u0026ndash;3 days: 32.3%, 4\u0026ndash;6 days: 39.7%, \u0026gt;\u0026thinsp;6 days: 27.9%.\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eRoute of Administration of Antibiotics\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eIV Therapy: 83%, Oral Therapy: 17% (primarily Watch group).\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003ePercentage of Culture-Sensitivity Guided Prescriptions\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAntibiotics changed post-culture in 12.5% of cases; sensitivity-based prescribing improved outcomes significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.00000005).\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eAppropriateness of Prescriptions\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eAppropriate: 65.4%, Partially Appropriate: 20.6%, Inappropriate: 13.9%.\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eType of Antibiotic Therapy (Empirical/Definitive)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eEmpirical: 50%, Definitive: 44.9%, Prophylactic: 5.1%.\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eClinical Outcome of Antibiotic Use\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eCure/Improvement: 70.6%, No Change: 25%, Worsening: 4.4%; Significant association with AWaRe category (p\u0026thinsp;=\u0026thinsp;0.0039).\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSummary of Chi-Square Analyses for Factors Influencing Clinical Outcomes\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eS. No.\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eComparative Factor\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u0026chi;\u0026sup2; Value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003edf\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ep-value\u003c/div\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSignificance\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e(\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05)\u003c/div\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eWHO AWaRe Classification vs. Clinical Outcomes\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e15.398\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e0.0039\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSignificant\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eSensitivity Pattern vs. Clinical Outcomes\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e39.400\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e4.758 x 10\u003c/span\u003e\u003csup\u003e\u003cspan class=\"Bold\"\u003e\u0026minus;\u0026thinsp;8\u003c/span\u003e\u003c/sup\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eHighly Significant\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003eTherapy Type (Monotherapy vs. Combination) vs. Clinical Outcomes\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e14.840\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e0.0050\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eSignificant\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003ePrescribing Type (Empirical vs. Definitive) vs. Clinical Outcomes\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e216.042\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e1.3328 x 10\u003c/span\u003e\u003csup\u003e\u003cspan class=\"Bold\"\u003e\u0026minus;\u0026thinsp;44\u003c/span\u003e\u003c/sup\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eExtremely Significant\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis research evaluated the use of antibiotics through the lens of the WHO AWaRe framework, concentrating specifically on usage patterns, appropriateness, and clinical outcomes. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Demographics and Usage Patterns (Table 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e demonstrates that the largest percentage of patients fell within the age range of 41–60 years (51.4%), with a notable predominance of males (66%) over females. This observation is consistent with the research conducted by Chizimu et al. in 2024 \u003cstrong\u003e\u003csup\u003e[16\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e, which indicated that middle-aged and older patients, especially men, exhibited a higher tendency for hospitalisation as a result of age-related immunosuppression and comorbid conditions. Conversely, Mugada Vinodkumar et al. in 2021\u003cstrong\u003e\u003csup\u003e[17\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e reported that younger patients (19–44 years) were more prevalent in outpatient environments, likely due to earlier access to healthcare services and variations in the severity of infections.\u003c/p\u003e\n\u003cp\u003eConcerning the administration of antibiotics, \u003cstrong\u003eTable 1\u003c/strong\u003e indicates that 39% of patients were treated with a single antibiotic, 37% with two antibiotics, and 24% with three or more. These results align closely with those presented by Gagliotti et al. in 2024 \u003cstrong\u003e\u003csup\u003e[18\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e, who noted that 40.6% of prescriptions in European hospital environments were for single antibiotics. Nevertheless, the comparatively elevated proportions of dual or triple therapy observed in our research may be attributed to the empiric management of severe infections, a phenomenon also highlighted by Chizimu et al. in 2024 \u003cstrong\u003e\u003csup\u003e[16\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e. This underscores the necessity for more precise therapeutic approaches informed by diagnostic evaluations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAWaRe Classification and Appropriateness (Table 2, Figure 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 2\u003c/strong\u003e, the predominant category of antibiotics prescribed was the Watch group, comprising 63.9% of the total, whereas Reserve antibiotics represented 22.8%, and Access antibiotics accounted for a mere 13.2%. This pattern is further depicted in Figure 1, which illustrates the clinical outcomes associated with these AWaRe categories. The Access group exhibited the highest cure rates at 94.4%, in contrast to the Watch group, which had a significantly lower cure rate of 59.8%. These results align with the findings of Pappalardo et al. in 2024 \u003cstrong\u003e\u003csup\u003e[19\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e and Gagliotti et al. in 2024 \u003cstrong\u003e\u003csup\u003e[18\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e, both of whom also noted the predominance of the Watch group in hospital environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe insufficient use of Access group antibiotics, in contrast to the WHO's target of at least 60%, may be attributed to prescribers favouring broad-spectrum agents, particularly in empirical treatment. Potential corrective measures could involve formulary management, the reinforcement of guidelines, and education regarding stewardship principles.\u003c/p\u003e\n\u003cp\u003eThe appropriateness of prescribing in our study was found to be relatively favourable, with 65.4% of prescriptions deemed appropriate, as indicated in \u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eThis figure surpasses the 54.9% reported by Sajjad et al. in 2024 \u003cstrong\u003e\u003csup\u003e[20\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e in a similar South Asian context, which may be attributed to the involvement of clinical pharmacists and strict adherence to hospital protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of Key Variables on Clinical Outcomes (Table 3, Figures 1–4)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e illustrates the outcomes of chi-square analyses, indicating that AWaRe classification, sensitivity patterns, therapy type, and prescribing type all had a significant impact on patient outcomes. These results are visually represented in Figures 1–4, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e (AWaRe vs. Clinical Outcomes) demonstrates a statistically significant correlation (p = 0.0039), indicating improved outcomes for patients treated with Access antibiotics. This finding reinforces the recommendations set forth by the WHO and underscores the necessity for stewardship initiatives aimed at prioritising these medications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e (Sensitivity Patterns vs. Outcomes) illustrates that the highest cure rates were observed in patients administered sensitive antibiotics (93.9%), while those receiving treatment for resistant infections exhibited elevated rates of no change or deteriorating outcomes. This observation is supported by the chi-square p-value of 5.758 × 10⁻⁸ presented in Table 3, and is consistent with the findings of Tadesse et al. in 2022 \u003cstrong\u003e\u003csup\u003e[21\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e and Rahman et al. in 2022 \u003cstrong\u003e\u003csup\u003e[22\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e, who similarly reported that culture-guided therapy significantly enhanced outcomes. Nevertheless, only 12.5% of prescriptions in our investigation were informed by sensitivity testing, highlighting the necessity to enhance microbiological services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e (Monotherapy vs. Combination Therapy) illustrates that monotherapy yielded superior outcomes compared to combination therapy (p = 0.0050), as indicated in Table 3. Despite the higher prevalence of combination therapy (56.6%), it is typically utilised for resistant or complex infections. These results are consistent with the research conducted by Massawe et al. in 2023 \u003cstrong\u003e\u003csup\u003e[23\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e and underscore the significance of de-escalating therapy once culture results are obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e (Prescribing Type vs. Outcomes) illustrates a particularly notable discovery: a highly significant correlation (p = 1.3328 × 10⁻⁴⁴) between the type of prescribing and the resulting outcomes. Although empirical therapy constituted 50% of all prescriptions, definitive therapy produced more favourable outcomes when sensitivity patterns were established. This observation aligns with the findings of Demoz et al. in 2020 \u003cstrong\u003e\u003csup\u003e[24\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e and Chizimu et al. in 2024 \u003cstrong\u003e\u003csup\u003e[16\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e, who cautioned against the long-term dangers associated with empirical prescribing. Enhancing investment in rapid diagnostics and antibiograms would facilitate a shift towards more definitive prescribing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrescribing Indicators and System-Wide Assessment (Table 4)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e4\u003c/strong\u003e assesses our study concerning the prescribing indicators established by the WHO. The mean number of antibiotics prescribed per patient was 2.1, which surpasses the WHO-recommended range of 1.6 to 1.8. This indicates a possible inclination towards overprescribing, which may be influenced by the practices associated with empirical therapy. The rate of generic prescriptions stood at 71%, reflecting moderate adherence to guidelines, yet it remains below the 96.5% compliance reported by Mudenda et al. in 2024 \u003cstrong\u003e\u003csup\u003e[25\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e]\u003c/sup\u003e. Implementing policies that encourage generic prescribing could contribute to cost reduction and enhance standardisation.\u003c/p\u003e\n\u003cp\u003eMoreover, 83% of antibiotics were administered through the intravenous route, indicating the seriousness of cases requiring hospitalisation. Nevertheless, the adoption of IV-to-oral switch protocols, when clinically suitable, has the potential to enhance cost-effectiveness and shorten the length of hospital stays.\u003c/p\u003e\n\u003cp\u003eBased on the study findings and comparisons with previous literature, several key corrective actions are essential to improve antibiotic utilisation practices. There is an urgent need to \u003cstrong\u003eencourage the use of Access antibiotics\u003c/strong\u003e through targeted prescriber education, formulary restrictions, and regular audit-feedback mechanisms. Simultaneously, efforts must be made to \u003cstrong\u003eexpand culture-guided prescribing\u003c/strong\u003e by enhancing microbiology infrastructure and ensuring timely sensitivity reports, which can significantly improve clinical outcomes. Clinicians should be encouraged to \u003cstrong\u003epromote rational monotherapy\u003c/strong\u003e wherever appropriate, as it has shown better success rates compared to combinations in less severe cases. Moreover, the tendency toward broad-spectrum and empirical prescribing must be addressed by \u003cstrong\u003elimiting empirical therapy\u003c/strong\u003e and promoting \u003cstrong\u003eempirical-to-definitive switch protocols\u003c/strong\u003e once diagnostic results are available. Institutional policies should also focus on \u003cstrong\u003eboosting generic prescribing\u003c/strong\u003e through clinician training and strict enforcement of guidelines. Lastly, the adoption of \u003cstrong\u003eIV-to-oral transition protocols\u003c/strong\u003e should be prioritised to reduce treatment costs and hospitalisation duration while maintaining therapeutic efficacy.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study, through the assessment of antibiotic usage trends in a tertiary care hospital located in South India, employs the WHO AWaRe framework alongside various prescribing indicators to reveal critical opportunities for enhancing antibiotic prescribing practices. The notable prevalence of antibiotics categorised as Watch group and the significant amount of empirical therapy, despite substantial evidence indicating improved outcomes with Access group antibiotics and definitive treatment, highlight the pressing necessity for more judicious prescribing. Furthermore, the strong correlations identified between AWaRe classification, sensitivity patterns, therapy type, and prescribing type with clinical outcomes accentuate the importance of strategic interventions aimed at optimising antibiotic utilisation and addressing the escalating challenge of antimicrobial resistance.\u003c/p\u003e\n\u003cp\u003eAlthough Antimicrobial Stewardship Programs are currently implemented in certain healthcare facilities across India, there exists an urgent requirement to enhance and broaden these initiatives by integrating real-time prescription audits, clinical decision support systems, and the automated incorporation of microbiological data into prescribing processes. It is crucial to focus on diagnostic stewardship, which guarantees the timely and suitable application of culture tests, facilitating a transition from empirical to definitive therapy. Furthermore, multidisciplinary stewardship teams—comprising clinical pharmacists, microbiologists, and specialists in infectious diseases—should be empowered to spearhead educational programs and offer feedback at the prescriber level. This cohesive, data-informed strategy is essential for enhancing clinical outcomes and creating a sustainable framework for antibiotic governance within tertiary care hospitals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAMR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntimicrobial Resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAWaRe\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAccess, Watch, Reserve (WHO antibiotic classification framework)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eASP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntimicrobial Stewardship Program\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003edf\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDegrees of Freedom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Health Record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGLASS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Antimicrobial Resistance and Use Surveillance System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntravenous\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMDRO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultidrug-Resistant Organisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organisation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eχ\u0026sup2;\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChi-Square\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e: This study obtained approval from the Institutional Ethics Committee of Aditya Pharmacy College (A), located in Surampalem (Ref: APC-IEC/2024-2025/02). The research was carried out in alignment with the ethical standards specified in the Declaration of Helsinki and its later amendments. Since the investigation entailed the examination of anonymised patient records without any direct engagement with participants, the Ethics Committee provided a waiver for the necessity of informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e Not relevant. This research did not encompass any individual-level information (such as images or personal details) that would necessitate consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets that were utilised and/or analysed throughout the present study can be obtained from the corresponding author upon a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no specific grant from any funding agency in the public, commercial, and not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eDr. Pavan Kumar Yanamadala was responsible for conceptualising the study, providing academic oversight, and facilitating the critical revision of the manuscript. Riton Shil, Divya Merugu, and Sara Aiswarya Bommeti undertook the research as part of their academic project, playing a significant role in data collection, statistical analysis, interpretation of findings, and drafting the manuscript. Dr. Praveen Sana guided clinically, providing supervision and support in the coordination of hospital-based data collection. Prasanna Sai Sree Vallabhareddy contributed to the literature review and assisted with the formatting of the manuscript. All authors have reviewed and approved the final version of the manuscript and accept responsibility for its content and integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors express their heartfelt gratitude to the healthcare professionals of Trust Multispeciality Hospitals in Kakinada for their unwavering support during the duration of this study. \u0026nbsp;Special acknowledgement is extended to Miss Hepzibah Rani Gidla, Pharm. D Intern of Aditya Pharmacy College (A), Surampalem, for her essential contributions to the statistical analysis, which significantly enhanced the accuracy and clarity of the research results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMurray CJL, Ikuta KS, Sharara F, Swetschinski L, Aguilar GR, Gray A et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet [Internet]. 2022;399 (10325): 629\u0026ndash;55. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)02724-0/fulltext\u003c/span\u003e\u003cspan address=\"https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)02724-0/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDadgostar P. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://books.google.co.in/books/about/Mathematical_Statistics_with_Application.html?id=5AJ0AwAAQBAJ\u0026amp;redir_esc=y\u003c/span\u003e\u003cspan address=\"https://books.google.co.in/books/about/Mathematical_Statistics_with_Application.html?id=5AJ0AwAAQBAJ\u0026amp;redir_esc=y\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChizimu JY, Mudenda S, Yamba K et al. Antibiotic use and adherence to the WHO AWaRe guidelines across 16 hospitals in Zambia: a point prevalence survey. JAC Antimicrob Resist. 2024; 6(5): dlae170. 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Assessment of in-hospital antibiotics consumption pattern according to the WHO AWaRe classification in a local health authority. Eur J Hosp Pharm. 2024; 31(Suppl 1): A58. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/ejhpharm-2024-eahp.119\u003c/span\u003e\u003cspan address=\"10.1136/ejhpharm-2024-eahp.119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSajjad U, Afzal N, Asif M, Rehman MB, Afridi AU, Kazmi T. Evaluation of antibiotic prescription patterns using WHO AWaRe classification. East Mediterr Health J. 2024; 30(2):156\u0026ndash;162. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26719/emhj.24.031\u003c/span\u003e\u003cspan address=\"10.26719/emhj.24.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTadesse TY, Molla M, Yimer YS, Kefale B. Evaluation of Antibiotic Prescribing Patterns among Inpatients Using World Health Organisation Prescribing Indicators. SAGE Open Med. 2022; 10:20503121221127390. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20503121221127390\u003c/span\u003e\u003cspan address=\"10.1177/20503121221127390\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MR, Chowdhury F, Rahman MM, Arefin Z, Chakrabarty S, Islam MA et al. Point prevalence survey of antibiotic use in tertiary and secondary level hospitals in Bangladesh using the WHO AWaRe classification. Antibiotics (Basel). 2022; 11(810):1\u0026ndash;14. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antibiotics11060810\u003c/span\u003e\u003cspan address=\"10.3390/antibiotics11060810\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassawe CS, Kivuyo AB, Mganga KT, Nyawawa SS. Antibiotic Prescribing Patterns and Utilisation in Tertiary Hospitals in Dar es Salaam, Tanzania. JAC Antimicrob Resist. 2023; 5(4): dlad093. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jacamr/dlad093\u003c/span\u003e\u003cspan address=\"10.1093/jacamr/dlad093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemoz GT, Gebreslassie G, Hagazy K, Woldu G, Wahdey S, Tadesse DB et al. Prescribing pattern of antibiotics using WHO prescribing indicators among inpatients in Ethiopia: a need for an antibiotic stewardship program. Infect Drug Resist. 2020; 13:2783\u0026ndash;94. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/IDR.S262104\u003c/span\u003e\u003cspan address=\"10.2147/IDR.S262104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMudenda S, Chilimboyi R, Matafwali SK et al. Hospital prescribing patterns of antibiotics in Zambia using the WHO prescribing indicators post-COVID-19 pandemic: Findings and implications. JAC Antimicrob Resist. 2024; 6(1): dlae023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jacamr/dlae023\u003c/span\u003e\u003cspan address=\"10.1093/jacamr/dlae023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anti-bacterial agents, Drug Prescriptions, Drug Resistance, Antimicrobial Stewardship, Clinical Outcomes, Tertiary hospital","lastPublishedDoi":"10.21203/rs.3.rs-6734591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6734591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eThe issue of antimicrobial resistance presents a significant obstacle to global health, largely driven by the inappropriate use of antibiotics, especially in tertiary care settings. In response, the WHO has introduced the AWaRe framework to promote the rational use of antibiotics; however, further investigation is required to effectively implement this framework within Indian hospital settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim: \u003c/strong\u003eThis research intends to assess the patterns of antibiotic prescriptions within a south-Indian tertiary care hospital, employing the WHO's AWaRe framework combined with a range of prescribing indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis observational, prospective study was carried out at a Tertiary Care Hospital in Kakinada from September 2024 to February 2025. Data was collected and analysed from 136 patient records receiving systemic antibiotics via EHR, and clinical outcomes were analysed using descriptive statistics and the Chi-Square test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our study showed a significant prevalence of Watch group antibiotics, accounting for 63.9% of prescriptions, in contrast to the mere 13.2% for Access group antibiotics. More common empirical prescribing raises concerns regarding the potential escalation of antibiotic resistance. It is crucial to recognise that the patterns of antibiotic prescribing have a significant influence on patient outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe antibiotic prescribing practices in this study suggest a significant need for improvement, particularly in the use of Watch group antibiotics. This offers an opportunity to align better with WHO AWaRe guidelines. Targeted antimicrobial stewardship initiatives are crucial for promoting responsible antibiotic use and enhancing patient outcomes. Future research should evaluate the effectiveness of these interventions and their long-term impact.\u003c/p\u003e","manuscriptTitle":"Assessment of Antibiotic Utilization Using Who’s AWaRe Framework in a South Indian Tertiary Care Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-29 09:12:39","doi":"10.21203/rs.3.rs-6734591/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-28T08:18:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T08:08:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-05-27T08:06:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"702e26c9-1682-4bf7-92de-5ac08ccf21b2","owner":[],"postedDate":"May 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-22T07:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-29 09:12:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6734591","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6734591","identity":"rs-6734591","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}