Clinical characteristics of bacterial infections in patients with decompensated cirrhosis and construction and verification of a risk prediction model

Clinical characteristics of bacterial infections in patients with decompensated cirrhosis and construction and verification of a risk prediction model | 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 Clinical characteristics of bacterial infections in patients with decompensated cirrhosis and construction and verification of a risk prediction model Jing Gao, Yong Chen, Lixiao Yan, Tengfei Liu, Teng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7515340/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Bacterial infections and disease progression are correlated in individuals with decompensated cirrhosis. We aimed to construct and verify a risk prediction model for bacterial infections in patients with decompensated cirrhosis. Methods Retrospectively, 588 patients with decompensated cirrhosis treated at the First Affiliated Hospital of Hebei North University were selected as the training set, and 224 patients with decompensated cirrhosis treated at Zhangjiakou Traditional Chinese Medicine Hospital were selected as the validation set. The participants were divided into infected and non-infected groups according to whether they had bacterial infection or not. Clinical data were collected before the positive culture results; the variables were screened by least absolute shrinkage and selection operator (LASSO) regression. Multivariate regression was used to analyze infection risk factors to construct a nomogram model; the predictive effect of the model was evaluated by the area under the receiver operating characteristic curve (AUC). Calibration and decision curves were used to evaluate the model’s clinical application value. Results Bacterial infections occurred in 34.9% of patients; peritonitis was the main infection. Escherichia was the most cultured infectious agent among 68 pathogenic bacterial strains. Multivariate logistic regression analysis showed that independent risk factors for bacterial infections (P < 0.05) were electrolyte and acid-base imbalance, renal function impairment, liver failure, abnormal platelet (PLT) counts, a high Child–Pugh score, and a high Model for End-Stage Liver Disease (MELD) score. The AUCs of the predicted model were 0.802 in the training cohort and 0.832 in the validation cohort. Hosmer–Lemeshow tests showed a good fit between the model and verification groups. Decision curve analysis and calibration curves showed a high value for the prediction model. Conclusions The nomogram model showed favorable differentiation and prediction of bacterial infection risk and might be able to identify high-risk patients early. Decompensated cirrhosis Bacterial infections LASSO Risk prediction model Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background “Decompensated cirrhosis” refers to late-stage hepatic cirrhosis with extremely poor prognosis [ 1 , 2 ]. Patients with decompensated cirrhosis show severe liver damage and compromised immune function, with significantly impaired resistance to exogenous and endogenous pathogens, rendering them highly susceptible to various infections. Bacterial infections are a crucial contributing factor to mortality in patients with decompensated cirrhosis [ 3 , 4 ]. The existing literature has shown a correlation between bacterial infections and disease progression in individuals diagnosed with cirrhosis [ 5 – 7 ]. During the progression of liver dysfunction in individuals with cirrhosis, susceptibility to infection is elevated, exacerbating the condition. To mitigate bacterial infections among patients with decompensated cirrhosis, public health measures must be implemented from multiple perspectives, encompassing enhanced patient education and personal protective measures, modifications in lifestyle and dietary habits, strengthened infection control in healthcare facilities, rational use of medication, and intensified monitoring and follow-up visits. The early prediction and implementation of proactive and efficient prevention and control measures can significantly reduce the risk of bacterial infection in patients with decompensated cirrhosis. However, there are still limited clinical studies on the risk factors for bacterial infection in patients with this disease, and no reports have yet used models for risk prediction. Nomogram models are capable of comprehensively considering multiple influencing factors and presenting them in an intuitive graphical format, allowing clinicians to easily access patients' infection risk. Therefore, this retrospective study aimed to analyze the clinical characteristics and risk factors for bacterial infection in patients with decompensated cirrhosis. Through least absolute shrinkage and selection operator regression, predictive variables were screened to construct a nomogram model, which visually presents the risk factors for bacterial infection in patients with decompensated cirrhosis. This model will provide clinicians with a simple and precise foundation for early intervention measures. Materials and Methods Study design and population Data spanning from January 2021 to December 2023 were collected by researchers at First Affiliated Hospital of Hebei North University and Zhangjiakou Hospital of Traditional Chinese Medicine.Inclusion criteria encompassed patients diagnosed with decompensated cirrhosis and those with complete clinical and laboratory data.The demographic and clinical data collected on the participants prior to testing positive included age, sex, time of cirrhosis diagnosis, etiology of cirrhosis, underlying conditions, complications, diagnosis of liver failure, PLT count, creatinine (Cr) levels, total bilirubin (TBil) levels, prothrombin (PT) time, hemoglobin(Hb) levels, Albumin (Alb) levels, aspartate aminotransferase (AST) levels, alanine aminotransferase (ALT) levels, international normalized ratio (INR) ,Child–Pugh score, and MELD score. Exclusion criteria comprised incomplete treatment, incomplete clinical or laboratory data, lacoinfection with a virus, occurrence of chronic inflammatory infection with uncertain etiology, and specimen contamination or pathogen colonization.Ultimately, a total of 588 patients with decompensated cirrhosis hospitalized in the First Affiliated Hospital of Hebei North University between 1 January, 2021 and 31 December, 2023 were retrospectively selected as the training set(Fig. 1A). A total of 224 patients with decompensated cirrhosis hospitalized in Zhangjiakou Hospital of Traditional Chinese Medicine between 1 January, 2020 and 31 December 2023 comprised the validation set.The training set was utilized for constructing the nomogram, while the test set was used for validation purposes(Fig. 1B). Definitions Decompensated cirrhosis was diagnosed based on the "EASL Clinical Practice Guidelines for the management of patients with decompensated cirrhosis " [ 8 ]. Liver failure is diagnosed based on the " EASL clinical practice guidelines on acute-on-chronic liver failure " [ 9 ]. Statistical analysis The data were analyzed using SPSS software (version 26.0) and R software (version 4.04; R Core Team). Frequency and percentages were used to express the count data. Comparisons were performed using the χ2 test, with the Fisher exact probability method employed if the data did not meet the test requirement. Non-normal data are described as median (quartile 1, quartile 3); comparison between groups was performed using the Mann–Whitney U test. Predictors were screened using LASSO regression with a 10-fold cross-validation method, followed by multivariate logistic regression analysis and the establishment of a nomogram model. The model’s prediction efficiency was evaluated using receiver operating characteristic (ROC) curve analysis; the consistency and fit between the predicted infection risk from the nomogram model and the actual infection risk were assessed using the Homer–Lemeshow test. Calibration curves were drawn to evaluate model accuracy; the clinical application value was assessed using decision curve analysis (DCA) models. P < 0.05 was considered statistically significant. Results Study population characteristics A total of 588 patients were included in the final analysis, including 346 men and 242 women, with a median age of 62 (range: 15–87) years. The validation set was analyzed according to the same criteria, consisting of 224 patient 125 men and 99 women, with a median age of 66 (range: 20–89) years. No differences were found in the basic characteristics of the participants between the two groups (P > 0.05, as presented in Table 1 ). Table 1 Comparison of basic data between the training set and the validation set Variables Training set(n = 588) Validation set(n = 224) P value Gender,n(%) female 242 (41.2%) 99 (44.2%) 0.433 male 346 (58.8%) 125(55.8%) Age,years 62(54,69) 62(52,70) 0.713 Diagnosis time of cirrhosis,years 2(0.25,7) 3(0.25,7) 0.898 Etiology Alcoholic liver disease (n,%) 169 (28.8%) 54 (24.1%) 0.186 Viral infection (n,%) 139 (23.6%) 60 (26.8%) 0.352 Autoimmune liver disease (n,%) 119 (20.2%) 47 (21.0%) 0.814 Drug-(n,%) 14 (2.4%) 9 (4.0%) 0.209 Heredity\Metabolism (n,%) 3 (0.5%) 0 (0.0%) 0.565 Circulatory disturbance (n,%) 2 (0.3%) 1 (0.4%) 1.000 Nonalcoholic fatty liver disease (n,%) 2 (0.3%) 1 (0.4%) 1.000 Alcoholic liver disease + Viral infection (n,%) 56 (9.5%) 24 (10.7%) 0.611 Viral infection + Autoimmune liver disease (n,%) 1 (0.2%) 0 (0.0%) 1.000 Unknown(n,%) 83 (14.1%) 28 (12.5%) 0.549 Concomitant disease Chronic obstructive pulmonary disease(COPD) 20 (3.4%) 13 (5.8%) 0.121 Respiratory failure 11 (1.9%) 5 (2.2%) 0.779 Lung cancer 10 (1.7%) 2 (0.9%) 0.527 Shock 6 (1.0%) 1 (0.4%) 0.680 Hypertension 111 (18.9%) 45 (20.1%) 0.695 Diabetes 128 (21.8%) 46 (20.5%) 0.702 Coronary heart disease 2 (0.3%) 0 (0.0%) 1.000 Heart failure 7 (1.2%) 7 (3.1%) 0.071 Renal failure 1 (0.2%) 0 (0.0%) 1.000 Three or more underlying diseases 6 (1.0%) 5 (2.2%) 0.187 Complication Electrolyte and acid-base balance disorders (n,%) 248 (42.2%) 97 (43.3%) 0.772 Cholelithiasis(n,%) 110 (18.7%) 45 (20.1%) 0.654 Gastrointestinal hemorrhage(n,%) 111 (18.9%) 48 (21.4%) 0.413 Effusion in the serous cavity 518 (88.1%) 195 (87.1%) 0.685 Renal impairment 54 (9.2%) 17 (7.6%) 0.472 Primary liver cancer 94 (16.0%) 37 (16.5%) 0.854 Portal vein thrombosis (PVT), (n,%) 24 (4.1%) 8 (3.6%) 0.738 Hepatic encephalopathy(n,%) 78 (13.3%) 30 (13.4%) 0.962 Three or more complications 205 (34.9%) 80 (35.7%) 0.820 Liver failure 49 (8.3%) 21 (9.4%) 0.636 Hb 115g/L ≤ Hb ≤ 150 g/L (n,%) 189 (32.1%) 83 (37.1%) 0.185 Hb 150 g/L (n,%) 399 (67.9%) 141 (62.9%) PLT 125*10 9 /L ≤ PLT ≤ 350*10 9 /L (n,%) 132 (22.4%) 52 (23.2%) 0.816 PLT 350*10 9 /L (n,%) 456 (77.6%) 172 (76.8%) Cr 35µmol/L ≤ Cr ≤ 80µmol/L (n,%) 414 (70.4%) 166 (74.1%) 0.297 Cr 80µmol/L (n,%) 174 (29.6%) 58 (25.9%) AST AST ≤ 50U/L(n,%) 329 (55.9%) 142 (63.4%) 0.055 AST>50U/L(n,%) 259 (44.1%) 82 (36.6%) ALT ALT ≤ 50U/L(n,%) 453 (77.0%) 182 (81.3%) 0.194 ALT>50U/L(n,%) 135 (23.0%) 42 (18.8%) TBil TBil ≤ 20.5µmol/L (n,%) 194 (33.0%) 81 (36.2%) 0.394 TBil>20.5µmol/L(n,%) 394 (67.0%) 143 (63.8%) Alb 35 g/L ≤ Alb ≤ 55g/L (n,%) 90 (15.3%) 43 (19.2%) 0.181 Alb 55g/L (n,%) 498 (84.7%) 181 (80.8%) PT PT ≤ 12.1s (n,%) 29 (4.9%) 16 (7.1%) 0.218 PT>12.1s(n,%) 559 (95.1%) 208 (92.9%) INR INR ≤ 1.5 (n,%) 420 (71.4%) 163 (72.8%) 0.705 INR>1.5(n,%) 168 (28.6%) 61 (27.2%) Child-Pugh Child-Pugh = A(n,%) 64 (10.9%) 34 (15.2%) 0.234 Child-Pugh = B(n,%) 299 (50.9%) 106 (47.3%) Child-Pugh = C(n,%) 225 (38.3%) 84 (37.5%) MELD 0 ≤ MELD < 10 (n,%) 390 (66.3%) 152 (67.9%) 0.731 10 ≤ MELD < 20 (n,%) 174 (29.6%) 61 (27.2%) 20 ≤ MELD (n,%) 24 (4.1%) 11 (4.9%) In the training set of 588 patients with decompensated cirrhosis, 205 were coinfected. The uninfected group comprised 383 patients. Among the infected patients, 231 cases of infection occurred, with clinical diagnosis in 163 cases and etiological diagnosis in 68 cases. Peritonitis was observed in 71cases (34.6%), pulmonary infection in 69 cases (33.6%), urinary tract infection in 12 cases (5.8%), blood infection in 11 cases (5.4%), skin and soft tissue infection in seven cases (3.4%), gastrointestinal infection in five cases (2.4%), biliary tract infection in three cases (1.4%),upper respiratory tract infection in one case (0.5%),intracranial infections in one case (0.5%), lung infection complicated with peritonitis in five cases (2.4%); lung infection complicated with bacteremia in five cases (2.4%); peritonitis complicated with bacteremia in four cases (2.0%); lung infection complicated with skin infection in four cases(2.0%); lung infection complicated with urinary tract infection in three cases (1.5%); peritonitis complicated with urinary tract infection in one case (0.5%); peritonitis complicated with skin infection in one case (0.5%); urinary tract infection complicated with bacteremia in one case (0.5%), and lung infection and urinary tract infection complicated with bacteremia in one case (0.5%). The identified pathogenic bacteria included Escherichia (n = 17), Enterococcus faecium (n = 12), Klebsiella pneumoniae (n = 10), Staphylococcus aureus (n = 8), Acinetobacter baumannii (n = 6), Enterobacter cloacae (n = 4), Streptococcus pneumoniae (n = 3), Staphylococcus haemolyticus (n = 3), Serratia marcescens (n = 3) and Enterococcus faecalis (n = 2). Drug susceptibility tests were conducted on all strains, and no multidrug-resistant bacteria were found. (as shown in Table 2 ) Table 2 Detection of bacteria Bacteria Number Percent(%) Escherichia 17 25.0% Enterococcus faecium 12 17.6% Klebsiella pneumoniae 10 14.7% Staphylococcus aureus 8 11.8% Acinetobacter baumannii 6 8.8% Enterobacter cloacae 4 5.9% Streptococcus pneumoniae 3 4.4% Staphylococcus haemolyticus 3 4.4% Serratia marcescens 3 4.4% Enterococcus faecalis 2 2.9% Identification of risk factors for bacterial infections in patients with decompensated cirrhosis Comparison of the demographic and clinical data upon hospitalization between the infected group and the non-infected group in the training set revealed significant differences in respiratory failure, electrolyte and acid-base balance disorders, serous cavity effusion, hepatorenal syndrome, hepatic encephalopathy, the presence of three or more complications of decompensated cirrhosis, diagnosis of liver failure, Cr, AST, ALT, TBIL, PLT count, ALB, INR, Child–Pugh score, and MELD score (all with P < 0.05, as shown in Table 3 ). Table 3 Comparison of basic data between the infected group and the non-infected groups Variables Infected group (n = 205) Non-infected group (n = 383) P value Gender,n(%) female 84(41.0%) 158(41.3%) 0.948 male 121(59.0%) 225(58.7%) Age,years 60(51,69) 63(54,69) 0.087 Diagnosis time of cirrhosis,years 2(0.25,7) 2(0.25,7) 0.763 Etiology Alcoholic liver disease (n,%) 59 (28.8%) 110 (28.7%) 0.988 Viral infection (n,%) 50 (24.4%) 89 (23.2%) 0.754 Autoimmune liver disease (n,%) 41 (20.0%) 78 (20.4%) 0.916 Drug-(n,%) 5 (2.4%) 9 (2.3%) 1.000 Heredity\Metabolism (n,%) 2 (1.0%) 1 (0.3%) 0.28 Circulatory disturbance (n,%) 1 (0.5%) 1 (0.3%) 1.000 Nonalcoholic fatty liver disease (n,%) 1 (0.5%) 1 (0.3%) 1.000 Alcoholic liver disease + Viral infection (n,%) 17 (8.3%) 39 (10.2%) 0.457 Viral infection + Autoimmune liver disease (n,%) 0 (0.0%) 1 (0.3%) 1.000 Unknown(n,%) 29 (14.1%) 54 (14.1%) 1.000 Concomitant disease COPD 3 (1.5%) 17 (4.4%) 0.058 Respiratory failure 9 (4.4%) 2 (0.5%) 0.002 Lung cancer 5 (2.4%) 5 (1.3%) 0.329 Shock 4 (2.0%) 2 (0.5%) 0.19 Hypertension 34 (16.6%) 77 (20.1%) 0.299 Diabetes 43 (21.0%) 85 (22.2%) 0.733 Coronary heart disease 1 (0.5%) 1 (0.3%) 1.000 Heart failure 5 (2.4%) 2 (0.5%) 0.054 Renal failure 0 (0.0%) 1 (0.3%) 1.000 Three or more underlying diseases 2 (1.0%) 4 (1.0%) 1.000 Complication Electrolyte and acid-base balance disorders (n,%) 127 (61.9%) 121 (31.6%) 0.000 Cholelithiasis(n,%) 44 (21.5%) 66 (17.2%) 0.210 gastrointestinal hemorrhage(n,%) 47 (22.9%) 87 (22.7%) 0.954 Effusion in the serous cavity 193 (94.1%) 325 (84.8%) 0.001 Renal impairment 39 (19.0%) 15 (3.9%) 0.000 Primary liver cancer 35 (17.1%) 59 (15.4%) 0.599 Portal thrombosis(n,%) 6 (2.9%) 18 (4.7%) 0.300 Hepatic encephalopathy(n,%) 45 (22.0%) 33 (8.6%) 0.000 Three or more complications 100 (48.8%) 105 (27.4%) 0.000 Hepatic failure 40 (19.5%) 9 (2.3%) 0.000 Hb 115g/L ≤ Hb ≤ 150 g/L (n,%) 70 (34.1%) 119 (31.1%) 0.447 Hb 150 g/L (n,%) 135 (65.9%) 264 (68.9%) PLT 125*10 9 /L ≤ PLT ≤ 350*10 9 /L (n,%) 29 (14.1%) 103 (26.9%) 0.000 PLT 350*10 9 /L (n,%) 176 (85.9%) 280 (73.1%) Cr 35µmol/L ≤ Cr ≤ 80µmol/L (n,%) 130 (63.4%) 284 (74.2%) 0.007 Cr 80µmol/L (n,%) 75 (36.6%) 99 (25.9%) AST AST ≤ 50U/L(n,%) 90 (43.9%) 239 (62.4%) 0.000 AST>50U/L(n,%) 115 (56.1%) 144 (37.6%) ALT ALT ≤ 50U/L(n,%) 142 (69.3%) 311 (81.2%) 0.001 ALT>50U/L(n,%) 63 (30.7%) 72 (18.8%) TBil TBil ≤ 20.5µmol/L (n,%) 37 (18.0%) 157 (41.0%) 0.000 TBil>20.5µmol/L(n,%) 168 (82.0%) 226 (59.0%) Alb 35 g/L ≤ Alb ≤ 55g/L (n,%) 12 (5.9%) 78 (20.4%) 0.000 Alb 55g/L (n,%) 193 (94.1%) 305 (79.6%) PT PT ≤ 12.1s (n,%) 7 (3.4%) 22 (5.7%) 0.214 PT>12.1s(n,%) 198 (96.6%) 361 (94.3%) INR INR ≤ 1.5 (n,%) 111 (54.1%) 309 (80.7%) 0.000 INR>1.5(n,%) 94 (45.9%) 74 (19.3%) Child-Pugh Child-Pugh = A(n,%) 2 (1.0%) 62 (16.2%) 0.000 Child-Pugh = B(n,%) 76 (37.1%) 223 (58.2%) Child-Pugh = C(n,%) 127 (61.9%) 98 (25.6%) MELD 0 ≤ MELD < 10 (n,%) 95 (46.3%) 295 (77.0%) 0.000 10 ≤ MELD < 20 (n,%) 88 (42.9%) 86 (22.5%) 20 ≤ MELD (n,%) 22 (10.7%) 2 (0.5%) Development of nomogram for bacterial infections in patients with decompensated cirrhosis The LASSO regression model was used to incorporate all variables for predictor screening (Fig. 2 A), resulting in the selection of 13 variables through a 10-fold cross-validation process(Fig. 2 B); these included respiratory failure, electrolyte and acid-base imbalance, renal function impairment, hepatic encephalopathy, diagnosis of liver failure, AST, ALT, TBil, PLT count, Hb, ALB, Child–Pugh classification, and MELD score. The 13 variables identified through the LASSO regression were incorporated into a multivariate logistic regression model. The findings from the analysis showed that an electrolyte and acid-base imbalance, renal function impairment, liver failure, abnormal PLT count, high Child–Pugh score and high MELD score contributed to the risk of bacterial infections in patients with decompensated cirrhosis (Table 4 ). Table 4 Multivariate analysis of influencing bacterial infections in patients with decompensated liver cirrhosis Variable β SE Waldχ2 P OR(95%CI) Respiratory failure 1.471 0.893 2.713 0.100 4.354(0.756–25.072) Electrolyte and acid-base imbalance 0.968 0.219 19.542 0.000 2.633(1.714–4.045) Renal function impairment 1.548 0.410 14.280 0.000 4.703(2.107–10.499) Hepatic encephalopathy 0.305 0.322 0.899 0.343 1.356(0.722–2.547) Diagnosis of liver failure 0.961 0.434 4.903 0.027 2.614(1.117–6.119) Hb -0.345 0.236 2.143 0.143 0.708(0.446–1.124) PLT 0.759 0.282 7.220 0.007 2.136(1.228–3.715) AST 0.410 0.262 2.451 0.117 1.508(0.902–2.520) ALT 0.371 0.298 1.551 0.213 1.449(0.808–2.596) TBil 0.126 0.290 0.187 0.665 1.134(0.642–2.004) ALB 0.466 0.389 1.433 0.231 1.593(0.743–3.417) Child–Pugh classification 0.782 0.233 11.260 0.001 2.185(1.384–3.450) MELD score 0.698 0.213 10.727 0.001 2.010(1.324–3.054) Constant -4.653 0.586 62.979 0.000 The results of the multivariate logistic regression model were used to construct a nomogram depicting bacterial infections in patients with decompensated cirrhosis (Fig. 3 ). Validation of nomogram for bacterial infections in patients with decompensated cirrhosis The ROC analysis demonstrated excellent discriminative ability of the model, with an area under the curve (AUC) of 0.802 (95% confidence interval (CI: 0.765–0.840) for the training set, sensitivity of 64.9%, and specificity of 82.2%, further details are shown in Fig. 4 A. The AUC for the validation set was 0.832 (95% CI: 0.777–0.886), with a sensitivity of 85.9% and specificity of 65.4%, further details are shown in Fig. 4 B. The Hosmer–Lemeshow test results indicated favorable calibration, with P-values of 0.905 in the training set (Fig. 5 A) and 0.097 in the validation set (Fig. 5 B), suggesting a satisfactory fit. The calibration curve demonstrated a strong concordance between the actual and predicted probabilities of bacterial infections in patients with decompensated cirrhosis. The DCA plot in Fig. 6 A shows a net benefit range of 0–0.310 within a threshold range of 0.05–1 in the training set. Within the corresponding threshold range of 0.08–0.90 in the validation set (Fig. 6 B), a benefit range of 0–0.275 was noted. The R language “DynNom” package layout was used to construct a nomogram model, accessible at https://gaojing.shinyapps.io/dynnomapp/ . The web calculator test results demonstrate stability after performance testing (Fig. 7 ). Discussion Liver cirrhosis is a severe liver disease that, if not treated promptly, can progress to decompensated cirrhosis, leading to liver failure. Furthermore, studies have shown that the age of patients with end-stage liver disease is gradually decreasing, further exacerbating the burden of liver cirrhosis on the healthcare system. When cirrhosis progresses to the decompensated stage, various complications gradually appear, including but not limited to liver dysfunction, portal hypertension, and spontaneous bacterial peritonitis. These complications can have an impact on prognosis. Bacterial infection can serve as not only one of the complications, but also act as a trigger for various complications, exacerbating the condition, and even leading to (sub)acute liver failure with a high mortality rate [ 10 – 12 ]. Therefore, developing a predictive model is crucial for the early identification of high-risk factors associated with bacterial infections in patients with decompensated cirrhosis and for implementing targeted management strategies. In this study, a total of 588 patients with decompensated cirrhosis were included in the training set, among which 205 patients developed bacterial infection, with an infection rate of 34.9%, which is similar to the research results reported by Fernández et al [ 13 ]. The main type of bacterial infection in patients with decompensated cirrhosis was peritonitis, which is consistent with that in previous studies [ 14 ]. Bacterial peritonitis is a serious complication of decompensated cirrhosis, and the pathogenic bacteria can enter the abdominal cavity through the lymphatic system, intestine, blood, and other routes, causing abdominal cavity infection. Among the main pathogens causing infection, E. coli was detected in 17 cases, E. faecium in 12 cases, and K. pneumoniae ranked among the top three bacterial infectious agents in 10 cases. It should be noted that E. coli , E. faecium , and K. pneumoniae are widely distributed within various environmental niches, as well as in the human respiratory tract, intestine, and skin [ 15 – 17 ]. Infection can occur in patients when their immune system is weakened and when the anti-infective defense barrier is impaired. During the process of model construction, LASSO regression demonstrated exceptional capabilities in effectively eliminating variables with minor contributions to the model, thereby significantly enhancing the robustness of the model and effectively counteracting the overfitting phenomenon. Additionally, LASSO regression can effectively circumvent the issue of multicollinearity, ensuring accurate estimation of model parameters [ 18 ]. Furthermore, LASSO regression exhibits flexibility in terms of in integrating with various models to achieve efficient variable screening and be applied in different scenarios [ 19 – 21 ]. In this study, we employed the LASSO-logistic regression method to identify six independent risk factors, namely electrolyte and acid-base imbalance, renal function injury, liver failure, abnormal PLT counts, high Child–Pugh scores, and high MELD scores. These risk factors possess significant guiding implications for the risk assessment of bacterial infection in patients with decompensated cirrhosis. By integrating these six independent risk factors, a nomogram model was established. This study conducted a comprehensive evaluation of the prediction model. The AUC value of the ROC curve was 0.802, indicating a high level of accuracy for the model. The Hosmer–Lemeshow test revealed a good fit for the model. The calibration curve demonstrated favorable consistency between the predicted and actual probabilities of infection, and the DCA curve indicated a strong clinical applicability for the model. Overall, the model exhibited excellent performance in all aspects. By using the nomogram model, healthcare professionals can gain a more intuitive understanding of the varying degrees of risk associated with different risk factors and subsequently implement corresponding interventions to reduce the likelihood of bacterial infections in patients with decompensated cirrhosis. According to the nomogram model, patients with decompensated cirrhosis and abnormal PLT counts are at a high risk of bacterial infection. As stated in one study [ 22 ], PLTs serve as a crucial component of the immune response in the body, interacting with various cell populations and participating in immune regulation processes. PLTs play a pivotal role in the occurrence, development, and ultimate outcome of infections. Abnormalities in PLT function or number can affect the body’s active defense and injury repair mechanisms, thereby increasing the probability of infection. Therefore, close monitoring of the PLT status of patients with decompensated cirrhosis is crucial for the prevention and control of infections. Several studies have demonstrated a strong correlation between electrolyte balance disturbances and elevated occurrence of diverse infections [ 23 , 24 ]. The underlying reasons are postulated to be the following: 1) Electrolyte imbalances can impair the body’s ability to eliminate pathogens by disrupting the microenvironment and causing dysfunction in vital organs. 2) Specific electrolytes, such as magnesium and zinc, participate in the body’s anti-inflammatory effect [ 25 , 26 ]. The presence of these metal ions can exert an anti-inflammatory effect by mitigating oxidative stress-induced damage in the host, suppressing the expression of pro-inflammatory surface markers on macrophages, and inhibiting the phosphorylation of nuclear transcription factor κB, toll-like receptors, and other inflammatory signaling pathways [ 27 ]. The disturbance of electrolytes can impair human physiology through various mechanisms, leading to decreased resistance and creating favorable conditions for pathogenic infections. The presence of renal dysfunction is a severe complication commonly associated with patients with cirrhosis, and such patients are more prone to deteriorating to renal failure, leading to a higher mortality rate, which has been widely confirmed in existing studies [ 28 – 30 ]. The present study demonstrated that patients with cirrhosis complicated with renal dysfunction had a higher risk of bacterial infection. A study by Xu et al [ 31 ]also showed that renal function impairment was an independent risk factors for poor outcome of patients with decompensated cirrhosis complicated with infection. Furthermore, the presence of bacterial infection can exacerbate the degree of renal dysfunction in patients with cirrhosis [ 32 ]. Through a deep analysis of the mechanisms underlying the increase in infection rates due to renal dysfunction, we found that bacterial infection may be closely related to multiple factors, such as hemodynamic disorders, immune inflammatory responses, autophagy, oxidative stress responses, and metabolic reprogramming. Specifically, renal dysfunction can lead to decreased renal perfusion capacity and disrupt the water-electrolyte balance and acid-base balance, potentially causing tubular necrosis and promoting the release of inflammatory factors. These factors act together in the body, disrupting immune homeostasis and thus increasing the risk of infection. The Child–Pugh scoring system, through a comprehensive evaluation of liver injury-related indicators such as ALB, TBil, prothrombin time, and ascites, not only reflects the patient's liver function reserve and the degree of cirrhosis but also indirectly assesses the patient's infection status and surgical risk. The MELD scoring system, a widely recognized assessment tool, effectively measures the severity of end-stage liver disease. The widespread application of these two scoring systems in the diagnosis and treatment of cirrhosis is mainly due to the objectivity of the selected variables, comparability of measurement results between different laboratories, ease of data acquisition, and scalability. A higher score represents poorer liver function. In this study, we observed significant differences in the Child–Pugh classification and MELD score between infected and non-infected patients, further confirming their importance as independent risk factors for infection in patients with decompensated cirrhosis. In other words, an increase in the Child–Pugh classification or MELD score in patients with decompensated cirrhosis often indicates a higher risk of infection. In the nomogram model, patients diagnosed with liver failure were at an increased risk of bacterial infection, further supporting the strong correlation between severe liver damage and infection. The massive necrosis of hepatocytes in patients with liver failure not only directly affects their liver function but also leads to the impairment of the monocyte-macrophage system. Macrophages can synthesize and release various acute-phase cytokines, facilitating bacterial and endotoxin clearance. Bacterial translocation and endotoxin release are facilitated by abnormal liver function, thereby exacerbating the patient’s condition [ 33 , 34 ]. Bacterial infection, as another significant factor, can further exacerbate liver damage, and the interaction between the two forms a vicious cycle that has a severe impact on the patient's condition [ 35 – 37 ]. In our current research, we have taken note of the study conducted by Sundaram et al. on the risk factors of infection in patients with decompensated cirrhosis [ 38 – 40 ]. Unlike their study, several distinctions can be drawn from our investigation. First, our research include a large number of cases of infected patients with decompensated cirrhosis for computation and constructed a nomogram model. Second, our study included a wider range of clinical data and laboratory indicators than previous studies, making it easier for healthcare professionals to assess the risk of bacterial infection in each individual patient with decompensated cirrhosis using the nomogram model. Lastly, our study comprehensively evaluated the predictive model and conducted external validation to ensure its practicality. The current study still faces some limitations at this stage: (1) Given that this study employed a retrospective analysis method, a cautious approach should be taken in interpreting the causal relationship between risk factors and infection. (2) The arbitrary selection in LASSO is a limitation, but it was mitigated in this study via the multivariate logistic regression, AUC, calibration plots, and decision curve analysis, these helped to reduce the impact on the final nomogram model and its validity. (3)This study is currently in the preliminary exploration stage. Hence, future work will incorporate more characteristic variables and expand the sample size to more comprehensively validate and improve the overall performance of the model. Conclusions In this study, a nomogram was developed to predict bacterial infections in patients with decompensated cirrhosis. The nomogram includes six predictor variables, which will effectively predict the likelihood of bacterial infections. This nomogram is valuable in predicting bacterial infections and will help clinicians to decide whether early intervention is needed based on the patient ' s specific situation. Abbreviations LASSO Least absolute shrinkage and selection operator AUC Area under the receiver operating characteristic curve PLT Platelet MELD Model for End-Stage Liver Disease Cr Creatinine TBil Total bilirubin PT Prothrombin Hb Hemoglobin Alb Albumin AST Aspartate aminotransferase ALT Alanine aminotransferase INR International normalized ratio ROC Receiver operating characteristic DCA Decision curve analysis COPD Chronic obstructive pulmonary disease PVT Portal vein thrombosis Declarations Clinical trial number Not applicable. Ethics approval and consent to participate This study adhered to the ethical principles outlined in the Declaration of Helsinki (1964) and its subsequent amendments. The present study adheres to the standards of medical ethics (institutional review board number (IRB)#: W2023045) and is a retrospective investigation. No intervention measures were implemented on the study participants, and informed consent was waived following a review by the hospital’s ethics committee. Consent for publication Not applicable. Data availability statement The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This study was supported by funding from the Scientific Research Project of Hebei Provincial Health Commission (20231413). The funder did not participate in the design of the study, collection or analysis of data, decision to publish, ordrafting of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions J.G. conceived and designed the paper, collected and analyzed the data, prepared figures andtables, wrote the original draft of the manuscript, verified and reviewed the paper. Y.C. conceived and designed the paper, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, verified and reviewed the paper. L.Y. collected and analyzed the data, prepared figures and tables. T.L. collected the data, prepared figures and tables. T.W. analyzed the data, prepared figures and table. All authors reviewed the manuscript and approved the final version of the manuscript. Acknowledgements The authors sincerely appreciate the support and assistance provided by the faculty members of the First Affiliated Hospital of Hebei North University. 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Invasive fungal infections in acute decompensation of cirrhosis: epidemiology, predictors of 28-day mortality, and outcomes. Indian J Microbiol. 2025;65:1366-70. https://doi.org/10.1007/s12088-024-01243-4. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 Oct, 2025 Reviewers invited by journal 25 Sep, 2025 Editor assigned by journal 23 Sep, 2025 Editor invited by journal 05 Sep, 2025 Submission checks completed at journal 04 Sep, 2025 First submitted to journal 04 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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16:38:18","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212876,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/423777e283461880057f9bee.png"},{"id":93062704,"identity":"b92f7615-5113-4aee-ac16-cc728a2c9112","added_by":"auto","created_at":"2025-10-08 16:38:02","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92457,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/188a1c2106419652f7e5fda2.png"},{"id":93062903,"identity":"58aeec67-cb63-4992-b62c-2db45924451c","added_by":"auto","created_at":"2025-10-08 16:38:05","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109091,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/fe7d6d01244824265d5dd41c.png"},{"id":93062713,"identity":"fb348090-4b7d-4c3f-9031-428520b52832","added_by":"auto","created_at":"2025-10-08 16:38:02","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110748,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/f1519b56ed62ef608f50458d.png"},{"id":93063214,"identity":"a3cc45ff-fd93-48ca-b252-58f6e6dabc5f","added_by":"auto","created_at":"2025-10-08 16:38:10","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191266,"visible":true,"origin":"","legend":"","description":"","filename":"3385796a56924167b50cf1d6347a0de11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/c15e3121bd9c5c1c6b87ce81.xml"},{"id":93062905,"identity":"1a26c152-a09e-486a-8026-d9ed631e1c23","added_by":"auto","created_at":"2025-10-08 16:38:05","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196797,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/ab0e6b92410ae946723fb641.html"},{"id":93062809,"identity":"7382da41-f9fd-4a82-9d72-ca88c61fc24c","added_by":"auto","created_at":"2025-10-08 16:38:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":157233,"visible":true,"origin":"","legend":"\u003cp\u003eThe Process of Patient Selection:\u003cstrong\u003eA\u003c/strong\u003e training set, \u003cstrong\u003eB\u003c/strong\u003e validation set\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/7f0f08146d490970e55b0a53.png"},{"id":93062888,"identity":"c904bbbb-ed94-429f-bac7-2ee9bd6304b2","added_by":"auto","created_at":"2025-10-08 16:38:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":401713,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO screening parameters (\u003cstrong\u003eA\u003c/strong\u003e) and LASSO compressing parameters (\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/f0bcaa7c4530b7ef67b5dd7f.png"},{"id":93063121,"identity":"8f950033-970f-4861-b55c-c3ff3190ed5d","added_by":"auto","created_at":"2025-10-08 16:38:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102414,"visible":true,"origin":"","legend":"\u003cp\u003eTraining set of infection risk prediction nomogram model\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/faee687c2e86be842928affd.png"},{"id":93063145,"identity":"2cf5621f-5c03-4b6e-bf9e-7e4dfe7abf6d","added_by":"auto","created_at":"2025-10-08 16:38:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":559126,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve analysis on the nomogram of training set (\u003cstrong\u003eA\u003c/strong\u003e) and validation set (\u003cstrong\u003eB\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/e19e215d44a600127134a5ef.png"},{"id":93063250,"identity":"fdf368e9-8236-4bbc-813e-20a0088d4a77","added_by":"auto","created_at":"2025-10-08 16:38:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":132559,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of training set(\u003cstrong\u003eA\u003c/strong\u003e) and validation set(\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/08d6ace20b07d628b56459eb.png"},{"id":93062844,"identity":"30d2364b-9195-4a41-a323-f992f773400b","added_by":"auto","created_at":"2025-10-08 16:38:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":231576,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of training set(\u003cstrong\u003eA\u003c/strong\u003e) and validation set(\u003cstrong\u003eB\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/0c252607989bcb481cb227f8.png"},{"id":93063126,"identity":"2476dea4-e645-4381-933a-cb677f5ebc02","added_by":"auto","created_at":"2025-10-08 16:38:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":130650,"visible":true,"origin":"","legend":"\u003cp\u003eWeb calculator\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/12d0b0a6c8d6be3cb40c3a49.png"},{"id":93064371,"identity":"7012fb91-10cf-45dd-8752-bd9dfbdc4a14","added_by":"auto","created_at":"2025-10-08 16:41:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2697419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7515340/v1/76a276b1-053e-4171-b7ff-bb13a54824c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical characteristics of bacterial infections in patients with decompensated cirrhosis and construction and verification of a risk prediction model","fulltext":[{"header":"Background","content":"\u003cp\u003e\u0026ldquo;Decompensated cirrhosis\u0026rdquo; refers to late-stage hepatic cirrhosis with extremely poor prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Patients with decompensated cirrhosis show severe liver damage and compromised immune function, with significantly impaired resistance to exogenous and endogenous pathogens, rendering them highly susceptible to various infections. Bacterial infections are a crucial contributing factor to mortality in patients with decompensated cirrhosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The existing literature has shown a correlation between bacterial infections and disease progression in individuals diagnosed with cirrhosis [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. During the progression of liver dysfunction in individuals with cirrhosis, susceptibility to infection is elevated, exacerbating the condition.\u003c/p\u003e\u003cp\u003eTo mitigate bacterial infections among patients with decompensated cirrhosis, public health measures must be implemented from multiple perspectives, encompassing enhanced patient education and personal protective measures, modifications in lifestyle and dietary habits, strengthened infection control in healthcare facilities, rational use of medication, and intensified monitoring and follow-up visits. The early prediction and implementation of proactive and efficient prevention and control measures can significantly reduce the risk of bacterial infection in patients with decompensated cirrhosis. However, there are still limited clinical studies on the risk factors for bacterial infection in patients with this disease, and no reports have yet used models for risk prediction. Nomogram models are capable of comprehensively considering multiple influencing factors and presenting them in an intuitive graphical format, allowing clinicians to easily access patients' infection risk.\u003c/p\u003e\u003cp\u003eTherefore, this retrospective study aimed to analyze the clinical characteristics and risk factors for bacterial infection in patients with decompensated cirrhosis. Through least absolute shrinkage and selection operator regression, predictive variables were screened to construct a nomogram model, which visually presents the risk factors for bacterial infection in patients with decompensated cirrhosis. This model will provide clinicians with a simple and precise foundation for early intervention measures.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design and population\u003c/h2\u003e\n \u003cp\u003eData spanning from January 2021 to December 2023 were collected by researchers at First Affiliated Hospital of Hebei North University and Zhangjiakou Hospital of Traditional Chinese Medicine.Inclusion criteria encompassed patients diagnosed with decompensated cirrhosis and those with complete clinical and laboratory data.The demographic and clinical data collected on the participants prior to testing positive included age, sex, time of cirrhosis diagnosis, etiology of cirrhosis, underlying conditions, complications, diagnosis of liver failure, PLT count, creatinine (Cr) levels, total bilirubin (TBil) levels, prothrombin (PT) time, hemoglobin(Hb) levels, Albumin (Alb) levels, aspartate aminotransferase (AST) levels, alanine aminotransferase (ALT) levels, international normalized ratio (INR) ,Child\u0026ndash;Pugh score, and MELD score. Exclusion criteria comprised incomplete treatment, incomplete clinical or laboratory data, lacoinfection with a virus, occurrence of chronic inflammatory infection with uncertain etiology, and specimen contamination or pathogen colonization.Ultimately, a total of 588 patients with decompensated cirrhosis hospitalized in the First Affiliated Hospital of Hebei North University between 1 January, 2021 and 31 December, 2023 were retrospectively selected as the training set(Fig. 1A). A total of 224 patients with decompensated cirrhosis hospitalized in Zhangjiakou Hospital of Traditional Chinese Medicine between 1 January, 2020 and 31 December 2023 comprised the validation set.The training set was utilized for constructing the nomogram, while the test set was used for validation purposes(Fig. 1B).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003eDecompensated cirrhosis was diagnosed based on the \u0026quot;EASL Clinical Practice Guidelines for the management of patients with decompensated cirrhosis \u0026quot; [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eLiver failure is diagnosed based on the \u0026quot; EASL clinical practice guidelines on acute-on-chronic liver failure \u0026quot; [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eThe data were analyzed using SPSS software (version 26.0) and R software (version 4.04; R Core Team). Frequency and percentages were used to express the count data. Comparisons were performed using the \u0026chi;2 test, with the Fisher exact probability method employed if the data did not meet the test requirement. Non-normal data are described as median (quartile 1, quartile 3); comparison between groups was performed using the Mann\u0026ndash;Whitney U test.\u003c/p\u003e\n \u003cp\u003ePredictors were screened using LASSO regression with a 10-fold cross-validation method, followed by multivariate logistic regression analysis and the establishment of a nomogram model. The model\u0026rsquo;s prediction efficiency was evaluated using receiver operating characteristic (ROC) curve analysis; the consistency and fit between the predicted infection risk from the nomogram model and the actual infection risk were assessed using the Homer\u0026ndash;Lemeshow test. Calibration curves were drawn to evaluate model accuracy; the clinical application value was assessed using decision curve analysis (DCA) models. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStudy population characteristics\u003c/h2\u003e\u003cp\u003eA total of 588 patients were included in the final analysis, including 346 men and 242 women, with a median age of 62 (range: 15\u0026ndash;87) years. The validation set was analyzed according to the same criteria, consisting of 224 patient 125 men and 99 women, with a median age of 66 (range: 20\u0026ndash;89) years. No differences were found in the basic characteristics of the participants between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, 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\u003eComparison of basic data between the training set and the validation set\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\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraining set(n\u0026thinsp;=\u0026thinsp;588)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValidation set(n\u0026thinsp;=\u0026thinsp;224)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender,n(%)\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\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e242 (41.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99 (44.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e346 (58.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125(55.8%)\u003c/p\u003e\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\u003e62(54,69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62(52,70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.713\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis time of cirrhosis,years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(0.25,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(0.25,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEtiology\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\u003eAlcoholic liver disease (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViral infection (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 (23.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (26.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutoimmune liver disease (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (21.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.814\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug-(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeredity\\Metabolism (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCirculatory disturbance (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.4%)\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\u003eNonalcoholic fatty liver disease (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.4%)\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\u003eAlcoholic liver disease\u0026thinsp;+\u0026thinsp;Viral infection (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViral infection\u0026thinsp;+\u0026thinsp;Autoimmune liver disease (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\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\u003eUnknown(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcomitant disease\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\u003eChronic obstructive pulmonary disease(COPD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.680\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (20.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128 (21.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\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\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\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\u003eThree or more underlying diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplication\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\u003eElectrolyte and acid-base balance disorders (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e248 (42.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 (43.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCholelithiasis(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (20.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal hemorrhage(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffusion in the serous cavity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e518 (88.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195 (87.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (9.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (7.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary liver cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (16.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePortal vein thrombosis (PVT), (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatic encephalopathy(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree or more complications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e205 (34.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb\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\u003e115g/L\u0026thinsp;\u0026le;\u0026thinsp;Hb\u0026thinsp;\u0026le;\u0026thinsp;150 g/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189 (32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb\u0026thinsp;\u0026lt;\u0026thinsp;115g/L or \u0026gt;\u0026thinsp;150 g/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e399 (67.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141 (62.9%)\u003c/p\u003e\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\u003ePLT\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\u003e125*10\u003csup\u003e9\u003c/sup\u003e/L\u0026thinsp;\u0026le;\u0026thinsp;PLT\u0026thinsp;\u0026le;\u0026thinsp;350*10\u003csup\u003e9\u003c/sup\u003e/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52 (23.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT\u0026thinsp;\u0026lt;\u0026thinsp;125*10\u003csup\u003e9\u003c/sup\u003e/L or \u0026gt;\u0026thinsp;350*10\u003csup\u003e9\u003c/sup\u003e/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e456 (77.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e172 (76.8%)\u003c/p\u003e\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\u003eCr\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\u003e35\u0026micro;mol/L\u0026thinsp;\u0026le;\u0026thinsp;Cr\u0026thinsp;\u0026le;\u0026thinsp;80\u0026micro;mol/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e414 (70.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166 (74.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u0026thinsp;\u0026lt;\u0026thinsp;35\u0026micro;mol/L or \u0026gt;\u0026thinsp;80\u0026micro;mol/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (25.9%)\u003c/p\u003e\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\u003eAST\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\u003eAST\u0026thinsp;\u0026le;\u0026thinsp;50U/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e329 (55.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142 (63.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u0026gt;50U/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259 (44.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 (36.6%)\u003c/p\u003e\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\u003eALT\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\u003eALT\u0026thinsp;\u0026le;\u0026thinsp;50U/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e453 (77.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e182 (81.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u0026gt;50U/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (23.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (18.8%)\u003c/p\u003e\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\u003eTBil\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\u003eTBil\u0026thinsp;\u0026le;\u0026thinsp;20.5\u0026micro;mol/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194 (33.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBil\u0026gt;20.5\u0026micro;mol/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e394 (67.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143 (63.8%)\u003c/p\u003e\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\u003eAlb\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\u003e35 g/L\u0026thinsp;\u0026le;\u0026thinsp;Alb\u0026thinsp;\u0026le;\u0026thinsp;55g/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (19.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlb\u0026thinsp;\u0026lt;\u0026thinsp;35g/L or \u0026gt;\u0026thinsp;55g/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e498 (84.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181 (80.8%)\u003c/p\u003e\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\u003ePT\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\u003ePT\u0026thinsp;\u0026le;\u0026thinsp;12.1s (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT\u0026gt;12.1s(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e559 (95.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208 (92.9%)\u003c/p\u003e\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\u003eINR\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\u003eINR\u0026thinsp;\u0026le;\u0026thinsp;1.5 (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e420 (71.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163 (72.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u0026gt;1.5(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (27.2%)\u003c/p\u003e\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\u003eChild-Pugh\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\u003eChild-Pugh\u0026thinsp;=\u0026thinsp;A(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild-Pugh\u0026thinsp;=\u0026thinsp;B(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e299 (50.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (47.3%)\u003c/p\u003e\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\u003eChild-Pugh\u0026thinsp;=\u0026thinsp;C(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225 (38.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (37.5%)\u003c/p\u003e\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\u003eMELD\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\u003e0\u0026thinsp;\u0026le;\u0026thinsp;MELD\u0026thinsp;\u0026lt;\u0026thinsp;10 (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e390 (66.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152 (67.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026thinsp;\u0026le;\u0026thinsp;MELD\u0026thinsp;\u0026lt;\u0026thinsp;20 (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (27.2%)\u003c/p\u003e\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\u003e20\u0026thinsp;\u0026le;\u0026thinsp;MELD (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the training set of 588 patients with decompensated cirrhosis, 205 were coinfected. The uninfected group comprised 383 patients. Among the infected patients, 231 cases of infection occurred, with clinical diagnosis in 163 cases and etiological diagnosis in 68 cases. Peritonitis was observed in 71cases (34.6%), pulmonary infection in 69 cases (33.6%), urinary tract infection in 12 cases (5.8%), blood infection in 11 cases (5.4%), skin and soft tissue infection in seven cases (3.4%), gastrointestinal infection in five cases (2.4%), biliary tract infection in three cases (1.4%),upper respiratory tract infection in one case (0.5%),intracranial infections in one case (0.5%), lung infection complicated with peritonitis in five cases (2.4%); lung infection complicated with bacteremia in five cases (2.4%); peritonitis complicated with bacteremia in four cases (2.0%); lung infection complicated with skin infection in four cases(2.0%); lung infection complicated with urinary tract infection in three cases (1.5%); peritonitis complicated with urinary tract infection in one case (0.5%); peritonitis complicated with skin infection in one case (0.5%); urinary tract infection complicated with bacteremia in one case (0.5%), and lung infection and urinary tract infection complicated with bacteremia in one case (0.5%).\u003c/p\u003e\u003cp\u003eThe identified pathogenic bacteria included \u003cem\u003eEscherichia\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;17), \u003cem\u003eEnterococcus faecium\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;12), \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;10), \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;8), \u003cem\u003eAcinetobacter baumannii\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;6), \u003cem\u003eEnterobacter cloacae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;4), \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;3), \u003cem\u003eStaphylococcus haemolyticus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;3), \u003cem\u003eSerratia marcescens\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;3) and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2). Drug susceptibility tests were conducted on all strains, and no multidrug-resistant bacteria were found. (as shown 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\u003eDetection of bacteria\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEscherichia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus faecium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnterobacter cloacae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStaphylococcus haemolyticus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSerratia marcescens\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of risk factors for bacterial infections in patients with decompensated cirrhosis\u003c/h2\u003e\u003cp\u003eComparison of the demographic and clinical data upon hospitalization between the infected group and the non-infected group in the training set revealed significant differences in respiratory failure, electrolyte and acid-base balance disorders, serous cavity effusion, hepatorenal syndrome, hepatic encephalopathy, the presence of three or more complications of decompensated cirrhosis, diagnosis of liver failure, Cr, AST, ALT, TBIL, PLT count, ALB, INR, Child\u0026ndash;Pugh score, and MELD score (all with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eComparison of basic data between the infected group and the non-infected groups\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\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInfected group (n\u0026thinsp;=\u0026thinsp;205)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-infected group (n\u0026thinsp;=\u0026thinsp;383)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender,n(%)\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\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84(41.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158(41.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.948\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121(59.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225(58.7%)\u003c/p\u003e\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\u003e60(51,69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63(54,69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis time of cirrhosis,years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(0.25,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(0.25,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEtiology\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\u003eAlcoholic liver disease (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (28.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViral infection (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (24.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (23.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutoimmune liver disease (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (20.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug-(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (2.3%)\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\u003eHeredity\\Metabolism (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCirculatory disturbance (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.3%)\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\u003eNonalcoholic fatty liver disease (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.3%)\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\u003eAlcoholic liver disease\u0026thinsp;+\u0026thinsp;Viral infection (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViral infection\u0026thinsp;+\u0026thinsp;Autoimmune liver disease (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.3%)\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\u003eUnknown(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (14.1%)\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\u003eConcomitant disease\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\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (1.3%)\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\u003eShock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (16.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (20.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (21.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.3%)\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\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.3%)\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\u003eThree or more underlying diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (1.0%)\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\u003eComplication\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\u003eElectrolyte and acid-base balance disorders (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127 (61.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCholelithiasis(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egastrointestinal hemorrhage(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffusion in the serous cavity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e193 (94.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e325 (84.8%)\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\u003eRenal impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (19.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary liver cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePortal thrombosis(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatic encephalopathy(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (22.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree or more complications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100 (48.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105 (27.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatic failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (19.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb\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\u003e115g/L\u0026thinsp;\u0026le;\u0026thinsp;Hb\u0026thinsp;\u0026le;\u0026thinsp;150 g/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (34.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (31.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.447\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb\u0026thinsp;\u0026lt;\u0026thinsp;115g/L or \u0026gt;\u0026thinsp;150 g/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (65.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e264 (68.9%)\u003c/p\u003e\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\u003ePLT\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\u003e125*10\u003csup\u003e9\u003c/sup\u003e/L\u0026thinsp;\u0026le;\u0026thinsp;PLT\u0026thinsp;\u0026le;\u0026thinsp;350*10\u003csup\u003e9\u003c/sup\u003e/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT\u0026thinsp;\u0026lt;\u0026thinsp;125*10\u003csup\u003e9\u003c/sup\u003e/L or \u0026gt;\u0026thinsp;350*10\u003csup\u003e9\u003c/sup\u003e/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176 (85.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e280 (73.1%)\u003c/p\u003e\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\u003eCr\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\u003e35\u0026micro;mol/L\u0026thinsp;\u0026le;\u0026thinsp;Cr\u0026thinsp;\u0026le;\u0026thinsp;80\u0026micro;mol/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (63.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e284 (74.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u0026thinsp;\u0026lt;\u0026thinsp;35\u0026micro;mol/L or \u0026gt;\u0026thinsp;80\u0026micro;mol/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (36.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99 (25.9%)\u003c/p\u003e\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\u003eAST\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\u003eAST\u0026thinsp;\u0026le;\u0026thinsp;50U/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (43.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e239 (62.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u0026gt;50U/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115 (56.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144 (37.6%)\u003c/p\u003e\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\u003eALT\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\u003eALT\u0026thinsp;\u0026le;\u0026thinsp;50U/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142 (69.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e311 (81.2%)\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\u003eALT\u0026gt;50U/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (30.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (18.8%)\u003c/p\u003e\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\u003eTBil\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\u003eTBil\u0026thinsp;\u0026le;\u0026thinsp;20.5\u0026micro;mol/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (18.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157 (41.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBil\u0026gt;20.5\u0026micro;mol/L(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168 (82.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226 (59.0%)\u003c/p\u003e\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\u003eAlb\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\u003e35 g/L\u0026thinsp;\u0026le;\u0026thinsp;Alb\u0026thinsp;\u0026le;\u0026thinsp;55g/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (20.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlb\u0026thinsp;\u0026lt;\u0026thinsp;35g/L or \u0026gt;\u0026thinsp;55g/L (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e193 (94.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e305 (79.6%)\u003c/p\u003e\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\u003ePT\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\u003ePT\u0026thinsp;\u0026le;\u0026thinsp;12.1s (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT\u0026gt;12.1s(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198 (96.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e361 (94.3%)\u003c/p\u003e\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\u003eINR\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\u003eINR\u0026thinsp;\u0026le;\u0026thinsp;1.5 (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (54.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e309 (80.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u0026gt;1.5(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (19.3%)\u003c/p\u003e\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\u003eChild-Pugh\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\u003eChild-Pugh\u0026thinsp;=\u0026thinsp;A(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild-Pugh\u0026thinsp;=\u0026thinsp;B(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223 (58.2%)\u003c/p\u003e\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\u003eChild-Pugh\u0026thinsp;=\u0026thinsp;C(n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127 (61.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (25.6%)\u003c/p\u003e\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\u003eMELD\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\u003e0\u0026thinsp;\u0026le;\u0026thinsp;MELD\u0026thinsp;\u0026lt;\u0026thinsp;10 (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (46.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e295 (77.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026thinsp;\u0026le;\u0026thinsp;MELD\u0026thinsp;\u0026lt;\u0026thinsp;20 (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88 (42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (22.5%)\u003c/p\u003e\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\u003e20\u0026thinsp;\u0026le;\u0026thinsp;MELD (n,%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDevelopment of nomogram for bacterial infections in patients with decompensated cirrhosis\u003c/p\u003e\u003cp\u003eThe LASSO regression model was used to incorporate all variables for predictor screening (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), resulting in the selection of 13 variables through a 10-fold cross-validation process(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB); these included respiratory failure, electrolyte and acid-base imbalance, renal function impairment, hepatic encephalopathy, diagnosis of liver failure, AST, ALT, TBil, PLT count, Hb, ALB, Child\u0026ndash;Pugh classification, and MELD score.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe 13 variables identified through the LASSO regression were incorporated into a multivariate logistic regression model. The findings from the analysis showed that an electrolyte and acid-base imbalance, renal function impairment, liver failure, abnormal PLT count, high Child\u0026ndash;Pugh score and high MELD score contributed to the risk of bacterial infections in patients with decompensated cirrhosis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eMultivariate analysis of influencing bacterial infections in patients with decompensated liver cirrhosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWaldχ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.354(0.756\u0026ndash;25.072)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElectrolyte and acid-base imbalance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.633(1.714\u0026ndash;4.045)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal function impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.703(2.107\u0026ndash;10.499)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatic encephalopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.356(0.722\u0026ndash;2.547)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis of liver failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.614(1.117\u0026ndash;6.119)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.345\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.708(0.446\u0026ndash;1.124)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.136(1.228\u0026ndash;3.715)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.508(0.902\u0026ndash;2.520)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.449(0.808\u0026ndash;2.596)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.134(0.642\u0026ndash;2.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.593(0.743\u0026ndash;3.417)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild\u0026ndash;Pugh classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.185(1.384\u0026ndash;3.450)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMELD score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.010(1.324\u0026ndash;3.054)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-4.653\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of the multivariate logistic regression model were used to construct a nomogram depicting bacterial infections in patients with decompensated cirrhosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eValidation of nomogram for bacterial infections in patients with decompensated cirrhosis\u003c/h3\u003e\n\u003cp\u003eThe ROC analysis demonstrated excellent discriminative ability of the model, with an area under the curve (AUC) of 0.802 (95% confidence interval (CI: 0.765\u0026ndash;0.840) for the training set, sensitivity of 64.9%, and specificity of 82.2%, further details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The AUC for the validation set was 0.832 (95% CI: 0.777\u0026ndash;0.886), with a sensitivity of 85.9% and specificity of 65.4%, further details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Hosmer\u0026ndash;Lemeshow test results indicated favorable calibration, with P-values of 0.905 in the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and 0.097 in the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), suggesting a satisfactory fit. The calibration curve demonstrated a strong concordance between the actual and predicted probabilities of bacterial infections in patients with decompensated cirrhosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe DCA plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA shows a net benefit range of 0\u0026ndash;0.310 within a threshold range of 0.05\u0026ndash;1 in the training set. Within the corresponding threshold range of 0.08\u0026ndash;0.90 in the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), a benefit range of 0\u0026ndash;0.275 was noted.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe R language \u0026ldquo;DynNom\u0026rdquo; package layout was used to construct a nomogram model, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gaojing.shinyapps.io/dynnomapp/\u003c/span\u003e\u003cspan address=\"https://gaojing.shinyapps.io/dynnomapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The web calculator test results demonstrate stability after performance testing (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLiver cirrhosis is a severe liver disease that, if not treated promptly, can progress to decompensated cirrhosis, leading to liver failure. Furthermore, studies have shown that the age of patients with end-stage liver disease is gradually decreasing, further exacerbating the burden of liver cirrhosis on the healthcare system. When cirrhosis progresses to the decompensated stage, various complications gradually appear, including but not limited to liver dysfunction, portal hypertension, and spontaneous bacterial peritonitis. These complications can have an impact on prognosis. Bacterial infection can serve as not only one of the complications, but also act as a trigger for various complications, exacerbating the condition, and even leading to (sub)acute liver failure with a high mortality rate [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, developing a predictive model is crucial for the early identification of high-risk factors associated with bacterial infections in patients with decompensated cirrhosis and for implementing targeted management strategies.\u003c/p\u003e\u003cp\u003eIn this study, a total of 588 patients with decompensated cirrhosis were included in the training set, among which 205 patients developed bacterial infection, with an infection rate of 34.9%, which is similar to the research results reported by Fern\u0026aacute;ndez et al [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The main type of bacterial infection in patients with decompensated cirrhosis was peritonitis, which is consistent with that in previous studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Bacterial peritonitis is a serious complication of decompensated cirrhosis, and the pathogenic bacteria can enter the abdominal cavity through the lymphatic system, intestine, blood, and other routes, causing abdominal cavity infection. Among the main pathogens causing infection, \u003cem\u003eE. coli\u003c/em\u003e was detected in 17 cases, \u003cem\u003eE. faecium\u003c/em\u003e in 12 cases, and \u003cem\u003eK. pneumoniae\u003c/em\u003e ranked among the top three bacterial infectious agents in 10 cases. It should be noted that \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eE. faecium\u003c/em\u003e, and \u003cem\u003eK. pneumoniae\u003c/em\u003e are widely distributed within various environmental niches, as well as in the human respiratory tract, intestine, and skin [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Infection can occur in patients when their immune system is weakened and when the anti-infective defense barrier is impaired.\u003c/p\u003e\u003cp\u003eDuring the process of model construction, LASSO regression demonstrated exceptional capabilities in effectively eliminating variables with minor contributions to the model, thereby significantly enhancing the robustness of the model and effectively counteracting the overfitting phenomenon. Additionally, LASSO regression can effectively circumvent the issue of multicollinearity, ensuring accurate estimation of model parameters [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, LASSO regression exhibits flexibility in terms of in integrating with various models to achieve efficient variable screening and be applied in different scenarios [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we employed the LASSO-logistic regression method to identify six independent risk factors, namely electrolyte and acid-base imbalance, renal function injury, liver failure, abnormal PLT counts, high Child\u0026ndash;Pugh scores, and high MELD scores. These risk factors possess significant guiding implications for the risk assessment of bacterial infection in patients with decompensated cirrhosis. By integrating these six independent risk factors, a nomogram model was established. This study conducted a comprehensive evaluation of the prediction model. The AUC value of the ROC curve was 0.802, indicating a high level of accuracy for the model. The Hosmer\u0026ndash;Lemeshow test revealed a good fit for the model. The calibration curve demonstrated favorable consistency between the predicted and actual probabilities of infection, and the DCA curve indicated a strong clinical applicability for the model. Overall, the model exhibited excellent performance in all aspects. By using the nomogram model, healthcare professionals can gain a more intuitive understanding of the varying degrees of risk associated with different risk factors and subsequently implement corresponding interventions to reduce the likelihood of bacterial infections in patients with decompensated cirrhosis.\u003c/p\u003e\u003cp\u003eAccording to the nomogram model, patients with decompensated cirrhosis and abnormal PLT counts are at a high risk of bacterial infection. As stated in one study [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], PLTs serve as a crucial component of the immune response in the body, interacting with various cell populations and participating in immune regulation processes. PLTs play a pivotal role in the occurrence, development, and ultimate outcome of infections. Abnormalities in PLT function or number can affect the body\u0026rsquo;s active defense and injury repair mechanisms, thereby increasing the probability of infection. Therefore, close monitoring of the PLT status of patients with decompensated cirrhosis is crucial for the prevention and control of infections.\u003c/p\u003e\u003cp\u003eSeveral studies have demonstrated a strong correlation between electrolyte balance disturbances and elevated occurrence of diverse infections [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The underlying reasons are postulated to be the following: 1) Electrolyte imbalances can impair the body\u0026rsquo;s ability to eliminate pathogens by disrupting the microenvironment and causing dysfunction in vital organs. 2) Specific electrolytes, such as magnesium and zinc, participate in the body\u0026rsquo;s anti-inflammatory effect [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The presence of these metal ions can exert an anti-inflammatory effect by mitigating oxidative stress-induced damage in the host, suppressing the expression of pro-inflammatory surface markers on macrophages, and inhibiting the phosphorylation of nuclear transcription factor κB, toll-like receptors, and other inflammatory signaling pathways [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The disturbance of electrolytes can impair human physiology through various mechanisms, leading to decreased resistance and creating favorable conditions for pathogenic infections.\u003c/p\u003e\u003cp\u003eThe presence of renal dysfunction is a severe complication commonly associated with patients with cirrhosis, and such patients are more prone to deteriorating to renal failure, leading to a higher mortality rate, which has been widely confirmed in existing studies [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The present study demonstrated that patients with cirrhosis complicated with renal dysfunction had a higher risk of bacterial infection. A study by Xu et al [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]also showed that renal function impairment was an independent risk factors for poor outcome of patients with decompensated cirrhosis complicated with infection. Furthermore, the presence of bacterial infection can exacerbate the degree of renal dysfunction in patients with cirrhosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Through a deep analysis of the mechanisms underlying the increase in infection rates due to renal dysfunction, we found that bacterial infection may be closely related to multiple factors, such as hemodynamic disorders, immune inflammatory responses, autophagy, oxidative stress responses, and metabolic reprogramming. Specifically, renal dysfunction can lead to decreased renal perfusion capacity and disrupt the water-electrolyte balance and acid-base balance, potentially causing tubular necrosis and promoting the release of inflammatory factors. These factors act together in the body, disrupting immune homeostasis and thus increasing the risk of infection.\u003c/p\u003e\u003cp\u003eThe Child\u0026ndash;Pugh scoring system, through a comprehensive evaluation of liver injury-related indicators such as ALB, TBil, prothrombin time, and ascites, not only reflects the patient's liver function reserve and the degree of cirrhosis but also indirectly assesses the patient's infection status and surgical risk. The MELD scoring system, a widely recognized assessment tool, effectively measures the severity of end-stage liver disease. The widespread application of these two scoring systems in the diagnosis and treatment of cirrhosis is mainly due to the objectivity of the selected variables, comparability of measurement results between different laboratories, ease of data acquisition, and scalability. A higher score represents poorer liver function. In this study, we observed significant differences in the Child\u0026ndash;Pugh classification and MELD score between infected and non-infected patients, further confirming their importance as independent risk factors for infection in patients with decompensated cirrhosis. In other words, an increase in the Child\u0026ndash;Pugh classification or MELD score in patients with decompensated cirrhosis often indicates a higher risk of infection.\u003c/p\u003e\u003cp\u003eIn the nomogram model, patients diagnosed with liver failure were at an increased risk of bacterial infection, further supporting the strong correlation between severe liver damage and infection. The massive necrosis of hepatocytes in patients with liver failure not only directly affects their liver function but also leads to the impairment of the monocyte-macrophage system. Macrophages can synthesize and release various acute-phase cytokines, facilitating bacterial and endotoxin clearance. Bacterial translocation and endotoxin release are facilitated by abnormal liver function, thereby exacerbating the patient\u0026rsquo;s condition [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Bacterial infection, as another significant factor, can further exacerbate liver damage, and the interaction between the two forms a vicious cycle that has a severe impact on the patient's condition [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our current research, we have taken note of the study conducted by Sundaram et al. on the risk factors of infection in patients with decompensated cirrhosis [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Unlike their study, several distinctions can be drawn from our investigation. First, our research include a large number of cases of infected patients with decompensated cirrhosis for computation and constructed a nomogram model. Second, our study included a wider range of clinical data and laboratory indicators than previous studies, making it easier for healthcare professionals to assess the risk of bacterial infection in each individual patient with decompensated cirrhosis using the nomogram model. Lastly, our study comprehensively evaluated the predictive model and conducted external validation to ensure its practicality.\u003c/p\u003e\u003cp\u003eThe current study still faces some limitations at this stage: (1) Given that this study employed a retrospective analysis method, a cautious approach should be taken in interpreting the causal relationship between risk factors and infection. (2) The arbitrary selection in LASSO is a limitation, but it was mitigated in this study via the multivariate logistic regression, AUC, calibration plots, and decision curve analysis, these helped to reduce the impact on the final nomogram model and its validity. (3)This study is currently in the preliminary exploration stage. Hence, future work will incorporate more characteristic variables and expand the sample size to more comprehensively validate and improve the overall performance of the model.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, a nomogram was developed to predict bacterial infections in patients with decompensated cirrhosis. The nomogram includes six predictor variables, which will effectively predict the likelihood of bacterial infections. This nomogram is valuable in predicting bacterial infections and will help clinicians to decide whether early intervention is needed based on the patient ' s specific situation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLASSO \u0026nbsp; \u0026nbsp; \u0026nbsp; Least absolute shrinkage and selection operator\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Area under the receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003ePLT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Platelet\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMELD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Model for End-Stage Liver Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCr \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Creatinine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTBil \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total bilirubin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Prothrombin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHb \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hemoglobin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlb \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Albumin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Aspartate aminotransferase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eALT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Alanine aminotransferase \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eINR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; International normalized ratio\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Decision curve analysis\u003c/p\u003e\n\u003cp\u003eCOPD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003ePVT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Portal vein thrombosis\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adhered to the ethical principles outlined in the Declaration of Helsinki (1964) and its subsequent amendments. The present study adheres to the standards of medical ethics (institutional review board number (IRB)#: W2023045) and is a retrospective investigation. No intervention measures were implemented on the study participants, and informed consent was waived following a review by the hospital’s ethics committee.\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\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed 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 no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by funding from the Scientific Research Project of Hebei\u003c/p\u003e\n\u003cp\u003eProvincial Health Commission (20231413). The funder did not participate in the design of the study, collection or analysis of data, decision to publish, ordrafting of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.G. conceived and designed the paper, collected and analyzed the data, prepared figures andtables, wrote the original draft of the manuscript, verified and reviewed the paper. Y.C. conceived and designed the paper, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, verified and reviewed the paper. \u0026nbsp;L.Y. collected and analyzed the data, prepared figures and tables. T.L. collected \u0026nbsp;the data, prepared figures and tables. T.W. analyzed the data, prepared figures and table. All authors reviewed the manuscript and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely appreciate the support and assistance provided by the faculty members of the First Affiliated Hospital of Hebei North University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Infection Management,The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Infectious Management, Zhangjiakou Hospital of Traditional Chinese Medicine, Zhangjiakou, Hebei, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTrebicka J, Aguilar F, Queiroz Farias A, Lozano JJ, S\u0026aacute;nchez-Garrido C, Us\u0026oacute;n-Raposo E, de la Pe\u0026ntilde;a-Ramirez C, Sidorova J, Curto-Vilalta A, Sierra-Casas P, Zitelli PM, Papp M, Pereira G, Caraceni P, Goncalves LL, Alessandria C, Torre A, Laleman W, Gadano A, Piano S, Mattos AZ, Gu W, Brol MJ, Schierwagen R, Uschner FE, Fischer J, Mendes LSC, Vargas V, Alvares-da-Silva MR, Mookerjee R, Bittencourt PL, Benitez C, Albillos A, Couto C, Mendizabal M, Ba\u0026ntilde;ares R, Toledo CL, Mazo DF, Janicko M, Castillo-Barradas M, Martin Padilla Machaca P, Gatti P, Zarela-Lozano Miranda A, Mal\u0026eacute;-Vel\u0026aacute;zquez R, Zipprich A, Castro-Lyra A, Gustot T, Bernal W, Gerbes AL, Jalan R, Fern\u0026aacute;ndez J, Angeli P, Carrilho FJ, Claria J, Moreau R, Arroyo V. 2025. 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Gut. 2024;73:1015-24. https://doi.org/10.1136/gutjnl-2023-330584.\u003c/li\u003e\n\u003cli\u003eArroyo V, Angeli P, Moreau R, Jalan R, Cl\u0026agrave;ria J, Trebicka J, Fern\u0026aacute;ndez J, Gustot T, Caraceni P, Bernardi M. The systemic inflammation hypothesis: Towards a new paradigm of acute decompensation and multiorgan failure in cirrhosis. Journal of Hepatology. 2020;74:670-85. https://doi.org/10.1016/j.jhep.2020.11.048.\u003c/li\u003e\n\u003cli\u003eRajapakse J, Khatiwada S, Akon AC, Yu KL, Shen S, Zekry A. Unveiling the complex relationship between gut microbiota and liver cancer: opportunities for novel therapeutic interventions. Gut Microbes. 2023;15:224003. https://doi.org/10.1080/19490976.2023.2240031.\u003c/li\u003e\n\u003cli\u003eKibble, H., Shawcross, DL. The assessment and management of cirrhotic patients with encephalopathy. United European Gastroenterology Journal. 2024;12:187-93. https://doi.org/10.1002/ueg2.12530\u003c/li\u003e\n\u003cli\u003eSundaram V, Jalan R, Ahn JC, Charlton MR, Goldberg DS, Karvellas CJ, Noureddin M, Wong RJ. Class III obesity is a risk factor for the development of acute-on-chronic liver failure in patients with decompensated cirrhosis. Journal of Hepatology. 2018;69:617-25. https://doi.org/10.1016/j.jhep.2018.04.016.\u003c/li\u003e\n\u003cli\u003eAlabsawy E, Shalimar S, Sheikh MF, Ballester MP, Acharya SK, Agarwal B, Jalan R. Overt hepatic encephalopathy is an independent risk factor for de novo infection in cirrhotic patients with acute decompensation. Alimentary Pharmacology \u0026amp; Therapeutics. 2022;55:722-32. https://doi.org/10.1111/apt.16790.\u003c/li\u003e\n\u003cli\u003eWang Y, Li C, Su HB, Hu JH. Invasive fungal infections in acute decompensation of cirrhosis: epidemiology, predictors of 28-day mortality, and outcomes. Indian J Microbiol. 2025;65:1366-70. https://doi.org/10.1007/s12088-024-01243-4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Decompensated cirrhosis, Bacterial infections, LASSO, Risk prediction model, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7515340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7515340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBacterial infections and disease progression are correlated in individuals with decompensated cirrhosis. We aimed to construct and verify a risk prediction model for bacterial infections in patients with decompensated cirrhosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eRetrospectively, 588 patients with decompensated cirrhosis treated at the First Affiliated Hospital of Hebei North University were selected as the training set, and 224 patients with decompensated cirrhosis treated at Zhangjiakou Traditional Chinese Medicine Hospital were selected as the validation set. The participants were divided into infected and non-infected groups according to whether they had bacterial infection or not. Clinical data were collected before the positive culture results; the variables were screened by least absolute shrinkage and selection operator (LASSO) regression. Multivariate regression was used to analyze infection risk factors to construct a nomogram model; the predictive effect of the model was evaluated by the area under the receiver operating characteristic curve (AUC). Calibration and decision curves were used to evaluate the model\u0026rsquo;s clinical application value.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBacterial infections occurred in 34.9% of patients; peritonitis was the main infection. Escherichia was the most cultured infectious agent among 68 pathogenic bacterial strains. Multivariate logistic regression analysis showed that independent risk factors for bacterial infections (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were electrolyte and acid-base imbalance, renal function impairment, liver failure, abnormal platelet (PLT) counts, a high Child\u0026ndash;Pugh score, and a high Model for End-Stage Liver Disease (MELD) score. The AUCs of the predicted model were 0.802 in the training cohort and 0.832 in the validation cohort. Hosmer\u0026ndash;Lemeshow tests showed a good fit between the model and verification groups. Decision curve analysis and calibration curves showed a high value for the prediction model.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe nomogram model showed favorable differentiation and prediction of bacterial infection risk and might be able to identify high-risk patients early.\u003c/p\u003e","manuscriptTitle":"Clinical characteristics of bacterial infections in patients with decompensated cirrhosis and construction and verification of a risk prediction model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 16:02:28","doi":"10.21203/rs.3.rs-7515340/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"297281370731611893658807555469107874323","date":"2025-10-05T02:44:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T14:23:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T09:49:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-05T21:14:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T02:26:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-09-05T02:23:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"482333d0-91df-45bf-8b8e-0a94ac1f4044","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T16:02:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 16:02:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7515340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7515340","identity":"rs-7515340","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}