Antimicrobial Resistance Trends in ICU-acquired Infections after the COVID Epidemic: A 5-year Retrospective Cohort and Comparative Review with MENA Region

Antimicrobial Resistance Trends in ICU-acquired Infections after the COVID Epidemic: A 5-year Retrospective Cohort and Comparative Review with MENA Region | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Antimicrobial Resistance Trends in ICU-acquired Infections after the COVID Epidemic: A 5-year Retrospective Cohort and Comparative Review with MENA Region Sahar Shadvar, Reza Bolandparvaz Jahromi, Seyedsina Ojaghi Haghighi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8682214/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Antimicrobial resistance (AR) in hospital-acquired infections (HAIs) poses a significant global health challenge, particularly in intensive care units (ICUs). The COVID-19 pandemic has exacerbated AR trends due to increased antibiotic misuse and strained infection control measures. However, comparative data on pre- and post-pandemic AR trends of Gram-negative pathogens in ICUs of the Middle East and North Africa (MENA) region remain scarce. Methods: This retrospective cohort study was conducted on 472 clinical isolates from 242 ICU patients. Bacterial identification and antibiotic susceptibility testing were performed according to updated standard microbiological protocols (e.g., CLSI guidelines). The distribution of bacterial species, resistance categories (MDR, XDR, PDR), and antibiotic-specific resistance were assessed with temporal comparisons drawn to pre- and post-COVID-19. Results: Klebsiella pneumoniae was the most frequent isolate, followed by Pseudomonas aeruginosa and Escherichia coli. Respiratory specimens dominated and surpassed urinary infections. Overall resistance was highest to ceftriaxone, fluoroquinolones, and carbapenems, while colistin remained the most effective antibiotic. AR rates were high, with 76.9% MDR, 64.8% XDR, and 1.5% PDR isolates. AR prevalence, particularly XDR isolates increased after the onset of the COVID-19 pandemic (>20%). Acinetobacter baumannii consistently exhibited the highest AR (>90% XDR, 100% carbapenem resistance). Conclusion: This study highlights the alarming rise in MDR and XDR pathogens in ICU settings following the COVID-19 pandemic. These findings underscore the urgent need for region-specific surveillance and antibiotic stewardship to guide rational empirical therapy and curb AR in ICU settings. Trial registration: Not applicable. Antimicrobial Resistance Hospital-Acquired Infections Intensive Care Units Gram-Negative Bacterial Infections COVID-19 Pandemic Middle East and North Africa (MENA) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The prevalence of antimicrobial resistance (AR) among nosocomial infections is rising dramatically. It is specifically a consequence of resistant bacterial strains, hence representing a significant global healthcare concern. This issue intensifies tremendously within intensive care units (ICUs), where critical patients are most vulnerable as a result of their compromised conditions. Some authors have proposed ICU-acquired infection during hospitalization as an independent factor linked to mortality, with a two-fold rise of the rate (1, 2). HAIs are a significant healthcare issue that causes economic losses and impacts productivity. They result in prolonged hospital length of stay, long-term disabilities, financial burdens on healthcare systems, increased costs for patients and families, AR, and higher in-hospital mortality rates (3). The recent World Health Organization (WHO) updates estimated AR was the main reason of 1.27 million deaths across the world and was accountable for 4.95 million deaths indirectly in 2019. Moreover, the burden of AR is disproportionately higher in medium- and lower-income countries, where ICU-acquired infections are 2-3 times more prone compared to high-income countries, with higher mortality (33.6% vs <20%) (4). Also, the challenge posed by AR at the Middle East and North Africa (MENA) region remains obscure (5). According to a nationwide study conducted in 2018 across 940 hospitals in Iran, the incidence rate of HAIs was 4.2 per 1000 patient-days with a 15.65% mortality rate (6); however, these figures predated the Coronavirus Disease-19 (COVID-19) pandemic. Bacterial AR refers to genotypic or phenotypic changes in bacteria that reduce the efficacy of drugs intended to treat infections, and it has emerged as one of the foremost public health challenges of the 21st century (7). Presumably a naturally occurring phenomenon, AR has been primarily aggravated on a global scale over recent decades due to the indiscriminate use of antibiotics in healthcare (e.g., prescriptions without indication, incorrect administration, and over-the-counter availability) as well as in agriculture and veterinarian utilization (8). Remarkable Gram-negative bacilli (GNB) pathogens capable of developing resistance to multiple antibiotics consist of Klebsiella species, Pseudomonas aeruginosa (P. aeruginosa), Acinetobacter baumannii (A. baumannii), and Escherichia coli (E. coli) species. The aforementioned bacteria are frequently implicated in hospital-acquired infections (HAIs), particularly within ICU settings, such as ventilator-associated pneumonia, bloodstream infections, and urinary tract infections (UTIs) (9, 10). The rapid increase in resistance among bacteria to first- and second-line antimicrobial agents, coupled with the emergence of multidrug-resistant (MDR) organisms, extensively drug resistant (XDR) organisms, and pan drug resistant (PDR) organisms is particularly alarming. This situation has significantly narrowed the range of available therapeutic options for treating these infections. The limited number of new antimicrobial agents approved recently further exacerbates this situation (3). The COVID-19 pandemic profoundly altered infection control practices and antibiotic indications worldwide. The Centers for Disease Control and Prevention (CDC) revealed a 20% increase of AR in HAIs during the pandemic, peaking in 2021 and remaining above pre-pandemic levels in 2022 (11). Such aspects remain insufficiently studied in Iran and across MENA region, which resulted in scientific gaps about potential changes in empirical antibiotic therapy that we intend to address. Sharing epidemiological information reporting the particularities of each region is a crucial measure to mitigate or degrade the advance of AR according to the WHO Global Action Plan (12). Healthcare strategists must carry out regular evaluation of surveillance data to monitor the imperative trends and proactively avert outbreaks (13). Yet, lack of updated national data on AR and HAIs in Iran, as the most recent comprehensive national report prior to this study dated back to 2018 before the COVID pandemic (6), led us to provide an updated insight to inform healthcare policies. In the current study, the cornerstone of our investigations was to introduce an update on local epidemiology of HAIs and AR trends of major bacterial pathogens in ICU settings over a five-year period, from 2018 to 2023. Bank Melli Hospital in Tehran provides an ideal setting to study the incidence of AR due to its high ICU patient turnover in this region. Methods Study Design and Subjects The current observational cohort study was conducted retrospectively on 358 hospitalized patients in three separate ICU wards to evaluate AR at Bank Melli Hospital, a tertiary center, Tehran, Iran over a 5-year period from March 2018 to December 2023, including the COVID-19 pandemic. The official onset of the COVID-19 epidemic in Iran dates back to February 17, 2020. All of the demographic and clinical data including age, gender of the patients, medical records, clinical course during hospitalization, and microbiological laboratory results were reviewed from hospital databases for all patients. ICU-acquired infections was defined according to CDC/NHSN criteria (14): new pathogen isolated >48 hours after admission with compatible clinical, radiological, and laboratory features. Positive cultures prior to 48 hours of admission, contaminated cultures (i.e., polymicrobial cultures), and duplicated samples (i.e., repeated positive cultures similar in both bacterial species and site during the treatment course or a simultaneous positive blood culture to another prior infection) were also excluded. All of the previously listed criteria were put into place to avoid the possibility of duplication bias or miscalculating recurring situations. We included 472 non-duplicate clinical samples from 242 ICU patients with positive cultures of A. baumanii, P. aeruginosa, K. pneumoniae, K. oxytoca and E. coli. In addition, a positive SARS-CoV-2 polymerase chain reaction (PCR) test was considered as the basis for confirming the diagnosis of COVID-19. The patients were treated in parallel to the results of clinical bacteriology identification procedures on the samples. They were classified by age into under 60, 60 to 75, and above 75; the duration of hospitalization was also categorized into less than 18 days and 18 days or longer. Identified risk factors were considered as smoking, drug use, and diabetes; additionally, patients were assessed for immunodeficiency. Immunodeficiency is acknowledged as primary or secondary based on medical references. Primary immunodeficiencies are mainly illustrated in rheumatological and pediatrics domains; on the other hand, secondary immunodeficiencies span from viral and malignancies to iatrogenic immunosuppression, hence a broader spectrum of immunological disorders (15). Laboratory Evaluations The samples were processed immediately by the microbiology laboratory in accordance with established protocols guidelines to identify isolates (16). In detail, conventional biochemical tests including oxidase, catalase, motility, metabolic procedures such as citrate, indole production, methyl red, Voges-Proskauer, and presence of lysine decarboxylase and arginine dehydrogenase enzymes were performed. Blood samples were loaded in the BD BACTEC™ Automated Blood Culture System (BD Diagnostics, Sparks, MD, USA) at 36 °C for up to 5 days and positive samples were subcultured. In addition, a molecular sepsis panel including PCR assays targeting both Gram-positive and Gram-negative bacteria was performed in selected suspicious bacteremia cases. Bronchoalveolar lavage (BAL), sputum, pus swab and wound specimens, catheters and third-space fluids (pleural and peritoneal tap) samples were inoculated on MacConkey agar, sheep blood agar, and chocolate blood agar (Hi Media Laboratories LLC, India) and incubated in 37 °C overnight at both ambient air and 5% CO2. Urine samples were inoculated on cysteine-lactose electrolyte deficient agar, as well as aerobically. Antimicrobial Susceptibility Testing Isolates confirmed by biochemical and molecular tests underwent the disc diffusion susceptibility test to each tested drug using antibiotic disks provided by Rosco™, Switzerland and Padtan Teb™, Iran. Quality control was performed annually according to the following guidelines. Pivotal points were interpreted according to the latest available clinical and laboratory standards institute (CLSI) guidelines (17-20). Intermediate results were classified as resistant. Interpretation of antibiotic susceptibility was performed based on the European center for disease prevention and control (ECDC) instructor as MDR: resistant to at least one drug in three or more antimicrobial classes, XDR: susceptible to only one or two classes, PDR: resistant to all drug classes (21). In addition, a gradient minimum inhibitory concentration (MIC) determining method according to manufacturer’s instructions was implemented in certain cases. In summary, a 0.5 McFarland suspension of each isolate was inoculated on a whole plate surface Mueller–Hinton agar plate by streaking the swab in back and forth motions. Then, they were incubated for 24 hours at 37°C. Following incubation, a ruler measured inhibition zone sizes to the nearest millimeter. Results Demographic characteristics of the study. A total of 472 clinical isolates from 242 hospitalized patients were recovered between March 2018 and December 2023. The mean age of the patients was 75.6 ± 15.5 years and the majority were elderly (over 75 years old, 60.4%). Men comprised 56.8% of the study cohort. Approximately half of the patients had a hospital stay of 18 days or longer. Diabetes mellitus (23.5%), immunodeficiency (21.6%) and active tobacco or substance use (14.4%) were the most common comorbidities. Notably, 62.1% of patients were hospitalized after the official declaration of the COVID-19 epidemic in Iran (Table 1). Causes of Hospitalization. Pneumonia (28.9%), neurologic diseases (18.6%), and cancer (16.1%) were the prevalent causes of hospitalization, respectively. Other reasons such as cardiovascular diseases, other respiratory system diseases rather than pneumonia, orthopedic surgeries, UTI and acute abdomen were also noted less frequently (Figure 1). Distribution of Isolates. K. pneumoniae (42.2%) was the most frequently isolated organism overall (42.2%), followed by P. aeruginosa (30.3) and E. coli (16.7%). K. oxytoca (8.5%) and A. baumannii (2.3%) were less frequently detected. Respiratory specimens (sputum and BAL) accounted for more than two-thirds of isolates, followed by urine (16.5%) and blood or wound cultures (each 6.8%) (Table 2). A. baumannii was isolated only from BAL, sputum, and blood cultures, respectively. Temporal observations revealed an increasing proportion of both K. pneumoniae and K. oxytoca after the onset of the COVID-19 pandemic (peaking in 2022), while P. aeruginosa and E. coli declined over time. Notably, A. baumannii was not detected until 2021 but appeared thereafter (Figures 2,3). AR categories. Resistance profiles were broadly similar across demographic and clinical subgroups. However, non-MDR strains were less frequent among females compared with males, and the prevalence of XDR increased with advancing age. Yet the highest proportion of non-MDR isolates was observed with a decreasing trend with age, and PDR was not detected in patients under 60 years of age. Both MDR and non-MDR strains were more commonly detected in immunocompromised patients (Table 3). The overall incidence of MDR, XDR and PDR pathogens accounted for 76.9%, 64.8% and 1.5% of our patients, respectively. The lowest AR rate was reported in E. coli. On the other hand, the highest XDR pattern was observed in A. baumannii (Figure 4). The prevalence of XDR increased during the study years, while PDR gradually declined and was absent in the last two years. After the onset of COVID-19, MDR and XDR isolates became more common (Figure 5). Antibiotic-Specific Resistance. AR varied considerably across antibiotic classes (Table 4). The highest overall AR was observed against ceftriaxone (90.3%), followed by trimethoprim-sulfamethoxazole (83%), fluoroquinolones (80.5%), carbapenems (79%), and ceftazidime (78.3%). Resistance to piperacillin–tazobactam was also high (70.4%). Aminoglycosides showed relatively lower AR (58.7%). Colistin retained the greatest activity, with resistance documented in only 2.8% of isolates. A. baumannii consistently exhibited the highest AR among species and across most antibiotic classes, while E. coli demonstrated the lowest, particularly against carbapenems and aminoglycosides. Discussion The overall rate of AR and MDR isolates in HAIs is more prevalent in the ICU than in other wards (22). This may be due to the frequent utilization of invasive medical devices among ICU patients and their heightened exposure to a greater diversity of antibiotic-resistant pathogens in addition to horizontal gene exchange of various resistant traits, namely plasmid-encoded betalactamases, aminoglycosides modifying enzymes, quinolone resistance gene (23, 24). WHO has defined the AWaRe (Access, Watch and Reserve) classification (Table 5) as a global action plan to counter the progression of AR (25). The Access group consists of wide-spectrum antibiotics with a lower risk of AR. The Watch group encompasses antibiotics that require careful monitoring because of the higher potential of AR. We must consider these groups as empirical therapy options and withhold the Reserve group as the last-resort option for confirmed MDR cases. Iran is formally among the countries with established national and hospital-level Infection Prevention and Control (IPC) policies yet; implementation gaps remain (26). According to a study conducted in 2018 across 940 hospitals in Iran, HAI incidence was 4.2 per 1000 patient-days with 15.65% mortality rate. The mode of afflicted patients had pneumonia, and after that in order to frequency, UTI, surgical site infections, and sepsis. In addition, the most frequently cultured pathogens included E. coli, K. pneumoniae, and A. baumannii (6). K. pneumoniae accounted for a substantial proportion of HAIs in our study (with the exception of E. coli emerging as the most common infectious agent in UTIs), comparable with the similar studies in the region (27-29). Although these discrepancies may reflect variations in patterns and epidemiological characteristics between healthcare settings and age groups, the excessive and unnecessary use of antibiotics in Iran has led to this striking rate of AR in recent decades (30). Qatar’s 3-year experience in introducing an effective stewardship policy managed to steadily diminish the MDR P. Aeruginosa prevalence from 9% to 5.46% in 2015 (31). Source of Isolates and Empirical Therapy The respiratory tract samples, sputum and BAL, were the most common type in terms of the type of HAIs similar to Carenjo Suarez and Litwin (32, 33), although most authors consistently found UTI emerging as the leading cause of HAI (34-36). This may be due to ventilators being indicated more frequently after the COVID pandemic. There was also a considerable prevalence (36.6%) of P. aeruginosa in respiratory tract samples of our study, compatible with others (31%) (8). The incidence of E. coli as a cause of UTI is reportedly three-fold higher than Klebsiella spp.; hence, nitrofurantoin may serve as an effective empirical therapy for uncomplicated UTIs. Other recommended agents in complex cases are aminoglycosides, ceftazidime, cefepime, or piperacillin-tazobactam (37). Studies have demonstrated higher cephalosporin-resistant E. coli in blood compared with urine isolates (38). Some researchers propose a concern as meropenem becomes less effective in vascular catheters and blood stream infections than other sites argues escalating reports of community-acquired carbapenem resistant infections and considerably attributable with higher mortality (8, 39). It is noteworthy to cover Gram-positive cocci in addition to GNB in skin/soft tissue, vascular catheter infections, and bacteremias because Staphylococcus aureus is generally the most common MDR isolate at these sites (8). Isolates GNBs are the proven culprits for most ICU HAIs (40-42). Our data regarding the microorganism types elucidated an overall preponderance of K. pneumoniae (especially respiratory samples) aligned with elevated risk of colonization during hospitalization. These were backed up with other Iranian studies (43-45). In the opposite view of us, all Klebsiella isolates depicted low AR to levofloxacin per Molana et al. findings (46) despite the AR level. Remarkable resistance to carbapenem in Klebsiella spp. is often observed in urinary tract and pus isolates (47). A 6.9% prevalence of colistin-resistant K. pneumoniae isolates, slightly higher than us (2.5%), was calculated over Iran in 2022 (48). Unlike Sekar et al. (47), the AR rate in Klebsiella spp. was significantly lower compared with E. coli for most antibiotics; however, the AR of carbapenems in both studies was significantly higher in Klebsiella spp. E. coli is a conditional pathogen with high uropathogenic propensity to trigger UTI and infections of the respiratory tract (49-51). This might be attributed to the production of virulence factors, and AR mechanisms like extended spectrum betalactamases (ESBL) production and mutations in Amp C enzymes and porin loss (52-55). E. coli had the least AR, though it is still concerning. Only piperacillin/tazobactam is considerably efficient among the penicillins (47). ESBL-producing bacteria are more resistant to aminoglycosides and levofloxacin than those with high AR to cephalosporins. Synthetic antibiotics such as sulfonamides demonstrated lower AR than ampicillin (56). Fluoroquinolone-resistant E. coli (associated with MDR) isolates from UTIs are also commonly resistant to ampicillin and sulfamethoxazole-trimethoprim (57). Our reported resistance to third-generation cephalosporins and fluoroquinolones was excessive in E. coli, in line with the current trend of studies reporting levofloxacin and ofloxacin as the most effective (47, 58). This can be attributed to administration of high-grade and often unwarranted fluoroquinolones or third-generation cephalosporins to provide empiric coverage of this pathogen in UTIs, which may restrict available treatment options (carbapenems as the only susceptible agent) (56, 59-62). Fortunately, the rate of carbapenem resistance was not as high as A. Baumannii and K. Pneumoniae isolates in our study. P. Aeruginosa was identified as the second most prevalent strain in our study. The rate of MDR P. Aeruginosa varies geographically and it is evident across the MENA region, where it ranges from 22.5% in Egypt to 61% in Saudi Arabia (5). 36.3% MDR and 18.1% XDR prevalence was assessed in Pakistan (63). A handful of Iranian investigations also portrayed considerable heterogeneity: Concerning 87% AR in VEB-1, OXA-10 and PER-1 producing genotypes are the established mechanisms of betalactam resistance in MDR P. aeruginosa and A. baumannii in Iran, all of which (100%) were resistant to cefotaxime, ceftazidime, and cefepime but susceptible to carbapenems (64, 65). Nasimmoghadas et al. (66), further confirmed 94% MDR and 85% as XDR strains with significant resistance to nearly all tested antibiotics, except colistin (2%) and ceftazidime (32%). P. Aeruginosa is a major pathogen cause of wound and burn HAIs in ICU (65). In two burn centers, 93.1% MDR strains were observed with high AR rates to ceftazidime 57.5%, ciprofloxacin 65-93.7%, gentamycin 67.5%, piperacillin 87.5%, amikacin 82-90%, and imipenem 79.2-97.5% (65, 67). On the contrary, other studies in the similar region reported 5.46-45.3% MDR and 15.53% XDR prevalence with ciprofloxacin (62.7%), amikacin (52%) and imipenem (64%) resistant phenotypes, respectively (68, 69). A Canadian multicenter investigation in 10 of 27 ICUs reported an elevation in Pseudomonas AR to broad-spectrum cephalosporins, piperacillin-tazobactam, and carbapenems over the study period (70). In our study, we also observed such an increase in XDR and PDR resistance patterns. In the current study, the frequency of fluoroquinolons, aminoglycosides and carbapenem resistant P. Aeruginosa was substantially lower compared to previous reports of the Tehran Children’s Medical Center, (44, 71, 72) and demonstrated quite high susceptibility to commonly tested antibiotics. The higher overall AR in our study may be attributable to differences in patient populations (adult ICU versus pediatric patients) and greater exposure to risk factors such as invasive devices. A. baumannii is an opportunistic pathogen with the capability to colonize in hospital settings, persist for an extended time, acquire diverse virulence factors, and emerging as a crucial cause of ICU-acquired infection outbreaks particularly in immunocompromised patients (73). The most prevalent HAI with A. baumannii is ventilator-associated pneumonia through skin and airway defects penetration and attachment to bronchial epithelial cells (74), as it was also apparent in our study. A sharp escalation of MDR A. baumannii was reported at Iran in 2015 (75) with outstanding regional prevalence of ESBL, MDR and XDR isolates far higher than MDR and XDR P. aeruginosa (76). So although A. baumannii is less virulent than P. aeruginosa, it represents the most common MDR GNB, exhibits the highest AR to tested antibiotics and contributes to the dissemination of plasmid-mediated AR genes contributing to outbreaks of nosocomial MDR (92.2%) and XDR (78.6%) A. baumannii. This is largely attributed to the synergic interaction between certain betalactamses (carbapenem hydrolyzing enzymes such as OXA-23), porin loss, and efflux pumps overproduction (74, 77, 78). Coinciding with OXA-23, the ArmA enzyme is also the predominant driver of global AR of A. baumannii to all aminoglycosides, including Iran (79). The widespread MDR pattern of A. Baumannii has established carbapenems as the mainstay treatment in practice; however, numerous reports of carabapenem-resistant isolates are noted locally and worldwide despite of ongoing narrowed options (32, 76, 77, 80, 81). Remarkable resistance to almost all tested agents (broad-spectrum betalactams, cephalosporins, carbapenems, aminoglycosides and fluoroquinolons) was similar between (74) and ours, at approximately 92-97%. Several Iranian studies (68, 82) have confirmed these mutual concerns with approximately 70-97% AR to tobramycin, ceftazidime, ciprofloxacin, and carbapenems, comparable to us; while Jafari et al. (83) documented lower AR rates to carbapenems (41%-60), ciprofloxacin (78%), and retained susceptibility to colistin and ampicillin-sulbactam. Nevertheless, emergence of colistin-resistant A. baumannii was reported in Iran in 2013 (84). In our observations, there was 100% carbapenem resistance and 0% colisitin resistance in A. baumannii isolates, while the abovementioned studies noted lower carbapenem resistance but higher colistin resistance. Antibiotic agents The Infectious Diseases Society of America discourages administration of antimicrobials for empirical treatment if the regional AR is greater than 10-20% (85). Convincing percentages of resistant strains of E. coli and Klebsiella spp. to third and fourth generations of cephalosporins were noted with a slightly lower rate in other developing countries and broadly (86). Cefepime combined with metronidazole or piperacillin-tazobactam are good alternatives for intra-abdominal infections, while meropenem must be reserved for sepsis (37). Fluoroquinolones are frequently favored in empirical use in UTI (61), yet we found a relatively high AR to E. coli (potential UTI pathogens). High level of ciprofloxacin-resistant GNB is interestingly associated with elevated AR of broad-spectrum antipseudomonal agents like piperacillin-tazobactam (90%) and carbapenems (88 to 90%), in favor of observations of non-enzymatic mechanisms such as efflux systems overexpression or low permeability in MDR isolates (87). The rising trends of AR of antipseudomonal agents in most countries of our region is alarming: Bahrain, Qatar, Saudi Arabia, Iraq, Egypt, Syria, Libya, Tunisia, and Lebanon consistently showed elevated AR levels for piperacillin-tazobactam, antipseudomonal cephalosporins (ceftazidime and cefepime), carbapenems, aminoglycosides, fluoroquinolones, and aztreonam. Carbapenems are the forefront class to counter severe MDR cases with the lowest AR to betalactams; thus, the propensity to spread rapidly at local and global outbreaks worryingly restricts available treatment options (47). Therefore, carbapenem-resistant K. pneumoniae, E. coli, P. aeruginosa, and A. baumannii pose an international public health concern, especially in developing countries and account for high morbidity, mortality, and healthcare costs (88). Ying Han claimed a 20% increase in carbapenem resistance in a 4-year period (89). We also found a concerning AR to carbapenems (80%). Carbapenem overuse might explain why there might be a difference between areas with patients from a higher socioeconomic status than lower socioeconomic areas (90). Carbapenem-resistant cases are managed by long-established drugs such as aminoglycosides, tigecycline, colistin, and ceftazidime-avibactam. Some authors established the correlation between colistin resistance in carbapenem-resistant K. pneumonia (multiple worldwide outbreaks), P. aeruginosa and A. baumannii bloodstream infections with mortality (48, 91-93). Colistin remains the highly active last-resort against MDR, XDR, or even PDR carbapenem-resistant GNB with roughly 100% efficacy in most regions, although Qatar, Saudi Arabia, Egypt, and Syria report developing AR to colistin (3.4 to 30%) (5, 94). A national survey calculated that Tehran had the highest resistance to colistin with 16% (7.3-31.5%), whereas Urmia had the lowest with 0.3% (0–4.2%). The overall rate of colistin resistance increased significantly over time (2013 2018, 2019–2021), which may be due to the increased use of this antibiotic in the recent years (48). The prevalence of carbapenem and colistin AR has increased along with alarming emergence of PDR organisms among critical wards (95). Nevertheless, colistin resistance was very low in our study, closely matching to an investigation across 30 of 57 centers in the UK reporting a colistin resistance rate of 3.1% (96). Colistin resistance evolution is largely attributed to alterations in the pathway of lipopolysaccharide biosynthesis (77). The novel betalactamase inhibitor combinations such as ceftazidime-avibactam and ceftolozane-tazobactam were often unavailable but depicted good antimicrobial susceptibilities in the Middle East. However, it is less compared to other regions probably because of high regional AR such as in Qatar (31.2 - 37.1%), even before their introduction into clinical practice. Furthermore, many clinicians also stated that specific last-line antibiotics (linezolid, colistin, tigecycline and daptomycin) were not available in their ICU (97, 98). Nonetheless, a slight increase in linezolid prescription was also associated with development and spread of AR (99). Some futuristic studies are evaluating the promising results of synergism of bacteriophage therapy and antimicrobial agents in MDR epidemics (100). COVID-19 K. pneumoniae became more prevalent concurrently with nosocomial respiratory infections after the pandemic in our observations. This is possibly due to more ventilator utilization and it might indicate more focus on ventilator-associated pneumonia in HAI empirical management guidelines. P. aueroginosa, A. baumannii, Mycoplasma pneumonia, and Haemophilus influenza were frequently suspected bacterial superinfections in the COVID-19 pandemic. An escalating trend of prescribing the Reserve agents out of fear of bacterial coinfections or overlap of paraclinical features with bacterial respiratory infections led to unjustified prescriptions of broad-spectrum antibiotics. For example, the wide prescription of azithromycin due to its additional immunomodulatory and antiviral properties resulted in rapid transformation of AR (101). Overall, we report a >20% increase in the prevalence of XDR isolates at ICUs after the pandemic. Risk factors We observed that patients with more than 18-day hospitalization, age over 60, and male gender were slightly more frequent in terms of MDR and XDR HAI. Some researchers introduced the independent predictors associated with MDR organism HAI in ICU as bacterial load, male gender, multiple invasive procedures, and prolonged hospitalization in ICU (1, 102). Importantly, prior hospitalization within the past year is a key determinant, and each additional day at ICU increased HAI risk by approximately 1% with a mean acquisition time of 11 days for MDR HAI and 24.5 days for XDR HAI (45, 103). Furthermore, patients with respiratory and cardiovascular comorbidities are five and six times more prone to a GNB HAI, respectively (103). Moreover, cirrhosis and impaired consciousness are also substantial underlying risk factors in the ICU patients (45, 104). MDR A. baumannii and P. aeruginosa survive in the hospital environment and are easily transmitted between patients through the hands of health care workers (105). The optimal healthcare preventive protocols to combat dissemination of AR should respond appropriately against risk factors, namely reducing invasive procedures, enhancing routine hygiene practices and stringent antibiotic stewardship program (106-108). Immunodeficiency A possible contributor to HAI and consequently morbidity and mortality in ICU patients is underlying conditions compromising the immune system; however, our study contradicts this theory. MDR GNB are a common cause of sepsis in patients undergoing transplantation or chemotherapy, which are typically managed by carbapenems (95). There are reports of invasive bloodstream infections caused by MDR Enterobacteriaceae in neutropenic hematopoietic stem cell recipients (even with sufficient prophylaxis) (109). A similar correlation was observed between malignancies and ESBL Enterobacteriaceae (56). Suggestions Improving, adaptation and adherence to antibiotic stewardship guidelines by considering regional AR patterns could effectively mitigate or degrade the spread of resistant bacteria (110). Overall, this study emphasizes the urge for medical staff to use antimicrobial agents rationally by designing indigenized empirical stewardship guidelines. Limitations Our retrospective observation had some limitations, as the genotypic and molecular data of the isolates were not documented. Furthermore, the disk diffusion method might not be sensitive enough as the broth microdilution method to identify AR measurements. Moreover, most patients were already under antimicrobial treatment before referring to our center. Differentiating between pathogen and contamination were sometimes challenging, particularly in patients with urine catheterization. We conducted a single territory study within the province, so the risk of local practice bias probably affects the generalizability of the findings. Therefore, we recommend futuristic replication of similar blinded analytical studies in various hospitals and cities in this region for a more definitive comprehensive analysis to monitor HAIs. Conclusion This study provides an updated overview of HAI and AR in ICU settings over a five-year period after the COVID-19 epidemic. The incidence of respiratory HAIs has surpassed UTIs compared to pre-COVID studies. The alarming prevalence of MDR, XDR and PDR was observed in Klebsiella species, P. aeruginosa, and E. coli. These findings were consistent with concerning trends reported across neighboring countries. Acinetobacter baumannii consistently exhibited the highest AR (>90% XDR, 100% carbapenem resistance) among species and across most antibiotic classes. AR was mostly noted against cephalosporins, fluoroquinolones, and carbapenems, while colistin remained the most effective agent in MDR cases. The burden of AR increased after the onset of the COVID-19 pandemic (>20% increase in XDR isolates), highlighting the urge for stricter surveillance and antibiotic stewardship policies. Our findings emphasize on the vitality of region-specific data to guide infection prevention and control measures and provide insights to update empirical guidelines, especially in ICUs. Abbreviations A. baumannii: Acinetobacter baumannii AR: Antimicrobial Resistance BAL: Bronchoalveolar Lavage CDC: Centers for Disease Control and Prevention COVID-19: Coronavirus Disease-19 E. coli: Escherichia coli ESBL: Extended Spectrum Betalactamases GNB: Gram-Negative Bacilli HAI: Hospital-Acquired Infections ICU: Intensive Care Units MDR: Multidrug Resistant MENA: Middle East and North Africa P. aeruginosa: Pseudomonas aeruginosa PDR: Pan Drug Resistant UTI: Urinary Tract Infections WHO: World Health Organization XDR: Extensively Drug Resistant Declarations Ethical approval and consent to participate The study protocol was registered and approved by the ethics committee of Farhikhtegan Hospital, Islamic Azad University, Tehran Medical Sciences (IAUTMU) (ethical code: IR.IAU.FARHIKHTEGANH.REC.1404.005 ||2026.01.07 ) (111) and consent for participation or anonymous publication was waived accordingly due to retrospective design and use of anonymized data. Hence, clinical trial registration was not applicable. All procedures were adhered to the tenets of the Declaration of Helsinki (1964) and its later amendments about biomedical studies involving human participants. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public or commercial sectors. Authors' Contributions R.B. and K.K. extracted the study data. A.E. and S.O. wrote the primary draft and together with K.K. and R.B. completed the final manuscript. S.S. supervised the current study and provided expert and critical opinions to resolve any discrepancies. 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Evaluation of a modified cleaning procedure in the prevention of carbapenem-resistant Acinetobacter baumannii clonal spread in a burn intensive care unit using a high-sensitivity luminometer. Journal of Hospital Infection. 2017;95(1):46–52. Satlin MJ, Chavda KD, Baker TM, Chen L, Shashkina E, Soave R, et al. Colonization with levofloxacin-resistant extended-spectrum β-lactamase-producing Enterobacteriaceae and risk of bacteremia in hematopoietic stem cell transplant recipients. Clinical Infectious Diseases. 2018;67(11):1720–8. Luo Y, Yang J, Ye L, Guo L, Zhao Q, Chen R, et al. Characterization of KPC-2-producing Escherichia coli, Citrobacter freundii, Enterobacter cloacae, Enterobacter aerogenes, and Klebsiella oxytoca isolates from a Chinese Hospital. Microbial Drug Resistance. 2014;20(4):264–9. [Available from: https://ethics.research.ac.ir/ProposalCertificateEn.php?id=838420&Print=true&NoPrintHeader=true&NoPrintFooter=true&NoPrintPageBorder=true&LetterPrint=true. Tables Table 1. Baseline characteristics of the included patients Variables Total: (n=120) SAPB group: (n=60) Control group: (n=60) P Age, years (mean ± SD) 47.87 ± 14.64 49.07 ± 14.31 46.67 ± 15.1 0.64 Gender, n (%) Female 44 (36.7) 14 (23.3) 30 (50.0) 0.03 Male 76 (63.3) 46 (76.7) 30 (50.0) Underlying disease, n (%) No 36 (30.00) 16 (26.70) 20 (33.30) 0.57 Yes 84 (70.00) 44 (73.30) 40 (66.70) BMI, kg/m 2 (mean ± SD) 24.19 ± 3.508 24.83 ± 3.06 23.55 ± 3.83 0.11 Surgery incision sites, n (%) 4th intercostal space 36 (30.0) 20 (33.3) 16 (26.7) 0.45 5th intercostal space 44 (36.7) 24 (40.0) 20 (33.3) 6th intercostal space 36 (30.0) 16 (26.7) 20 (33.3) 7th intercostal space 4 (3.3) 0 (0.0) 4 (6.7) Surgical Procedure, n (%) Hydatid Cystectomy 16 (13.3) 9 (15) 7 (11.7) 0.44 Lobectomy 21 (17.5) 11 (18.3) 10 (16.7) Esophagectomy 10 (8.3) 4 (6.7) 6 (10) Metastatectomy 8 (6.7) 4 (6.7) 4 (6.7) Mediastinal 2 (1.7) 1 (1.7) 1 (1.7) Others 3 (2.5) 1 (1.7) 2 (3.3) Operation duration, hours (mean ± SD) 3.87 ± 1.742 3.73 ± 1.187 4 ± 2.17 0.90 Complications, n (%) Nausea 22 (18.3) 12 (20.0) 10 (16.7) 0.73 Vomiting 16 (13.3) 6 (10) 10 (16.7) No complication 82 (68.3) 42 (70.0) 40 (66.6) Table 2. Comparison of pain scores (VAS) in the three assessment checkpoints between two groups Pain intensity SAPB group: (N=60) Control group: (N=60) P 6 h after surgery Mean ± SD 3.30 ± 1.56 6.27 ± 1.80 <0.001 95% CI 2.91 – 3.69 5.82 – 6.73 12 h after surgery Mean ± SD 1.73 ± 1.23 5 ± 1.23 <0.001 95% CI 1.42 – 2.04 4.69 – 5.31 24 h after surgery Mean ± SD 1.30 ± 1.39 3.77 ± 1.46 <0.001 95% CI 0.95 – 1.65 3.40 – 4.14 First 24 h after surgery P-value <0.001 <0.001 Table 3. Multivariate regression analysis for the first time checkpoint (6h) Pain score (6h) Coefficient P 95% CI Group Control baseline Intervention -2.95 0.00 -3.85 – -2.05 Age -0.01 0.36 -0.05 – 0.02 Gender Female baseline M 0.10 0.84 -0.90 – 1.10 BMI 0.06 0.38 -0.08 – 0.21 Surgical procedure cystectomy baseline lobectomy 0.43 0.46 -0.73 – 1.59 esophagectomy 2.55 0.01 0.72 – 4.37 metastatectomy 0.57 0.45 -0.94 – 2.08 mediastinal 2.00 0.13 -0.59 – 4.58 others -0.56 0.61 -2.78 – 1.66 Operation duration -0.18 0.26 -0.51 – 014 Table 4. Multivariate regression analysis for the second time checkpoint (12h) Pain score (12h) Coefficient P 95% CI Group Control baseline Intervention -3.20 0.00 -3.91 – -2.48 Age -0.01 0.71 -0.03 – 0.02 Gender Female baseline M -0.08 0.84 -0.87 – 0.71 BMI -0.03 0.59 -0.14 – 0.08 Surgical procedure cystectomy baseline lobectomy 0.27 0.56 -0.64 – 1.18 esophagectomy 0.90 0.21 -0.54 – 2.34 metastatectomy 0.17 0.77 -1.02 – 1.36 mediastinal 0.17 0.86 -1.86 – 2.22 others -0.77 0.38 -2.52 – 0.98 Operation duration -0.10 0.42 -0.36 – 0.15 Table 5. Multivariate regression analysis for the third time checkpoint (24h) Pain score (24h) Coefficient P 95% CI Group Control baseline Intervention -2.46 0.00 -3.30 – -1.62 Age 0.01 0.66 -0.02 – 0.04 Gender Female baseline M -0.11 0.81 -1.05 – 0.83 BMI -0.02 0.74 -0.16 – 0.11 Surgical procedure cystectomy baseline lobectomy -0.14 0.80 -1.22 – 0.94 esophagectomy -0.28 0.74 -1.99 – 1.42 metastatectomy -0.34 0.63 -1.75 – 1.07 mediastinal -0.13 0.92 -2.54 – 2.29 others -1.13 0.28 -3.20 – 0.94 Operation duration 0.07 0.64 -0.23 – 0.37 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. 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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-8682214","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584929929,"identity":"7c6e5f2d-57c3-4278-b372-96fb0d2d8b1f","order_by":0,"name":"Sahar Shadvar","email":"","orcid":"","institution":"Islamic Azad University Medical Branch of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"","lastName":"Shadvar","suffix":""},{"id":584929930,"identity":"86da6e1e-cd36-43c4-8369-32cb24e73fa4","order_by":1,"name":"Reza Bolandparvaz Jahromi","email":"","orcid":"","institution":"Islamic Azad University Medical Branch of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"Bolandparvaz","lastName":"Jahromi","suffix":""},{"id":584929931,"identity":"0cf5ca5d-6ceb-4465-804b-a1594d7936a9","order_by":2,"name":"Seyedsina Ojaghi Haghighi","email":"","orcid":"","institution":"Islamic Azad University Medical Branch of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Seyedsina","middleName":"Ojaghi","lastName":"Haghighi","suffix":""},{"id":584929932,"identity":"85b7e9b6-dd93-416b-b18d-821c60e12cbf","order_by":3,"name":"Kamyar Khazaei","email":"","orcid":"","institution":"Islamic Azad University Medical Branch of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Kamyar","middleName":"","lastName":"Khazaei","suffix":""},{"id":584929933,"identity":"89405425-2297-4c40-bf28-8697ab132722","order_by":4,"name":"Amir 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19:53:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8682214/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8682214/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101864842,"identity":"a4082e35-5b7a-4464-8bc2-c6eca0db6b14","added_by":"auto","created_at":"2026-02-04 12:17:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60485,"visible":true,"origin":"","legend":"\u003cp\u003eCause of hospitalization\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8682214/v1/6eb548e47e9b42965e8a217b.png"},{"id":101864846,"identity":"b61d6a0a-a594-4943-8936-c8f30372b441","added_by":"auto","created_at":"2026-02-04 12:17:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50817,"visible":true,"origin":"","legend":"\u003cp\u003eIsolated strains prevalence during study period\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8682214/v1/3cf8efb19179fa7c43fd0746.png"},{"id":101864844,"identity":"a88814e4-6ccc-416b-a6ab-0373c6f990b4","added_by":"auto","created_at":"2026-02-04 12:17:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83331,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of GNBs before and after COVID-19\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8682214/v1/23aadb2cee1828ec26cfa5d5.png"},{"id":101864843,"identity":"77a8d600-07e4-4d73-9ac9-e72ed006ddfc","added_by":"auto","created_at":"2026-02-04 12:17:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46774,"visible":true,"origin":"","legend":"\u003cp\u003eAR prevalence of isolated strains\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8682214/v1/495328e9ad50c42b80a61996.png"},{"id":101864845,"identity":"018bf8b6-3ebb-41e0-9506-3cdabca7b3b9","added_by":"auto","created_at":"2026-02-04 12:17:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41280,"visible":true,"origin":"","legend":"\u003cp\u003eOverall AR during study period\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8682214/v1/c0a6ddf2a62319ca777a94d6.png"},{"id":104397830,"identity":"237896ab-504c-4c07-a740-cedbf23b6bf3","added_by":"auto","created_at":"2026-03-11 11:57:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1499871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8682214/v1/755db1cb-e0b8-4499-aca9-ba655ae7ba4a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Antimicrobial Resistance Trends in ICU-acquired Infections after the COVID Epidemic: A 5-year Retrospective Cohort and Comparative Review with MENA Region","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe prevalence of antimicrobial resistance (AR) among nosocomial infections is rising dramatically. It is specifically a consequence of resistant bacterial strains, hence representing a significant global healthcare concern. This issue intensifies tremendously within intensive care units (ICUs), where critical patients are most vulnerable as a result of their compromised conditions. Some authors have proposed ICU-acquired infection during hospitalization as an independent factor linked to mortality, with a two-fold rise of the rate (1, 2).\u003c/p\u003e\n\u003cp\u003eHAIs are a significant healthcare issue that causes economic losses and impacts productivity. They result in prolonged hospital length of stay, long-term disabilities, financial burdens on healthcare systems, increased costs for patients and families, AR, and higher in-hospital mortality rates (3). The recent World Health Organization (WHO) updates estimated AR was the main reason of 1.27 million deaths across the world and was accountable for 4.95 million deaths indirectly in 2019. Moreover, the burden of AR is disproportionately higher in medium- and lower-income countries, where ICU-acquired infections are 2-3 times more prone compared to high-income countries, with higher mortality (33.6% vs \u0026lt;20%) (4). Also, the challenge posed by AR at the Middle East and North Africa (MENA) region remains obscure (5). According to a nationwide study conducted in 2018 across 940 hospitals in Iran, the incidence rate of HAIs was 4.2 per 1000 patient-days with a 15.65% mortality rate (6); however, these figures predated the Coronavirus Disease-19 (COVID-19) pandemic.\u003c/p\u003e\n\u003cp\u003eBacterial AR refers to genotypic or phenotypic changes in bacteria that reduce the efficacy of drugs intended to treat infections, and it has emerged as one of the foremost public health challenges of the 21st century (7). Presumably a naturally occurring phenomenon, AR has been primarily aggravated on a global scale\u0026nbsp;over recent decades due to the indiscriminate use of antibiotics in healthcare (e.g., prescriptions without indication, incorrect administration, and over-the-counter availability) as well as in agriculture and veterinarian utilization (8).\u003c/p\u003e\n\u003cp\u003eRemarkable Gram-negative bacilli (GNB) pathogens capable of developing resistance to multiple antibiotics consist of Klebsiella species, Pseudomonas aeruginosa (P. aeruginosa), Acinetobacter baumannii (A. baumannii), and Escherichia coli (E. coli)\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003especies. The aforementioned bacteria are frequently implicated in hospital-acquired infections (HAIs), particularly within ICU settings, such as ventilator-associated pneumonia, bloodstream infections, and urinary tract infections (UTIs) (9, 10).\u0026nbsp;The rapid increase in resistance among bacteria to first- and second-line antimicrobial agents, coupled with the emergence of multidrug-resistant (MDR) organisms, extensively drug resistant (XDR) organisms, and pan drug resistant (PDR) organisms is particularly alarming. This situation has significantly narrowed the range of available therapeutic options for treating these infections. The limited number of new antimicrobial agents approved recently further exacerbates this situation\u0026nbsp;(3).\u003c/p\u003e\n\u003cp\u003eThe COVID-19 pandemic profoundly altered infection control practices and antibiotic indications worldwide. The Centers for Disease Control and Prevention (CDC) revealed a 20% increase of AR in HAIs during the pandemic, peaking in 2021 and remaining above pre-pandemic levels in 2022 (11). Such aspects remain insufficiently studied in Iran and across MENA region, which resulted in scientific gaps about potential changes in empirical antibiotic therapy that we intend to address. Sharing epidemiological information reporting the particularities of each region is a crucial measure to mitigate or degrade the advance of AR according to the WHO Global Action Plan (12). Healthcare strategists must carry out regular evaluation of surveillance data to monitor the imperative trends and proactively avert outbreaks (13). Yet, lack of updated national data on AR and HAIs in Iran, as the most recent comprehensive national report prior to this study dated back to 2018 before the COVID pandemic (6), led us to provide an updated insight to inform healthcare policies. In the current study, the cornerstone of our investigations was to introduce an update on local epidemiology of HAIs and AR trends of major bacterial pathogens in ICU settings over a five-year period, from 2018 to 2023. Bank Melli Hospital in Tehran provides an ideal setting to study the incidence of AR due to its high ICU patient turnover in this region.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy Design and Subjects\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe current observational cohort study was conducted retrospectively on 358 hospitalized patients in three separate ICU wards to evaluate AR at Bank Melli Hospital, a tertiary center, Tehran, Iran over a 5-year period from March 2018 to December 2023, including the COVID-19 pandemic. The official onset of the COVID-19 epidemic in Iran dates back to February 17, 2020. All of the demographic and clinical data including age, gender of the patients, medical records, clinical course during hospitalization, and microbiological laboratory results were reviewed from hospital databases for all patients. ICU-acquired infections was defined according to CDC/NHSN criteria (14): new pathogen isolated \u0026gt;48 hours after admission with compatible clinical, radiological, and laboratory features. Positive cultures prior to 48 hours of admission, contaminated cultures (i.e., polymicrobial cultures), and duplicated samples (i.e., repeated positive cultures similar in both bacterial species and site during the treatment course or a simultaneous positive blood culture to another prior infection) were also excluded. All of the previously listed criteria were put into place to avoid the possibility of duplication bias or miscalculating recurring situations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe included 472 non-duplicate clinical samples from 242 ICU patients with positive cultures of A. baumanii, P. aeruginosa, K. pneumoniae, K. oxytoca and E. coli. In addition, a positive SARS-CoV-2 polymerase chain reaction (PCR) test was considered as the basis for confirming the diagnosis of COVID-19. The patients were treated in parallel to the results of clinical bacteriology identification procedures on the samples. They were classified by age into under 60, 60 to 75, and above 75; the duration of hospitalization was also categorized into less than 18 days and 18 days or longer. Identified risk factors were considered as smoking, drug use, and diabetes; additionally, patients were assessed for immunodeficiency. Immunodeficiency is acknowledged as primary or secondary based on medical references. Primary immunodeficiencies are mainly illustrated in rheumatological and pediatrics domains; on the other hand, secondary immunodeficiencies span from viral and malignancies to iatrogenic immunosuppression, hence a broader spectrum of immunological disorders (15).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLaboratory Evaluations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe samples were processed immediately by the microbiology laboratory in accordance with established protocols guidelines to identify isolates (16). In detail, conventional biochemical tests including oxidase, catalase, motility, metabolic procedures such as citrate, indole production, methyl red, Voges-Proskauer, and presence of lysine decarboxylase and arginine dehydrogenase enzymes were performed. Blood samples were loaded in the BD BACTEC™ Automated Blood Culture System (BD Diagnostics, Sparks, MD, USA) at 36\u0026nbsp;°C for up to 5\u0026nbsp;days and positive samples were subcultured. In addition, a molecular sepsis panel including PCR assays targeting both Gram-positive and Gram-negative bacteria was performed in selected suspicious bacteremia cases. Bronchoalveolar lavage (BAL), sputum, pus swab and wound specimens, catheters and third-space fluids (pleural and peritoneal tap) samples were inoculated on MacConkey agar, sheep blood agar, and chocolate blood agar (Hi Media Laboratories LLC, India) and incubated in 37\u0026nbsp;°C overnight at both ambient air and 5% CO2. Urine samples were inoculated on cysteine-lactose electrolyte deficient agar, as well as aerobically.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAntimicrobial Susceptibility Testing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIsolates confirmed by biochemical and molecular tests underwent the disc diffusion susceptibility test to each tested drug using antibiotic disks provided by Rosco™, Switzerland and Padtan Teb™, Iran. Quality control was performed annually according to the following guidelines. Pivotal points were interpreted according to the latest available clinical and laboratory standards institute (CLSI) guidelines (17-20). Intermediate results were classified as resistant. Interpretation of antibiotic susceptibility was performed based on the European center for disease prevention and control (ECDC) instructor as MDR: resistant to at least one drug in three or more antimicrobial classes, XDR: susceptible to only one or two classes, PDR: resistant to all drug classes (21). In addition, a gradient minimum inhibitory concentration (MIC) determining method according to manufacturer’s instructions was implemented in certain cases. In summary, a 0.5 McFarland suspension of each isolate was inoculated on a whole plate surface Mueller–Hinton agar plate by streaking the swab in back and forth motions. Then, they were incubated for 24 hours at 37°C. Following incubation, a ruler measured inhibition zone sizes to the nearest millimeter.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eDemographic characteristics of the study.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 472 clinical isolates from 242 hospitalized patients were recovered between March 2018 and December 2023. The mean age of the patients was 75.6 ± 15.5 years and the majority were elderly (over 75 years old, 60.4%). Men comprised 56.8% of the study cohort. Approximately half of the patients had a hospital stay of 18 days or longer. Diabetes mellitus (23.5%), immunodeficiency (21.6%) and active tobacco or substance use (14.4%) were the most common comorbidities. Notably, 62.1% of patients were hospitalized after the official declaration of the COVID-19 epidemic in Iran (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCauses of Hospitalization.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePneumonia (28.9%), neurologic diseases (18.6%), and cancer (16.1%) were the prevalent causes of hospitalization, respectively. Other reasons such as cardiovascular diseases, other respiratory system diseases rather than pneumonia, orthopedic surgeries, UTI and acute abdomen were also noted less frequently (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDistribution of Isolates.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eK. pneumoniae (42.2%) was the most frequently isolated organism overall (42.2%), followed by P.\u0026nbsp;aeruginosa\u0026nbsp;(30.3) and E. coli (16.7%). K. oxytoca (8.5%) and A.\u0026nbsp;baumannii\u0026nbsp;(2.3%) were less frequently detected. \u003cstrong\u003eRespiratory specimens (sputum and BAL) accounted for more than two-thirds of isolates, followed by urine (16.5%) and blood or wound cultures (each 6.8%) (Table 2).\u0026nbsp;\u003c/strong\u003eA.\u0026nbsp;baumannii\u0026nbsp;was isolated only from BAL, sputum, and blood cultures, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTemporal observations revealed an increasing proportion of both K. pneumoniae and K. oxytoca after the onset of the COVID-19 pandemic (peaking in 2022), while P.\u0026nbsp;aeruginosa\u0026nbsp;and E. coli declined over time. Notably, A.\u0026nbsp;baumannii\u0026nbsp;was not detected until 2021 but appeared thereafter (Figures 2,3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAR categories.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eResistance profiles were broadly similar across demographic and clinical subgroups. However, non-MDR strains were less frequent among females compared with males, and the prevalence of XDR increased with advancing age. Yet the highest proportion of non-MDR isolates was observed with a decreasing trend with age, and PDR was not detected in patients under 60 years of age. Both MDR and non-MDR strains were more commonly detected in immunocompromised patients (Table 3).\u0026nbsp;The overall incidence of MDR, XDR and PDR pathogens accounted for 76.9%, 64.8% and 1.5% of our patients, respectively. The lowest AR rate was reported in E. coli. On the other hand,\u0026nbsp;the highest XDR pattern was observed in A. baumannii (Figure 4).\u003c/p\u003e\n\u003cp\u003eThe prevalence of XDR increased during the study years, while PDR gradually declined and was absent in the last two years. After the onset of COVID-19, MDR and XDR isolates became more common (Figure 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAntibiotic-Specific Resistance.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAR varied considerably across antibiotic classes (Table 4). The highest overall AR was observed against ceftriaxone (90.3%), followed by trimethoprim-sulfamethoxazole (83%), fluoroquinolones (80.5%), carbapenems (79%), and ceftazidime (78.3%). Resistance to piperacillin–tazobactam was also high (70.4%). Aminoglycosides showed relatively lower AR (58.7%). Colistin retained the greatest activity, with resistance documented in only 2.8% of isolates. A. baumannii consistently exhibited the highest AR among species and across most antibiotic classes, while E. coli demonstrated the lowest, particularly against carbapenems and aminoglycosides.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe overall rate of AR and MDR isolates in HAIs is more prevalent in the ICU than in other wards (22). This may be due to the frequent utilization of invasive medical devices among ICU patients and their heightened exposure to a greater diversity of antibiotic-resistant pathogens in addition to horizontal gene exchange of various resistant traits, namely plasmid-encoded betalactamases, aminoglycosides modifying enzymes, quinolone resistance gene (23, 24). WHO has defined the AWaRe (Access, Watch and Reserve) classification (Table 5) as a global action plan to counter the progression of AR (25). The Access group consists of wide-spectrum antibiotics with a lower risk of AR. The Watch group encompasses antibiotics that require careful monitoring because of the higher potential of AR. We must consider these groups as empirical therapy options and withhold the Reserve group as the last-resort option for confirmed MDR cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIran is formally among the countries with established national and hospital-level Infection Prevention and Control (IPC) policies yet; implementation gaps remain (26). According to a study conducted in 2018 across 940 hospitals in Iran, HAI incidence was 4.2 per 1000 patient-days with 15.65% mortality rate. The mode of afflicted patients had pneumonia, and after that in order to frequency, UTI, surgical site infections, and sepsis. In addition, the most frequently cultured pathogens included E. coli, K. pneumoniae, and A. baumannii (6). K. pneumoniae accounted for a substantial proportion of HAIs in our study (with the exception of E. coli emerging as the most common infectious agent in UTIs), comparable with the similar studies in the region (27-29). Although these discrepancies may reflect variations in patterns and epidemiological characteristics between healthcare settings and age groups, the excessive and unnecessary use of antibiotics in Iran has led to this striking rate of AR in recent decades (30). Qatar’s 3-year experience in introducing an effective stewardship policy managed to steadily diminish the MDR P. Aeruginosa prevalence from 9% to 5.46% in 2015 (31).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource of Isolates and Empirical Therapy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe respiratory tract samples, sputum and BAL, were the most common type in terms of the type of HAIs similar to Carenjo Suarez and Litwin (32, 33), although most authors consistently found UTI emerging as the leading cause of HAI (34-36). This may be due to ventilators being indicated more frequently after the COVID pandemic. There was also a considerable prevalence (36.6%) of P. aeruginosa in respiratory tract samples of our study, compatible with others (31%) (8). The incidence of E. coli as a cause of UTI is reportedly three-fold higher than Klebsiella spp.; hence, nitrofurantoin may serve as an effective empirical therapy for uncomplicated UTIs. Other recommended agents in complex cases are aminoglycosides, ceftazidime, cefepime, or piperacillin-tazobactam (37). Studies have demonstrated higher cephalosporin-resistant E. coli in blood compared with urine isolates (38). Some researchers propose a concern as meropenem becomes less effective in vascular catheters and blood stream infections than other sites argues escalating reports of community-acquired carbapenem resistant infections and considerably attributable with higher mortality (8, 39).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is noteworthy to cover Gram-positive cocci in addition to GNB in skin/soft tissue, vascular catheter infections, and bacteremias because Staphylococcus aureus is generally the most common MDR isolate at these sites (8).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIsolates\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGNBs are the proven culprits for most ICU HAIs (40-42). Our data regarding the microorganism types elucidated an overall preponderance of K. pneumoniae (especially respiratory samples) aligned with elevated risk of colonization during hospitalization. These were backed up with other Iranian studies (43-45). In the opposite view of us, all Klebsiella isolates depicted low AR to levofloxacin per Molana et al. findings (46) despite the AR level. Remarkable resistance to carbapenem in Klebsiella spp. is often observed in urinary tract and pus isolates (47). A 6.9% prevalence of colistin-resistant K. pneumoniae isolates, slightly higher than us (2.5%), was calculated over Iran in 2022 (48). Unlike Sekar et al. (47), the AR rate in Klebsiella spp. was significantly lower compared with E. coli for most antibiotics; however, the AR of carbapenems in both studies was significantly higher in Klebsiella spp.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eE. coli is a conditional pathogen with high uropathogenic propensity to trigger UTI and infections of the respiratory tract (49-51). This might be attributed to the production of virulence factors, and AR mechanisms like extended spectrum betalactamases (ESBL) production and mutations in Amp C enzymes and porin loss (52-55). E. coli had the least AR, though it is still concerning. Only piperacillin/tazobactam is considerably efficient among the penicillins (47). ESBL-producing bacteria are more resistant to aminoglycosides and levofloxacin than those with high AR to cephalosporins. Synthetic antibiotics such as sulfonamides demonstrated lower AR than ampicillin (56). Fluoroquinolone-resistant E. coli (associated with MDR) isolates from UTIs are also commonly resistant to ampicillin and sulfamethoxazole-trimethoprim (57). Our reported resistance to third-generation cephalosporins and fluoroquinolones was excessive in E. coli, in line with the current trend of studies reporting levofloxacin and ofloxacin as the most effective (47, 58). This can be attributed to administration of high-grade and often unwarranted fluoroquinolones or third-generation cephalosporins to provide empiric coverage of this pathogen in UTIs, which may restrict available treatment options (carbapenems as the only susceptible agent) (56, 59-62). Fortunately, the rate of carbapenem resistance was not as high as A. Baumannii and K. Pneumoniae isolates in our study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP. Aeruginosa was identified as the second most prevalent strain in our study. The rate of MDR P. Aeruginosa varies geographically and it is evident across the MENA region, where it ranges from 22.5% in Egypt to 61% in Saudi Arabia (5). 36.3% MDR and 18.1% XDR prevalence was assessed in Pakistan (63). A handful of Iranian investigations also portrayed considerable heterogeneity: Concerning 87% AR in VEB-1, OXA-10 and PER-1 producing genotypes are the established mechanisms of betalactam resistance in MDR P. aeruginosa and A. baumannii in Iran, all of which (100%) were resistant to cefotaxime, ceftazidime, and cefepime but susceptible to carbapenems (64, 65). Nasimmoghadas et al. (66), further confirmed 94% MDR and 85% as XDR strains with significant resistance to nearly all tested antibiotics, except colistin (2%) and ceftazidime (32%). P. Aeruginosa is a major pathogen cause of wound and burn HAIs in ICU (65). In two burn centers, 93.1% MDR strains were observed with high AR rates to ceftazidime 57.5%, ciprofloxacin 65-93.7%, gentamycin 67.5%, piperacillin 87.5%, amikacin 82-90%, and imipenem 79.2-97.5% (65, 67). On the contrary, other studies in the similar region reported 5.46-45.3% MDR and 15.53% XDR prevalence with ciprofloxacin (62.7%), amikacin (52%) and imipenem (64%) resistant phenotypes, respectively (68, 69). A Canadian multicenter investigation in 10 of 27 ICUs reported an elevation in Pseudomonas AR to broad-spectrum cephalosporins, piperacillin-tazobactam, and carbapenems over the study period (70). In our study, we also observed such an increase in XDR and PDR resistance patterns. In the current study, the frequency of fluoroquinolons, aminoglycosides and carbapenem resistant P. Aeruginosa was substantially lower compared to previous reports of the Tehran Children’s Medical Center, (44, 71, 72) and demonstrated quite high susceptibility to commonly tested antibiotics. The higher overall AR in our study may be attributable to differences in patient populations (adult ICU versus pediatric patients) and greater exposure to risk factors such as invasive devices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA. baumannii is an opportunistic pathogen with the capability to colonize in hospital settings, persist for an extended time, acquire diverse virulence factors, and emerging as a crucial cause of ICU-acquired infection outbreaks particularly in immunocompromised patients (73). The most prevalent HAI with A. baumannii is ventilator-associated pneumonia through skin and airway defects penetration and attachment to bronchial epithelial cells (74), as it was also apparent in our study. A sharp escalation of MDR A. baumannii was reported at Iran in 2015 (75) with outstanding regional prevalence of ESBL, MDR and XDR isolates far higher than MDR and XDR P. aeruginosa (76). So although A. baumannii is less virulent than P. aeruginosa, it represents the most common MDR GNB, exhibits the highest AR to tested antibiotics and contributes to the dissemination of plasmid-mediated AR genes contributing to outbreaks of nosocomial MDR (92.2%) and XDR (78.6%) A. baumannii. This is largely attributed to the synergic interaction between certain betalactamses (carbapenem hydrolyzing enzymes such as OXA-23), porin loss, and efflux pumps overproduction (74, 77, 78). Coinciding with OXA-23, the ArmA enzyme is also the predominant driver of global AR of A. baumannii to all aminoglycosides, including Iran (79). The widespread MDR pattern of A. Baumannii has established carbapenems as the mainstay treatment in practice; however, numerous reports of carabapenem-resistant isolates are noted locally and worldwide despite of ongoing narrowed options (32, 76, 77, 80, 81). Remarkable resistance to almost all tested agents (broad-spectrum betalactams, cephalosporins, carbapenems, aminoglycosides and fluoroquinolons) was similar between (74) and ours, at approximately 92-97%. Several Iranian studies (68, 82) have confirmed these mutual concerns with approximately 70-97% AR to tobramycin, ceftazidime, ciprofloxacin, and carbapenems, comparable to us; while Jafari et al. (83) documented lower AR rates to carbapenems (41%-60), ciprofloxacin (78%), and retained susceptibility to colistin and ampicillin-sulbactam. Nevertheless, emergence of colistin-resistant A. baumannii was reported in Iran in 2013 (84). In our observations, there was 100% carbapenem resistance and 0% colisitin resistance in A. baumannii isolates, while the abovementioned studies noted lower carbapenem resistance but higher colistin resistance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAntibiotic agents\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Infectious Diseases Society of America discourages administration of antimicrobials for empirical treatment if the regional AR is greater than 10-20% (85). Convincing percentages of resistant strains of E. coli and Klebsiella spp. to third and fourth generations of cephalosporins were noted with a slightly lower rate in other developing countries and broadly (86). Cefepime combined with metronidazole or piperacillin-tazobactam are good alternatives for intra-abdominal infections, while meropenem must be reserved for sepsis (37). Fluoroquinolones are frequently favored in empirical use in UTI (61), yet we found a relatively high AR to E. coli (potential UTI pathogens). High level of ciprofloxacin-resistant GNB is interestingly associated with elevated AR of broad-spectrum antipseudomonal agents like piperacillin-tazobactam (90%) and carbapenems (88 to 90%), in favor of observations of non-enzymatic mechanisms such as efflux systems overexpression or low permeability in MDR isolates (87).\u003c/p\u003e\n\u003cp\u003eThe rising trends of AR of antipseudomonal agents in most countries of our region is alarming: Bahrain, Qatar, Saudi Arabia, Iraq, Egypt, Syria, Libya, Tunisia, and Lebanon consistently showed elevated AR levels for piperacillin-tazobactam, antipseudomonal cephalosporins (ceftazidime and cefepime), carbapenems, aminoglycosides, fluoroquinolones, and aztreonam. Carbapenems are the forefront class to counter severe MDR cases with the lowest AR to betalactams; thus, the propensity to spread rapidly at local and global outbreaks worryingly restricts available treatment options (47). Therefore, carbapenem-resistant K. pneumoniae, E. coli, P. aeruginosa, and A. baumannii pose an international public health concern, especially in developing countries and account for high morbidity, mortality, and healthcare costs (88). Ying Han claimed a 20% increase in carbapenem resistance in a 4-year period (89). We also found a concerning AR to carbapenems (80%). Carbapenem overuse might explain why there might be a difference between areas with patients from a higher socioeconomic status than lower socioeconomic areas (90). Carbapenem-resistant cases are managed by long-established drugs such as aminoglycosides, tigecycline, colistin, and ceftazidime-avibactam. Some authors established the correlation between colistin resistance in carbapenem-resistant K. pneumonia (multiple worldwide outbreaks), P. aeruginosa and A. baumannii bloodstream infections with mortality (48, 91-93).\u003c/p\u003e\n\u003cp\u003eColistin remains the highly active last-resort against MDR, XDR, or even PDR carbapenem-resistant GNB with roughly 100% efficacy in most regions, although Qatar, Saudi Arabia, Egypt, and Syria report developing AR to colistin (3.4 to 30%) (5, 94). A national survey calculated that Tehran had the highest resistance to colistin with 16% (7.3-31.5%), whereas Urmia had the lowest with 0.3% (0–4.2%). The overall rate of colistin resistance increased significantly over time (2013 2018, 2019–2021), which may be due to the increased use of this antibiotic in the recent years (48). The prevalence of carbapenem and colistin AR has increased along with alarming emergence of PDR organisms among critical wards (95). Nevertheless, colistin resistance was very low in our study, closely matching to an investigation across 30 of 57 centers in the UK reporting a colistin resistance rate of 3.1% (96). Colistin resistance evolution is largely attributed to alterations in the pathway of lipopolysaccharide biosynthesis (77).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe novel betalactamase inhibitor combinations such as ceftazidime-avibactam and ceftolozane-tazobactam were often unavailable but depicted good antimicrobial susceptibilities in the Middle East. However, it is less compared to other regions probably because of high regional AR such as in Qatar (31.2 - 37.1%), even before their introduction into clinical practice. Furthermore, many clinicians also stated that specific last-line antibiotics (linezolid, colistin, tigecycline and daptomycin) were not available in their ICU (97, 98). Nonetheless, a slight increase in linezolid prescription was also associated with development and spread of AR (99). Some futuristic studies are evaluating the promising results of synergism of bacteriophage therapy and antimicrobial agents in MDR epidemics (100).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCOVID-19\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eK. pneumoniae became more prevalent concurrently with nosocomial respiratory infections after the pandemic in our observations. This is possibly due to more ventilator utilization and it might indicate more focus on ventilator-associated pneumonia in HAI empirical management guidelines. P. aueroginosa, A. baumannii, Mycoplasma pneumonia, and Haemophilus influenza were frequently suspected bacterial superinfections in the COVID-19 pandemic. An escalating trend of prescribing the Reserve agents out of fear of bacterial coinfections or overlap of paraclinical features with bacterial respiratory infections led to unjustified prescriptions of broad-spectrum antibiotics. For example, the wide prescription of azithromycin due to its additional immunomodulatory and antiviral properties resulted in rapid transformation of AR (101). Overall, we report a \u0026gt;20% increase in the prevalence of XDR isolates at ICUs after the pandemic.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk factors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe observed that patients with more than 18-day hospitalization, age over 60, and male gender were slightly more frequent in terms of MDR and XDR HAI. Some researchers introduced the independent predictors associated with MDR organism HAI in ICU as bacterial load, male gender, multiple invasive procedures, and prolonged hospitalization in ICU (1, 102). Importantly, prior hospitalization within the past year is a key determinant, and each additional day at ICU increased HAI risk by approximately 1% with a mean acquisition time of 11 days for MDR HAI and 24.5 days for XDR HAI \u0026nbsp;(45, 103). Furthermore, patients with respiratory and cardiovascular comorbidities are five and six times more prone to a GNB HAI, respectively (103). Moreover, cirrhosis and impaired consciousness are also substantial underlying risk factors in the ICU patients (45, 104).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDR A. baumannii and P. aeruginosa survive in the hospital environment\u0026nbsp;and are easily transmitted between patients through the hands of health care workers (105). The optimal healthcare preventive protocols to combat dissemination of AR should respond appropriately against risk factors, namely reducing invasive procedures, enhancing routine hygiene practices and stringent antibiotic stewardship program (106-108). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImmunodeficiency\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA possible contributor to HAI and consequently morbidity and mortality in ICU patients is underlying conditions compromising the immune system; however, our study contradicts this theory. MDR GNB are a common cause of sepsis in patients undergoing transplantation or chemotherapy, which are typically managed by carbapenems (95). There are reports of invasive bloodstream infections caused by MDR Enterobacteriaceae in neutropenic hematopoietic stem cell recipients (even with sufficient prophylaxis) (109). A similar correlation was observed between malignancies and ESBL Enterobacteriaceae (56).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSuggestions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eImproving, adaptation and adherence to antibiotic stewardship guidelines by considering regional AR patterns could effectively mitigate or degrade the spread of resistant bacteria (110). Overall, this study emphasizes the urge for medical staff to use antimicrobial agents rationally by designing indigenized empirical stewardship guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur retrospective observation had some limitations, as the genotypic and molecular data of the isolates were not documented. Furthermore, the disk diffusion method might not be sensitive enough as the broth microdilution method to identify AR measurements. Moreover, most patients were already under antimicrobial treatment before referring to our center. Differentiating between pathogen and contamination were sometimes challenging, particularly in patients with urine catheterization. We conducted a single territory study within the province, so the risk of local practice bias probably affects the generalizability of the findings. Therefore, we recommend futuristic replication of similar blinded analytical studies in various hospitals and cities in this region for a more definitive comprehensive analysis to monitor HAIs.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eThis study provides an updated overview of HAI and AR in ICU settings over a five-year period after the COVID-19 epidemic. The incidence of respiratory HAIs has surpassed UTIs compared to pre-COVID studies. The alarming prevalence of MDR, XDR and PDR was observed in Klebsiella species, P. aeruginosa, and E. coli. These findings were consistent with concerning trends reported across neighboring countries. Acinetobacter baumannii consistently exhibited the highest AR (\u0026gt;90% XDR, 100% carbapenem resistance) among species and across most antibiotic classes. AR was mostly noted against cephalosporins, fluoroquinolones, and carbapenems, while colistin remained the most effective agent in MDR cases. The burden of AR increased after the onset of the COVID-19 pandemic (\u0026gt;20% increase in XDR isolates), highlighting the urge for stricter surveillance and antibiotic stewardship policies. Our findings emphasize on the vitality of region-specific data to guide infection prevention and control measures and provide insights to update empirical guidelines, especially in ICUs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eA. baumannii: Acinetobacter baumannii\u003c/p\u003e\n\u003cp\u003eAR: Antimicrobial Resistance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBAL: Bronchoalveolar Lavage\u003c/p\u003e\n\u003cp\u003eCDC: Centers for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eCOVID-19: Coronavirus Disease-19\u003c/p\u003e\n\u003cp\u003eE. coli: Escherichia coli\u003c/p\u003e\n\u003cp\u003eESBL: Extended Spectrum Betalactamases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGNB: Gram-Negative Bacilli\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHAI: Hospital-Acquired Infections\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICU: Intensive Care Units\u003c/p\u003e\n\u003cp\u003eMDR: Multidrug Resistant\u003c/p\u003e\n\u003cp\u003eMENA: Middle East and North Africa\u003c/p\u003e\n\u003cp\u003eP. aeruginosa: Pseudomonas aeruginosa\u003c/p\u003e\n\u003cp\u003ePDR: Pan Drug Resistant\u003c/p\u003e\n\u003cp\u003eUTI: Urinary Tract Infections\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organization\u003c/p\u003e\n\u003cp\u003eXDR: Extensively Drug Resistant\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was registered and approved by the ethics committee of Farhikhtegan Hospital, Islamic Azad University, Tehran Medical Sciences (IAUTMU) (ethical code: IR.IAU.FARHIKHTEGANH.REC.1404.005\u003cem\u003e||2026.01.07\u003c/em\u003e) (111) and consent for participation or anonymous publication was waived accordingly due to retrospective design and use of anonymized data. Hence, clinical trial registration was not applicable. All procedures were adhered to the tenets of the Declaration of Helsinki (1964) and its later amendments about biomedical studies involving human participants.\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\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eCompeting interests\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThe authors declare that they have no competing interests.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eFunding\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eThis research received no specific grant from any funding agency in the public or commercial sectors.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eAuthors\u0026apos; Contributions\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eR.B. and K.K. extracted the study data. A.E. and S.O. wrote the primary draft and together with K.K. and R.B. completed the final manuscript. S.S. supervised the current study and provided expert and critical opinions to resolve any discrepancies. All authors take full responsibility for their contribution to this study and also they all reviewed and approved the final manuscript.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eAcknowledgments\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eNone.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eUse of AI tools\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors used AI-based tools only for language editing and grammar checking. No content was generated by AI, and the authors take full responsibility for the integrity and originality of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMehrad B, Clark NM, Zhanel GG, Lynch III JP. Antimicrobial resistance in hospital-acquired gram-negative bacterial infections. Chest. 2015;147(5):1413\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eYlipalosaari P, Ala-Kokko TI, Laurila J, Ohtonen P, Syrj\u0026auml;l\u0026auml; H. Intensive care acquired infection is an independent risk factor for hospital mortality: a prospective cohort study. Critical Care. 2006;10:1\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eCerceo E, Deitelzweig SB, Sherman BM, Amin AN. Multidrug-resistant gram-negative bacterial infections in the hospital setting: overview, implications for clinical practice, and emerging treatment options. Microbial Drug Resistance. 2016;22(5):412\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eOrganization WH. 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Journal of antimicrobial chemotherapy. 2019;74(12):3497\u0026ndash;504.\u003c/li\u003e\n\u003cli\u003eAlatoom A, Elsayed H, Lawlor K, AbdelWareth L, El-Lababidi R, Cardona L, et al. Comparison of antimicrobial activity between ceftolozane\u0026ndash;tazobactam and ceftazidime\u0026ndash;avibactam against multidrug-resistant isolates of Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. International Journal of Infectious Diseases. 2017;62:39\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eGrau S, Hern\u0026aacute;ndez S, Echeverr\u0026iacute;a-Esnal D, Almendral A, Ferrer R, Lim\u0026oacute;n E, et al. on behalf of the Catalan Infection Control Antimicrobial Stewardship Program. Antimicrobial Consumption among 66 Acute Care Hospitals in Catalonia: Impact of the COVID-19 Pandemic. Antibiotics 2021, 10, 943. s Note: MDPI stays neutral with regard to jurisdictional claims in published \u0026hellip;; 2021.\u003c/li\u003e\n\u003cli\u003eAlyousef AA, Al-Kadmy IM. The effect of immune modulation of Streptococcus constellatus SC10 strain upon Acinetobactor baumannii infection. Microbial Pathogenesis. 2017;111:370\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eLucien MAB, Canarie MF, Kilgore PE, Jean-Denis G, F\u0026eacute;n\u0026eacute;lon N, Pierre M, et al. Antibiotics and antimicrobial resistance in the COVID-19 era: Perspective from resource-limited settings. International journal of infectious diseases. 2021;104:250\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eWu C, Lu J, Ruan L, Yao J. Tracking epidemiological characteristics and risk factors of multi-drug resistant bacteria in intensive care units. Infection and Drug Resistance. 2023:1499\u0026ndash;509.\u003c/li\u003e\n\u003cli\u003eMaina JW, Onyambu FG, Kibet PS, Musyoki AM. Multidrug-resistant Gram-negative bacterial infections and associated factors in a Kenyan intensive care unit: a cross-sectional study. Annals of Clinical Microbiology and Antimicrobials. 2023;22(1):85.\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-Mart\u0026iacute;nez NF, C\u0026aacute;rcel-Fern\u0026aacute;ndez S, De la Fuente-Martos C, Ruiz-Montero R, Guzm\u0026aacute;n-Herrador BR, Le\u0026oacute;n-L\u0026oacute;pez R, et al. Risk factors for multidrug-resistant gram-negative bacteria carriage upon admission to the intensive care unit. International Journal of Environmental Research and Public Health. 2022;19(3):1039.\u003c/li\u003e\n\u003cli\u003eTacconelli E. Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development. 2017.\u003c/li\u003e\n\u003cli\u003eStrich JR, Palmore TN. Preventing transmission of multidrug-resistant pathogens in the intensive care unit. Infectious disease clinics of North America. 2017;31(3):535.\u003c/li\u003e\n\u003cli\u003eGray A, Allard R, Par\u0026eacute; R, Tannenbaum T, Lefebvre B, L\u0026eacute;vesque S, et al. Management of a hospital outbreak of extensively drug-resistant Acinetobacter baumannii using a multimodal intervention including daily chlorhexidine baths. Journal of Hospital Infection. 2016;93(1):29\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eCasini B, Selvi C, Cristina ML, Totaro M, Costa AL, Valentini P, et al. Evaluation of a modified cleaning procedure in the prevention of carbapenem-resistant Acinetobacter baumannii clonal spread in a burn intensive care unit using a high-sensitivity luminometer. Journal of Hospital Infection. 2017;95(1):46\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eSatlin MJ, Chavda KD, Baker TM, Chen L, Shashkina E, Soave R, et al. Colonization with levofloxacin-resistant extended-spectrum \u0026beta;-lactamase-producing Enterobacteriaceae and risk of bacteremia in hematopoietic stem cell transplant recipients. Clinical Infectious Diseases. 2018;67(11):1720\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eLuo Y, Yang J, Ye L, Guo L, Zhao Q, Chen R, et al. Characterization of KPC-2-producing Escherichia coli, Citrobacter freundii, Enterobacter cloacae, Enterobacter aerogenes, and Klebsiella oxytoca isolates from a Chinese Hospital. Microbial Drug Resistance. 2014;20(4):264\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003e[Available from: https://ethics.research.ac.ir/ProposalCertificateEn.php?id=838420\u0026amp;Print=true\u0026amp;NoPrintHeader=true\u0026amp;NoPrintFooter=true\u0026amp;NoPrintPageBorder=true\u0026amp;LetterPrint=true.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eBaseline characteristics of the included patients\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal: (n=120)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAPB group: (n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl group: (n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years (mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e47.87 \u0026plusmn; 14.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e49.07 \u0026plusmn; 14.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e46.67 \u0026plusmn; 15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e44 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e14 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e30 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e76 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e46 (76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e30 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderlying disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e36 (30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e16 (26.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e20 (33.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e84 (70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e44 (73.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e40 (66.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e (mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e24.19 \u0026plusmn; 3.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e24.83 \u0026plusmn; 3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e23.55 \u0026plusmn; 3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery incision sites, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4th intercostal space\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e36 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e20 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5th intercostal space\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e44 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e24 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e20 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6th intercostal space\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e36 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e16 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e20 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7th intercostal space\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e4 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical Procedure, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydatid Cystectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e16 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e9 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e7 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLobectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e21 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e11 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEsophagectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e10 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e4 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e6 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastatectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e8 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e4 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMediastinal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOthers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2 \u0026nbsp;(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperation duration, hours\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3.87 \u0026plusmn; 1.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.73 \u0026plusmn; 1.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4 \u0026plusmn; 2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNausea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e22 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e12 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVomiting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e16 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e6 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo complication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e82 (68.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e42 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e40 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eComparison of pain scores (VAS) in the three assessment checkpoints between two groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePain intensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAPB group: (N=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl group: (N=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 h after surgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e3.30 \u0026plusmn; 1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e6.27 \u0026plusmn; 1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e2.91 \u0026ndash; 3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e5.82 \u0026ndash; 6.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12 h after surgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.73 \u0026plusmn; 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e5 \u0026plusmn; 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.42 \u0026ndash; 2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e4.69 \u0026ndash; 5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24 h after surgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.30 \u0026plusmn; 1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e3.77 \u0026plusmn; 1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e0.95 \u0026ndash; 1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e3.40 \u0026ndash; 4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst 24 h after surgery P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eMultivariate regression analysis for the first time checkpoint (6h)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePain score (6h)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ebaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.85 \u0026ndash; -2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.05 \u0026ndash; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ebaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.90 \u0026ndash; 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.08 \u0026ndash; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical procedure\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ecystectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ebaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003elobectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.73 \u0026ndash; 1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eesophagectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72 \u0026ndash; 4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emetastatectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.94 \u0026ndash; 2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emediastinal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.59 \u0026ndash; 4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eothers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.78 \u0026ndash; 1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperation duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.51 \u0026ndash; 014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eMultivariate regression analysis for the second time checkpoint (12h)\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePain score (12h)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ebaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.91 \u0026ndash; -2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03 \u0026ndash; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ebaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.87 \u0026ndash; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.14 \u0026ndash; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical procedure\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ecystectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ebaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003elobectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.64 \u0026ndash; 1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eesophagectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.54 \u0026ndash; 2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emetastatectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.02 \u0026ndash; 1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emediastinal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.86 \u0026ndash; 2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eothers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.52 \u0026ndash; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperation duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.36 \u0026ndash; 0.15\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\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eMultivariate regression analysis for the third time checkpoint (24h)\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePain score (24h)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ebaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.30 \u0026ndash; -1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.02 \u0026ndash; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ebaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.05 \u0026ndash; 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.16 \u0026ndash; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical procedure\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ecystectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ebaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003elobectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.22 \u0026ndash; 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eesophagectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.99 \u0026ndash; 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emetastatectomy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.75 \u0026ndash; 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emediastinal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.54 \u0026ndash; 2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eothers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.20 \u0026ndash; 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperation duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23 \u0026ndash; 0.37\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\u003e\u0026nbsp;\u003c/p\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":true,"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":"Antimicrobial Resistance, Hospital-Acquired Infections, Intensive Care Units, Gram-Negative Bacterial Infections, COVID-19 Pandemic, Middle East and North Africa (MENA)","lastPublishedDoi":"10.21203/rs.3.rs-8682214/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8682214/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Antimicrobial resistance (AR) in hospital-acquired infections (HAIs) poses a significant global health challenge, particularly in intensive care units (ICUs). The COVID-19 pandemic has exacerbated AR trends due to increased antibiotic misuse and strained infection control measures. However, comparative data on pre- and post-pandemic AR trends of Gram-negative pathogens in ICUs of the Middle East and North Africa (MENA) region remain scarce.\u003c/p\u003e\n\u003cp\u003eMethods: This retrospective cohort study was conducted on 472 clinical isolates from 242 ICU patients. Bacterial identification and antibiotic susceptibility testing were performed according to updated standard microbiological protocols (e.g., CLSI guidelines). The distribution of bacterial species, resistance categories (MDR, XDR, PDR), and antibiotic-specific resistance were assessed with temporal comparisons drawn to pre- and post-COVID-19.\u003c/p\u003e\n\u003cp\u003eResults: Klebsiella pneumoniae was the most frequent isolate, followed by Pseudomonas aeruginosa and Escherichia coli. Respiratory specimens dominated and surpassed urinary infections. Overall resistance was highest to ceftriaxone, fluoroquinolones, and carbapenems, while colistin remained the most effective antibiotic. AR rates were high, with 76.9% MDR, 64.8% XDR, and 1.5% PDR isolates. AR prevalence, particularly XDR isolates increased after the onset of the COVID-19 pandemic (\u0026gt;20%). Acinetobacter baumannii consistently exhibited the highest AR (\u0026gt;90% XDR, 100% carbapenem resistance).\u003c/p\u003e\n\u003cp\u003eConclusion: This study highlights the alarming rise in MDR and XDR pathogens in ICU settings following the COVID-19 pandemic. These findings underscore the urgent need for region-specific surveillance and antibiotic stewardship to guide rational empirical therapy and curb AR in ICU settings.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTrial registration:\u003c/em\u003e \u003cem\u003eNot applicable.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Antimicrobial Resistance Trends in ICU-acquired Infections after the COVID Epidemic: A 5-year Retrospective Cohort and Comparative Review with MENA Region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 12:17:42","doi":"10.21203/rs.3.rs-8682214/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":"e407309c-0a74-4760-9f95-510385c8b7a8","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T09:12:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 12:17:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8682214","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8682214","identity":"rs-8682214","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}