Changes in Antibiotic Resistance Before and During the COVID-19 Pandemic: Retrospective Surveillance Study in a Single Indonesian Tertiary Hospital

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Antibiotic resistance related to inappropriate empiric antibiotics, particularly during the COVID-19 pandemic. However, limited information is available about changes in antibiotic resistance before and during the pandemic in Indonesia. This study aimed to describe changes in the prevalence of antibiotic resistance among patients with proven bacterial infections before and during the COVID-19 pandemic. Methods: A retrospective surveillance study was carried out at a single tertiary hospital to review medical records containing culture and antibiotic susceptibility data among hospitalized patients diagnosed with sepsis and COVID-19 according to the International Classification of Disease (ICD). In this context, the predefined periods were 1 January–31December 2019 and 1 March 2020–31 December 2021. The result was the percentage of resistance to selected antibiotics among the study population, stratified by gram-bacteria type, with the evaluation of changes in antibiotic resistance over time. Results: During the observation period, 596 adult patients were diagnosed with sepsis (before COVID-19), and 2786 were diagnosed with confirmed COVID-19 (during COVID-19). The rate of culture growth in patients with sepsis was greater than that in patients with COVID-19, with values of 51.6% and 29.2%, respectively. Gram-negative bacterial isolates were predominantly found in all observation periods, accounting for 41.2% - 47.3% of the adult middle-aged group. Changes in antibiotic resistance against GNB were observed during COVID-19 (peak phase, above 20%) compared to the early phase. For gram-positive bacteria, the greatestchanges were found in the late phase, reaching 70%. Conclusions: This study revealed that changes in antibiotic resistance before and during the COVID-19 pandemicaffected both GNB and GPB. High antibiotic use and age-related immune responses (i.e., immunosenescence) contributed to these rapid changes. Strengthening strategies, including implementing surveillance systems and antimicrobial stewardship programs and enhancing the capacity of healthcare workers, are recommended for combatting antibiotic resistance. antibiotic resistance antibiotic surveillance bacterial infection COVID-19 sepsis Figures Figure 1 Figure 2 Introduction Antibiotic resistance is recognized as a public health threat and has received great attention due to its significant impact on mortality and increased economic burdens. The incidence is driven by inappropriate use of empiric antibiotics, which often occur in severe or critical situations, such as sepsis. 1 This life-threatening condition is associated with a dysregulated immune response to infection and leads to organ dysfunction. In 2020, the World Health Organization (WHO) reported that approximately 20% of global deaths were due to sepsis. 2 Subsequently, in the year, a new rapidly spreading respiratory disease, namely, COVID-19, was declared a global pandemic disease caused by severe acute respiratory coronavirus 2 (SARS-CoV-2). 3 Similar to sepsis, COVID-19 also results in a dysregulated immune response and organ dysfunction. Several factors have contributed to the use of antibiotics for treating COVID-19. These include ( 1 ) rapid progression of the disease, ( 2 ) limited information on disease management, and ( 3 ) difficulties in differentiating between COVID-19 and bacterial pneumonia. 4–8 Moreover, both sepsis and COVID-19 patients require long-term hospitalization, increasing the risk of hospital-acquired infections, often bacteria in nature. 9,10 This suggests that the complex factor of sepsis and COVID-19 may contribute to the development of antibiotic resistance. Surveillance of antibiotics has been identified as one of the strategy pillars for combatting resistance by the WHO. This strategy is essential for identifying local antibiotic situations and providing evidence for the development of empirical guideline therapies. Consequently, continuous monitoring is crucial, particularly in severe or critical situations, such as sepsis and COVID-19. This will help in clinician decision-making management to prevent inappropriate antibiotic use. Previous studies have reported the prevalence of antibiotic resistance during the COVID-19 pandemic. 11,12 There is limited information about changes before and during the pandemic, particularly in Indonesia. Therefore, a laboratory-based surveillance study was conducted at a single tertiary hospital, Dr. Hasan Sadikin Hospital (RSHS), to describe the changes in the prevalence of antibiotic resistance among patients with proven bacterial infections during the following time frames: (a) before the COVID-19 pandemic (2019), (b) early (2020), and (c) the peak phase (2021). Materials and Methods Study Design A retrospective descriptive study was conducted to review the medical records of hospitalized patients diagnosed with sepsis and COVID-19 according to the International Classification of Disease 10th Revision (ICD-10). The predefined periods were 1 January–31 December 2019 and 1 March 2020–31 December 2021, while the study was conducted at a tertiary hospital, RSHS. This hospital is the main province hospital in the western part of Java Island, with a maximum capacity of 1000 bed inpatients. It acted as a referral hospital, but later during the pandemic, the status changed to a primary referral COVID-19 hospital. The medical records were obtained and extracted from the hospital information system (Sistem Informasi Rumah Sakit Dr. Hasan Sadikin, Bandung, Indonesia) following the standard operating procedure. Subsequently, this list was merged with culture and antibiotic susceptibility test data from a laboratory information system (HCLAB Micro, Sysmex, Asia Pacific). Merged data were screened for population eligibility, and medical records were manually searched to select patients according to the inclusion and exclusion criteria. Baseline demographic data, including age, sex, type of ward, clinical outcome, bacterial species, and antibiotic susceptibility, were also collected. Time Frame Before the Pandemic: Sepsis Population The medical records of hospitalized patients diagnosed with sepsis according to the ICD-10 codes A40-A41.9 between 1 January and 31 December 2019 were identified, screened, and merged with culture data. Merged data were retrospectively hand-searched to identify the inclusion/exclusion criteria. The inclusion criteria were as follows: ( 1 ) adult patients aged 18 years or older, ( 2 ) admitted to intensive or nonintensive wards, and ( 3 ) who submitted any clinical specimens (blood, sputum, or urine) during hospital admission for culture. The exclusion criteria were as follows: ( 1 ) had conditions, including HIV, malignancy, use of immunosuppressant drugs, or autoimmune diseases (systemic lupus erythematosus, rheumatoid arthritis); and ( 2 ) had commensal bacteria, namely, Viridans group streptococci , Micrococcus sp., and Bacillus sp., as well as fungi. Time Frame During the Pandemic: COVID-19 Population Similar to the sepsis population, the medical records of hospitalized patients diagnosed with COVID-19 according to the ICD-10 code U70.1 between 1 March 2020 and 31 December 2021 were identified, screened, and merged with culture data. Subsequently, merged data were manually searched to identify the inclusion/exclusion criteria. The inclusion criteria were as follows: ( 1 ) adult patients aged 18 years or older, ( 2 ) patients whose clinical specimens (blood, urine, sputum) were submitted for culture during hospital admission, and ( 3 ) patients who were admitted to intensive or nonintensive wards. The exclusion criteria were ( 1 ) patients who were rehospitalized during the same period and ( 2 ) patients with commensal bacteria, namely, Viridans group streptococci , Micrococcus sp., Bacillus sp., and fungi growing on clinical specimens. Among the COVID-19 population, a specific time frame was applied and categorized into three periods according to demographic distribution by the Indonesian Ministry of Health 13,14 : ( 1 ) the early phase, 1 March–30 November 2020; ( 2 ) the peak phase, 1 December 2020–30 June 2021; and ( 3 ) the late phase, 1 July–31 December 2021. Cumulative Antibiotic Resistance Report The clinical specimen collection procedure for the study population followed the hospital laboratory protocol and WHO recommendations. 15 Bacterial identification and antibiotic susceptibility testing (AST) were performed using an automatic microbiology analyzer (Vitek2 Compact, Biomerieux, France). The protocol followed the WHO and Clinical and Laboratory Standards Institute (CLSI) guidelines. 15,16 To fulfill the surveillance report according to the WHO recommendation, the dedicated software WHONET 5.6 (WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Boston, USA) was used to produce the cumulative reports of organisms and ASTs. This includes the surveillance rules according to the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS). 16,17 The antibiotics reported in this study were selected based on the American Thoracic Society Guidelines for Pneumonia and CLSI Guidelines for gram-negative (GNB) and gram-positive Bacteria (GPB). 18,19 The antibiotics used for GPB were ampicillin/sulbactam, ceftriaxone, ciprofloxacin, gentamicin, oxacillin, and vancomycin. Moreover, the antibiotics used for GNB were amikacin, ampicillin/sulbactam, aztreonam, cefepime, ceftriaxone, ceftazidime, ciprofloxacin, gentamicin, and meropenem. In this study, antibiotics with intermediate results were interpreted as resistant. The selected antibiotic resistance percentage was considered high when it was equal to or greater than 20%. 20 Data Analysis The data were entered into Microsoft Excel 2013 (Microsoft Corp.) and merged using the statistical software STATA 12.0 (Stata, Texas, USA). The characteristic data were categorized into age groups according to Peng et al. 21 The type of ward was categorized into intensive and nonintensive, while clinical outcome was classified into surviving and nonsurviving. The prevalence of resistance to selected antibiotics among the study population was defined as the percentage of bacteria tested, stratified by gram-bacteria type and time (before, during the early, and peak phases of COVID-19) with changes over time. Patient characteristics and cumulative antibiotic resistance results were summarized as frequencies and percentages using STATA 12.0 and WHONET 5.6. Results Population characteristics During the study, 596 adult patients were identified as having sepsis (before the pandemic), and 2786 were confirmed to have COVID-19 (during the pandemic). In the sepsis population, 77.7% (463 specimens) were submitted for culture, while only 26.3% (732 specimens) were submitted for culture in the COVID-19 population. The percentage of positive cultures in the sepsis population was greater than that in the COVID-19 population, with values of 51.6% and 29.2%, respectively. GNB isolates were predominantly found during the observation period ( Figure 1 ). Overall, 41.2% - 47.3% of the adult patients were in the middle-aged group. In contrast, the occupancy of intensive care for patients with bacterial infections was low during the COVID-19 pandemic, at 12.6%, 19.2%, and 23.6% in the early, peak, and late phases, respectively. This also corresponded with the survival rate among this population, ranging from 82.4% - 95.8%, which was categorized as surviving and discharged from hospitalization ( Table 1 ). Figure 1. Study flowchart. Notes: ($), number of submitted cultures from any clinical suspicion, including blood, urine, sputum; (*), multiple isolates can be identified among submitted specimens. Table 1. Patient Characteristics Based on Submitted Specimens. Variable Before COVID-19 n=239 During COVID-19 n=214 Early Phase n=78 Peak Phase n=119 Late Phase n=17 n % n % n % n % Age Group Young Age 6 2.5 5 6.4 12 10.1 2 11.8 Adult 48 20.1 17 21.8 19 15.9 0 0.0 Middle Age 113 47.3 35 44.8 65 54.6 7 41.2 Old Age 72 30.1 21 27.0 23 19.4 8 47.0 Ward Type Intensive 132 55.2 15 19.2 15 12.6 4 23.5 Nonintensive 107 44.8 63 80.8 104 87.4 13 76.5 Clinical Outcome Surviving 94 39.3 71 91.0 114 95.8 14 82.4 Nonsurviving 145 60.7 7 9.0 5 4.2 3 17.6 Notes: n, number of patients based on the submitted specimen; %, percentage; young age, 18-25 years; adult, 26-44 years; middle-aged, 45-59 years; old age, 60 years; before the pandemic, 1 January–31 December 2019; early phase, 1 March–30 November 2020; peak phase, 1 December 2020–30 June 2021; late phase, 1 July–31 December 2021. Bacterial identification profiles GNB were predominantly observed before and during the COVID-19 pandemic. Critical priority bacteria, including Klebsiella pneumoniae (31.7%, 531 out of 1673 GNB isolates), Acinetobacter baumanii (20.3%, 340 out of 1673 GNB isolates), and Pseudomonas aeruginosa (10.8%, 180 out of 1673 GNB isolates), were commonly identified among both populations as the etiology of infection. Environmentally related GNB, such as Stenotrophomonas maltophilia and Burkholderia cepacia , were more commonly found during the COVID-19 pandemic than during the previous period. Moreover, coagulase-negative Staphylococci (48.5%, 161 out of 331 GPB isolates) were the predominant bacteria among both populations. Streptococcus sp. was more commonly identified during the COVID-19 pandemic than during the previous period. In general, there were no changes in the distribution of bacterial pathogens before or during the COVID-19 pandemic, particularly in GNB, the most common pathogen in hospital settings ( Figure 2 ). Figure 2. Bacterial distribution . Notes: before the pandemic, 1 January–31 December 2019; early phase, 1 March–30 November 2020; peak phase, 1 December 2020–30 June 2021; late phase, 1 July–31 December 2021; x-axis, number of isolates identified; y-axis, organism identified stratified by gram type. Changes in Antibiotic Resistance Based on the results, there were changes in antibiotic resistance against GNB based on the time frame before and during COVID-19 ( Table 2 ). Before the pandemic, there was high-range resistance among GNB isolates (above 20%), except for amikacin (16.7%). In the early phase of COVID-19, the percentage of patients with antibiotic resistance decreased compared with that in the previous period, particularly for primary empirical pneumonia treatment. This effect was detected for cephalosporin (35.4-68.1% vs 27.8-43.8%), beta-lactam combinations (36.8-68.7% vs 26.8-52.5%), fluoroquinolone (58.3% vs 28.9%) and monobactam (63.2% vs 20.4%). Subsequently, elevated resistance to beta-lactam combinations (28.6-56.8% vs 26.8-52.5%), antipseudomonal cephalosporins (ceftazidime, 34.7% vs 26.8%; cefepime, 27.8% vs 30.6%), fluoroquinolone (38.9% vs 41.9%), monobactam (20.4% vs 29.9%) and carbapenem (26.4% vs 27.1%) was detected among GNB strains during the peak phase compared to the early phase. In the late phase, high resistance against GNB was observed, particularly to restricted intravenous broad-spectrum antibiotics such as ceftazidime, cefepime, piperacillin-tazobactam, amikacin, and meropenem. The lowest prevalence of extended-spectrum beta-lactamases (ESBLs) among GNB occurred in the early phase of COVID-19 (20.4%). Moreover, increasing carbapenem resistance among GNB strains was recorded throughout the study period, ranging between 22.9% and 47.2%. In the GPB, the greatest changes in antibiotic resistance were found in the late phase of COVID-19 (above 70%), except for vancomycin (0%) ( Table 3 ). The resistance of GNB to fluoroquinolone (ciprofloxacin) ranged from 56.8% to 87.5% before and during the COVID-19 pandemic. Furthermore, increased resistance to oxacillin, a surrogate marker for methicillin-resistant staphylococci, particularly coagulase-negative staphylococci, was observed during the peak and late phases of COVID-19, with values of 70.8% and 100%, respectively. Vancomycin resistance against GPB was low, ranging between 0% and 1.1%. Table 2. Percent antibiotic resistance of GNB before and during COVID-19 pandemic. Antibiotic Class Antibiotic Agent Before COVID-19 During COVID-19 2019 2020 2021 Early Phase Peak Phase Late Phase n= 144 n= 232 n= 1096 n= 201 n %R n %R n %R n %R Beta-lactam combination Ampicillin-Sulbactam 144 68.7 232 52.5 1096 56.8 201 37.5 Piperacillin-Tazobactam 144 36.8 232 26.8 1096 28.6 201 43.4 Cephalosporin Ceftriaxone 144 68.1 232 43.8 1096 43.1 201 36.6 Ceftazidime 144 48.6 232 34.7 1096 36.8 201 39.6 Cefepime 144 35.4 232 27.8 1096 30.6 201 49.1 Monobactam Aztreonam 144 63.2 232 20.4 1096 29.9 201 52.9 Fluoroquinolone Ciprofloxacin 144 58.3 232 38.9 1096 41.9 201 37.7 Aminoglycoside Amikacin 144 16.7 232 9.7 1096 15.5 201 77.4 Gentamicin 144 42.4 232 31.9 1096 34.9 201 41.5 Folate pathway antagonist Trimethoprim-sulfamethoxazole 144 55.6 232 22.1 1096 35.0 201 55.6 Carbapenem Meropenem 144 22.9 232 26.4 1096 27.1 201 47.2 Notes: n, number of isolates tested for a certain antibiotic; %R, percentage of antibiotic resistance; before the pandemic, 1 January–31 December 2019; early phase, 1 March–30 November 2020; peak phase, 1 December 2020–30 June 2021; late phase, 1 July–31 December 2021. Table 3. Percent antibiotic resistance of GPB before and during the COVID-19 pandemic. Antibiotic Class Antibiotic Agent Before COVID-19 During COVID-19 2019 2020 2021 Early Phase Peak Phase Late Phase n= 95 n= 59 n= 136 n=42 n %R n %R n %R n %R Beta-lactam combination Ampicillin-Sulbactam 95 60.0 59 61.1 136 64.5 42 100 Fluoroquinolone Ciprofloxacin 95 56.8 59 70.6 136 77.4 42 87.5 Cephalosporin Ceftriaxone 95 63.2 59 30.0 136 42.5 42 72.7 Folate pathway antagonist Trimethoprim-sulfamethoxazole 95 43.2 59 46.7 136 37.5 42 85.7 Penicillin Oxacillin 95 65.3 59 64.3 136 70.8 42 100 Aminoglycoside Gentamicin 95 38.9 59 31.2 136 35.7 42 85.7 Glycopeptide Vancomycin 95 1.1 59 0.0 136 0.0 42 0.0 Notes: n, number of isolates tested for a certain antibiotic; %R, percentage of antibiotic resistance; before the pandemic, 1 January–31 December 2019; early phase, 1 March–30 November 2020; peak phase, 1 December 2020–30 June 2021; late phase, 1 July–31 December 2021 Discussion The COVID-19 pandemic has sparked global concern and raised awareness about antibiotic resistance. This study revealed changes in antibiotic resistance before and during the COVID-19 pandemic. These changes can be influenced by several factors, including health regulation, antibiotic usage, health workers, and hospital equipment, as reported previously. 9 In the early phase of COVID-19, the lowest prevalence of antibiotic resistance was observed against several GNB isolates compared to other phases. During this phase, several health regulations were established, including social restrictions, self-awareness, hand hygiene, and the use of medical masks. These regulations were deemed effective for reducing the transmission of infection and mitigating the spread of multidrug-resistant organisms, particularly in the hospital setting. 10,22 As the pandemic progressed, national or international guidelines for COVID-19 were published, recommending the use of antibiotics for patient management. 23,24 The empirical use of antibiotics has impacted and driven increasing resistance, as observed in the peak and late phases. However, this is unavoidable since COVID-19 is a risk factor for the development of healthcare-associated infections (HAIs) and multidrug-resistant (MDR) pathogens. Prolonged hospital stays and increased usage of equipment also contributed to the development of HAIs. These complex factors collectively contribute to the development of multidrug-resistant bacteria, which leads to treatment failure and increased mortality. 11 However, issues with HAI were already present before the pandemic, as observed in the sepsis population. Both populations in this study showed a similar frequency of bacteria identified across all periods, with GNB, including Klebsiella pneumoniae, Acinetobacter baumanii, Escherichia coli, and Pseudomonas aeruginosa , as the critical priority of hospital-associated pathogens 25 . Environmentally related GNB, such as Stenotrophomonas maltophilia and Burkholderia cepacia, were also detected . Although limited information was provided for this surveillance study, the age group of patients for both populations was recorded. The middle-aged and older groups were more prone to bacterial infection among the sepsis and COVID-19 populations. The aging of the immune response, or immunosenescence, is the dysregulated state of an aged immune system, including short-lived memory responses, a defective response to new antigens, a greater disposition of autoimmunity, and the development of chronic low-grade systemic inflammation. As described in a previous study 26 , both sepsis and COVID-19 resulted in severe inflammation associated with the activation and proliferation of lymphocytes, including cytotoxic T and natural killer cells. This reaction is related to the general immune response to viral infection or to neutrophil activation and recruitment during bacterial infection. Subsequently, with the secretion of antibodies or cytokines/lymphokines (interferon), the immune system eliminates the infected cell and performs viral clearance. 26 Immunosenescence enhances the severe dysregulation of immune responses, leading to a severe hyperinflammatory state. This state also facilitates the type of bacteria, as reported in a previous study. Based on previous study results, more severe responses were observed in GNB sepsis patients than in GPB sepsis patients because lipopolysaccharide may induce alterations in complement protein levels. 27 Moreover, in this age group, an impaired immune response contributes to the development of antibiotic resistance. This hypothesis has been shown in a previous study indicating that a synergism between the immune response and antibiotic drug concentrations reduces the development of resistance to the pathogen. For example, in situations where resistance has not yet developed at the beginning of the treatment period, an immune response helps to eradicate and minimize the chance of creating a resistant pathogen. Correspondingly, an impaired immune response creates selective pressure and leads to the development of resistant pathogens. 28–30 This study also revealed rapid changes in the resistance to meropenem and oxacillin, which serve as surrogate markers for carbapenem-resistant GNB and methicillin-resistant GPB, respectively. As reported previously, there has been an increase in MDR pathogens, including carbapenem-resistant Acinetobacter baumanii (CRAB), ESBL-producing Enterobacterales , carbapenem-resistant Enterobacterales (CRE), MDR Pseudomonas sp. and methicillin-resistant Staphylococci . 9,27 The surge in COVID-19 admissions, many of which require mechanical ventilation, suggests the occurrence of MDR outbreaks. Furthermore, the lack of up-to-date guidance, shortage of personnel-protective equipment, lack of infection prevention due to increased workload, and decreased time for patient care caused by healthcare personnel shortages have contributed to rapid changes in resistance. Previous studies also reported the time lag between antibiotic use and the increase in the number of resistant pathogens among hospitalized patients. Data obtained across all pathogens (GNB or GPB) showed that the development of antibiotic resistance tends to occur over 0 to 6 months following exposure to antibiotics. 31 Therefore, the data agreed with the rapid changes in antibiotic resistance before and during the COVID-19 pandemic. There are several limitations of the study. First, the potential for selection bias for both populations was unavoidable due to the use of a laboratory-based surveillance approach. 32 The clinical-symptom diagnosis approach was adapted and used as part of laboratory surveillance based on a previous study to minimize selection bias. 33 However, the selection of a culture based on clinician decisions may still introduce bias to this study. Second, due to the limited information available, antibiotic resistance was not stratified into other categories, including disease severity, type of infection (community or hospital-onset), hospital equipment use such as mechanical ventilation, and urinary catheterization. Third, this study was designed as a surveillance report; hence, statistical analysis to measure the effect of changes in antibiotic resistance could not be performed. Conclusion In conclusion, this study revealed changes in antibiotic resistance before and during the COVID-19 pandemic for both GNB and GPB. High antibiotic use and age-related immune response (immunosenescence) were identified as factors contributing to these rapid changes. This underscores the need for strengthened recommendations in combatting HAIs and MDR pathogens. These recommendations included ( 1 ) having a sustainable antibiotic resistance and antibiotic usage surveillance system at the local (hospital) and national levels (country), ( 2 ) continuous monitoring for prevention infection programs, together with antimicrobial stewardship programs in the hospital, and ( 3 ) enhancing knowledge and skills among healthcare personnel about HAI and MDR pathogens, as well as treatment options. Abbreviations World Health Organization (WHO); severe acute respiratory coronavirus 2 (SARS-CoV-2); Dr. Hasan Sadikin Hospital (RSHS); International Classification of Disease 10th Revision (ICD-10); antibiotic susceptibility testing (AST); Clinical and Laboratory Standards Institute (CLSI); Global Antimicrobial Resistance and Use Surveillance System (GLASS); gram negative bacteria (GNB); gram positive bacteria (GPB); healthcare-associated infections (HAI); multi-drug resistance (MDR); carbapenem-resistant Acinetobacter baumanii (CRAB); carbapenem-resistant Enterobacterales (CRE) Declarations Ethical approval and consent to participate This study was conducted under the Declaration of Helsinki, and the study protocol was approved by the Ethics Committee of Dr. Hasan Sadikin General Hospital (LB.02.01/X.6.5/94/2022). Informed consent was not required to obtain data from the hospital or laboratory information system. Therefore, the ethics committee waived the need for written patient consent. Consent for publication Not applicable Availability of data and materials All the data generated or analyzed during this study are included in this published article. Competing interests The authors declare that they have no competing interests. Funding Not applicable Authors’ contributions All the authors contributed equally to this research. Acknowledgments The authors are grateful to (1) the healthcare workers at Dr. Hasan Sadikin General Hospital for supporting data availability; (2) DRPM Universitas Padjadjaran for assistance and financial support; and (3) all participating subjects at Dr. Hasan Sadikin General Hospital between 2019 and 2021 for data contribution. References Ginting F, Sugianli AK, Barimbing M, Ginting N, Mardianto M, Kusumawati RL, et al. Appropriateness of diagnosis and antibiotic use in sepsis patients admitted to a tertiary hospital in Indonesia. 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Exploring the role of the immune response in preventing antibiotic resistance. Journal of Theoretical Biology. 2009;256(4):655–62. Hasan CM, Dutta D, Nguyen ANT. Revisiting Antibiotic Resistance: Mechanistic Foundations to Evolutionary Outlook. Antibiotics. 2021;11(1):40. Poku E, Cooper K, Cantrell A, Harnan S, Sin MA, Zanuzdana A, et al. A systematic review of the time lag between antibiotic use and rise of resistant pathogens among hospitalized adults in Europe. JAC-Antimicrobial Resistance. 2022;5(1):dlad001. Sugianli AK, Ginting F, Kusumawati RL, Parwati I, de Jong MD, van Leth F, et al. Laboratory-based versus population-based surveillance of antimicrobial resistance to inform empirical treatment for suspected urinary tract infection in Indonesia. PLOS ONE. 2020;15(3):e0230489. Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A. Demonstration of the Weighted-Incidence Syndromic Combination Antibiogram: An Empiric Prescribing Decision Aid. Infect Control Hosp Epidemiol. 2012;33(4):381–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4430480","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304485822,"identity":"cb97c51a-492f-43dd-a625-744594b4a3d0","order_by":0,"name":"Adhi Kristianto Sugianli","email":"data:image/png;base64,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","orcid":"","institution":"Padjadjaran University","correspondingAuthor":true,"prefix":"","firstName":"Adhi","middleName":"Kristianto","lastName":"Sugianli","suffix":""},{"id":304485827,"identity":"99fdbd7c-2e75-4446-b0ec-7d36b98cfe84","order_by":1,"name":"Rachel Amelia","email":"","orcid":"","institution":"Padjadjaran University","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Amelia","suffix":""},{"id":304485829,"identity":"81a78025-b2e2-4ad7-9ea9-d019fe286636","order_by":2,"name":"Jerry Tjoanatan","email":"","orcid":"","institution":"Padjadjaran University","correspondingAuthor":false,"prefix":"","firstName":"Jerry","middleName":"","lastName":"Tjoanatan","suffix":""},{"id":304485831,"identity":"73940851-78ec-4276-99d8-0b5ba7428fa5","order_by":3,"name":"Anna Tjandrawati","email":"","orcid":"","institution":"Padjadjaran University","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Tjandrawati","suffix":""},{"id":304485833,"identity":"3f94606c-7209-444d-9196-d5eec0c1a91d","order_by":4,"name":"Dewi Kartika Turbawaty","email":"","orcid":"","institution":"Padjadjaran University","correspondingAuthor":false,"prefix":"","firstName":"Dewi","middleName":"Kartika","lastName":"Turbawaty","suffix":""}],"badges":[],"createdAt":"2024-05-16 10:36:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4430480/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4430480/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57720796,"identity":"67381816-74e3-4a8e-9807-87bc94005ef7","added_by":"auto","created_at":"2024-06-04 18:54:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1259390,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart. Notes: ($), number of submitted cultures from any clinical suspicion, including blood, urine, sputum; (*), multiple isolates can be identified among submitted specimens.\u003c/p\u003e","description":"","filename":"20240516BMCFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4430480/v1/7e195707aa54791c926d94a3.jpg"},{"id":57720795,"identity":"6f277e1f-3e9b-46f5-b2bd-2a1117b49cbe","added_by":"auto","created_at":"2024-06-04 18:54:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":420627,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial distribution\u003cstrong\u003e. \u003c/strong\u003eNotes: before the pandemic, 1 January–31 December 2019; early phase, 1 March–30 November 2020; peak phase, 1 December 2020–30 June 2021; late phase, 1 July–31 December 2021; x-axis, number of isolates identified; y-axis, organism identified stratified by gram type.\u003c/p\u003e","description":"","filename":"20240516BMCFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4430480/v1/4f2dfc3725ad9ee032eb11e5.jpg"},{"id":58934359,"identity":"55b254d8-729f-4f41-87c7-36d2d58c7a0b","added_by":"auto","created_at":"2024-06-24 09:44:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2367811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4430480/v1/0e2cd36d-8ad0-43c7-94c0-624609c1d063.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changes in Antibiotic Resistance Before and During the COVID-19 Pandemic: Retrospective Surveillance Study in a Single Indonesian Tertiary Hospital","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntibiotic resistance is recognized as a public health threat and has received great attention due to its significant impact on mortality and increased economic burdens. The incidence is driven by inappropriate use of empiric antibiotics, which often occur in severe or critical situations, such as sepsis.\u003csup\u003e1\u003c/sup\u003e This life-threatening condition is associated with a dysregulated immune response to infection and leads to organ dysfunction. In 2020, the World Health Organization (WHO) reported that approximately 20% of global deaths were due to sepsis.\u003csup\u003e2\u003c/sup\u003e Subsequently, in the year, a new rapidly spreading respiratory disease, namely, COVID-19, was declared a global pandemic disease caused by severe acute respiratory coronavirus 2 (SARS-CoV-2).\u003csup\u003e3\u003c/sup\u003e Similar to sepsis, COVID-19 also results in a dysregulated immune response and organ dysfunction. Several factors have contributed to the use of antibiotics for treating COVID-19. These include (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) rapid progression of the disease, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) limited information on disease management, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) difficulties in differentiating between COVID-19 and bacterial pneumonia.\u003csup\u003e4\u0026ndash;8\u003c/sup\u003e Moreover, both sepsis and COVID-19 patients require long-term hospitalization, increasing the risk of hospital-acquired infections, often bacteria in nature.\u003csup\u003e9,10\u003c/sup\u003e This suggests that the complex factor of sepsis and COVID-19 may contribute to the development of antibiotic resistance.\u003c/p\u003e \u003cp\u003eSurveillance of antibiotics has been identified as one of the strategy pillars for combatting resistance by the WHO. This strategy is essential for identifying local antibiotic situations and providing evidence for the development of empirical guideline therapies. Consequently, continuous monitoring is crucial, particularly in severe or critical situations, such as sepsis and COVID-19. This will help in clinician decision-making management to prevent inappropriate antibiotic use. Previous studies have reported the prevalence of antibiotic resistance during the COVID-19 pandemic.\u003csup\u003e11,12\u003c/sup\u003e There is limited information about changes before and during the pandemic, particularly in Indonesia. Therefore, a laboratory-based surveillance study was conducted at a single tertiary hospital, Dr. Hasan Sadikin Hospital (RSHS), to describe the changes in the prevalence of antibiotic resistance among patients with proven bacterial infections during the following time frames: (a) before the COVID-19 pandemic (2019), (b) early (2020), and (c) the peak phase (2021).\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eA retrospective descriptive study was conducted to review the medical records of hospitalized patients diagnosed with sepsis and COVID-19 according to the International Classification of Disease 10th Revision (ICD-10). The predefined periods were 1 January\u0026ndash;31 December 2019 and 1 March 2020\u0026ndash;31 December 2021, while the study was conducted at a tertiary hospital, RSHS. This hospital is the main province hospital in the western part of Java Island, with a maximum capacity of 1000 bed inpatients. It acted as a referral hospital, but later during the pandemic, the status changed to a primary referral COVID-19 hospital.\u003c/p\u003e \u003cp\u003eThe medical records were obtained and extracted from the hospital information system (Sistem Informasi Rumah Sakit Dr. Hasan Sadikin, Bandung, Indonesia) following the standard operating procedure. Subsequently, this list was merged with culture and antibiotic susceptibility test data from a laboratory information system (HCLAB Micro, Sysmex, Asia Pacific). Merged data were screened for population eligibility, and medical records were manually searched to select patients according to the inclusion and exclusion criteria. Baseline demographic data, including age, sex, type of ward, clinical outcome, bacterial species, and antibiotic susceptibility, were also collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTime Frame Before the Pandemic: Sepsis Population\u003c/h2\u003e \u003cp\u003eThe medical records of hospitalized patients diagnosed with sepsis according to the ICD-10 codes A40-A41.9 between 1 January and 31 December 2019 were identified, screened, and merged with culture data. Merged data were retrospectively hand-searched to identify the inclusion/exclusion criteria. The inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) adult patients aged 18 years or older, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) admitted to intensive or nonintensive wards, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) who submitted any clinical specimens (blood, sputum, or urine) during hospital admission for culture. The exclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) had conditions, including HIV, malignancy, use of immunosuppressant drugs, or autoimmune diseases (systemic lupus erythematosus, rheumatoid arthritis); and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) had commensal bacteria, namely, \u003cem\u003eViridans group streptococci\u003c/em\u003e, \u003cem\u003eMicrococcus\u003c/em\u003e sp., and \u003cem\u003eBacillus\u003c/em\u003e sp., as well as fungi.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTime Frame During the Pandemic: COVID-19 Population\u003c/h2\u003e \u003cp\u003eSimilar to the sepsis population, the medical records of hospitalized patients diagnosed with COVID-19 according to the ICD-10 code U70.1 between 1 March 2020 and 31 December 2021 were identified, screened, and merged with culture data. Subsequently, merged data were manually searched to identify the inclusion/exclusion criteria. The inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) adult patients aged 18 years or older, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) patients whose clinical specimens (blood, urine, sputum) were submitted for culture during hospital admission, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) patients who were admitted to intensive or nonintensive wards. The exclusion criteria were (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients who were rehospitalized during the same period and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) patients with commensal bacteria, namely, \u003cem\u003eViridans group streptococci\u003c/em\u003e, \u003cem\u003eMicrococcus\u003c/em\u003e sp., \u003cem\u003eBacillus\u003c/em\u003e sp., and fungi growing on clinical specimens. Among the COVID-19 population, a specific time frame was applied and categorized into three periods according to demographic distribution by the Indonesian Ministry of Health \u003csup\u003e13,14\u003c/sup\u003e: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the early phase, 1 March\u0026ndash;30 November 2020; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the peak phase, 1 December 2020\u0026ndash;30 June 2021; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the late phase, 1 July\u0026ndash;31 December 2021.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCumulative Antibiotic Resistance Report\u003c/h2\u003e \u003cp\u003eThe clinical specimen collection procedure for the study population followed the hospital laboratory protocol and WHO recommendations.\u003csup\u003e15\u003c/sup\u003e Bacterial identification and antibiotic susceptibility testing (AST) were performed using an automatic microbiology analyzer (Vitek2 Compact, Biomerieux, France). The protocol followed the WHO and Clinical and Laboratory Standards Institute (CLSI) guidelines.\u003csup\u003e15,16\u003c/sup\u003e To fulfill the surveillance report according to the WHO recommendation, the dedicated software WHONET 5.6 (WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Boston, USA) was used to produce the cumulative reports of organisms and ASTs. This includes the surveillance rules according to the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS).\u003csup\u003e16,17\u003c/sup\u003e The antibiotics reported in this study were selected based on the American Thoracic Society Guidelines for Pneumonia and CLSI Guidelines for gram-negative (GNB) and gram-positive Bacteria (GPB).\u003csup\u003e18,19\u003c/sup\u003e The antibiotics used for GPB were ampicillin/sulbactam, ceftriaxone, ciprofloxacin, gentamicin, oxacillin, and vancomycin. Moreover, the antibiotics used for GNB were amikacin, ampicillin/sulbactam, aztreonam, cefepime, ceftriaxone, ceftazidime, ciprofloxacin, gentamicin, and meropenem. In this study, antibiotics with intermediate results were interpreted as resistant. The selected antibiotic resistance percentage was considered high when it was equal to or greater than 20%.\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe data were entered into Microsoft Excel 2013 (Microsoft Corp.) and merged using the statistical software STATA 12.0 (Stata, Texas, USA). The characteristic data were categorized into age groups according to Peng et al.\u003csup\u003e21\u003c/sup\u003e The type of ward was categorized into intensive and nonintensive, while clinical outcome was classified into surviving and nonsurviving. The prevalence of resistance to selected antibiotics among the study population was defined as the percentage of bacteria tested, stratified by gram-bacteria type and time (before, during the early, and peak phases of COVID-19) with changes over time. Patient characteristics and cumulative antibiotic resistance results were summarized as frequencies and percentages using STATA 12.0 and WHONET 5.6.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePopulation characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study, 596 adult patients were identified as having sepsis (before the pandemic), and 2786 were confirmed to have COVID-19 (during the pandemic). In the sepsis population, 77.7% (463 specimens) were submitted for culture, while only 26.3% (732 specimens) were submitted for culture in the COVID-19 population. The percentage of positive cultures in the sepsis population was greater than that in the COVID-19 population, with values of 51.6% and 29.2%, respectively. GNB isolates were predominantly found during the observation period (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Overall, 41.2% - 47.3% of the adult patients were in the middle-aged group. In contrast, the occupancy of intensive care for patients with bacterial infections was low during the COVID-19 pandemic, at 12.6%, 19.2%, and 23.6% in the early, peak, and late phases, respectively. This also corresponded with the survival rate among this population, ranging from 82.4% - 95.8%, which was categorized as surviving and discharged from hospitalization (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u0026nbsp;\u003c/strong\u003eStudy flowchart. Notes: ($), number of submitted cultures from any clinical suspicion, including blood, urine, sputum; (*), multiple isolates can be identified among submitted specimens.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003ePatient Characteristics Based on Submitted Specimens.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore COVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003en=239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.494949494949495%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuring COVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003en=214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.416666666666664%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eEarly Phase\u003c/p\u003e\n \u003cp\u003en=78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePeak Phase\u003c/p\u003e\n \u003cp\u003en=119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.25%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLate Phase\u003c/p\u003e\n \u003cp\u003en=17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.309859154929576%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.67605633802817%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.267605633802816%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.450704225352112%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.67605633802817%\" valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eYoung Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eOld Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWard Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eIntensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eNonintensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e80.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e87.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e76.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eSurviving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e39.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e91.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e95.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e82.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eNonsurviving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e60.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: n, number of patients based on the submitted specimen; %, percentage; young age, 18-25 years; adult, 26-44 years; middle-aged, 45-59 years; old age, 60 years; before the pandemic, 1 January\u0026ndash;31 December 2019; early phase, 1 March\u0026ndash;30 November 2020; peak phase, 1 December 2020\u0026ndash;30 June 2021; late phase, 1 July\u0026ndash;31 December 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBacterial identification profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGNB were predominantly observed before and during the COVID-19 pandemic. Critical priority bacteria, including \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (31.7%, 531 out of 1673 GNB isolates), \u003cem\u003eAcinetobacter baumanii\u003c/em\u003e (20.3%, 340 out of 1673 GNB isolates), and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (10.8%, 180 out of 1673 GNB isolates), were commonly identified among both populations as the etiology of infection. Environmentally related GNB, such as \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e and \u003cem\u003eBurkholderia cepacia\u003c/em\u003e, were more commonly found during the COVID-19 pandemic than during the previous period. Moreover, coagulase-negative Staphylococci (48.5%, 161 out of 331 GPB isolates) were the predominant bacteria among both populations. \u003cem\u003eStreptococcus\u003c/em\u003e sp. was more commonly identified during the COVID-19 pandemic than during the previous period. In general, there were no changes in the distribution of bacterial pathogens before or during the COVID-19 pandemic, particularly in GNB, the most common pathogen in hospital settings (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2.\u0026nbsp;\u003c/strong\u003eBacterial distribution\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eNotes: before the pandemic, 1 January\u0026ndash;31 December 2019; early phase, 1 March\u0026ndash;30 November 2020; peak phase, 1 December 2020\u0026ndash;30 June 2021; late phase, 1 July\u0026ndash;31 December 2021; x-axis, number of isolates identified; y-axis, organism identified stratified by gram type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in Antibiotic Resistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the results, there were changes in antibiotic resistance against GNB based on the time frame before and during COVID-19 (\u003cstrong\u003eTable 2\u003c/strong\u003e). Before the pandemic, there was high-range resistance among GNB isolates (above 20%), except for amikacin (16.7%). In the early phase of COVID-19, the percentage of patients with antibiotic resistance decreased compared with that in the previous period, particularly for primary empirical pneumonia treatment. This effect was detected for cephalosporin (35.4-68.1% vs 27.8-43.8%), beta-lactam combinations (36.8-68.7% vs 26.8-52.5%), fluoroquinolone (58.3% vs 28.9%) and monobactam (63.2% vs 20.4%). Subsequently, elevated resistance to beta-lactam combinations (28.6-56.8% vs 26.8-52.5%), antipseudomonal cephalosporins (ceftazidime, 34.7% vs 26.8%; cefepime, 27.8% vs 30.6%), fluoroquinolone (38.9% vs 41.9%), monobactam (20.4% vs 29.9%) and carbapenem (26.4% vs 27.1%) was detected among GNB strains during the peak phase compared to the early phase. In the late phase, high resistance against GNB was observed, particularly to restricted intravenous broad-spectrum antibiotics such as ceftazidime, cefepime, piperacillin-tazobactam, amikacin, and meropenem. The lowest prevalence of extended-spectrum beta-lactamases (ESBLs) among GNB occurred in the early phase of COVID-19 (20.4%). Moreover, increasing carbapenem resistance among GNB strains was recorded throughout the study period, ranging between 22.9% and 47.2%.\u003c/p\u003e\n\u003cp\u003eIn the GPB, the greatest changes in antibiotic resistance were found in the late phase of COVID-19 (above 70%), except for vancomycin (0%) (\u003cstrong\u003eTable 3\u003c/strong\u003e). The resistance of GNB to fluoroquinolone (ciprofloxacin) ranged from 56.8% to 87.5% before and during the COVID-19 pandemic. Furthermore, increased resistance to oxacillin, a surrogate marker for methicillin-resistant staphylococci, particularly coagulase-negative staphylococci, was observed during the peak and late phases of COVID-19, with values of 70.8% and 100%, respectively. Vancomycin resistance against GPB was low, ranging between 0% and 1.1%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePercent antibiotic resistance of GNB before and during COVID-19 pandemic.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.387755102040817%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotic Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotic Agent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore COVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.857142857142854%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuring\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.074074074074073%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.074074074074073%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.851851851851855%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.70731707317073%\" colspan=\"2\"\u003e\n \u003cp\u003eEarly Phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.58536585365854%\" colspan=\"2\"\u003e\n \u003cp\u003ePeak\u003c/p\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.70731707317073%\" colspan=\"2\"\u003e\n \u003cp\u003eLate Phase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.074074074074073%\" colspan=\"2\"\u003e\n \u003cp\u003en= 144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.074074074074073%\" colspan=\"2\"\u003e\n \u003cp\u003en= 232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.77777777777778%\" colspan=\"2\"\u003e\n \u003cp\u003en= 1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.074074074074073%\" colspan=\"2\"\u003e\n \u003cp\u003en= 201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e%R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e%R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e%R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.320754716981131%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.20754716981132%\"\u003e\n \u003cp\u003e%R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" rowspan=\"2\"\u003e\n \u003cp\u003eBeta-lactam combination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eAmpicillin-Sulbactam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e68.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e52.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e56.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.16883116883117%\"\u003e\n \u003cp\u003ePiperacillin-Tazobactam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e43.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" rowspan=\"3\"\u003e\n \u003cp\u003eCephalosporin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eCeftriaxone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e68.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e43.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e43.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.16883116883117%\"\u003e\n \u003cp\u003eCeftazidime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e48.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e34.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.16883116883117%\"\u003e\n \u003cp\u003eCefepime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e30.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eMonobactam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eAztreonam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e63.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e52.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eFluoroquinolone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eCiprofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" rowspan=\"2\"\u003e\n \u003cp\u003eAminoglycoside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eAmikacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.16883116883117%\"\u003e\n \u003cp\u003eGentamicin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e42.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e34.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.792207792207792%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" valign=\"top\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eFolate pathway antagonist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eTrimethoprim-sulfamethoxazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003eCarbapenem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eMeropenem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\" valign=\"top\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e47.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: n, number of isolates tested for a certain antibiotic; %R, percentage of antibiotic resistance; before the pandemic, 1 January\u0026ndash;31 December 2019; early phase, 1 March\u0026ndash;30 November 2020; peak phase, 1 December 2020\u0026ndash;30 June 2021; late phase, 1 July\u0026ndash;31 December 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003ePercent antibiotic resistance of GPB before and during the COVID-19 pandemic.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.387755102040817%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotic Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.551020408163264%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibiotic Agent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore COVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.775510204081634%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuring\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.45098039215686%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.529411764705884%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.01960784313726%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\" colspan=\"2\"\u003e\n \u003cp\u003eEarly Phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.13513513513514%\" colspan=\"2\"\u003e\n \u003cp\u003ePeak Phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.432432432432435%\" colspan=\"2\"\u003e\n \u003cp\u003eLate Phase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.45098039215686%\" colspan=\"2\"\u003e\n \u003cp\u003en= 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.529411764705884%\" colspan=\"2\"\u003e\n \u003cp\u003en= 59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.49019607843137%\" colspan=\"2\"\u003e\n \u003cp\u003en= 136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.529411764705884%\" colspan=\"2\"\u003e\n \u003cp\u003en=42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.764705882352942%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.686274509803921%\"\u003e\n \u003cp\u003e%R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.803921568627452%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\"\u003e\n \u003cp\u003e%R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\"\u003e\n \u003cp\u003e%R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.803921568627452%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.72549019607843%\"\u003e\n \u003cp\u003e%R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003eBeta-lactam combination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eAmpicillin-Sulbactam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e61.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003eFluoroquinolone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eCiprofloxacin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e56.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e70.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003eCephalosporin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eCeftriaxone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e63.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e42.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003eFolate pathway antagonist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eTrimethoprim-sulfamethoxazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003ePenicillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eOxacillin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e65.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e70.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003eAminoglycoside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eGentamicin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003eGlycopeptide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\"\u003e\n \u003cp\u003eVancomycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\" valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"top\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\" valign=\"top\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: n, number of isolates tested for a certain antibiotic; %R, percentage of antibiotic resistance; before the pandemic, 1 January\u0026ndash;31 December 2019; early phase, 1 March\u0026ndash;30 November 2020; peak phase, 1 December 2020\u0026ndash;30 June 2021; late phase, 1 July\u0026ndash;31 December 2021\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eThe\u003c/b\u003e COVID-19 pandemic has sparked global concern and raised awareness about antibiotic resistance. This study revealed changes in antibiotic resistance before and during the COVID-19 pandemic. These changes can be influenced by several factors, including health regulation, antibiotic usage, health workers, and hospital equipment, as reported previously.\u003csup\u003e9\u003c/sup\u003e In the early phase of COVID-19, the lowest prevalence of antibiotic resistance was observed against several GNB isolates compared to other phases. During this phase, several health regulations were established, including social restrictions, self-awareness, hand hygiene, and the use of medical masks. These regulations were deemed effective for reducing the transmission of infection and mitigating the spread of multidrug-resistant organisms, particularly in the hospital setting. \u003csup\u003e10,22\u003c/sup\u003e As the pandemic progressed, national or international guidelines for COVID-19 were published, recommending the use of antibiotics for patient management.\u003csup\u003e23,24\u003c/sup\u003e The empirical use of antibiotics has impacted and driven increasing resistance, as observed in the peak and late phases. However, this is unavoidable since COVID-19 is a risk factor for the development of healthcare-associated infections (HAIs) and multidrug-resistant (MDR) pathogens. Prolonged hospital stays and increased usage of equipment also contributed to the development of HAIs. These complex factors collectively contribute to the development of multidrug-resistant bacteria, which leads to treatment failure and increased mortality.\u003csup\u003e11\u003c/sup\u003e However, issues with HAI were already present before the pandemic, as observed in the sepsis population. Both populations in this study showed a similar frequency of bacteria identified across all periods, with GNB, including \u003cem\u003eKlebsiella pneumoniae, Acinetobacter baumanii, Escherichia coli, and Pseudomonas aeruginosa\u003c/em\u003e, as the critical priority of hospital-associated pathogens\u003csup\u003e25\u003c/sup\u003e. Environmentally related GNB, such as \u003cem\u003eStenotrophomonas maltophilia\u003c/em\u003e and \u003cem\u003eBurkholderia cepacia, were also detected\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAlthough limited information was provided for this surveillance study, the age group of patients for both populations was recorded. The middle-aged and older groups were more prone to bacterial infection among the sepsis and COVID-19 populations. The aging of the immune response, or immunosenescence, is the dysregulated state of an aged immune system, including short-lived memory responses, a defective response to new antigens, a greater disposition of autoimmunity, and the development of chronic low-grade systemic inflammation. As described in a previous study \u003csup\u003e26\u003c/sup\u003e, both sepsis and COVID-19 resulted in severe inflammation associated with the activation and proliferation of lymphocytes, including cytotoxic T and natural killer cells. This reaction is related to the general immune response to viral infection or to neutrophil activation and recruitment during bacterial infection. Subsequently, with the secretion of antibodies or cytokines/lymphokines (interferon), the immune system eliminates the infected cell and performs viral clearance.\u003csup\u003e26\u003c/sup\u003e Immunosenescence enhances the severe dysregulation of immune responses, leading to a severe hyperinflammatory state. This state also facilitates the type of bacteria, as reported in a previous study. Based on previous study results, more severe responses were observed in GNB sepsis patients than in GPB sepsis patients because lipopolysaccharide may induce alterations in complement protein levels.\u003csup\u003e27\u003c/sup\u003e Moreover, in this age group, an impaired immune response contributes to the development of antibiotic resistance. This hypothesis has been shown in a previous study indicating that a synergism between the immune response and antibiotic drug concentrations reduces the development of resistance to the pathogen. For example, in situations where resistance has not yet developed at the beginning of the treatment period, an immune response helps to eradicate and minimize the chance of creating a resistant pathogen. Correspondingly, an impaired immune response creates selective pressure and leads to the development of resistant pathogens.\u003csup\u003e28\u0026ndash;30\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study also revealed rapid changes in the resistance to meropenem and oxacillin, which serve as surrogate markers for carbapenem-resistant GNB and methicillin-resistant GPB, respectively. As reported previously, there has been an increase in MDR pathogens, including carbapenem-resistant \u003cem\u003eAcinetobacter baumanii\u003c/em\u003e (CRAB), ESBL-producing \u003cem\u003eEnterobacterales\u003c/em\u003e, carbapenem-resistant \u003cem\u003eEnterobacterales\u003c/em\u003e (CRE), MDR \u003cem\u003ePseudomonas\u003c/em\u003e sp. and methicillin-resistant \u003cem\u003eStaphylococci\u003c/em\u003e.\u003csup\u003e9,27\u003c/sup\u003e The surge in COVID-19 admissions, many of which require mechanical ventilation, suggests the occurrence of MDR outbreaks. Furthermore, the lack of up-to-date guidance, shortage of personnel-protective equipment, lack of infection prevention due to increased workload, and decreased time for patient care caused by healthcare personnel shortages have contributed to rapid changes in resistance. Previous studies also reported the time lag between antibiotic use and the increase in the number of resistant pathogens among hospitalized patients. Data obtained across all pathogens (GNB or GPB) showed that the development of antibiotic resistance tends to occur over 0 to 6 months following exposure to antibiotics.\u003csup\u003e31\u003c/sup\u003e Therefore, the data agreed with the rapid changes in antibiotic resistance before and during the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eThere are several limitations of the study. First, the potential for selection bias for both populations was unavoidable due to the use of a laboratory-based surveillance approach.\u003csup\u003e32\u003c/sup\u003e The clinical-symptom diagnosis approach was adapted and used as part of laboratory surveillance based on a previous study to minimize selection bias.\u003csup\u003e33\u003c/sup\u003e However, the selection of a culture based on clinician decisions may still introduce bias to this study. Second, due to the limited information available, antibiotic resistance was not stratified into other categories, including disease severity, type of infection (community or hospital-onset), hospital equipment use such as mechanical ventilation, and urinary catheterization. Third, this study was designed as a surveillance report; hence, statistical analysis to measure the effect of changes in antibiotic resistance could not be performed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study revealed changes in antibiotic resistance before and during the COVID-19 pandemic for both GNB and GPB. High antibiotic use and age-related immune response (immunosenescence) were identified as factors contributing to these rapid changes. This underscores the need for strengthened recommendations in combatting HAIs and MDR pathogens. These recommendations included (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) having a sustainable antibiotic resistance and antibiotic usage surveillance system at the local (hospital) and national levels (country), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) continuous monitoring for prevention infection programs, together with antimicrobial stewardship programs in the hospital, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) enhancing knowledge and skills among healthcare personnel about HAI and MDR pathogens, as well as treatment options.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWorld Health Organization (WHO); severe acute respiratory coronavirus 2 (SARS-CoV-2); Dr. Hasan Sadikin Hospital (RSHS); International Classification of Disease 10th Revision (ICD-10); antibiotic susceptibility testing (AST); Clinical and Laboratory Standards Institute (CLSI); Global Antimicrobial Resistance and Use Surveillance System (GLASS); gram negative bacteria (GNB); gram positive bacteria (GPB); healthcare-associated infections (HAI); multi-drug resistance (MDR); carbapenem-resistant \u003cem\u003eAcinetobacter baumanii\u003c/em\u003e (CRAB); carbapenem-resistant \u003cem\u003eEnterobacterales\u003c/em\u003e (CRE)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted under the Declaration of Helsinki, and the study protocol was approved by the Ethics Committee of Dr. Hasan Sadikin General Hospital (LB.02.01/X.6.5/94/2022). Informed consent was not required to obtain data from the hospital or laboratory information system. Therefore, the ethics committee waived the need for written patient consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors contributed equally to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to (1) the healthcare workers at Dr. Hasan Sadikin General Hospital for supporting data availability; (2) DRPM Universitas Padjadjaran for assistance and financial support; and (3) all participating subjects at Dr. Hasan Sadikin General Hospital between 2019 and 2021 for data contribution.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGinting F, Sugianli AK, Barimbing M, Ginting N, Mardianto M, Kusumawati RL, et al. Appropriateness of diagnosis and antibiotic use in sepsis patients admitted to a tertiary hospital in Indonesia. Postgraduate Medicine. 2020;133(6):674\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eLegese MH, Asrat D, Swedberg G, Hasan B, Mekasha A, Getahun T, et al. Sepsis: emerging pathogens and antimicrobial resistance in Ethiopian referral hospitals. Antimicrob Resist Infect Control. 2022;11(1):83.\u003c/li\u003e\n\u003cli\u003eChen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet. 2020;395(10223):507\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eGarcia-Vidal C, Sanjuan G, Moreno-Garc\u0026iacute;a E, Puerta-Alcalde P, Garcia-Pouton N, Chumbita M, et al. Incidence of coinfections and superinfections in hospitalized patients with COVID-19: a retrospective cohort study. Clinical Microbiology and Infection. 2021;27(1):83\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eLangford BJ, So M, Raybardhan S, Leung V, Westwood D, MacFadden DR, et al. Bacterial coinfection and secondary infection in patients with COVID-19: a living rapid review and meta-analysis. Clinical Microbiology and Infection. 2020;26(12):1622\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eGhosh S, Bornman C, Zafer MM. Antimicrobial Resistance Threats in the emerging COVID-19 pandemic: Where do we stand? Journal of Infection and Public Health. 2021;14(5):555\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eNori P, Szymczak W, Puius Y, Sharma A, Cowman K, Gialanella P, et al. Emerging Co-Pathogens: New Delhi Metallo-beta-lactamase Producing Enterobacterales Infections in New York City COVID-19 Patients. Int J Antimicrob Agents. 2020;56(6):106179.\u003c/li\u003e\n\u003cli\u003eOwoicho O, Tapela K, Djomkam Zune AL, Nghochuzie NN, Isawumi A, Mosi L. Suboptimal antimicrobial stewardship in the COVID-19 era: is humanity staring at a postantibiotic future? Future Microbiology. 2021;16(12):919\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eWitt LS, Howard-Anderson JR, Jacob JT, Gottlieb LB. The impact of COVID-19 on multidrug-resistant organisms causing healthcare-associated infections: a narrative review. JAC-Antimicrobial Resistance. 2022;5(1):dlac130.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Toole RF. The interface between COVID-19 and bacterial healthcare-associated infections. Clinical Microbiology and Infection. 2021;27(12):1772\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eSantoso P, Sung M, Hartantri Y, Andriyoko B, Sugianli AK, Alisjahbana B, et al. MDR Pathogens Organisms as Risk Factor of Mortality in Secondary Pulmonary Bacterial Infections Among COVID-19 Patients: Observational Studies in Two Referral Hospitals in West Java, Indonesia. IJGM. 2022;15:4741\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eSubagdja MFM, Sugianli AK, Prodjosoewojo S, Hartantri Y, Parwati I. Antibiotic Resistance in COVID-19 with Bacterial Infection: Laboratory-Based Surveillance Study at Single Tertiary Hospital in Indonesia. IDR. 2022;15:5849\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003ePusat Informasi \u0026amp; Koordinasi Provinsi Jawa Barat. Sebaran Kasus Covid-19 di Jawa Barat [Internet]. Pusat Informasi \u0026amp; Koordinasi Provinsi Jawa Barat. 2022 [cited 2022 Jan 13]. Available from: https://pikobar.jabarprov.go.id/distribution-case\u003c/li\u003e\n\u003cli\u003eKementerian Kesehatan RI. Pedoman Pencegahan dan Pengendalian Coronavirus Disease (COVID-19). Kementerian Kesehatan RI; 2020.\u003c/li\u003e\n\u003cli\u003eVandepitte J, World Health Organization, editors. Basic laboratory procedures in clinical bacteriology. 2nd ed. Geneva: World Health Organization; 2003. 167 p.\u003c/li\u003e\n\u003cli\u003eClinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Disk Susceptibility Tests - Eleventh Edition: Approved Standard M02-A11. Wayne, Pennsylvania: Clinical and Laboratory Standards Institute; 2012.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Global Antimicrobial Resistance Surveillance System: Manual for Early Implementation. World Health Organization; 2015.\u003c/li\u003e\n\u003cli\u003eMetlay JP, Waterer GW, Long AC, Anzueto A, Brozek J, Crothers K, et al. Diagnosis and Treatment of Adults with Community-acquired Pneumonia. An Official Clinical Practice Guideline of the American Thoracic Society and Infectious Diseases Society of America. Am J Respir Crit Care Med. 2019;200(7):e45\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eClinical and Laboratory Standards Institute. Performance standards for antimicrobial susceptibility testing: supplement M100. 30th ed. Wayne, Pa.: Clinical and Laboratory Standards Institute; 2020.\u003c/li\u003e\n\u003cli\u003eIndonesian Ministry of Health. Peraturan Menteri Kesehatan Republik Indonesia Nomor 8 Tahun 2015 Tentang Program Pengendalian Resistensi Antimikroba di Rumah Sakit. Indonesian Ministry of Health; 2015.\u003c/li\u003e\n\u003cli\u003ePeng Y, Zhu Q, Wang B, Ren J. A cross-sectional study on interference control: age affects reactive control but not proactive control. PeerJ. 2020;8:e8365.\u003c/li\u003e\n\u003cli\u003eBaker MA, Sands KE, Huang SS, Kleinman K, Septimus EJ, Varma N, et al. The Impact of Coronavirus Disease 2019 (COVID-19) on Healthcare-Associated Infections. Clinical Infectious Diseases. 2021;ciab688.\u003c/li\u003e\n\u003cli\u003eGillies MB, Burgner DP, Ivancic L, Nassar N, Miller JE, Sullivan SG, et al. Changes in antibiotic prescribing following COVID‐19 restrictions: Lessons for post‐pandemic antibiotic stewardship. Br J Clin Pharmacol. 2021;bcp. 15000.\u003c/li\u003e\n\u003cli\u003eStaub MB, Beaulieu RM, Graves J, Nelson GE. Changes in antimicrobial utilization during the coronavirus disease 2019 (COVID-19) pandemic after implementation of a multispecialty clinical guidance team. Infect Control Hosp Epidemiol. 2021;42(7):810\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eTacconelli E, Carrara E, Savoldi A, Harbarth S, Mendelson M, Monnet DL, et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. The Lancet Infectious Diseases. 2018;18(3):318\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eBulut O, Kilic G, Dom\u0026iacute;nguez-Andr\u0026eacute;s J, Netea MG. Overcoming immune dysfunction in elderly individuals: trained immunity as a novel approach. International Immunology. 2020;32(12):741\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eDuan C, Wang Y, Wang Q, Li J, Xie J, Liu S, et al. Gram-negative bacterial infection causes aggravated innate immune response in sepsis: Studies from clinical samples and cellular models. Biochemical and Biophysical Research Communications. 2023;650:137\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eBerti A, Rose W, Nizet V, Sakoulas G. Antibiotics and Innate Immunity: A Cooperative Effort Toward the Successful Treatment of Infections. Open Forum Infectious Diseases. 2020;ofaa302.\u003c/li\u003e\n\u003cli\u003eHandel A, Margolis E, Levin BR. Exploring the role of the immune response in preventing antibiotic resistance. Journal of Theoretical Biology. 2009;256(4):655\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eHasan CM, Dutta D, Nguyen ANT. Revisiting Antibiotic Resistance: Mechanistic Foundations to Evolutionary Outlook. Antibiotics. 2021;11(1):40.\u003c/li\u003e\n\u003cli\u003ePoku E, Cooper K, Cantrell A, Harnan S, Sin MA, Zanuzdana A, et al. A systematic review of the time lag between antibiotic use and rise of resistant pathogens among hospitalized adults in Europe. JAC-Antimicrobial Resistance. 2022;5(1):dlad001.\u003c/li\u003e\n\u003cli\u003eSugianli AK, Ginting F, Kusumawati RL, Parwati I, de Jong MD, van Leth F, et al. Laboratory-based versus population-based surveillance of antimicrobial resistance to inform empirical treatment for suspected urinary tract infection in Indonesia. PLOS ONE. 2020;15(3):e0230489.\u003c/li\u003e\n\u003cli\u003eHebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A. Demonstration of the Weighted-Incidence Syndromic Combination Antibiogram: An Empiric Prescribing Decision Aid. Infect Control Hosp Epidemiol. 2012;33(4):381\u0026ndash;8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"antibiotic resistance, antibiotic surveillance, bacterial infection, COVID-19, sepsis","lastPublishedDoi":"10.21203/rs.3.rs-4430480/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4430480/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Antibiotic resistance is recognized as a public health threat with significant impacts on mortality and economic burdens. Antibiotic resistance related to inappropriate empiric antibiotics, particularly during the COVID-19 pandemic. However, limited information is available about changes in antibiotic resistance before and during the pandemic in Indonesia. This study aimed to describe changes in the prevalence of antibiotic resistance among patients with proven bacterial infections before and during the COVID-19 pandemic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective surveillance study was carried out at a single tertiary hospital to review medical records containing culture and antibiotic susceptibility data among hospitalized patients diagnosed with sepsis and COVID-19 according to the International Classification of Disease (ICD). In this context, the predefined periods were 1 January–31December 2019 and 1 March 2020–31 December 2021. The result was the percentage of resistance to selected antibiotics among the study population, stratified by gram-bacteria type, with the evaluation of changes in antibiotic resistance over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDuring the observation period, 596 adult patients were diagnosed with sepsis (before COVID-19), and 2786 were diagnosed with confirmed COVID-19 (during COVID-19). The rate of culture growth in patients with sepsis was greater than that in patients with COVID-19, with values of 51.6% and 29.2%, respectively. Gram-negative bacterial isolates were predominantly found in all observation periods, accounting for 41.2% - 47.3% of the adult middle-aged group. Changes in antibiotic resistance against GNB were observed during COVID-19 (peak phase, above 20%) compared to the early phase. For gram-positive bacteria, the greatestchanges were found in the late phase, reaching 70%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study revealed that changes in antibiotic resistance before and during the COVID-19 pandemicaffected both GNB and GPB. High antibiotic use and age-related immune responses (i.e., immunosenescence) contributed to these rapid changes. Strengthening strategies, including implementing surveillance systems and antimicrobial stewardship programs and enhancing the capacity of healthcare workers, are recommended for combatting antibiotic resistance.\u003c/p\u003e","manuscriptTitle":"Changes in Antibiotic Resistance Before and During the COVID-19 Pandemic: Retrospective Surveillance Study in a Single Indonesian Tertiary Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 18:54:48","doi":"10.21203/rs.3.rs-4430480/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c98a086b-22cb-440d-a601-e7b83895a7eb","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-24T09:44:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-04 18:54:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4430480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4430480","identity":"rs-4430480","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}