Epidemiology, antimicrobial resistance and risk factors of infection among liver transplant patients in East China: a retrospective study 2010 to 2023

Epidemiology, antimicrobial resistance and risk factors of infection among liver transplant patients in East China: a retrospective study 2010 to 2023 | 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 Epidemiology, antimicrobial resistance and risk factors of infection among liver transplant patients in East China: a retrospective study 2010 to 2023 Pusen Wang, Zhongyi Jiang, Huanjin Liao, Shubin Zhang, Weitao Que, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3891314/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 Liver transplantation (LT) recipients exhibit heightened susceptibility to infection and sepsis, which have emerged as the most prevalent and life-threatening complications significantly impacting prognosis. The etiological spectrum of organisms following LT has undergone substantial changes over recent decades. Methods This retrospective monocentric study included a consecutive cohort of 776 LT patients from 2010 to 2023, in contrast to our previous study conducted from 2007 to 2010. Infection was diagnosed as per the established definition, and sepsis was diagnosed based on the sepsis-3 criteria. Infection was diagnosed as per the established definition, and sepsis was diagnosed based on the sepsis-3 criteria. Samples were collected from infection sites, cultured, and isolated for further analysis. Results A total of 207 pathogens were isolated from 180 infection sites of 156 (20.1%) patients, comprising of 82 (39.6%) gram-positive bacteria, 90 (43.5%) gram-negative bacteria, and 35 (16.9%) fungi. Among the gram-positive bacteria, we identified Methicillin-resistant Staphylococcus aureus (MRSA) in 18 cases, Methicillin-resistant coagulase-negative staphylococci (MRCNS) in 25 cases, and Vancomycin-resistant Enterococcus faecium (VRE) in 1 case. In terms of gram-negative bacteria, Carbapenem-resistant Enterobacteriaceae (CRE) was found in 8 cases (7 Klebsiella pneumoniae and 1 Escherichia coli), Extended-spectrum beta-lactamases (ESBLs)-producing bacteria were detected in 7 cases (5 Escherichia coli and 2 Enterobacter cloacae), Carbapenem-resistant Acinetobacter baumannii (CRAB) was found in 14 cases, and 2 cases had Carbapenem-resistant Pseudomonas aeruginosa (CRPA). Advanced age, prolonged mechanical ventilation, and extended ICU stay were significantly associated with increased susceptibility to post-LT infections. Infected patients with bilirubin levels exceeding 90 μmol/L (OR 3.46, 95% CI 1.46-8.24; P = 0.005) as well as drug-resistance bacterial infections (OR 2.35, 95% CI 1.07-5.15; P = 0.033) were more likely to develop sepsis. Conclusions More than 45% of bacterial infections were caused by drug-resistant pathogens, with over 30% of gram-negative bacteria exhibiting carbapenem resistance. Implementation of strategies aimed at reducing the duration of mechanical ventilation and ICU stay may effectively decrease the incidence of post-liver transplantation infection. Furthermore, pre-transplant interventions targeting reduction in jaundice could potentially mitigate the risk of post-transplant sepsis. Liver transplantation Pathogen Spectrum Resistance Sepsis Figures Figure 1 Background Liver transplantation (LT) has emerged as the best therapeutic approach for end-stage liver disease since 1963 [ 1 ]. With significant advancements in LT surgical techniques, anti-rejection therapies, and organ preservation methods, perioperative infection has become a major challenge affecting recipients' prognosis [ 2 – 4 ]. Particularly during the period of donation after cardiac death (DCD) in China, the incidence and severity of infections are considerably higher due to donor ischemia reperfusion injury and donor-derived infections. Furthermore, recipients are highly susceptible to infections due to prolonged administration of potent immunosuppressive agents. Studies have reported that infection occurs in 40–89% of recipients within one-year post-LT [ 5 , 6 ] Infection following LT is frequently caused by multi-drug resistant (MDR) bacteria, such as Methicillin-resistant Staphylococcus aureus (MRSA), Carbapenem-resistant Enterobacteriaceae (CRE), and others [ 3 , 4 ]. These infections often manifest as severe mixed infections involving fungi, bacteria, and viruses, which can progress to sepsis. Sepsis represents the most lethal infectious disease and contributes significantly to mortality rates in LT patients [ 7 , 8 ]. Kim et al. reported that bacteremia developed in 24% of LT recipients [ 9 ]. According to the latest Sepsis-3 criteria, up to 67% of patients develop sepsis after LT [ 10 ]. Previous studies have shown that LT patients have an approximately 15-fold increased risk of developing MRSA infection with a corresponding 30-day mortality rate of up to 21% [ 11 , 12 ]. Our own observations also revealed a strong association between MRSA infection and a one-month mortality rate of 41% [ 3 ]. The mortality rate for Carbapenem-resistant Klebsiella pneumoniae (CRKP) infection after LT has been reported as high as 33–42% [ 13 , 14 ]. Our previous study in 2012 demonstrated the spectrum and risk factors associated with MDR-negative bacilli infection in LT, which were the predominant causative pathogens leading to mortality at that time [ 4 ]. However, there has been a shift in the infection spectrum after LT over these years, with an increased incidence of gram-positive bacilli and fungal infections. In 2022 and 2023, we also reported a high prevalence of MRSA infection and sepsis, providing further insights into the underlying molecular mechanisms [ 2 , 3 ]. Therefore, to investigate this evolving pattern of infections after LT, we conducted a large-scale retrospective study. It is well recognized that therapeutic options for MDR bacteria and sepsis are extremely limited. The variation in infection patterns among different transplantation centers worldwide remains poorly understood. This study represents the first comprehensive analysis considering the infection pattern and associated risk factors of LT in East China, offering valuable guidance for clinical practice against infections and improving LT prognosis globally. Methods Patients Patients aged 18 years or older who underwent orthotopic LT between June 2010 and January 2023 were included in this study. Clinical data was obtained from the prospectively maintained database at Shanghai General Hospital. The exclusion criteria encompassed individuals who had undergone re-transplantation, combined organ transplantation, or ABO-incompatible transplantation. All LT procedures were performed by the same surgical team and managed in the intensive care unit (ICU) of Shanghai General Hospital. Definition of infection and sepsis The occurrence of infection following LT was confirmed through culture or isolation, and subsequent resistance testing was conducted as previously described [ 4 ]. Evaluation of sepsis after LT was performed in accordance with the sepsis-3 criteria as previously outlined [ 2 ]. Data collection The recipient baseline and clinical data, including age, sex, etiology, diabetes, cirrhosis, albumin, creatinine, bilirubin, ascites, alpha-fetoprotein (AFP) levels, Child-Pugh grading, model for end stage liver disease (MELD) score were recorded. Additionally, the donor baseline and clinical data consisted of age, sex, albumin, bilirubin, aspartate transaminase (AST), alanine transaminase (ALT) as well as surgery characteristics such as warm ischemia time, cold ischemia time, time of anhepatic phase, time of operation, blood loss, time of mechanical ventilation, duration in ICU after LT, and infectious complications comprising peritonitis, pleural effusion, cholangitis, catheter-related sepsis, pulmonary infection, urinary infection, incision infection, and pathogens consisting gram-positive bacteria, gam-negative bacteria, fungus. Statistical analysis The mean and standard deviation were used to express continuous data, while frequencies were used for discrete variables. Categorical variables were compared using Pearson's χ2 test or Fisher's exact test, whereas continuous variables were analyzed using Student's t-test, the Mann-Whitney U test, or a one-way analysis of variance. Logistic regression models were employed to investigate independent risk factors for infection and sepsis. The final models included dichotomized variables with P values < 0.10 from univariate analysis in multivariate logistic regression analysis. All statistical analyses were performed using SPSS ver.24.0 statistical software (SPSS Inc., Chicago, IL, USA). Statistical significance was defined as P < 0.05. Results Recipient, donor and surgery characteristics A total of 776 patients who underwent LT were included in this study. The median age of the recipients was 47, with 83.4% being male. Hepatitis B virus (HBV) infection was the predominant etiology, accounting for 80.3%. Among the recipients, 11% had diabetes and 76.9% had cirrhosis. The mean levels of albumin, creatinine, and bilirubin were measured at 34.05 g/L, 79.02 µmol/L, and 89.81 µmol/L respectively. Severe ascites was present in 21.4% of the recipients while mild to moderate ascites was observed in 30.9%. The AFP level was found to be at a value of 622.6 ng/mL. Regarding disease severity classification, Child-Pugh grade A accounted for approximately 35%, grade B accounted for around 39%, and grade C represented about 25% of all recipients enrolled in this study. The average MELD score calculated among these patients was 14.43. The median age of donors was 28, with 96.5% being male. The mean levels of albumin, bilirubin, AST, and ALT were 33.97 g/L, 17.02 µmol/L, 48.17 U/L, and 49.72 U/L respectively. Regarding surgical characteristics, the average warm ischemia time and cold ischemia time were recorded as 3.56 minutes and 8.19 hours respectively. The mean duration of the anhepatic phase and the overall operation were found to be approximately 45.42 minutes and 6.62 hours respectively. The average blood loss during surgery amounted to approximately 1895 mL. Furthermore, the mechanical ventilation period post-LT averaged at around 72.7 hours while the ICU stay lasted for an average duration of 446.3 hours. 1.7% of the patients received immune checkpoint inhibitors (ICI) prior to LT, while 37.5% of the patients were able to discontinue prednisone treatment after LT. These specific characteristics are presented in Supplementary Table 1 . Infection sites, pathogens and antibiotic resistance Among the 776 enrolled patients, a total of 156 recipients were found to have acquired infections after LT. Infection sites were identified in a cumulative count of 180 cases, encompassing peritonitis (15 cases; 1.9%), cholangitis (10 cases; 1.3%), incision infections (19 cases; 2.4%), abdominal abscesses (5 cases; 0.6%), pulmonary infections (110 cases; 14.2%), infectious pleural effusion (7 cases; 0.95%), catheter-related sepsis (6 cases; 0.8%), and urinary tract infections (8 cases; 1%) as presented in Table 1 . Table 1 Summary of infection sites post-LT Infection sites N = 180 Peritonitis 15 (1.9%) Cholangitis 10 (1.3%) Incision infection 19 (2.4%) Abdominal abscess 5 (0.6%) Pulmonary infection 110 (14.2%) Infectious Pleural effusion 7 (0.9%) Catheter-related sepsis 6 (0.8%) Urinary infection 8 (1.0%) LT, liver transplantation. A total of 207 pathogens were isolated from 156 patients and 180 infection sites. The specimens collected from corresponding infection sites included abdominal drainage fluids, bile, ascites, incision secretions, sputum, throat swabs, thoracic drainage fluid, catheter samples, and urine. Among the identified pathogens, gram-positive bacteria accounted for 39.6% (82 isolates), gram-negative bacteria accounted for 43.55% (90 isolates), and fungi accounted for 16.9% (35 isolates). Specifically, regarding gram-positive bacterial infections, Staphylococcus aureus (S. aureus) constituted 3.9% (30 isolates), Coagulase-negative staphylococci (CNS) constituted 4.1% (32 isolates), Enterococcus constituted 2.2% (17 isolates), while others were present in smaller proportions as well. Among these strains of gram-positive bacteria that were identified, a total of 44 antibiotic-resistant strains were found including 18 MRSA, 25 methicillin-resistant CNS (MRCNS) and 1 vancomycin resistant enterococcus (VRE). In terms of gram-negative bacterial infections, Klebsiella pneumoniae (K. pneumoniae) accounted for 3.1% (24 isolates), Escherichia coli (E. coli) accounted for 1.8% (14 isolates), Enterobacter cloacae (E. cloacae) accounted for 0.6% (5 isolates), Acinetobacter baumannii (A. baumannii) accounted for 4.3% (33 isolates), Pseudomonas aeruginosa (P. aeruginosa) accounted for 0.8% (6 isolates), Stenotrophomonas maltophilia (S. maltophilia) accounted for 0.5% (4 isolates). Other gram-negative bacteria of smaller proportions were also identified in the study population. Among these gram-negative bacteria, a total of 35 antibiotic-resistant strains were detected, including 8 CRE, 7 extended-spectrum beta-lactamases (ESBLs)-producing bacteria, 14 carbapenem-resistant A. baumannii (CRAB), 2 carbapenem-resistant P. aeruginosa (CRPA), and 4 intrinsically carbapenem-resistant S. maltophilia. In addition to bacterial infections, 35 fungal infections were observed in this study, with Candida albicans (C. albicans) accounting for the majority (32 isolates). The detailed summary of pathogens and their corresponding antibiotic resistance profiles can be found in Table 2 . Table 2 Summary of pathogens and antibiotics resistance Pathogens Pathogens (n = 207) Antibiotics resistance Gram-positive bacteria Staphylococcus aureus (SA) 30 (3.9%) MRSA 18 (60.0%) Coagulase-negative staphylococci (CNS) 32 (4.1%) MRCNS 25 (78.1%) Enterococcus 17 (2.2%) VRE 1 (5.9%) Others 3 (0.4%) Gram-negative bacteria Klebsiella pneumoniae (KP) 24 (3.1%) CRE 7 (29.2%) Escherichia coli 14 (1.8%) CRE 1 (7.1%) ESBLs 5 (35.7%) Enterobacter cloacae 5 (0.6%) ESBLs 2 (40.0%) Acinetobacter baumannii (AB) 33 (4.3%) CRAB 14 (42.4%) Pseudomonas aeruginosa (PA) 6 (0.8%) CRPA 2 (33.3%) Stenotrophomonas maltophilia 4 (0.5%) Intrinsically CR Others 4 (0.5%) Fungus Candida albicans 32 (4.1%) Others 3 (0.4%) CR, carbapenem-resistant; ESBLs, extended-spectrum β-lactamases; MR, methicillin-resistant; VRE, vancomycin-resistant enterococcus. Comparisons between infection group and non-infection group Subsequently, we conducted an analysis of recipient, donor, and surgery characteristics between patients with and without infection after LT (Table 3 ). The age of the infection group was significantly higher than that of the non-infection group (49.44 vs. 46.71, P = 0.001). There was a significantly higher proportion of female patients in the infection group compared to the non-infection group (21.4% vs. 14.6%, P = 0.049). Bilirubin levels before LT were significantly elevated in the infection group compared to the non-infection group (113.4 vs. 83.9, P = 0.045). Furthermore, there were significant differences observed in terms of operation duration, mechanical ventilation duration, and ICU stay between the infection and non-infection groups (P = 0.003, P < 0.001, and P = ​​0.024, respectively). All other characteristics remained similar between both groups. Table 3 Comparisons between infection group and non-infection group Infection (n = 156) Non-infection (n = 620) P value Recipient Age (years) 49.44 ± 9.20 46.71 ± 10.23 0.001 Sex Male Female 121 (78.6%) 33 (21.4%) 526 (85.4%) 90 (14.6%) 0.049 Etiology HBV HCV AIH Alcoholic hepatitis Congenital liver disease Wilson disease Drug-induced liver injury Iatrogenic liver injury Unknown 119 (77.3%) 2 (1.3%) 17 (11.0%) 7 (4.5%) 0 (0%) 3 (1.9%) 0 (0%) 0 (0%) 6 (3.9%) 510 (82.4%) 6 (1.0%) 47 (7.6%) 12 (1.9%) 3 (0.5%) 15 (2.4%) 2 (0.3%) 8 (1.3%) 16 (2.6%) 0.279 Diabetes Yes No 14 (9.0%) 142 (91.0%) 71 (12.2%) 510 (87.8%) 0.260 Cirrhosis Yes No 126 (80.8%) 30 (19.2%) 497 (80.3%) 122 (19.7%) 0.893 Albumin (g/L) 34.82 ± 6.91 33.86 ± 6.39 0.105 Creatinine (µmol/L) 71.99 ± 64.85 80.78 ± 88.37 0.245 Bilirubin (µmol/L) 113.4 ± 169.6 83.90 ± 131.7 0.045 Ascites No Mild to moderate Severe 76 (48.7%) 42 (26.9%) 38 (24.4%) 294 (47.4%) 198 (31.9%) 128 (20.6%) 0.395 AFP > 400 ng/mL Yes No 15 (9.6%) 141 (90.4%) 94 (15.2%) 526 (84.8%) 0.075 Child-Pugh A B C 54 (34.6%) 57 (36.5%) 45 (28.8%) 220 (35.5%) 249 (40.2%) 151 (24.4%) 0.488 MELD 14.99 ± 7.95 14.24 ± 8.88 0.367 Donor Age (years) 30.52 ± 9.29 31.03 ± 10.78 0.766 Sex Male Female 101 (98.1%) 2 (1.9%) 455 (96.2%) 18 (3.8%) 0.349 Albumin (g/L) 35.04 ± 6.66 33.69 ± 9.04 0.605 Bilirubin (µmol/L) 22.30 ± 19.16 15.65 ± 12.24 0.103 ALT (U/L) 54.38 ± 37.06 48.49 ± 67.51 0.747 Surgery Warm ischemia time (minutes) 3.51 ± 0.85 3.57 ± 1.23 0.517 Cold ischemia time (hours) 8.13 ± 2.60 8.22 ± 2.49 0.806 Time of anthepatic phase (minutes) 43.90 ± 10.40 46.12 ± 21.41 0.113 Time of operation (hours) 7.00 ± 1.76 6.52 ± 1.76 0.003 Blood loss (mL) 2155 ± 2461 1782 ± 2163 0.178 Time of mechanical ventilation (hours) 157.0 ± 331.9 51.65 ± 128.4 < 0.001 Time in ICU after LT (hours) 488.9 ± 1582 183.5 ± 295.7 0.024 Pre-LT ICI therapy 3 (1.9%) 10 (1.6%) 1.000 Post-LT without prednisone 52 (33.3%) 239 (38.5%) 0.267 AFP, alpha fetoprotein; AIH, autoimmune hepatitis; ALT, alanine transaminase; HBV, hepatitis B virus; HCV, hepatitis C virus; ICU, intensive care unit; ICI, immune checkpoint inhibitor; LT, liver transplantation; MELD, model for end-stage liver disease. Comparisons between sepsis group and non-sepsis group According to the sepsis-3 criteria, a total of 156 infected patients were categorized into either the sepsis or non-sepsis group. Subsequently, we conducted a comparative analysis of recipient, donor, and surgery characteristics between these two groups consisting of 62 septic patients and 94 non-septic patients (Table 4 ). Regarding the presence of infected pathogens, it was observed that drug-resistant bacteria accounted for 53.2% in the septic patient group compared to only 30.9% in the non-septic patient group (P = 0.005). Furthermore, there was a significant difference in mechanical ventilation duration between the sepsis and non-sepsis groups with values of 242.8 and 97.85 respectively (P = 0.020). Table 4 Comparisons between sepsis group and non-sepsis group Sepsis (n = 62) Non-sepsis (n = 94) P value Recipient Age (years) 49.02 ± 9.52 49.72 ± 9.01 0.640 Sex Male Female 50 (80.6%) 12 (19.4%) 73 (77.7%) 21 (22.3%) 0.655 Etiology HBV HCV AIH Alcoholic hepatitis Wilson disease Unknown 48 (78.7%) 0 (0%) 7 (11.5%) 2 (3.3%) 1 (1.6%) 3 (4.9%) 71 (76.3%) 2 (2.2%) 10 (10.8%) 5 (5.4%) 2 (2.2%) 3 (3.2%) 0.927 Diabetes Yes No 8 (12.9%) 54 (87.1%) 6 (6.4%) 88 (93.6%) 0.163 Cirrhosis Yes No 46 (74.2%) 16 (25.8%) 80 (85.1%) 14 (14.9%) 0.091 Albumin (g/L) 34.46 ± 6.67 35.05 ± 7.09 0.612 Creatinine (µmol/L) 79.56 ± 98.21 67.07 ± 26.06 0.243 Bilirubin (µmol/L) 146.04 ± 186.91 92.29 ± 154.81 0.064 Ascites No Mild to moderate Severe 28 (45.2%) 15 (24.2%) 19 (30.6%) 48 (51.1%) 27 (28.7%) 19 (20.2%) 0.329 AFP > 400 ng/mL Yes No 7 (11.3%) 55 (88.7%) 8 (8.5%) 86 (91.5%) 0.564 Child-Pugh A B C 18 (29.0%) 20 (32.3%) 24 (38.7%) 36 (38.3%) 37 (39.4%) 21 (22.3%) 0.086 MELD 14.99 ± 7.95 14.24 ± 8.88 0.061 Drug-resistance bacteria Yes No 33 (53.2%) 29 (46.8%) 29 (30.9%) 65 (69.1%) 0.005 Donor Age (years) 28.91 ± 8.24 32.13 ± 10.16 0.245 Sex Male Female 45 (97.8%) 1 (2.2%) 56 (98.2%) 1 (1.8%) 1.000 Albumin (g/L) 34.22 ± 6.09 35.65 ± 7.41 0.706 Bilirubin (µmol/L) 28.70 ± 24.30 16.71 ± 12.30 0.240 ALT (U/L) 65.29 ± 46.01 44.84 ± 26.54 0.304 Surgery Warm ischemia time (minutes) 3.56 ± 0.88 3.46 ± 0.82 0.514 Cold ischemia time (hours) 8.17 ± 2.61 8.10 ± 2.61 0.920 Time of anthepatic phase (minutes) 59.07 ± 11.14 58.77 ± 9.87 0.876 Time of operation (hours) 7.19 ± 1.90 6.87 ± 1.66 0.264 Blood loss (mL) 3459 ± 2661 2964 ± 2327 0.317 Time of mechanical ventilation (hours) 242.8 ± 421.5 97.85 ± 237.6 0.020 Time in ICU after LT (hours) 625.6 ± 473.7 733.0 ± 2028 0.693 Pre-LT ICI therapy 1 (1.6%) 2 (2.1%) 1.000 Post-LT without prednisone 15 (24.2%) 37 (39.4%) 0.057 AFP, alpha fetoprotein; AIH, autoimmune hepatitis; ALT, alanine transaminase; HBV, hepatitis B virus; HCV, hepatitis C virus; ICU, intensive care unit; ICI, immune checkpoint inhibitor; LT, liver transplantation; MELD, model for end-stage liver disease. Risk factors of infection and sepsis The univariate analysis revealed that recipient age, sex, bilirubin levels, AFP levels, time of operation, duration of mechanical ventilation, and length of stay in the ICU were identified as potential variables associated with post-LT infection (P < 0.10). Additionally, recipient cirrhosis status, bilirubin levels, Child-Pugh grade, MELD score, drug-resistant bacterial infection and duration of mechanical ventilation were found to be potential variables associated with post-LT sepsis (P 50 years (OR 1.70, P = 0.009), with mechanical ventilation duration > 72 h (OR 3.04, P 10 days (OR 3.00, P 72 h (OR 2.57, P = 0.021), bilirubin levels > 90 µmol/L (OR 3.46, P = 0.005), and drug-resistant bacterial infections (OR 2.35, P = 0.033) were more likely to develop sepsis ( Table 5 ) . Table 5 Independent risk factors for infection and developing sepsis after LT Model Ⅰ (n = 669) Model Ⅱ (n = 133) Age > 50 years 1.70 (1.15–2.54) 0.009 Time of mechanical ventilation > 72h 3.04 (1.94–4.75) 10 days 3.00 (1.76–5.14) 90 µmol/L 3.46 (1.46–8.24) 0.005 Drug-resistance bacteria 2.35 (1.07–5.15) 0.033 ICU, intensive care unit; LT, liver transplantation. Discussion Infection and sepsis significantly impact the prognosis of LT patients, even in an era where 5-year survival rates are nearly 80% [ 2 , 3 , 15 ]. Post-LT infections caused by antibiotic-resistant bacteria have become increasingly common, including the lethal carbapenem-resistant gram-negative bacteria. A decade ago, we previously reported on the spectrum of MDR gram-negative bacterial infections post-LT [ 4 ]. However, most studies have focused only on specific infectious pathogens and there is a lack of comprehensive analysis on serious post-LT infections such as sepsis, which plays a crucial role in determining prognosis. Therefore, this study aims to provide a comprehensive analysis of infection patterns and resistance profiles while simultaneously investigating risk factors for sepsis in LT patients. Furthermore, building upon our previous study conducted from 2007–2010, we aim to demonstrate the shifting pattern of bacterial infections in recent decades within the field of LT. In our previous study, the incidence of bacterial infection was as high as 59.9%. Specifically, 29.5% were attributed to gram-positive bacteria and 30.4% to gram-negative bacteria (Fig. 1 A) [ 4 ]. In this large cohort study, a total of 20.1% of patients were infected by either bacteria or fungi (Fig. 1 B). Although there has been a remarkable decrease in the overall bacterial infection rate over recent years, the ratio between gram-negative and gram-positive bacteria remains nearly unchanged at approximately 1:1 (Fig. 1 A and B ). This reduction in infection rates can be attributed to advancements in infection prevention protocols, immunosuppressive therapies, surgical procedures, and peri-operative management practices. The pathogen spectrums of 2007–2010 (MDR gram-negative bacteria) and 2010–2023 were shown in Fig. 1 C and D . Among specific gram-negative pathogens identified, the top three were A. baumannii (36.7%), K. pneumoniae (26.7%), and E. coli (15.6%), which aligns with previous reports from years ago [ 4 ]. However, it is noteworthy that both A. baumannii and K. pneumoniae have shown an increase of approximately 6–7%, while E. coli has decreased by 8.3%. Furthermore, carbapenem resistance was observed in up to 31.1% of gram-negative bacteria including A. baumannii (42.4%), K. pneumoniae (29.2%), E. coli (7.1%), P. aeruginosa (33.3%) and S. maltophilia (100%). Without a doubt, bacterial infection is the most prevalent post-LT infection. The incidence of bacterial infection varies among different transplantation centers and eras. Previous studies have reported incidences of post-LT infection ranging from 18.4% [ 16 ], 26.6% [ 17 ], 44% [ 18 ], to 68.6% [ 19 ] in these centers, respectively. In our study, the infection rate was determined to be 20.1%. Notably, the spectrum of infections was significantly associated with geographical regions and environmental factors, while perioperative management further influenced post-LT infections' occurrence and characteristics. Therefore, studying the spectrum of post-LT infections within a single center holds great significance. This large-scale study conducted at a single center in East China successfully revealed the regional spectrum of post-LT infections. Infections caused by MDR bacteria, particularly carbapenem-resistant gram-negative bacteria, have emerged as a significant public health challenge due to limited antibiotic options and high case-fatality rates [ 20 ]. The Centers for Disease Control and Prevention (CDC) has identified CRE and CRAB as two of the five urgent threats to public health ( https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf ). Additionally, the World Health Organization (WHO) has classified CRAB, CRPA, and CRE as Priority 1 (critical) pathogens requiring research, discovery, and development of new antibiotics [ 21 ]. According to the most recent data from CHINET ( www.chinets.com ), approximately 20% of P. aeruginosa isolates are carbapenem-resistant along with 78% of A. baumannii isolates and 30% of K. pneumoniae isolates. Overall, our study found that 31.1% of GNB were resistant to carbapenems. Furthermore, we observed common resistance among post-LT infections caused by MRSA, MRCNS, VRE, and ESBLs. Importantly, LT patients infected with these drug-resistant pathogens had a higher likelihood of developing sepsis (OR 2.351; P = 0.033). Previous studies have reported that infection with MDR pathogens significantly increases the risk of inappropriate empiric therapy administration and mortality [ 22 – 24 ]. Based on our findings presented in this study, we propose that the association between infection with MDR pathogens and increased mortality may be attributed to a higher incidence rate of subsequent sepsis. Other independent risk factors for infection and sepsis in our study include advanced age, prolonged mechanical ventilation, extended ICU stay, and elevated bilirubin levels. Advanced age has been widely recognized as a well-known risk factor for infection and has been consistently identified in previous studies [ 9 , 25 ]. In our previous study [ 4 ], we also established prolonged mechanical ventilation as an independent risk factor for both GNB infection and MDR organisms. Here, we further demonstrate that prolonged mechanical ventilation is associated with an increased risk of sepsis development among recipients. LT patients are particularly susceptible to sepsis due to various factors such as cirrhosis-associated immune dysfunction, frequent hospital admissions, multiple antibiotic courses, lengthy stays in the ICU, and immunosuppressive therapies [ 2 , 26 ]. Yoshizumi T et al. reported that a MELD > 15 was an independent risk factor for bacterial sepsis development [ 27 ]. Regarding MELD score, our findings indicate that bilirubin levels exceeding 90 µmol/L constitute a significant risk factor for the development of sepsis. Furthermore, other studies have also reported an association between elevated bilirubin levels, sepsis occurrence, and subsequent outcomes [ 28 , 29 ]. Notably, bilirubin > 90 µmol/L serves as a more specific and modifiable indicator of liver function impairment. This finding suggests the importance of implementing jaundice-reducing treatments prior to LT to mitigate the occurrence of post-LT sepsis. In our previous study, we identified post-LT without the administration of prednisone as an independent protective factor against GNB infection. In contrast, in this study, the use of post-LT prednisone showed only a non-significant trend. This discrepancy may be attributed to a significantly lower incidence of infection or advancements in perioperative management. There are several limitations inherent to this retrospective study. Firstly, the retrospective nature of the study inherently imposes certain limitations. Secondly, it is important to note that this study was conducted at a single center. While conducting a multi-center study on infection patterns may be impractical, it would be valuable to validate and compare these findings using data from other transplantation centers. Finally, the correlation between ICI and infection after LT remains uncertain and warrants further investigation; however, it should be noted that this study has a limited sample size. Conclusions Bacterial infection was predominant in post-LT infections. Currently, the proportion of gram-negative bacterial infections after LT is comparable to that of gram-positive bacterial infections. More than 45% of bacterial infections were caused by drug-resistant pathogens, with over 30% of gram-negative bacteria exhibiting carbapenem resistance. Recipients aged > 50 years, those requiring mechanical ventilation for > 72 h, and individuals with an ICU stay > 10 days after LT were significantly more susceptible to post-LT infections. Among infected patients, those who required mechanical ventilation for > 72 h, had bilirubin levels > 90 µmol/L, and experienced drug-resistant bacterial infections were at a higher risk of developing sepsis. Based on these factors, strategies aimed at reducing the duration of mechanical ventilation and ICU stay may help decrease the incidence of post-LT infections. Additionally, implementing pre-LT treatments targeting jaundice reduction could potentially aid in preventing post-LT sepsis. Abbreviations AFP alpha-fetoprotein ALT alanine transaminase AST aspartate transaminase A. baumannii Acinetobacter baumannii CNS coagulase-negative staphylococci CR carbapenem-resistant C. albicans Candida albicans E. cloacae Enterobacter cloacae E. coli Escherichia coli ESBLs extended-spectrum beta-lactamases HBV Hepatitis B virus ICI immune checkpoint inhibitor ICU intensive care unit K. pneumoniae Klebsiella pneumoniae LT liver transplantation MDR multi-drug resistant MELD model for end stage liver disease MR methicillin-resistant OR odds ratio P. aeruginosa Pseudomonas aeruginosa S. aureus Staphylococcus aureus S. maltophilia Stenotrophomonas maltophilia VRE vancomycin resistant enterococcus. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, and conducted in accordance with the 1964 World Medical Association Declaration of Helsinki and its subsequent amendments[30]. Consent for publication Not applicable. Availability of data and materials The datasets used 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 work was supported, in part, by grants from the National Key R&D Program of China (2022YFC2304700), the National Natural Science Foundation of China (82302427), the Science and Technology Commission of Shanghai Municipality (23141902000, 22YF1435600), and the Fundamental Research Funds for the Central Universities (YG2023QNA26). Authors' contributions WPS, YY and ZL proposed the study. WPS, JZY and LHJ analyzed and interpreted the patient data. WPS, ZSB, QWT, WCG, HYM, ZY, ZD collected the data. WPS wrote the first draft. All authors contributed to the design and interpretation of the study and to further drafts. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Dutkowski P, Linecker M, DeOliveira ML, Mullhaupt B, Clavien PA. Challenges to liver transplantation and strategies to improve outcomes. Gastroenterology. 2015;148(2):307–23. Wang P, Shi B, Wang C, Wang Y, Que W, Jiang Z, Liu X, Jiang Q, Li H, Peng Z, et al. Hepatic pannexin-1 mediates ST2(+) regulatory T cells promoting resolution of inflammation in lipopolysaccharide-induced endotoxemia. Clin Transl Med. 2022;12(5):e849. Li H, Yu X, Shi B, Zhang K, Yuan L, Liu X, Wang P, Lv J, Meng G, Xuan Q et al. Reduced pannexin 1-IL-33 axis function in donor livers increases risk of MRSA infection in liver transplant recipients. Science translational medicine 2021, 13(606). Zhong L, Men TY, Li H, Peng ZH, Gu Y, Ding X, Xing TH, Fan JW. Multidrug-resistant gram-negative bacterial infections after liver transplantation - spectrum and risk factors. J Infect. 2012;64(3):299–310. Fishman JA. Infection in solid-organ transplant recipients. N Engl J Med. 2007;357(25):2601–14. Martin-Gandul C, Mueller NJ, Pascual M, Manuel O. The Impact of Infection on Chronic Allograft Dysfunction and Allograft Survival After Solid Organ Transplantation. Am J transplantation: official J Am Soc Transplantation Am Soc Transpl Surg. 2015;15(12):3024–40. Jones SL, Moore LW, Li XC, Mobley CM, Fields PA, Graviss EA, Nguyen DT, Nolte Fong J, Saharia A, Hobeika MJ, et al. Pre-transplant T-cell Clonality: An Observational Study of a Biomarker for Prediction of Sepsis in Liver Transplant Recipients. Ann Surg. 2021;274(3):411–8. Gotur DB, Masud FN, Ezeana CF, Nisar T, Paranilam J, Chen S, Puppala M, Wong STC, Zimmerman JL. Sepsis outcomes in solid organ transplant recipients. Transpl Infect disease: official J Transplantation Soc. 2020;22(1):e13214. Kim SI, Kim YJ, Jun YH, Wie SH, Kim YR, Choi JY, Yoon SK, Moon IS, Kim DG, Lee MD, et al. Epidemiology and risk factors for bacteremia in 144 consecutive living-donor liver transplant recipients. Yonsei Med J. 2009;50(1):112–21. Takeda K, Sawada Y, Kumamoto T, Tanaka K, Endo I. Severe Sepsis After Living Donor Liver Transplantation: Risk Factors and Outcomes. Transplantation proceedings 2016, 48(6):2124–2129. Russell DL, Flood A, Zaroda TE, Acosta C, Riley MM, Busuttil RW, Pegues DA. Outcomes of colonization with MRSA and VRE among liver transplant candidates and recipients. Am J transplantation: official J Am Soc Transplantation Am Soc Transpl Surg. 2008;8(8):1737–43. Singh N, Paterson DL, Chang FY, Gayowski T, Squier C, Wagener MM, Marino IR. Methicillin-resistant Staphylococcus aureus: the other emerging resistant gram-positive coccus among liver transplant recipients. Clin Infect Dis. 2000;30(2):322–7. Wang M, Earley M, Chen L, Hanson BM, Yu Y, Liu Z, Salcedo S, Cober E, Li L, Kanj SS, et al. Clinical outcomes and bacterial characteristics of carbapenem-resistant Klebsiella pneumoniae complex among patients from different global regions (CRACKLE-2): a prospective, multicentre, cohort study. Lancet Infect Dis. 2022;22(3):401–12. Agyeman AA, Bergen PJ, Rao GG, Nation RL, Landersdorfer CB. A systematic review and meta-analysis of treatment outcomes following antibiotic therapy among patients with carbapenem-resistant Klebsiella pneumoniae infections. Int J Antimicrob Agents. 2020;55(1):105833. Hand J, Patel G. Multidrug-resistant organisms in liver transplant: Mitigating risk and managing infections. Liver transplantation: official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society. 2016;22(8):1143–53. Li C, Wen TF, Mi K, Wang C, Yan LN, Li B. Analysis of infections in the first 3-month after living donor liver transplantation. World J Gastroenterol. 2012;18(16):1975–80. Liu M, Li C, Liu J, Wan Q. Risk factors of early bacterial infection and analysis of bacterial composition, distribution and drug susceptibility after cadaveric liver transplantation. Ann Clin Microbiol Antimicrob. 2023;22(1):63. Vera A, Contreras F, Guevara F. Incidence and risk factors for infections after liver transplant: single-center experience at the University Hospital Fundacion Santa Fe de Bogota, Colombia. Transpl Infect disease: official J Transplantation Soc. 2011;13(6):608–15. Tu Z, Xiang P, Xu X, Zhou L, Zhuang L, Wu J, Wang W, Zheng S. DCD liver transplant infection: experience from a single centre in China. Int J Clin Pract. 2016;70(Suppl):3–10. Jean SS, Harnod D, Hsueh PR. Global Threat of Carbapenem-Resistant Gram-Negative Bacteria. Front Cell Infect Microbiol. 2022;12:823684. Tacconelli E, Carrara E, Savoldi A, Harbarth S, Mendelson M, Monnet DL, Pulcini C, Kahlmeter G, Kluytmans J, Carmeli Y, et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect Dis. 2018;18(3):318–27. Zilberberg MD, Nathanson BH, Sulham K, Fan W, Shorr AF. Carbapenem resistance, inappropriate empiric treatment and outcomes among patients hospitalized with Enterobacteriaceae urinary tract infection, pneumonia and sepsis. BMC Infect Dis. 2017;17(1):279. Zilberberg MD, Nathanson BH, Sulham K, Fan W, Shorr AF. Multidrug resistance, inappropriate empiric therapy, and hospital mortality in Acinetobacter baumannii pneumonia and sepsis. Crit Care. 2016;20(1):221. Mouloudi E, Massa E, Papadopoulos S, Iosifidis E, Roilides I, Theodoridou T, Piperidou M, Orphanou A, Passakiotou M, Imvrios G et al. Bloodstream infections caused by carbapenemase-producing Klebsiella pneumoniae among intensive care unit patients after orthotopic liver transplantation: risk factors for infection and impact of resistance on outcomes. Transplantation proceedings 2014, 46(9):3216–3218. Zhang ML, Xu J, Zhang W, Liu XY, Zhang M, Wang WL, Zheng SS. Microbial epidemiology and risk factors of infections in recipients after DCD liver transplantation. Int J Clin Pract. 2016;70(Suppl):17–21. Bajaj JS, Kamath PS, Reddy KR. The Evolving Challenge of Infections in Cirrhosis. N Engl J Med. 2021;384(24):2317–30. Yoshizumi T, Shirabe K, Ikegami T, Yamashita N, Mano Y, Yoshiya S, Matono R, Harimoto N, Uchiyama H, Toshima T, et al. Decreased immunoglobulin G levels after living-donor liver transplantation is a risk factor for bacterial infection and sepsis. Transpl Infect disease: official J Transplantation Soc. 2014;16(2):225–31. Weng J, Hou R, Zhou X, Xu Z, Zhou Z, Wang P, Wang L, Chen C, Wu J, Wang Z. Development and validation of a score to predict mortality in ICU patients with sepsis: a multicenter retrospective study. J translational Med. 2021;19(1):322. Koozi H, Lidestam A, Lengquist M, Johnsson P, Frigyesi A. A simple mortality prediction model for sepsis patients in intensive care. J Intensive Care Soc. 2023;24(4):372–8. World Medical Association declaration of Helsinki. Recommendations guiding physicians in biomedical research involving human subjects. JAMA. 1997;277(11):925–6. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx 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-3891314","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268950517,"identity":"030021d5-2764-400b-b3fc-18948ba6ec75","order_by":0,"name":"Pusen 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2024-01-23 14:18:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3891314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3891314/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50171534,"identity":"8fa1e3dd-1236-4e53-a26e-0efa2204f30b","added_by":"auto","created_at":"2024-01-25 15:46:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":591418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfection and pathogen spectrum during the periods of 2007-2010 and 2010-2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Bacterial infection spectrum during the period of 2007-2010; B. Infection spectrum during the period of 2010-2023; C. MDR gram-negative bacteria spectrum during the period of 2007-2010; D. Pathogen spectrum during the period of 2010-2023.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3891314/v1/dfbf296de89a478fbaf5c45d.jpg"},{"id":50273471,"identity":"b0da3f7f-c443-4de9-bf36-7efa8c0f4551","added_by":"auto","created_at":"2024-01-28 20:22:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":601472,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3891314/v1/e209aee0-949e-41e1-99ba-895d04ff5554.pdf"},{"id":50171535,"identity":"fa65abb8-881d-4a71-9163-e88aa22121bf","added_by":"auto","created_at":"2024-01-25 15:46:53","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18172,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-3891314/v1/fd9d095a80c04a4216a7b789.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiology, antimicrobial resistance and risk factors of infection among liver transplant patients in East China: a retrospective study 2010 to 2023","fulltext":[{"header":"Background","content":"\u003cp\u003eLiver transplantation (LT) has emerged as the best therapeutic approach for end-stage liver disease since 1963 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With significant advancements in LT surgical techniques, anti-rejection therapies, and organ preservation methods, perioperative infection has become a major challenge affecting recipients' prognosis [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Particularly during the period of donation after cardiac death (DCD) in China, the incidence and severity of infections are considerably higher due to donor ischemia reperfusion injury and donor-derived infections. Furthermore, recipients are highly susceptible to infections due to prolonged administration of potent immunosuppressive agents. Studies have reported that infection occurs in 40\u0026ndash;89% of recipients within one-year post-LT [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eInfection following LT is frequently caused by multi-drug resistant (MDR) bacteria, such as Methicillin-resistant Staphylococcus aureus (MRSA), Carbapenem-resistant Enterobacteriaceae (CRE), and others [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These infections often manifest as severe mixed infections involving fungi, bacteria, and viruses, which can progress to sepsis. Sepsis represents the most lethal infectious disease and contributes significantly to mortality rates in LT patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Kim et al. reported that bacteremia developed in 24% of LT recipients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. According to the latest Sepsis-3 criteria, up to 67% of patients develop sepsis after LT [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have shown that LT patients have an approximately 15-fold increased risk of developing MRSA infection with a corresponding 30-day mortality rate of up to 21% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our own observations also revealed a strong association between MRSA infection and a one-month mortality rate of 41% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The mortality rate for Carbapenem-resistant Klebsiella pneumoniae (CRKP) infection after LT has been reported as high as 33\u0026ndash;42% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur previous study in 2012 demonstrated the spectrum and risk factors associated with MDR-negative bacilli infection in LT, which were the predominant causative pathogens leading to mortality at that time [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, there has been a shift in the infection spectrum after LT over these years, with an increased incidence of gram-positive bacilli and fungal infections. In 2022 and 2023, we also reported a high prevalence of MRSA infection and sepsis, providing further insights into the underlying molecular mechanisms [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, to investigate this evolving pattern of infections after LT, we conducted a large-scale retrospective study. It is well recognized that therapeutic options for MDR bacteria and sepsis are extremely limited. The variation in infection patterns among different transplantation centers worldwide remains poorly understood. This study represents the first comprehensive analysis considering the infection pattern and associated risk factors of LT in East China, offering valuable guidance for clinical practice against infections and improving LT prognosis globally.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003ePatients aged 18 years or older who underwent orthotopic LT between June 2010 and January 2023 were included in this study. Clinical data was obtained from the prospectively maintained database at Shanghai General Hospital. The exclusion criteria encompassed individuals who had undergone re-transplantation, combined organ transplantation, or ABO-incompatible transplantation. All LT procedures were performed by the same surgical team and managed in the intensive care unit (ICU) of Shanghai General Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of infection and sepsis\u003c/h2\u003e \u003cp\u003eThe occurrence of infection following LT was confirmed through culture or isolation, and subsequent resistance testing was conducted as previously described [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Evaluation of sepsis after LT was performed in accordance with the sepsis-3 criteria as previously outlined [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe recipient baseline and clinical data, including age, sex, etiology, diabetes, cirrhosis, albumin, creatinine, bilirubin, ascites, alpha-fetoprotein (AFP) levels, Child-Pugh grading, model for end stage liver disease (MELD) score were recorded. Additionally, the donor baseline and clinical data consisted of age, sex, albumin, bilirubin, aspartate transaminase (AST), alanine transaminase (ALT) as well as surgery characteristics such as warm ischemia time, cold ischemia time, time of anhepatic phase, time of operation, blood loss, time of mechanical ventilation, duration in ICU after LT, and infectious complications comprising peritonitis, pleural effusion, cholangitis, catheter-related sepsis, pulmonary infection, urinary infection, incision infection, and pathogens consisting gram-positive bacteria, gam-negative bacteria, fungus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe mean and standard deviation were used to express continuous data, while frequencies were used for discrete variables. Categorical variables were compared using Pearson's χ2 test or Fisher's exact test, whereas continuous variables were analyzed using Student's t-test, the Mann-Whitney U test, or a one-way analysis of variance. Logistic regression models were employed to investigate independent risk factors for infection and sepsis. The final models included dichotomized variables with P values\u0026thinsp;\u0026lt;\u0026thinsp;0.10 from univariate analysis in multivariate logistic regression analysis. All statistical analyses were performed using SPSS ver.24.0 statistical software (SPSS Inc., Chicago, IL, USA). Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRecipient, donor and surgery characteristics\u003c/h2\u003e \u003cp\u003eA total of 776 patients who underwent LT were included in this study. The median age of the recipients was 47, with 83.4% being male. Hepatitis B virus (HBV) infection was the predominant etiology, accounting for 80.3%. Among the recipients, 11% had diabetes and 76.9% had cirrhosis. The mean levels of albumin, creatinine, and bilirubin were measured at 34.05 g/L, 79.02 \u0026micro;mol/L, and 89.81 \u0026micro;mol/L respectively. Severe ascites was present in 21.4% of the recipients while mild to moderate ascites was observed in 30.9%. The AFP level was found to be at a value of 622.6 ng/mL. Regarding disease severity classification, Child-Pugh grade A accounted for approximately 35%, grade B accounted for around 39%, and grade C represented about 25% of all recipients enrolled in this study. The average MELD score calculated among these patients was 14.43.\u003c/p\u003e \u003cp\u003eThe median age of donors was 28, with 96.5% being male. The mean levels of albumin, bilirubin, AST, and ALT were 33.97 g/L, 17.02 \u0026micro;mol/L, 48.17 U/L, and 49.72 U/L respectively. Regarding surgical characteristics, the average warm ischemia time and cold ischemia time were recorded as 3.56 minutes and 8.19 hours respectively. The mean duration of the anhepatic phase and the overall operation were found to be approximately 45.42 minutes and 6.62 hours respectively. The average blood loss during surgery amounted to approximately 1895 mL. Furthermore, the mechanical ventilation period post-LT averaged at around 72.7 hours while the ICU stay lasted for an average duration of 446.3 hours. 1.7% of the patients received immune checkpoint inhibitors (ICI) prior to LT, while 37.5% of the patients were able to discontinue prednisone treatment after LT. These specific characteristics are presented in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eInfection sites, pathogens and antibiotic resistance\u003c/h2\u003e \u003cp\u003eAmong the 776 enrolled patients, a total of 156 recipients were found to have acquired infections after LT. Infection sites were identified in a cumulative count of 180 cases, encompassing peritonitis (15 cases; 1.9%), cholangitis (10 cases; 1.3%), incision infections (19 cases; 2.4%), abdominal abscesses (5 cases; 0.6%), pulmonary infections (110 cases; 14.2%), infectious pleural effusion (7 cases; 0.95%), catheter-related sepsis (6 cases; 0.8%), and urinary tract infections (8 cases; 1%) as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of infection sites post-LT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection sites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;180\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeritonitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholangitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncision infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal abscess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious Pleural effusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatheter-related sepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eLT, liver transplantation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of 207 pathogens were isolated from 156 patients and 180 infection sites. The specimens collected from corresponding infection sites included abdominal drainage fluids, bile, ascites, incision secretions, sputum, throat swabs, thoracic drainage fluid, catheter samples, and urine. Among the identified pathogens, gram-positive bacteria accounted for 39.6% (82 isolates), gram-negative bacteria accounted for 43.55% (90 isolates), and fungi accounted for 16.9% (35 isolates). Specifically, regarding gram-positive bacterial infections, Staphylococcus aureus (S. aureus) constituted 3.9% (30 isolates), Coagulase-negative staphylococci (CNS) constituted 4.1% (32 isolates), Enterococcus constituted 2.2% (17 isolates), while others were present in smaller proportions as well. Among these strains of gram-positive bacteria that were identified, a total of 44 antibiotic-resistant strains were found including 18 MRSA, 25 methicillin-resistant CNS (MRCNS) and 1 vancomycin resistant enterococcus (VRE).\u003c/p\u003e \u003cp\u003eIn terms of gram-negative bacterial infections, Klebsiella pneumoniae (K. pneumoniae) accounted for 3.1% (24 isolates), Escherichia coli (E. coli) accounted for 1.8% (14 isolates), Enterobacter cloacae (E. cloacae) accounted for 0.6% (5 isolates), Acinetobacter baumannii (A. baumannii) accounted for 4.3% (33 isolates), Pseudomonas aeruginosa (P. aeruginosa) accounted for 0.8% (6 isolates), Stenotrophomonas maltophilia (S. maltophilia) accounted for 0.5% (4 isolates). Other gram-negative bacteria of smaller proportions were also identified in the study population. Among these gram-negative bacteria, a total of 35 antibiotic-resistant strains were detected, including 8 CRE, 7 extended-spectrum beta-lactamases (ESBLs)-producing bacteria, 14 carbapenem-resistant A. baumannii (CRAB), 2 carbapenem-resistant P. aeruginosa (CRPA), and 4 intrinsically carbapenem-resistant S. maltophilia. In addition to bacterial infections, 35 fungal infections were observed in this study, with Candida albicans (C. albicans) accounting for the majority (32 isolates). The detailed summary of pathogens and their corresponding antibiotic resistance profiles can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of pathogens and antibiotics resistance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathogens\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathogens (n\u0026thinsp;=\u0026thinsp;207)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAntibiotics resistance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGram-positive bacteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaphylococcus aureus (SA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRSA 18 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulase-negative staphylococci (CNS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRCNS 25 (78.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnterococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVRE 1 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGram-negative bacteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKlebsiella pneumoniae (KP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRE 7 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEscherichia coli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRE 1 (7.1%)\u003c/p\u003e \u003cp\u003eESBLs 5 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnterobacter cloacae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eESBLs 2 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcinetobacter baumannii (AB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRAB 14 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudomonas aeruginosa (PA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRPA 2 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStenotrophomonas maltophilia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntrinsically CR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFungus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCandida albicans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCR, carbapenem-resistant; ESBLs, extended-spectrum β-lactamases; MR, methicillin-resistant; VRE, vancomycin-resistant enterococcus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eComparisons between infection group and non-infection group\u003c/h2\u003e \u003cp\u003eSubsequently, we conducted an analysis of recipient, donor, and surgery characteristics between patients with and without infection after LT (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The age of the infection group was significantly higher than that of the non-infection group (49.44 vs. 46.71, P\u0026thinsp;=\u0026thinsp;0.001). There was a significantly higher proportion of female patients in the infection group compared to the non-infection group (21.4% vs. 14.6%, P\u0026thinsp;=\u0026thinsp;0.049). Bilirubin levels before LT were significantly elevated in the infection group compared to the non-infection group (113.4 vs. 83.9, P\u0026thinsp;=\u0026thinsp;0.045). Furthermore, there were significant differences observed in terms of operation duration, mechanical ventilation duration, and ICU stay between the infection and non-infection groups (P\u0026thinsp;=\u0026thinsp;0.003, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and P = ​​0.024, respectively). All other characteristics remained similar between both groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons between infection group and non-infection group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfection (n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-infection (n\u0026thinsp;=\u0026thinsp;620)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecipient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.44\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.71\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (78.6%)\u003c/p\u003e \u003cp\u003e33 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e526 (85.4%)\u003c/p\u003e \u003cp\u003e90 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtiology\u003c/p\u003e \u003cp\u003eHBV\u003c/p\u003e \u003cp\u003eHCV\u003c/p\u003e \u003cp\u003eAIH\u003c/p\u003e \u003cp\u003eAlcoholic hepatitis\u003c/p\u003e \u003cp\u003eCongenital liver disease\u003c/p\u003e \u003cp\u003eWilson disease\u003c/p\u003e \u003cp\u003eDrug-induced liver injury\u003c/p\u003e \u003cp\u003eIatrogenic liver injury\u003c/p\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (77.3%)\u003c/p\u003e \u003cp\u003e2 (1.3%)\u003c/p\u003e \u003cp\u003e17 (11.0%)\u003c/p\u003e \u003cp\u003e7 (4.5%)\u003c/p\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003cp\u003e3 (1.9%)\u003c/p\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003cp\u003e6 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e510 (82.4%)\u003c/p\u003e \u003cp\u003e6 (1.0%)\u003c/p\u003e \u003cp\u003e47 (7.6%)\u003c/p\u003e \u003cp\u003e12 (1.9%)\u003c/p\u003e \u003cp\u003e3 (0.5%)\u003c/p\u003e \u003cp\u003e15 (2.4%)\u003c/p\u003e \u003cp\u003e2 (0.3%)\u003c/p\u003e \u003cp\u003e8 (1.3%)\u003c/p\u003e \u003cp\u003e16 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (9.0%)\u003c/p\u003e \u003cp\u003e142 (91.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (12.2%)\u003c/p\u003e \u003cp\u003e510 (87.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (80.8%)\u003c/p\u003e \u003cp\u003e30 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e497 (80.3%)\u003c/p\u003e \u003cp\u003e122 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.82\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.86\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.99\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;64.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.78\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;88.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.4\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;169.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.90\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;131.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eMild to moderate\u003c/p\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (48.7%)\u003c/p\u003e \u003cp\u003e42 (26.9%)\u003c/p\u003e \u003cp\u003e38 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e294 (47.4%)\u003c/p\u003e \u003cp\u003e198 (31.9%)\u003c/p\u003e \u003cp\u003e128 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u0026thinsp;\u0026gt;\u0026thinsp;400 ng/mL\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (9.6%)\u003c/p\u003e \u003cp\u003e141 (90.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (15.2%)\u003c/p\u003e \u003cp\u003e526 (84.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh\u003c/p\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (34.6%)\u003c/p\u003e \u003cp\u003e57 (36.5%)\u003c/p\u003e \u003cp\u003e45 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220 (35.5%)\u003c/p\u003e \u003cp\u003e249 (40.2%)\u003c/p\u003e \u003cp\u003e151 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.99\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.24\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.52\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.03\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (98.1%)\u003c/p\u003e \u003cp\u003e2 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e455 (96.2%)\u003c/p\u003e \u003cp\u003e18 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.04\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.69\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.30\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;19.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.65\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;12.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.38\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;37.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.49\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;67.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarm ischemia time (minutes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.51\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold ischemia time (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.13\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.22\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of anthepatic phase (minutes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.90\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.12\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;21.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of operation (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.00\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.52\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood loss (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2155\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1782\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of mechanical ventilation (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157.0\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;331.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.65\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;128.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime in ICU after LT (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e488.9\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183.5\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;295.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-LT ICI therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-LT without prednisone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAFP, alpha fetoprotein; AIH, autoimmune hepatitis; ALT, alanine transaminase; HBV, hepatitis B virus; HCV, hepatitis C virus; ICU, intensive care unit; ICI, immune checkpoint inhibitor; LT, liver transplantation; MELD, model for end-stage liver disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparisons between sepsis group and non-sepsis group\u003c/h2\u003e \u003cp\u003eAccording to the sepsis-3 criteria, a total of 156 infected patients were categorized into either the sepsis or non-sepsis group. Subsequently, we conducted a comparative analysis of recipient, donor, and surgery characteristics between these two groups consisting of 62 septic patients and 94 non-septic patients (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Regarding the presence of infected pathogens, it was observed that drug-resistant bacteria accounted for 53.2% in the septic patient group compared to only 30.9% in the non-septic patient group (P\u0026thinsp;=\u0026thinsp;0.005). Furthermore, there was a significant difference in mechanical ventilation duration between the sepsis and non-sepsis groups with values of 242.8 and 97.85 respectively (P\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons between sepsis group and non-sepsis group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSepsis (n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-sepsis (n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecipient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.02\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.72\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (80.6%)\u003c/p\u003e \u003cp\u003e12 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (77.7%)\u003c/p\u003e \u003cp\u003e21 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtiology\u003c/p\u003e \u003cp\u003eHBV\u003c/p\u003e \u003cp\u003eHCV\u003c/p\u003e \u003cp\u003eAIH\u003c/p\u003e \u003cp\u003eAlcoholic hepatitis\u003c/p\u003e \u003cp\u003eWilson disease\u003c/p\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (78.7%)\u003c/p\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003cp\u003e7 (11.5%)\u003c/p\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003cp\u003e1 (1.6%)\u003c/p\u003e \u003cp\u003e3 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (76.3%)\u003c/p\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003cp\u003e10 (10.8%)\u003c/p\u003e \u003cp\u003e5 (5.4%)\u003c/p\u003e \u003cp\u003e2 (2.2%)\u003c/p\u003e \u003cp\u003e3 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.9%)\u003c/p\u003e \u003cp\u003e54 (87.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.4%)\u003c/p\u003e \u003cp\u003e88 (93.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (74.2%)\u003c/p\u003e \u003cp\u003e16 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (85.1%)\u003c/p\u003e \u003cp\u003e14 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.46\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.05\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.56\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;98.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.07\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;26.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146.04\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;186.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.29\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;154.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eMild to moderate\u003c/p\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (45.2%)\u003c/p\u003e \u003cp\u003e15 (24.2%)\u003c/p\u003e \u003cp\u003e19 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (51.1%)\u003c/p\u003e \u003cp\u003e27 (28.7%)\u003c/p\u003e \u003cp\u003e19 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u0026thinsp;\u0026gt;\u0026thinsp;400 ng/mL\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.3%)\u003c/p\u003e \u003cp\u003e55 (88.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (8.5%)\u003c/p\u003e \u003cp\u003e86 (91.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh\u003c/p\u003e \u003cp\u003eA\u003c/p\u003e \u003cp\u003eB\u003c/p\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (29.0%)\u003c/p\u003e \u003cp\u003e20 (32.3%)\u003c/p\u003e \u003cp\u003e24 (38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (38.3%)\u003c/p\u003e \u003cp\u003e37 (39.4%)\u003c/p\u003e \u003cp\u003e21 (22.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.99\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.24\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug-resistance bacteria\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (53.2%)\u003c/p\u003e \u003cp\u003e29 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (30.9%)\u003c/p\u003e \u003cp\u003e65 (69.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.91\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.13\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (97.8%)\u003c/p\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (98.2%)\u003c/p\u003e \u003cp\u003e1 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.22\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.65\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.70\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;24.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.71\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;12.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.29\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;46.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.84\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;26.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarm ischemia time (minutes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.56\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold ischemia time (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.17\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.10\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of anthepatic phase (minutes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.07\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;11.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.77\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of operation (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.19\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.87\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood loss (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3459\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2964\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of mechanical ventilation (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e242.8\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;421.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.85\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;237.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime in ICU after LT (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e625.6\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;473.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e733.0\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-LT ICI therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-LT without prednisone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAFP, alpha fetoprotein; AIH, autoimmune hepatitis; ALT, alanine transaminase; HBV, hepatitis B virus; HCV, hepatitis C virus; ICU, intensive care unit; ICI, immune checkpoint inhibitor; LT, liver transplantation; MELD, model for end-stage liver disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors of infection and sepsis\u003c/h2\u003e \u003cp\u003eThe univariate analysis revealed that recipient age, sex, bilirubin levels, AFP levels, time of operation, duration of mechanical ventilation, and length of stay in the ICU were identified as potential variables associated with post-LT infection (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10). Additionally, recipient cirrhosis status, bilirubin levels, Child-Pugh grade, MELD score, drug-resistant bacterial infection and duration of mechanical ventilation were found to be potential variables associated with post-LT sepsis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10). These variables were subsequently dichotomized and subjected to multivariate analysis.\u003c/p\u003e \u003cp\u003eRecipients aged\u0026thinsp;\u0026gt;\u0026thinsp;50 years (OR 1.70, P\u0026thinsp;=\u0026thinsp;0.009), with mechanical ventilation duration\u0026thinsp;\u0026gt;\u0026thinsp;72 h (OR 3.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ICU stay after LT\u0026thinsp;\u0026gt;\u0026thinsp;10 days (OR 3.00, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exhibited a significantly higher risk of post-LT infection. Among these infected recipients, those with mechanical ventilation duration\u0026thinsp;\u0026gt;\u0026thinsp;72 h (OR 2.57, P\u0026thinsp;=\u0026thinsp;0.021), bilirubin levels\u0026thinsp;\u0026gt;\u0026thinsp;90 \u0026micro;mol/L (OR 3.46, P\u0026thinsp;=\u0026thinsp;0.005), and drug-resistant bacterial infections (OR 2.35, P\u0026thinsp;=\u0026thinsp;0.033) were more likely to develop sepsis \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndependent risk factors for infection and developing sepsis after LT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel Ⅰ (n\u0026thinsp;=\u0026thinsp;669)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel Ⅱ (n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.70 (1.15\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of mechanical ventilation\u0026thinsp;\u0026gt;\u0026thinsp;72h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.04 (1.94\u0026ndash;4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.57 (1.15\u0026ndash;5.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration in ICU after LT\u0026thinsp;\u0026gt;\u0026thinsp;10 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00 (1.76\u0026ndash;5.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin\u0026thinsp;\u0026gt;\u0026thinsp;90 \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.46 (1.46\u0026ndash;8.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug-resistance bacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.35 (1.07\u0026ndash;5.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eICU, intensive care unit; LT, liver transplantation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eInfection and sepsis significantly impact the prognosis of LT patients, even in an era where 5-year survival rates are nearly 80% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Post-LT infections caused by antibiotic-resistant bacteria have become increasingly common, including the lethal carbapenem-resistant gram-negative bacteria. A decade ago, we previously reported on the spectrum of MDR gram-negative bacterial infections post-LT [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, most studies have focused only on specific infectious pathogens and there is a lack of comprehensive analysis on serious post-LT infections such as sepsis, which plays a crucial role in determining prognosis. Therefore, this study aims to provide a comprehensive analysis of infection patterns and resistance profiles while simultaneously investigating risk factors for sepsis in LT patients. Furthermore, building upon our previous study conducted from 2007\u0026ndash;2010, we aim to demonstrate the shifting pattern of bacterial infections in recent decades within the field of LT.\u003c/p\u003e \u003cp\u003eIn our previous study, the incidence of bacterial infection was as high as 59.9%. Specifically, 29.5% were attributed to gram-positive bacteria and 30.4% to gram-negative bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this large cohort study, a total of 20.1% of patients were infected by either bacteria or fungi (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Although there has been a remarkable decrease in the overall bacterial infection rate over recent years, the ratio between gram-negative and gram-positive bacteria remains nearly unchanged at approximately 1:1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA \u003cb\u003eand B\u003c/b\u003e). This reduction in infection rates can be attributed to advancements in infection prevention protocols, immunosuppressive therapies, surgical procedures, and peri-operative management practices. The pathogen spectrums of 2007\u0026ndash;2010 (MDR gram-negative bacteria) and 2010\u0026ndash;2023 were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC \u003cb\u003eand D\u003c/b\u003e. Among specific gram-negative pathogens identified, the top three were A. baumannii (36.7%), K. pneumoniae (26.7%), and E. coli (15.6%), which aligns with previous reports from years ago [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, it is noteworthy that both A. baumannii and K. pneumoniae have shown an increase of approximately 6\u0026ndash;7%, while E. coli has decreased by 8.3%. Furthermore, carbapenem resistance was observed in up to 31.1% of gram-negative bacteria including A. baumannii (42.4%), K. pneumoniae (29.2%), E. coli (7.1%), P. aeruginosa (33.3%) and S. maltophilia (100%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWithout a doubt, bacterial infection is the most prevalent post-LT infection. The incidence of bacterial infection varies among different transplantation centers and eras. Previous studies have reported incidences of post-LT infection ranging from 18.4% [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], 26.6% [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], 44% [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], to 68.6% [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in these centers, respectively. In our study, the infection rate was determined to be 20.1%. Notably, the spectrum of infections was significantly associated with geographical regions and environmental factors, while perioperative management further influenced post-LT infections' occurrence and characteristics. Therefore, studying the spectrum of post-LT infections within a single center holds great significance. This large-scale study conducted at a single center in East China successfully revealed the regional spectrum of post-LT infections.\u003c/p\u003e \u003cp\u003eInfections caused by MDR bacteria, particularly carbapenem-resistant gram-negative bacteria, have emerged as a significant public health challenge due to limited antibiotic options and high case-fatality rates [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Centers for Disease Control and Prevention (CDC) has identified CRE and CRAB as two of the five urgent threats to public health (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, the World Health Organization (WHO) has classified CRAB, CRPA, and CRE as Priority 1 (critical) pathogens requiring research, discovery, and development of new antibiotics [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. According to the most recent data from CHINET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf\" target=\"_blank\"\u003ewww.chinets.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.chinets.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), approximately 20% of P. aeruginosa isolates are carbapenem-resistant along with 78% of A. baumannii isolates and 30% of K. pneumoniae isolates. Overall, our study found that 31.1% of GNB were resistant to carbapenems. Furthermore, we observed common resistance among post-LT infections caused by MRSA, MRCNS, VRE, and ESBLs. Importantly, LT patients infected with these drug-resistant pathogens had a higher likelihood of developing sepsis (OR 2.351; P\u0026thinsp;=\u0026thinsp;0.033). Previous studies have reported that infection with MDR pathogens significantly increases the risk of inappropriate empiric therapy administration and mortality [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Based on our findings presented in this study, we propose that the association between infection with MDR pathogens and increased mortality may be attributed to a higher incidence rate of subsequent sepsis.\u003c/p\u003e \u003cp\u003eOther independent risk factors for infection and sepsis in our study include advanced age, prolonged mechanical ventilation, extended ICU stay, and elevated bilirubin levels. Advanced age has been widely recognized as a well-known risk factor for infection and has been consistently identified in previous studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In our previous study [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], we also established prolonged mechanical ventilation as an independent risk factor for both GNB infection and MDR organisms. Here, we further demonstrate that prolonged mechanical ventilation is associated with an increased risk of sepsis development among recipients. LT patients are particularly susceptible to sepsis due to various factors such as cirrhosis-associated immune dysfunction, frequent hospital admissions, multiple antibiotic courses, lengthy stays in the ICU, and immunosuppressive therapies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Yoshizumi T et al. reported that a MELD\u0026thinsp;\u0026gt;\u0026thinsp;15 was an independent risk factor for bacterial sepsis development [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Regarding MELD score, our findings indicate that bilirubin levels exceeding 90 \u0026micro;mol/L constitute a significant risk factor for the development of sepsis. Furthermore, other studies have also reported an association between elevated bilirubin levels, sepsis occurrence, and subsequent outcomes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Notably, bilirubin\u0026thinsp;\u0026gt;\u0026thinsp;90 \u0026micro;mol/L serves as a more specific and modifiable indicator of liver function impairment. This finding suggests the importance of implementing jaundice-reducing treatments prior to LT to mitigate the occurrence of post-LT sepsis. In our previous study, we identified post-LT without the administration of prednisone as an independent protective factor against GNB infection. In contrast, in this study, the use of post-LT prednisone showed only a non-significant trend. This discrepancy may be attributed to a significantly lower incidence of infection or advancements in perioperative management.\u003c/p\u003e \u003cp\u003eThere are several limitations inherent to this retrospective study. Firstly, the retrospective nature of the study inherently imposes certain limitations. Secondly, it is important to note that this study was conducted at a single center. While conducting a multi-center study on infection patterns may be impractical, it would be valuable to validate and compare these findings using data from other transplantation centers. Finally, the correlation between ICI and infection after LT remains uncertain and warrants further investigation; however, it should be noted that this study has a limited sample size.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBacterial infection was predominant in post-LT infections. Currently, the proportion of gram-negative bacterial infections after LT is comparable to that of gram-positive bacterial infections. More than 45% of bacterial infections were caused by drug-resistant pathogens, with over 30% of gram-negative bacteria exhibiting carbapenem resistance. Recipients aged\u0026thinsp;\u0026gt;\u0026thinsp;50 years, those requiring mechanical ventilation for \u0026gt;\u0026thinsp;72 h, and individuals with an ICU stay\u0026thinsp;\u0026gt;\u0026thinsp;10 days after LT were significantly more susceptible to post-LT infections. Among infected patients, those who required mechanical ventilation for \u0026gt;\u0026thinsp;72 h, had bilirubin levels\u0026thinsp;\u0026gt;\u0026thinsp;90 \u0026micro;mol/L, and experienced drug-resistant bacterial infections were at a higher risk of developing sepsis. Based on these factors, strategies aimed at reducing the duration of mechanical ventilation and ICU stay may help decrease the incidence of post-LT infections. Additionally, implementing pre-LT treatments targeting jaundice reduction could potentially aid in preventing post-LT sepsis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAFP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealpha-fetoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine transaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate transaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eA. baumannii\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcinetobacter baumannii\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoagulase-negative staphylococci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecarbapenem-resistant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC. albicans\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCandida albicans\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE. cloacae\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnterobacter cloacae\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE. coli\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEscherichia coli\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESBLs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eextended-spectrum beta-lactamases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHepatitis B virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune checkpoint inhibitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eK. pneumoniae\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKlebsiella pneumoniae\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eliver transplantation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emulti-drug resistant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMELD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emodel for end stage liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emethicillin-resistant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eP. aeruginosa\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePseudomonas aeruginosa\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS. aureus\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStaphylococcus aureus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS. maltophilia\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStenotrophomonas maltophilia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVRE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evancomycin resistant enterococcus.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, and conducted in accordance with the 1964 World Medical Association Declaration of Helsinki and its subsequent amendments[30].\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 used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported, in part, by grants from the National Key R\u0026amp;D Program of China (2022YFC2304700), the National Natural Science Foundation of China (82302427), the Science and Technology Commission of Shanghai Municipality (23141902000, 22YF1435600), and the Fundamental Research Funds for the Central Universities (YG2023QNA26).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWPS, YY and ZL proposed the study. WPS, JZY and LHJ analyzed and interpreted the patient data. WPS, ZSB, QWT, WCG, HYM, ZY, ZD collected the data. WPS wrote the first draft. All authors contributed to the design and interpretation of the study and to further drafts. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDutkowski P, Linecker M, DeOliveira ML, Mullhaupt B, Clavien PA. Challenges to liver transplantation and strategies to improve outcomes. Gastroenterology. 2015;148(2):307\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang P, Shi B, Wang C, Wang Y, Que W, Jiang Z, Liu X, Jiang Q, Li H, Peng Z, et al. Hepatic pannexin-1 mediates ST2(+) regulatory T cells promoting resolution of inflammation in lipopolysaccharide-induced endotoxemia. Clin Transl Med. 2022;12(5):e849.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Yu X, Shi B, Zhang K, Yuan L, Liu X, Wang P, Lv J, Meng G, Xuan Q et al. Reduced pannexin 1-IL-33 axis function in donor livers increases risk of MRSA infection in liver transplant recipients. \u003cem\u003eScience translational medicine\u003c/em\u003e 2021, 13(606).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong L, Men TY, Li H, Peng ZH, Gu Y, Ding X, Xing TH, Fan JW. Multidrug-resistant gram-negative bacterial infections after liver transplantation - spectrum and risk factors. J Infect. 2012;64(3):299\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFishman JA. Infection in solid-organ transplant recipients. N Engl J Med. 2007;357(25):2601\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin-Gandul C, Mueller NJ, Pascual M, Manuel O. The Impact of Infection on Chronic Allograft Dysfunction and Allograft Survival After Solid Organ Transplantation. Am J transplantation: official J Am Soc Transplantation Am Soc Transpl Surg. 2015;15(12):3024\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones SL, Moore LW, Li XC, Mobley CM, Fields PA, Graviss EA, Nguyen DT, Nolte Fong J, Saharia A, Hobeika MJ, et al. Pre-transplant T-cell Clonality: An Observational Study of a Biomarker for Prediction of Sepsis in Liver Transplant Recipients. Ann Surg. 2021;274(3):411\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGotur DB, Masud FN, Ezeana CF, Nisar T, Paranilam J, Chen S, Puppala M, Wong STC, Zimmerman JL. Sepsis outcomes in solid organ transplant recipients. Transpl Infect disease: official J Transplantation Soc. 2020;22(1):e13214.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SI, Kim YJ, Jun YH, Wie SH, Kim YR, Choi JY, Yoon SK, Moon IS, Kim DG, Lee MD, et al. Epidemiology and risk factors for bacteremia in 144 consecutive living-donor liver transplant recipients. Yonsei Med J. 2009;50(1):112\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakeda K, Sawada Y, Kumamoto T, Tanaka K, Endo I. Severe Sepsis After Living Donor Liver Transplantation: Risk Factors and Outcomes. \u003cem\u003eTransplantation proceedings\u003c/em\u003e 2016, 48(6):2124\u0026ndash;2129.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell DL, Flood A, Zaroda TE, Acosta C, Riley MM, Busuttil RW, Pegues DA. Outcomes of colonization with MRSA and VRE among liver transplant candidates and recipients. Am J transplantation: official J Am Soc Transplantation Am Soc Transpl Surg. 2008;8(8):1737\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh N, Paterson DL, Chang FY, Gayowski T, Squier C, Wagener MM, Marino IR. Methicillin-resistant Staphylococcus aureus: the other emerging resistant gram-positive coccus among liver transplant recipients. Clin Infect Dis. 2000;30(2):322\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Earley M, Chen L, Hanson BM, Yu Y, Liu Z, Salcedo S, Cober E, Li L, Kanj SS, et al. Clinical outcomes and bacterial characteristics of carbapenem-resistant Klebsiella pneumoniae complex among patients from different global regions (CRACKLE-2): a prospective, multicentre, cohort study. Lancet Infect Dis. 2022;22(3):401\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgyeman AA, Bergen PJ, Rao GG, Nation RL, Landersdorfer CB. A systematic review and meta-analysis of treatment outcomes following antibiotic therapy among patients with carbapenem-resistant Klebsiella pneumoniae infections. Int J Antimicrob Agents. 2020;55(1):105833.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHand J, Patel G. Multidrug-resistant organisms in liver transplant: Mitigating risk and managing infections. Liver transplantation: official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society. 2016;22(8):1143\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Wen TF, Mi K, Wang C, Yan LN, Li B. Analysis of infections in the first 3-month after living donor liver transplantation. World J Gastroenterol. 2012;18(16):1975\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu M, Li C, Liu J, Wan Q. Risk factors of early bacterial infection and analysis of bacterial composition, distribution and drug susceptibility after cadaveric liver transplantation. Ann Clin Microbiol Antimicrob. 2023;22(1):63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVera A, Contreras F, Guevara F. Incidence and risk factors for infections after liver transplant: single-center experience at the University Hospital Fundacion Santa Fe de Bogota, Colombia. Transpl Infect disease: official J Transplantation Soc. 2011;13(6):608\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTu Z, Xiang P, Xu X, Zhou L, Zhuang L, Wu J, Wang W, Zheng S. DCD liver transplant infection: experience from a single centre in China. Int J Clin Pract. 2016;70(Suppl):3\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJean SS, Harnod D, Hsueh PR. Global Threat of Carbapenem-Resistant Gram-Negative Bacteria. Front Cell Infect Microbiol. 2022;12:823684.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTacconelli E, Carrara E, Savoldi A, Harbarth S, Mendelson M, Monnet DL, Pulcini C, Kahlmeter G, Kluytmans J, Carmeli Y, et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect Dis. 2018;18(3):318\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZilberberg MD, Nathanson BH, Sulham K, Fan W, Shorr AF. Carbapenem resistance, inappropriate empiric treatment and outcomes among patients hospitalized with Enterobacteriaceae urinary tract infection, pneumonia and sepsis. BMC Infect Dis. 2017;17(1):279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZilberberg MD, Nathanson BH, Sulham K, Fan W, Shorr AF. Multidrug resistance, inappropriate empiric therapy, and hospital mortality in Acinetobacter baumannii pneumonia and sepsis. Crit Care. 2016;20(1):221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMouloudi E, Massa E, Papadopoulos S, Iosifidis E, Roilides I, Theodoridou T, Piperidou M, Orphanou A, Passakiotou M, Imvrios G et al. Bloodstream infections caused by carbapenemase-producing Klebsiella pneumoniae among intensive care unit patients after orthotopic liver transplantation: risk factors for infection and impact of resistance on outcomes. \u003cem\u003eTransplantation proceedings\u003c/em\u003e 2014, 46(9):3216\u0026ndash;3218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang ML, Xu J, Zhang W, Liu XY, Zhang M, Wang WL, Zheng SS. Microbial epidemiology and risk factors of infections in recipients after DCD liver transplantation. Int J Clin Pract. 2016;70(Suppl):17\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBajaj JS, Kamath PS, Reddy KR. The Evolving Challenge of Infections in Cirrhosis. N Engl J Med. 2021;384(24):2317\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshizumi T, Shirabe K, Ikegami T, Yamashita N, Mano Y, Yoshiya S, Matono R, Harimoto N, Uchiyama H, Toshima T, et al. Decreased immunoglobulin G levels after living-donor liver transplantation is a risk factor for bacterial infection and sepsis. Transpl Infect disease: official J Transplantation Soc. 2014;16(2):225\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeng J, Hou R, Zhou X, Xu Z, Zhou Z, Wang P, Wang L, Chen C, Wu J, Wang Z. Development and validation of a score to predict mortality in ICU patients with sepsis: a multicenter retrospective study. J translational Med. 2021;19(1):322.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoozi H, Lidestam A, Lengquist M, Johnsson P, Frigyesi A. A simple mortality prediction model for sepsis patients in intensive care. J Intensive Care Soc. 2023;24(4):372\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Medical Association declaration of Helsinki. Recommendations guiding physicians in biomedical research involving human subjects. JAMA. 1997;277(11):925\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Liver transplantation, Pathogen, Spectrum, Resistance, Sepsis","lastPublishedDoi":"10.21203/rs.3.rs-3891314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3891314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiver transplantation (LT) recipients exhibit heightened susceptibility to infection and sepsis, which have emerged as the most prevalent and life-threatening complications significantly impacting prognosis. The etiological spectrum of organisms following LT has undergone substantial changes over recent decades.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective monocentric study included a consecutive cohort of 776 LT patients from 2010 to 2023, in contrast to our previous study conducted from 2007 to 2010. Infection was diagnosed as per the established definition, and sepsis was diagnosed based on the sepsis-3 criteria. Infection was diagnosed as per the established definition, and sepsis was diagnosed based on the sepsis-3 criteria. Samples were collected from infection sites, cultured, and isolated for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 207 pathogens were isolated from 180 infection sites of 156 (20.1%) patients, comprising of 82 (39.6%) gram-positive bacteria, 90 (43.5%) gram-negative bacteria, and 35 (16.9%) fungi. Among the gram-positive bacteria, we identified Methicillin-resistant Staphylococcus aureus (MRSA) in 18 cases, Methicillin-resistant coagulase-negative staphylococci (MRCNS) in 25 cases, and Vancomycin-resistant Enterococcus faecium (VRE) in 1 case. In terms of gram-negative bacteria, Carbapenem-resistant Enterobacteriaceae (CRE) was found in 8 cases (7 Klebsiella pneumoniae and 1 Escherichia coli), Extended-spectrum beta-lactamases (ESBLs)-producing bacteria were detected in 7 cases (5 Escherichia coli and 2 Enterobacter cloacae), Carbapenem-resistant Acinetobacter baumannii (CRAB) was found in 14 cases, and 2 cases had Carbapenem-resistant Pseudomonas aeruginosa (CRPA). Advanced age, prolonged mechanical ventilation, and extended ICU stay were significantly associated with increased susceptibility to post-LT infections. Infected patients with bilirubin levels exceeding 90 μmol/L (OR 3.46, 95% CI 1.46-8.24; P = 0.005) as well as drug-resistance bacterial infections (OR 2.35, 95% CI 1.07-5.15; P = 0.033) were more likely to develop sepsis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMore than 45% of bacterial infections were caused by drug-resistant pathogens, with over 30% of gram-negative bacteria exhibiting carbapenem resistance. Implementation of strategies aimed at reducing the duration of mechanical ventilation and ICU stay may effectively decrease the incidence of post-liver transplantation infection. Furthermore, pre-transplant interventions targeting reduction in jaundice could potentially mitigate the risk of post-transplant sepsis.\u003c/p\u003e","manuscriptTitle":"Epidemiology, antimicrobial resistance and risk factors of infection among liver transplant patients in East China: a retrospective study 2010 to 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 15:46:48","doi":"10.21203/rs.3.rs-3891314/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":"65365bd4-0d7e-43c3-9ebb-44fee6a06122","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-28T20:14:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 15:46:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3891314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3891314","identity":"rs-3891314","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}