Bile Over Blood: A Predictive Model for Guiding Antimicrobial Therapy When Blood Cultures Miss the Pathogen in Biliary Infections

Bile Over Blood: A Predictive Model for Guiding Antimicrobial Therapy When Blood Cultures Miss the Pathogen in Biliary Infections | 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 Bile Over Blood: A Predictive Model for Guiding Antimicrobial Therapy When Blood Cultures Miss the Pathogen in Biliary Infections Xin Zheng, Xue Bai, Ce Bian, Xuewei Zhuang, Yanli Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7821955/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Blood culture (BC) is considered the gold standard for diagnosing bacteremia; however, its sensitivity is notably limited in cases of acute biliary tract infections, often resulting in under-detection. Despite this, the patterns and contributing factors leading to such under-detection have not been systematically investigated. This study aims to quantify the discrepancies in pathogen profiles between bile and paired blood cultures, and to identify pathogen-specific under-detection rates and independent predictors of blood culture leakage. Methods This retrospective study included 398 patients with radiologically confirmed biliary obstruction who underwent concurrent bile and blood culture collection between January 2017 and December 2024. Patients were categorized into four groups based on culture results: leakage (bile+/blood − OR bile+/blood + without identical pathogen) non-leakage (bile+/blood + with ≥ 1 identical pathogen), double-negative, and blood-only-positive. Multivariable logistic regression was used to identify independent factors associated with blood culture leakage. Results The positivity rate of bile cultures (83.67%, 333/398) was significantly higher than that of blood cultures (36.18%, 144/398) ( P < 0.001). The overall leakage rate was 70.27% (234/333). Significant differences in leakage rates were observed among different pathogens ( P < 0.001), with Enterococcus faecalis exhibiting the highest leakage rate (94.23%), while Escherichia coli (31.93%) and Klebsiella pneumoniae (25.88%) showed higher concordance. Multivariable analysis identified fever (aOR = 0.45, 95% CI: 0.26–0.79, P = 0.005), presence of E. coli in bile (aOR = 0.50, 95% CI: 0.30–0.82, P = 0.007), and polymicrobial infection (aOR = 0.50, 95% CI: 0.30–0.82, P = 0.006) as protective factors, whereas antibiotic use (aOR = 2.08, 95% CI: 1.23–3.52, P = 0.006) was an independent risk factor. The predictive model exhibited moderate discriminative capacity with an AUC of 0.693. Conclusion This study confirms that the under-detection of blood cultures in biliary tract infections is highly pathogen-specific, with E. faecalis being most frequently missed. A clinical prediction model based on fever, antibiotic use, E. coli colonization, and infection complexity was developed. These findings emphasize that in afebrile patients, those receiving antibiotic therapy, or those with Enterococcus -dominated bile cultures, clinical decision-making should rely more heavily on bile culture Acute biliary tract infection Bacteremia Bile culture Blood culture Under-detection Figures Figure 1 Figure 2 Figure 3 Introduction Acute biliary tract infection is one of the most life-threatening gastrointestinal emergencies, characterized by bacterial infection secondary to biliary obstruction, which can rapidly progress to sepsis and multiple organ failure, with mortality rates as high as 30–50% in severe cases [ 1 – 3 ]. Globally, the increasing incidence of biliary obstruction due to gallstone disease and malignancies has heightened the disease burden of biliary infections, posing significant challenges to public health systems [ 4 , 5 ]. Recent large-scale epidemiological studies have corroborated an increasing trend in the incidence of biliary tract-associated bloodstream infections (BSIs), with clinical outcomes closely linked to the causative pathogens and the appropriateness of empirical therapy [ 6 , 7 ]. The cornerstone of sepsis management lies in early diagnosis and treatment, with accurate microbiological identification being essential for effective antimicrobial therapy [ 8 , 9 ]. Since the publication of the Tokyo Guidelines (TG18/TG13), the diagnosis and severity stratification of biliary infections have been standardized; however, strategies for microbiological diagnosis remain contentious [ 1 , 10 , 11 ]. Currently, blood culture is regarded as the gold standard for diagnosing bacteremia and guiding systemic antimicrobial treatment [ 12 ]. Nevertheless, in the context of biliary infections, the sensitivity of blood culture is notably diminished, with positivity rates typically ranging between 30% and 50% [ 13 , 14 ]. Moreover, the inherent turnaround time (TAT) often exceeds 48 hours, failing to meet the need for early targeted treatment in critically ill patients [ 15 ]. More importantly, blood culture results are highly susceptible to false negatives after empirical antibiotic administration, thereby diminishing their clinical utility [ 16 ]. The mechanisms underlying this under-detection are complex, potentially involving filtration by the gut-blood barrier, phagocytic clearance by hepatic Kupffer cells, and the intrinsic growth characteristics of pathogens [ 17 , 18 ]. Notably, the microbial ecology of biliary infections is rapidly evolving. While traditional Gram-negative bacilli such as Escherichia coli and Klebsiella pneumoniae remain predominant, Enterococcus species, particularly Enterococcus faecalis and Enterococcus faecium , have emerged as significant opportunistic pathogens. Their detection rates have been increasing worldwide, associated with healthcare-related infections, prior biliary interventions, and the emergence of antimicrobial resistance [ 13 , 19 – 21 ]. Of particular concern, polymicrobial infections, especially those involving both Gram-negative and Gram-positive bacteria, are more common in patients with cholangitis and have been linked to more severe clinical presentations, higher rates of organ dysfunction, and increased mortality [ 22 ]. The intrinsic resistance of enterococci to cephalosporins and their unique pathogenic mechanisms—including biofilm formation, immune evasion, and potential intracellular persistence—render these infections more insidious and challenging to eradicate [ 23 ]. Clarifying the differences in hematogenous dissemination among pathogens is crucial for interpreting culture results, optimizing empirical antibiotic regimens, and improving patient outcomes. However, large-scale studies that systematically quantify the concordance between bile and blood cultures and delineate the pathogen-specific and clinical circumstances predisposing to blood culture under-detection remain scarce.This knowledge gap directly impacts clinical decision-making: For a patient with positive bile culture but negative blood culture, what is the actual risk of bacteremia? Does a negative blood culture result justify avoiding targeted therapy against the pathogen isolated from bile? Therefore, this study aims to address the following key questions through a large retrospective cohort: (1) quantify the discordance in pathogen profiles between paired bile and blood cultures in patients with biliary obstruction; (2) elucidate the differences in the ability of various pathogens to disseminate from the biliary tract to the bloodstream (i.e., blood culture leakage rates); and (3) identify clinical and microbiological factors independently associated with blood culture leakage and construct a clinical prediction model. The findings are expected to provide high-level evidence for precise microbiological diagnosis and individualized antimicrobial therapy in biliary tract infections. Methods 1. Study Design and Population This retrospective cohort study was approved by the Ethics Committee of Shandong Provincial Third Hospital (KYLL-2024064) with informed consent waived. Consecutive patients hospitalized between January 2017 and December 2024 for radiologically confirmed biliary obstruction who underwent bile and blood culture collection within 48 hours of admission were included. Exclusion criteria: (1) Missing microbiological data for bile or blood cultures . (2) Concurrent active infection at other sites . A total sample size of 398 patients was included based on all consecutive eligible cases during the study period. This sample size provides approximately 80% power to detect an odds ratio of 1.8 for the primary outcome (blood culture leakage) at a two-sided alpha level of 0.05, assuming a baseline leakage rate of 50%. 2. Specimen Collection and Microbiological Testing Bile samples: Collected aseptically via ERCP or PTBD, inoculated onto blood agar and MacConkey agar, and incubated aerobically at 35°C for 2 days. Blood samples: Each culture set included aerobic and anaerobic bottles, with 8–10 mL of blood inoculated per bottle in adults. Positive samples were subcultured. Pathogen identification and susceptibility testing: Species identification and antibiotic susceptibility testing were performed using an automated microbial analysis system (VITEK 2 COMPACT, BioMérieux, France). Quality control was performed using standard strains ( Klebsiella oxytoca ATCC 700324, E. faecalis ATCC 700327). 3. Key Definitions Blood culture leakage: Detection of a pathogen (at species level) in bile but not in the paired blood culture. This includes two specific scenarios: 1.Bile culture positive and blood culture negative (bile+/blood−). 2.Both bile and blood cultures positive, but without an identical pathogen detected in the blood (bile+/blood+ but no matching pathogen). Bacteremia: Detection of a pathogenic microorganism in ≥1 bottle of blood cultures (contaminants excluded; coagulase-negative staphylococci, viridans group streptococci, Corynebacterium spp., etc., were considered based on clinical context). Polymicrobial infection: Detection of ≥2 distinct pathogens in a single bile or blood culture specimen. Groups: Patients were classified into leakage (bile+/blood− OR bile+/blood+ without identical pathogen), non-leakage (bile+/blood+, ≥1 identical pathogen), double-negative (bile−/blood−), and blood-only-positive (bile−/blood+) groups. 4. Statistical Analysis Statistical analyses were performed using Python 3.10 (scikit-learn, SciPy) and R 4.2.2. A two-sided significance level of α = 0.05 was adopted. Descriptive statistics: Continuous variables are presented as median (interquartile range, IQR) and categorical variables as frequency (percentage). Group comparisons: For continuous variables, Kruskal–Wallis H test was used for multiple groups and Mann–Whitney U test for two groups, with Bonferroni correction where applicable; For categorical variables, the χ² test or Fisher’s exact test was used, with Bonferroni correction where applicable. For variables showing significant overall differences ( P < 0.05), post-hoc pairwise comparisons were conducted using Dunn's test with Bonferroni correction for continuous variables and pairwise Fisher's exact tests with Bonferroni correction for categorical variables. Analysis of factors associated with blood culture leakage: Univariable analysis was used to screen potential variables ( P < 0.1). Multivariable analysis was then conducted using binary logistic regression (forward stepwise method),reporting adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs). Model goodness-of-fit was assessed using the Hosmer–Lemeshow test ( P > 0.05 indicating good fit), and discriminative ability was evaluated using the area under the receiver operating characteristic curve (AUC). Variables with P < 0.1 in univariable analysis were eligible for inclusion in the multivariable model. Multicollinearity was assessed using variance inflation factors (VIF < 10 indicating acceptable collinearity). Missing data for continuous variables were handled by median imputation. Results 1. Microbiological Characteristics Among 398 patients with biliary obstruction who underwent concurrent bile and blood cultures, the pathogen detection rate in bile was 83.67% (333/398), significantly higher than that in blood cultures at 36.18% (144/398) (McNemar χ² = 169.11, P < 0.001). A total of 490 microbial isolates were obtained from bile cultures, with 40.54% (135/333) of cases being polymicrobial infections (≥2 pathogens). Gram-negative bacteria accounted for 70.82% (347/490), Gram-positive bacteria for 27.76% (136/490), and fungi for 1.43% (7/490). The top five pathogens isolated were Escherichia coli (33.88%, 166/490), Klebsiella pneumoniae (17.35%, 85/490), Enterococcus faecium (12.24%, 60/490), Enterococcus faecalis (10.61%, 52/490), and Pseudomonas aeruginosa (6.12%, 30/490). Blood cultures yielded 162 microbial isolates, with a polymicrobial infection rate of 12.50% (18/144), significantly lower than that in bile cultures (χ² = 35.004, P < 0.001). Gram-negative bacteria accounted for 79.63% (129/162), Gram-positive bacteria for 19.75% (32/162), and fungi for 0.63% (1/162), showing no significant difference in microbial distribution compared to bile cultures ( P > 0.05) (Figure 1A). The top five pathogens in blood cultures were E. coli (46.30%, 75/162), K. pneumoniae (19.14%, 31/162), E. faecium (9.88%, 16/162), P. aeruginosa (4.94%, 8/162), and E. faecalis (3.70%, 6/162). Figure 1. Pathogen Distribution and Culture Discrepancies (A) Stacked bar chart showing microbial composition in bile and blood cultures. Gram-negative bacteria predominated in both sample types. (B) Forest plot of leakage rates for the top 5 pathogens. Error bars indicate 95% confidence intervals. The dashed vertical line represents the overall leakage rate (70.27%). E. faecalis showed the highest leakage rate (94.23%, P 20%, red bars <20%. 2. Concordance Between Bile and Blood Cultures Overall concordance between bile and blood cultures was 38.69% (154/398), with a Cohen’s Kappa coefficient of 0.077 ( P < 0.001), indicating weak agreement between the two methods (Figure S1). The performance of bile culture in predicting bacteremia was as follows: sensitivity 90.83% (95% CI: 83.9–95.6), specificity 19.03% (95% CI: 14.7–24.0), positive predictive value (PPV) 29.73% (95% CI: 24.7–35.1), and negative predictive value (NPV) 84.62% (95% CI: 73.2–92.3). Figure S1. Concordance Between Bile and Blood Cultures Venn diagram showing overlap in positive culture results.The bile-only positive group (n=199) constituted the majority of the leakage group (total n=234).Cohen's Kappa coefficient was 0.077 ( P <0.001), indicating poor agreement. 3. Pathogen-Specific Concordance Analysis Among 134 patients with both bile and blood cultures positive, 73.88% (99/134) had at least one identical pathogen. Significant differences were observed in concordance rates across major pathogens (χ² = 10.94, P = 0.027) (Figure 1C). E. coli showed the highest concordance rate (31.93%, 53/166), followed by K. pneumoniae (25.88%, 22/85), P. aeruginosa (23.33%, 7/30), E. faecium (23.33%, 14/60), and E. faecalis (5.77%, 3/52). Among 333 patients with positive bile cultures, the overall blood culture leakage rate was 70.27% (234/333; 95% CI: 65.1–75.1%). Leakage rates varied significantly across pathogens (χ² = 19.49, P < 0.001) (Figure 1B). E. faecalis exhibited the highest leakage rate (94.23%, 95% CI: 84.1–98.7%), which was significantly greater than that of all other major pathogens ( P < 0.001). 4. Differential Clinical and Microbiological Profiles Across Bile and Blood Culture Concordance Groups Patients were categorized into four distinct groups based on bile and blood culture concordance (Table 1). The leakage group (n = 234) included patients with positive bile culture but no identical pathogen detected in blood culture, comprising two scenarios: those with negative blood cultures (n=199) and those with positive blood cultures but where the pathogen(s) did not match those found in bile (n=35). The non-leakage group (n = 99) included patients with at least one identical pathogen in both bile and blood. The double-negative (n = 55) and blood-only-positive (n = 10) groups were defined as having both cultures negative or only blood culture positive, respectively. Key clinical and microbiological characteristics across the four groups are summarized in Table 2 and visualized in Figure 3. Significant differences were observed for several variables. The prevalence of fever was highest in the non-leakage group (66.7%) and lowest in the double-negative group (38.2%, P = 0.005), with post-hoc analysis confirming a significant difference between these two groups (corrected P = 0.004). Procalcitonin levels showed overall significant differences ( P = 0.032), though no significant pairwise differences were identified after correction. Microbiological characteristics demonstrated more pronounced intergroup variations. Bile E. coli detection was significantly higher in the non-leakage group (64.6%) compared to the leakage group (43.6%, corrected P = 0.003), and both bile-positive groups showed significantly higher rates than bile-negative groups (all corrected P < 0.001). Similarly, polymicrobial infection was more common in the non-leakage group (53.5%) than in the leakage group (35.0%, corrected P = 0.013), with both bile-positive groups demonstrating significantly higher rates than bile-negative groups (all corrected P < 0.001). While overall antibiotic use differed among groups ( P = 0.041), post-hoc analysis did not identify significant pairwise differences after correction. Figure 3. Differential Distribution of Key Clinical Features Across Study Groups (A) Fever prevalence across study groups. Different letters indicate significant differences based on post-hoc pairwise comparisons with Bonferroni correction ( P <0.05). (B) Procalcitonin (PCT) levels (median) showed overall significant differences ( P =0.032) but no significant pairwise differences after correction. (C) Bile E. coli detection rate. Different letters indicate significant differences between bile-positive groups. (D) Polymicrobial infection prevalence. Different letters indicate significant differences between bile-positive groups. Groups sharing the same letter are not significantly different. Bile-negative groups (double-negative and blood-only) were not included in pairwise comparisons for bile-specific variables. Table 1. Definition of patient groups based on bile and blood culture results Group Bile Culture Blood Culture Identical Pathogen? n Description / Scenario Leakage Positive Negative — 199 Bile culture positive, blood culture negative Positive Positive No 35 Both cultures positive, but no matching pathogen Non-Leakage Positive Positive Yes 99 Both cultures positive, with ≥1 identical pathogen Blood-Only-Positive Negative Positive — 10 Blood culture positive, bile culture negative Double-Negative Negative Negative — 55 Note: The leakage group comprises two distinct scenarios. 5. Factors Associated with Blood Culture Leakage Univariable analysis identified fever (OR = 0.58, 95% CI: 0.36-0.95, P = 0.031), bile E. coli (OR = 0.42, 95% CI: 0.26-0.69, P < 0.001), polymicrobial infection (OR = 0.47, 95% CI: 0.29-0.75, P = 0.002), and antibiotic use (OR = 1.76, 95% CI: 1.09-2.82, P = 0.020) as significant factors associated with blood culture leakage. Multivariable logistic regression using forward selection identified bile E. coli (aOR = 0.50, 95% CI: 0.30-0.82, P = 0.007), polymicrobial infection (aOR = 0.50, 95% CI: 0.30-0.82, P = 0.006), fever (aOR = 0.45, 95% CI: 0.26-0.79, P = 0.005), and antibiotic use (aOR = 2.08, 95% CI: 1.23-3.52, P = 0.006) as independent factors associated with blood culture leakage. The prediction model demonstrated moderate discriminative ability with an AUC of 0.693 (95% CI: 0.631-0.756) (Figure S2) and a pseudo R-squared of 0.081. Figure S2. Receiver Operating Characteristic (ROC) Curve ROC curve for the multivariable prediction model of blood culture leakage. The area under the curve (AUC) was 0.693 , indicating moderate discriminative ability. The shaded area represents the 95% confidence interval. No significant multicollinearity was observed among the predictor variables, as all variance inflation factors were below 10 (range: 1.30-2.31).The results of the multivariable logistic regression analysis are presented in Table 3 and illustrated in Figure 2. Figure 2. Multivariable Predictors of Blood Culture Leakage Forest plot showing adjusted odds ratios (aOR) with 95% confidence intervals. Protective factors (aOR 1) are in red. Antibiotic use was associated with a 2.08-fold increased risk of leakage ( P =0.006). Fever (aOR =0.45, P =0.005), presence of E. coli in bile (aOR =0.50, P =0.007), and polymicrobial infection (aOR =0.50, P =0.006) were protective factors. Table 2. Clinical and microbiological characteristics across bile and blood culture concordance groups. Characteristic Leakage (n=234) Non-leakage (n=99) Double-negative (n=55) Blood-only (n=10) P -value Clinical features Fever, n (%) 126 (53.8) 66 (66.7) 21 (38.2) 4 (40.0) 0.005 Laboratory results PCT, ng/mL, median (IQR) 1.2 (0.3-8.7) 5.3 (0.5-31.2) 0.7 (0.3-5.6) 11.0 (0.7-16.6) 0.032 Microbiological features Bile E. coli , n (%) 102 (43.6) 64 (64.6) 0 (0.0) 0 (0.0) <0.001 Polymicrobial infection, n (%) 82 (35.0) 53 (53.5) 0 (0.0) 0 (0.0) <0.001 Treatment Antibiotic use, n (%) 132 (56.4) 42 (42.4) 34 (61.8) Data are presented as median (interquartile range) for continuous variables and n (%) for categorical variables. PCT, procalcitonin. *Post-hoc pairwise comparisons with Bonferroni correction revealed significant differences (P < 0.05) between the Non-leakage and Double-negative groups for fever, and between the Non-leakage and Leakage groups for bile E. coli and polymicrobial infection. Table 3. Factors Associated with Blood Culture Leakage in Multivariable Logistic Regression Analysis Variable Univariable OR (95% CI) Univariable P-value Multivariable aOR (95% CI) Multivariable P -value Fever 0.58 (0.36-0.95) 0.031 0.45 (0.26-0.79) 0.005 Bile E. coli 0.42 (0.26-0.69) <0.001 0.50 (0.30-0.82) 0.007 Polymicrobial infection 0.47 (0.29-0.75) 0.002 0.50 (0.30-0.82) 0.006 Antibiotic use 1.76 (1.09-2.82) 0.020 2.08 (1.23-3.52) 0.006 Discussion Through paired microbiological analysis of 398 patients with biliary obstruction, this study reveals the existence of a highly variable and pathogen-selective "microbial barrier" between the bile and the circulatory system. We confirmed a high rate of blood culture under-detection (70.27%) in biliary-derived bacteremia and, for the first time, systematically demonstrated that this phenomenon is non-random and strongly associated with specific pathogens, particularly Enterococcus faecalis , and identifiable clinical scenarios such as antibiotic use, absence of fever, and monomicrobial infection. Based on these findings, we developed a clinical prediction model with moderate discriminative ability (AUC = 0.693). These insights have profound implications for both the clinical management of biliary infections and fundamental research on host–pathogen interactions. Our study indicates that bile culture provides richer and more direct microbiological information than blood culture in diagnosing biliary infections. With a high positivity rate of 83.67% and a polymicrobial infection rate of 40.54%, bile culture accurately reflects the complexity of bacterial infections in the biliary tract, consistent with previous studies [ 22 , 24 , 25 ]. Notably, polymicrobial infections, associated with worse clinical outcomes in both this study and the literature [ 22 ], were also identified as a protective factor against blood culture leakage (aOR = 0.50). This may reflect more severe barrier disruption and higher bacterial load, closely related to the activation of systemic inflammatory response syndrome (SIRS). A key contribution of this study is providing a contextual framework for interpreting blood culture results. We found that the sensitivity of blood culture highly depends on clinical context. Blood culture is more likely to be positive in patients presenting with fever, elevated inflammatory markers like PCT, presence of Escherichia coli in bile, or polymicrobial infection. These factors collectively indicate a state of strong SIRS, which often implies greater bacterial translocation or impaired host clearance capacity [ 26 , 27 ]. Therefore, blood culture offers the highest diagnostic value in such patients. Conversely, in afebrile patients, those who have received antibiotic therapy, or those with Enterococcus -dominated infections, clinicians should maintain a high index of suspicion regarding negative blood culture results. Treatment decisions should primarily rely on bile culture and susceptibility results to avoid undertreatment. This finding aligns with a recent Japanese study emphasizing that blood cultures should be routinely collected regardless of cholangitis severity, as a considerable proportion of mild cases also carry a risk of bacteremia [ 28 ]. The significant variation in leakage rates among pathogens observed in this study carries important clinical implications. Gram-negative bacilli such as E. coli and K. pneumoniae showed relatively high concordance rates (31.93% and 25.88%, respectively) attributable to their ability to produce potent inflammatory mediators like lipopolysaccharide (LPS). LPS can trigger a vigorous systemic inflammatory response (SIRS) by activating innate immune responses such as the TLR4 pathway. This inflammation increases vascular permeability, disrupts endothelial barriers, and promotes bacterial dissemination from the primary infection site into the bloodstream, making these pathogens more likely to be detected by blood culture [ 29 , 30 ]. E. faecalis exhibited an extremely high blood culture leakage rate (94.23%), a finding of major clinical significance. Several mechanisms may underlie this phenomenon. First, regarding pathogenic mode, unlike Gram-negative bacteria that induce a "cytokine storm," enterococci may favor a "latent" or "chronic" mode of infection. Their strong adhesins and biofilm-forming capacity [ 31 , 32 ] enable firm colonization on mucosal surfaces, leading to continuous low-level antigen release without causing massive bacteremia. Second, immune evasion and intracellular persistence: evidence suggests that E. faecalis can be phagocytosed by immune cells like macrophages but survives intracellularly through various mechanisms, evading extracellular bactericidal drugs and immune clearance [ 23 ]. This intracellular persistence may allow it to exist with low bacteremic load. Furthermore, as Gram-positive bacteria, enterococci weakly induce potent inflammatory cytokines like TNF-α, and their complement resistance may lead to rapid clearance from the bloodstream, making them difficult to capture by conventional blood culture [ 33 ]. These findings suggest that in patients at risk factors for Enterococcus infection (e.g., history of endoscopic sphincterotomy (EST), biliary stent placement, cholangiocarcinoma) [ 20 , 34 ], even with negative blood cultures, if bile culture indicates Enterococcus as the predominant pathogen, empirical therapy should carefully consider covering enterococci. Multivariable analysis firmly established prior antibiotic use as an independent risk factor for blood culture leakage (aOR = 2.08), consistent with classical principles and recent studies in infectious diseases [ 16 , 35 ]. Antibiotics can significantly reduce circulating bacterial loads below the detection threshold of cultures within hours. This finding strongly reaffirms the absolute necessity of routinely collecting blood cultures prior to initiating empirical antibiotic therapy. The results of this study have direct implications for clinical practice and Antimicrobial Stewardship Programs (ASP). First, bile culture should be elevated as the core of the diagnostic workflow for biliary infections, with results should serve as the primary basis for guiding targeted therapy, especially when blood cultures are negative. Second, empirical therapy should be optimized with pathogen-specific considerations. For patients at high risk for Enterococcus infection, empirical regimens should avoid relying solely on third-generation cephalosporins. Based on this study and recent literature [ 21 , 34 ], piperacillin-tazobactam or carbapenems may be preferable choices due to their coverage against enterococci and most Gram-negative bacteria. In regions with high VRE prevalence or in high-risk patients, early consideration of vancomycin or daptomycin may be necessary. Third, blood culture results require careful contextual interpretation. In patients identified by our model as high-risk for leakage, a negative blood culture must not justify the discontinuation or de-escalation of targeted therapy against pathogens isolated from bile. We honestly acknowledge several limitations. First, its single-center retrospective design may introduce selection bias, as all included patients underwent invasive procedures for bile collection, potentially representing a more severely ill population than general biliary infection patients. Second, although pathogen concordance was performed at the species level, the absence of molecular typing (e.g., MLST or WGS) prevents confirmation of clonal identity between bile and blood isolates, leaving open the possibility of multi-strain infections. Third, we were unable to comprehensively analyze all potential confounding factors, such as the host's detailed immune status, specific antibiotic types, and timing of administration. These limitations precisely highlight directions for future research: 1) conduct prospective multicenter studies to validate this prediction model and incorporate more variables; 2) perform whole-genome sequencing on paired isolates to precisely trace bacterial dissemination pathways at the molecular level; 3) integrate rapid molecular diagnostic technologies (e.g., rapid MALDI-TOF MS identification from positive blood culture bottles, direct nanopore sequencing or mNGS on bile samples) [ 36 – 38 ] to obtain comprehensive pathogen and resistance information within hours rather than days, thereby revolutionizing the diagnostic paradigm for biliary infections and enabling truly precise anti-infective therapy. Abbreviations BC Blood culture aOR Adjusted odds ratio CI Confidence interval AUC Area under the curve ERCP Endoscopic retrograde cholangiopancreatography PTBD Percutaneous transhepatic biliary drainage PCT Procalcitonin IQR Interquartile range SIRS Systemic inflammatory response syndrome LPS Lipopolysaccharide EST Endoscopic sphincterotomy ASP Antimicrobial Stewardship Program VRE Vancomycin-resistant enterococci Declarations Ethics approval and consent to participate This retrospective study was approved by the Ethics Committee of Shandong Provincial Third Hospital (Approval No. KYLL-2024064). The need for informed consent was waived by the ethics committee due to the retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by a grant from the Shandong Provincial Public Health Association [Grant Number SDPHA202424] Authors' contributions XZ contributed to the study conception and design. Material preparation, data collection and analysis were performed by XZ, XB and CB. The first draft of the manuscript was written by XZ and all authors commented on previous versions of the manuscript. XZ and YZ supervised the study. All authors read and approved the final manuscript. Acknowledgements Not applicable. Authors' information Not applicable. References Miura F, et al. Guidelines 2018: initial management of acute biliary infection and flowchart for acute cholangitis. J Hepatobiliary Pancreat Sci. 2018;25(1):31–40. Smith SE. 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Jeong HT, et al. Changing Patterns of Causative Pathogens over Time and Efficacy of Empirical Antibiotic Therapies in Acute Cholangitis with Bacteremia. Gut Liver. 2022;16(6):985–94. Tian S, et al. Clinical characteristics of Gram-negative and Gram-positive bacterial infection in acute cholangitis: a retrospective observational study. BMC Infect Dis. 2022;22(1):269. Archambaud C, et al. Enterococcus faecalis: an overlooked cell invader. Microbiol Mol Biol Rev. 2024;88(3):e0006924. Stathopoulos P, et al. Endoscopic retrograde cholangiopancreatography-obtained bile culture in acute cholangitis: retrospective analysis of bile cultures and risk factors in a tertiary care center. J Gastroenterol Hepatol. 2024;39(5):935–41. Kaya M, et al. Microbial profile and antibiotic sensitivity pattern in bile cultures from endoscopic retrograde cholangiography patients. World J Gastroenterol. 2012;18(27):3585–9. Deitch EA, Berg R. Bacterial translocation from the gut: a mechanism of infection. J Burn Care Rehabil. 1987;8(6):475–82. Wacker C, et al. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect Dis. 2013;13(5):426–35. Otani T, et al. Blood cultures should be collected for acute cholangitis regardless of severity. J Infect Chemother. 2022;28(2):181–6. Rietschel ET, et al. Bacterial endotoxin: molecular relationships of structure to activity and function. FASEB J. 1994;8(2):217–25. Poltorak A, et al. Defective LPS signaling in C3H/HeJ and C57BL/10ScCr mice: mutations in Tlr4 gene. Science. 1998;282(5396):2085–8. Schiopu P et al. An Overview of the Factors Involved in Biofilm Production by the Enterococcus Genus. Int J Mol Sci, 2023. 24(14). Ch'Ng J, et al. Biofilm-associated infection by enterococci. Nat Rev Microbiol. 2019;17(2):82–94. Stewart L et al. Cholangitis: bacterial virulence factors that facilitate cholangiovenous reflux and tumor necrosis factor-alpha production. J Gastrointest Surg, 2003. 7(2): pp. 191-8; discussion 198-9. Mussa M, et al. Risk Factors and Predictive Score for Bacteremic Biliary Tract Infections Due to Enterococcus faecalis and Enterococcus faecium: a Multicenter Cohort Study from the PROBAC Project. Microbiol Spectr. 2022;10(4):e0005122. Xu Y, et al. Diagnostic performance of metagenomic next-generation sequencing among hematological malignancy patients with bloodstream infections after antimicrobial therapy. J Infect. 2025;90(2):106395. Zhu Y, et al. Role of plasma and blood-cell co-metagenomic sequencing in precise diagnosis and severity evaluation of sepsis, a prospective cohort study in sepsis patients. J Infect. 2025;90(3):106434. Whittle E, et al. Optimizing Nanopore Sequencing for Rapid Detection of Microbial Species and Antimicrobial Resistance in Patients at Risk of Surgical Site Infections. mSphere. 2022;7(1):e0096421. Karadag D, Ergon MC. Investigation of different methods in rapid microbial identification directly from positive blood culture bottles by MALDI-TOF MS. Microbiol Spectr. 2024;12(8):e0063824. Additional Declarations No competing interests reported. Supplementary Files Onlinefloatimage2.png Figure S1. Concordance Between Bile and Blood Cultures Venn diagram showing overlap in positive culture results.The bile-only positive group (n=199) constituted the majority of the leakage group (total n=234).Cohen's Kappa coefficient was 0.077 ( P <0.001), indicating poor agreement. Onlinefloatimage4.png Figure S2. Receiver Operating Characteristic (ROC) Curve ROC curve for the multivariable prediction model of blood culture leakage. The area under the curve (AUC) was 0.693, indicating moderate discriminative ability. The shaded area represents the 95% confidence interval. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7821955","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":547361322,"identity":"3dc40435-9809-43e9-b4f5-ec48ad08606a","order_by":0,"name":"Xin Zheng","email":"","orcid":"","institution":"Department of Clinical Laboratory, Shandong Provincial Third Hospital, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zheng","suffix":""},{"id":547361326,"identity":"adbcce6e-ca6a-4459-8c98-ca527e7cccea","order_by":1,"name":"Xue Bai","email":"","orcid":"","institution":"School of Nursing and Rehabilitation, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Bai","suffix":""},{"id":547361329,"identity":"ac88c976-4eed-4d1c-bed2-6fdfb87a1c28","order_by":2,"name":"Ce Bian","email":"","orcid":"","institution":"Department of Clinical Laboratory, Jiyang People's Hospital of Jinan","correspondingAuthor":false,"prefix":"","firstName":"Ce","middleName":"","lastName":"Bian","suffix":""},{"id":547361330,"identity":"f771eeef-dfaf-49af-896d-f0029ddc69c8","order_by":3,"name":"Xuewei Zhuang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDACCST2gQ8MzCDagHgtB2eQrIWZhxgt8rN7DD8X/Dqc2C+RfPCwzR/rxAb25m0SDDV3cGphnHPGWHpmX1rizBlpCYdz29ITG3iOlUkwHHuGUwuzRI6BNG+PTeKG2zkGh3MbDic2SOSYSTA2HMaphU0ix/g3b49E4v7b+R8OW/wBapF/g18LD9BMaZ4fQFukcxgOM7CBbOHBr0VCIq3MmrchzXjG/WcGB3vb0o3beNKKLRKO4dYiPyN5822eP4dl+3sOP/7w44+1bD/74Y03PtTg1gIGjG3IvgMRCfg1AMEfgipGwSgYBaNgJAMADnpWFQywm8AAAAAASUVORK5CYII=","orcid":"","institution":"Department of Clinical Laboratory, Shandong Provincial Third Hospital, Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Xuewei","middleName":"","lastName":"Zhuang","suffix":""},{"id":547361331,"identity":"c760497f-2bde-46a3-9096-cb37b5f555e8","order_by":4,"name":"Yanli Zhang","email":"","orcid":"","institution":"Department of Clinical Laboratory, Shandong Provincial Third Hospital, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yanli","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-10-10 02:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7821955/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7821955/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96402072,"identity":"ab1beb5e-ba4d-499d-9714-42df766801c6","added_by":"auto","created_at":"2025-11-20 16:19:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":637230,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7821955/v1/89c306adb0b3ef163441fb84.docx"},{"id":96453978,"identity":"3acb761b-ebb7-4130-9e56-1e639217350e","added_by":"auto","created_at":"2025-11-21 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10:02:49","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110198,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7821955/v1/ab81d1337c3dd15fb54d3f08.html"},{"id":96402069,"identity":"7dfdd5ad-d4da-4147-8670-3a539721c1e5","added_by":"auto","created_at":"2025-11-20 16:19:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathogen Distribution and Culture Discrepancies\u003cbr\u003e\n \u003c/strong\u003e(A) Stacked bar chart showing microbial composition in bile and blood cultures. Gram-negative bacteria predominated in both sample types. (B) Forest plot of leakage rates for the top 5 pathogens. Error bars indicate 95% confidence intervals. The dashed vertical line represents the overall leakage rate (70.27%). \u003cem\u003eE. faecalis\u003c/em\u003e showed the highest leakage rate (94.23%, \u003cem\u003eP \u003c/em\u003e\u0026lt;0.001). (C) Matching rates of predominant pathogens from bile to blood cultures. Blue bars indicate rates \u0026gt;20%, red bars \u0026lt;20%.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7821955/v1/67230c0d70836c4887063f20.png"},{"id":96402071,"identity":"fcb08480-c224-4b95-b413-b1ad94340f3a","added_by":"auto","created_at":"2025-11-20 16:19:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariable Predictors of Blood Culture Leakage\u003c/strong\u003e\u003cbr\u003e\nForest plot showing adjusted odds ratios (aOR) with 95% confidence intervals. Protective factors (aOR \u0026lt;1) are shown in blue, while risk factors (aOR \u0026gt;1) are in red. Antibiotic use was associated with a 2.08-fold increased risk of leakage (\u003cem\u003eP \u003c/em\u003e=0.006). Fever (aOR =0.45, \u003cem\u003eP \u003c/em\u003e=0.005), presence of \u003cem\u003eE. coli \u003c/em\u003ein bile (aOR =0.50, P =0.007), and polymicrobial infection (aOR =0.50, \u003cem\u003eP \u003c/em\u003e=0.006) were protective factors.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7821955/v1/5c416a53430cd9cabc0c626f.png"},{"id":96402070,"identity":"71943b69-2413-406c-b4e2-517be7b0e68c","added_by":"auto","created_at":"2025-11-20 16:19:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32982,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential Distribution of Key Clinical Features Across Study Groups\u003cbr\u003e\n \u003c/strong\u003e(A) Fever prevalence across study groups. Different letters indicate significant differences based on post-hoc pairwise comparisons with Bonferroni correction (\u003cem\u003eP \u003c/em\u003e\u0026lt;0.05). (B) Procalcitonin (PCT) levels (median) showed overall significant differences (\u003cem\u003eP\u003c/em\u003e=0.032) but no significant pairwise differences after correction. (C) Bile \u003cem\u003eE. coli\u003c/em\u003e detection rate. Different letters indicate significant differences between bile-positive groups. (D) Polymicrobial infection prevalence. Different letters indicate significant differences between bile-positive groups. Groups sharing the same letter are not significantly different. Bile-negative groups (double-negative and blood-only) were not included in pairwise comparisons for bile-specific variables.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7821955/v1/b8efa9e1e074bccf40409b4f.png"},{"id":103398168,"identity":"50cf9c6e-009b-4140-b7f3-0f8d79decb67","added_by":"auto","created_at":"2026-02-25 08:59:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1534954,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7821955/v1/3a6d4de8-aaee-4873-a846-25225bdb3889.pdf"},{"id":96454721,"identity":"1b7b2dde-1f6b-465a-b727-2f3d4317e80a","added_by":"auto","created_at":"2025-11-21 10:03:04","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. Concordance Between Bile and Blood Cultures\u003c/strong\u003e\u003cbr\u003e\nVenn diagram showing overlap in positive culture results.The bile-only positive group (n=199) constituted the majority of the leakage group (total n=234).Cohen's Kappa coefficient was 0.077 (\u003cem\u003eP \u003c/em\u003e\u0026lt;0.001), indicating poor agreement.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7821955/v1/249f2b4c7db6bcfd52c8b1cc.png"},{"id":96402076,"identity":"c17f18ab-5a5b-469d-bb3f-b22ce883c25f","added_by":"auto","created_at":"2025-11-20 16:19:45","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. Receiver Operating Characteristic (ROC) Curve\u003c/strong\u003e\u003cbr\u003e\nROC curve for the multivariable prediction model of blood culture leakage. The area under the curve (AUC) was \u003cstrong\u003e0.693\u003c/strong\u003e, indicating moderate discriminative ability. The shaded area represents the 95% confidence interval.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7821955/v1/9a75d91c91a205c757b0006f.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bile Over Blood: A Predictive Model for Guiding Antimicrobial Therapy When Blood Cultures Miss the Pathogen in Biliary Infections","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute biliary tract infection is one of the most life-threatening gastrointestinal emergencies, characterized by bacterial infection secondary to biliary obstruction, which can rapidly progress to sepsis and multiple organ failure, with mortality rates as high as 30–50% in severe cases [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Globally, the increasing incidence of biliary obstruction due to gallstone disease and malignancies has heightened the disease burden of biliary infections, posing significant challenges to public health systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent large-scale epidemiological studies have corroborated an increasing trend in the incidence of biliary tract-associated bloodstream infections (BSIs), with clinical outcomes closely linked to the causative pathogens and the appropriateness of empirical therapy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe cornerstone of sepsis management lies in early diagnosis and treatment, with accurate microbiological identification being essential for effective antimicrobial therapy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Since the publication of the Tokyo Guidelines (TG18/TG13), the diagnosis and severity stratification of biliary infections have been standardized; however, strategies for microbiological diagnosis remain contentious [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Currently, blood culture is regarded as the gold standard for diagnosing bacteremia and guiding systemic antimicrobial treatment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, in the context of biliary infections, the sensitivity of blood culture is notably diminished, with positivity rates typically ranging between 30% and 50% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, the inherent turnaround time (TAT) often exceeds 48 hours, failing to meet the need for early targeted treatment in critically ill patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. More importantly, blood culture results are highly susceptible to false negatives after empirical antibiotic administration, thereby diminishing their clinical utility [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The mechanisms underlying this under-detection are complex, potentially involving filtration by the gut-blood barrier, phagocytic clearance by hepatic Kupffer cells, and the intrinsic growth characteristics of pathogens [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, the microbial ecology of biliary infections is rapidly evolving. While traditional Gram-negative bacilli such as \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e remain predominant, \u003cem\u003eEnterococcus\u003c/em\u003e species, particularly \u003cem\u003eEnterococcus faecalis\u003c/em\u003e and \u003cem\u003eEnterococcus faecium\u003c/em\u003e, have emerged as significant opportunistic pathogens. Their detection rates have been increasing worldwide, associated with healthcare-related infections, prior biliary interventions, and the emergence of antimicrobial resistance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Of particular concern, polymicrobial infections, especially those involving both Gram-negative and Gram-positive bacteria, are more common in patients with cholangitis and have been linked to more severe clinical presentations, higher rates of organ dysfunction, and increased mortality [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The intrinsic resistance of enterococci to cephalosporins and their unique pathogenic mechanisms—including biofilm formation, immune evasion, and potential intracellular persistence—render these infections more insidious and challenging to eradicate [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Clarifying the differences in hematogenous dissemination among pathogens is crucial for interpreting culture results, optimizing empirical antibiotic regimens, and improving patient outcomes.\u003c/p\u003e\u003cp\u003eHowever, large-scale studies that systematically quantify the concordance between bile and blood cultures and delineate the pathogen-specific and clinical circumstances predisposing to blood culture under-detection remain scarce.This knowledge gap directly impacts clinical decision-making: For a patient with positive bile culture but negative blood culture, what is the actual risk of bacteremia? Does a negative blood culture result justify avoiding targeted therapy against the pathogen isolated from bile?\u003c/p\u003e\u003cp\u003eTherefore, this study aims to address the following key questions through a large retrospective cohort: (1) quantify the discordance in pathogen profiles between paired bile and blood cultures in patients with biliary obstruction; (2) elucidate the differences in the ability of various pathogens to disseminate from the biliary tract to the bloodstream (i.e., blood culture leakage rates); and (3) identify clinical and microbiological factors independently associated with blood culture leakage and construct a clinical prediction model. The findings are expected to provide high-level evidence for precise microbiological diagnosis and individualized antimicrobial therapy in biliary tract infections.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1. Study Design and Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study was approved by the Ethics Committee of Shandong Provincial Third Hospital (KYLL-2024064) with informed consent waived. Consecutive patients hospitalized between January 2017 and December 2024 for radiologically confirmed biliary obstruction who underwent bile and blood culture collection within 48 hours of admission were included.\u003c/p\u003e\n\u003cp\u003eExclusion criteria:\u003cbr\u003e\u0026nbsp;(1) Missing microbiological data for bile or blood cultures .\u003cbr\u003e\u0026nbsp;(2) Concurrent active infection at other sites .\u003c/p\u003e\n\u003cp\u003eA total sample size of 398 patients was included based on all consecutive eligible cases during the study period. This sample size provides approximately 80% power to detect an odds ratio of 1.8 for the primary outcome (blood culture leakage) at a two-sided alpha level of 0.05, assuming a baseline leakage rate of 50%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Specimen Collection and Microbiological Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBile samples: Collected aseptically via ERCP or PTBD, inoculated onto blood agar and MacConkey agar, and incubated aerobically at 35\u0026deg;C for 2 days.\u003c/p\u003e\n\u003cp\u003eBlood samples: Each culture set included aerobic and anaerobic bottles, with 8\u0026ndash;10 mL of blood inoculated per bottle in adults. Positive samples were subcultured.\u003c/p\u003e\n\u003cp\u003ePathogen identification and susceptibility testing: Species identification and antibiotic susceptibility testing were performed using an automated microbial analysis system (VITEK 2 COMPACT, BioM\u0026eacute;rieux, France). Quality control was performed using standard strains (\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e ATCC 700324,\u0026nbsp;\u003cem\u003eE. faecalis\u003c/em\u003e ATCC 700327).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Key Definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood culture leakage: Detection of a pathogen (at species level) in bile but not in the paired blood culture. This includes two specific scenarios:\u003c/p\u003e\n\u003cp\u003e1.Bile culture positive and blood culture negative (bile+/blood\u0026minus;).\u003c/p\u003e\n\u003cp\u003e2.Both bile and blood cultures positive, but without an identical pathogen detected in the blood (bile+/blood+ but no matching pathogen).\u003c/p\u003e\n\u003cp\u003eBacteremia: Detection of a pathogenic microorganism in \u0026ge;1 bottle of blood cultures (contaminants excluded; coagulase-negative staphylococci, viridans group streptococci, Corynebacterium spp., etc., were considered based on clinical context).\u003c/p\u003e\n\u003cp\u003ePolymicrobial infection: Detection of \u0026ge;2 distinct pathogens in a single bile or blood culture specimen.\u003c/p\u003e\n\u003cp\u003eGroups: Patients were classified into leakage (bile+/blood\u0026minus; OR bile+/blood+ without identical pathogen), non-leakage (bile+/blood+, \u0026ge;1 identical pathogen), double-negative (bile\u0026minus;/blood\u0026minus;), and blood-only-positive (bile\u0026minus;/blood+) groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using Python 3.10 (scikit-learn, SciPy) and R 4.2.2. A two-sided significance level of \u0026alpha; = 0.05 was adopted. Descriptive statistics: Continuous variables are presented as median (interquartile range, IQR) and categorical variables as frequency (percentage). Group comparisons: For continuous variables, Kruskal\u0026ndash;Wallis H test was used for multiple groups and Mann\u0026ndash;Whitney U test for two groups, with Bonferroni correction where applicable; For categorical variables, the \u0026chi;\u0026sup2; test or Fisher\u0026rsquo;s exact test\u0026nbsp;was used, with Bonferroni correction where applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor variables showing significant overall differences (\u003cem\u003eP\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), post-hoc pairwise comparisons were conducted using Dunn\u0026apos;s test with Bonferroni correction for continuous variables and pairwise Fisher\u0026apos;s exact tests with Bonferroni correction for categorical variables.\u003c/p\u003e\n\u003cp\u003eAnalysis of factors associated with blood culture leakage:\u0026nbsp;Univariable analysis was used to screen potential variables (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.1). Multivariable analysis\u0026nbsp;was then conducted using binary logistic regression (forward stepwise method),reporting adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs). Model goodness-of-fit was assessed using the Hosmer\u0026ndash;Lemeshow test (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05 indicating good fit), and discriminative ability was evaluated using the area under the receiver operating characteristic curve (AUC).\u003c/p\u003e\n\u003cp\u003eVariables with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.1 in univariable analysis were eligible for inclusion in the multivariable model. Multicollinearity was assessed using variance inflation factors (VIF \u0026lt; 10 indicating acceptable collinearity). Missing data for continuous variables were handled by median imputation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Microbiological Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 398 patients with biliary obstruction who underwent concurrent bile and blood cultures, the pathogen detection rate in bile was 83.67% (333/398), significantly higher than that in blood cultures at 36.18% (144/398) (McNemar \u0026chi;\u0026sup2; = 169.11,\u0026nbsp;\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). A total of 490 microbial isolates were obtained from bile cultures, with 40.54% (135/333) of cases being polymicrobial infections (\u0026ge;2 pathogens). Gram-negative bacteria accounted for 70.82% (347/490), Gram-positive bacteria for 27.76% (136/490), and fungi for 1.43% (7/490). The top five pathogens isolated were \u003cem\u003eEscherichia coli\u003c/em\u003e (33.88%, 166/490), \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (17.35%, 85/490), \u003cem\u003eEnterococcus faecium\u003c/em\u003e (12.24%, 60/490), \u003cem\u003eEnterococcus faecalis\u003c/em\u003e (10.61%, 52/490), and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (6.12%, 30/490).\u003c/p\u003e\n\u003cp\u003eBlood cultures yielded 162 microbial isolates, with a polymicrobial infection rate of 12.50% (18/144), significantly lower than that in bile cultures (\u0026chi;\u0026sup2; = 35.004,\u0026nbsp;\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). Gram-negative bacteria accounted for 79.63% (129/162), Gram-positive bacteria for 19.75% (32/162), and fungi for 0.63% (1/162), showing no significant difference in microbial distribution compared to bile cultures (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05) (Figure 1A). The top five pathogens in blood cultures were\u0026nbsp;\u003cem\u003eE. coli\u003c/em\u003e (46.30%, 75/162),\u0026nbsp;\u003cem\u003eK. pneumoniae\u0026nbsp;\u003c/em\u003e(19.14%, 31/162),\u0026nbsp;\u003cem\u003eE. faecium\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e(9.88%, 16/162), \u003cem\u003eP.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eaeruginosa\u0026nbsp;\u003c/em\u003e(4.94%, 8/162), and\u0026nbsp;\u003cem\u003eE. faecalis\u003c/em\u003e (3.70%, 6/162).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1. Pathogen Distribution and Culture Discrepancies\u003cbr\u003e\u003c/strong\u003e(A) Stacked bar chart showing microbial composition in bile and blood cultures. Gram-negative bacteria predominated in both sample types. (B) Forest plot of leakage rates for the top 5 pathogens. Error bars indicate 95% confidence intervals. The dashed vertical line represents the overall leakage rate (70.27%). \u003cem\u003eE. faecalis\u003c/em\u003e showed the highest leakage rate (94.23%, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;0.001). (C) Matching rates of predominant pathogens from bile to blood cultures. Blue bars indicate rates \u0026gt;20%, red bars \u0026lt;20%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Concordance Between Bile and Blood Cultures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall concordance between bile and blood cultures was 38.69% (154/398), with a Cohen\u0026rsquo;s Kappa coefficient of 0.077 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), indicating weak agreement between the two methods (Figure S1). The performance of bile culture in predicting bacteremia was as follows: sensitivity 90.83% (95% CI: 83.9\u0026ndash;95.6), specificity\u0026nbsp;19.03% (95% CI: 14.7\u0026ndash;24.0), positive predictive value (PPV)\u0026nbsp;29.73% (95% CI:\u0026nbsp;24.7\u0026ndash;35.1), and negative predictive value (NPV) 84.62% (95% CI: 73.2\u0026ndash;92.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1. Concordance Between Bile and Blood Cultures\u003c/strong\u003e\u003cbr\u003eVenn diagram showing overlap in positive culture results.The bile-only positive group (n=199) constituted the majority of the leakage group (total n=234).Cohen\u0026apos;s Kappa coefficient was 0.077 (\u003cem\u003eP\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt;0.001), indicating poor agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Pathogen-Specific Concordance Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 134 patients with both bile and blood cultures positive, 73.88% (99/134) had at least one identical pathogen. Significant differences were observed in concordance rates across major pathogens (\u0026chi;\u0026sup2; = 10.94,\u0026nbsp;\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.027) (Figure 1C).\u0026nbsp;\u003cem\u003eE. coli\u003c/em\u003e showed the highest concordance rate (31.93%, 53/166), followed by\u0026nbsp;\u003cem\u003eK. pneumoniae\u003c/em\u003e (25.88%, 22/85), \u003cem\u003eP.\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eaeruginosa\u0026nbsp;\u003c/em\u003e(23.33%, 7/30),\u0026nbsp;\u003cem\u003eE. faecium\u003c/em\u003e (23.33%, 14/60), and\u0026nbsp;\u003cem\u003eE. faecalis\u003c/em\u003e (5.77%, 3/52).\u003c/p\u003e\n\u003cp\u003eAmong 333 patients with positive bile cultures, the overall blood culture leakage rate was 70.27% (234/333; 95% CI: 65.1\u0026ndash;75.1%). Leakage rates varied significantly across pathogens (\u0026chi;\u0026sup2; = 19.49,\u0026nbsp;\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) (Figure 1B).\u0026nbsp;\u003cem\u003eE. faecalis\u0026nbsp;\u003c/em\u003eexhibited the highest leakage rate (94.23%, 95% CI: 84.1\u0026ndash;98.7%), which was significantly greater than that of all other major pathogens (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Differential Clinical and Microbiological Profiles Across Bile and Blood Culture Concordance Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were categorized into four distinct groups based on bile and blood culture concordance (Table 1). The leakage group (n = 234) included patients with positive bile culture but no identical pathogen detected in blood culture, comprising two scenarios: those with negative blood cultures (n=199) and those with positive blood cultures but where the pathogen(s) did not match those found in bile (n=35). The non-leakage group (n = 99) included patients with at least one identical pathogen in both bile and blood. The double-negative (n = 55) and blood-only-positive (n = 10) groups were defined as having both cultures negative or only blood culture positive, respectively.\u003c/p\u003e\n\u003cp\u003eKey clinical and microbiological characteristics across the four groups are summarized in Table 2 and visualized in Figure 3. Significant differences were observed for several variables. The prevalence of fever was highest in the non-leakage group (66.7%) and lowest in the double-negative group (38.2%, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.005), with post-hoc analysis confirming a significant difference between these two groups (corrected \u003cem\u003eP\u003c/em\u003e = 0.004). Procalcitonin levels showed overall significant differences (\u003cem\u003eP\u003c/em\u003e = 0.032), though no significant pairwise differences were identified after correction.\u003c/p\u003e\n\u003cp\u003eMicrobiological characteristics demonstrated more pronounced intergroup variations. Bile \u003cem\u003eE. coli\u003c/em\u003e detection was significantly higher in the non-leakage group (64.6%) compared to the leakage group (43.6%, corrected \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.003), and both bile-positive groups showed significantly higher rates than bile-negative groups (all corrected \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). Similarly, polymicrobial infection was more common in the non-leakage group (53.5%) than in the leakage group (35.0%, corrected \u003cem\u003eP\u003c/em\u003e = 0.013), with both bile-positive groups demonstrating significantly higher rates than bile-negative groups (all corrected \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). While overall antibiotic use differed among groups (\u003cem\u003eP\u003c/em\u003e = 0.041), post-hoc analysis did not identify significant pairwise differences after correction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3. Differential Distribution of Key Clinical Features Across Study Groups\u003cbr\u003e\u003c/strong\u003e(A) Fever prevalence across study groups. Different letters indicate significant differences based on post-hoc pairwise comparisons with Bonferroni correction (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;0.05). (B) Procalcitonin (PCT) levels (median) showed overall significant differences (\u003cem\u003eP\u003c/em\u003e =0.032) but no significant pairwise differences after correction. (C) Bile \u003cem\u003eE. coli\u003c/em\u003e detection rate. Different letters indicate significant differences between bile-positive groups. (D) Polymicrobial infection prevalence. Different letters indicate significant differences between bile-positive groups. Groups sharing the same letter are not significantly different. Bile-negative groups (double-negative and blood-only) were not included in pairwise comparisons for bile-specific variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Definition of patient groups based on bile and blood culture results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBile Culture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood Culture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdentical Pathogen?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription / Scenario\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeakage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eBile culture positive, blood culture negative\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eBoth cultures positive, but no matching pathogen\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Leakage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eBoth cultures positive, with \u0026ge;1 identical pathogen\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood-Only-Positive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eBlood culture positive, bile culture negative\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDouble-Negative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote: The leakage group comprises two distinct scenarios.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Factors Associated with Blood Culture Leakage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable analysis identified fever (OR = 0.58, 95% CI: 0.36-0.95, \u003cem\u003eP\u003c/em\u003e = 0.031), bile \u003cem\u003eE. coli\u003c/em\u003e (OR = 0.42, 95% CI: 0.26-0.69, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), polymicrobial infection (OR = 0.47, 95% CI: 0.29-0.75, \u003cem\u003eP\u003c/em\u003e = 0.002), and antibiotic use (OR = 1.76, 95% CI: 1.09-2.82, \u003cem\u003eP\u003c/em\u003e = 0.020) as significant factors associated with blood culture leakage.\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression using forward selection identified bile \u003cem\u003eE. coli\u003c/em\u003e (aOR = 0.50, 95% CI: 0.30-0.82, \u003cem\u003eP\u003c/em\u003e = 0.007), polymicrobial infection (aOR = 0.50, 95% CI: 0.30-0.82, \u003cem\u003eP\u003c/em\u003e = 0.006), fever (aOR = 0.45, 95% CI: 0.26-0.79, \u003cem\u003eP\u003c/em\u003e = 0.005), and antibiotic use (aOR = 2.08, 95% CI: 1.23-3.52, \u003cem\u003eP\u003c/em\u003e = 0.006) as independent factors associated with blood culture leakage. The prediction model demonstrated moderate discriminative ability with an AUC of 0.693 (95% CI: 0.631-0.756) (Figure S2) and a pseudo R-squared of 0.081.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2. Receiver Operating Characteristic (ROC) Curve\u003c/strong\u003e\u003cbr\u003e ROC curve for the multivariable prediction model of blood culture leakage. The area under the curve (AUC) was \u003cstrong\u003e0.693\u003c/strong\u003e, indicating moderate discriminative ability. The shaded area represents the 95% confidence interval.\u003c/p\u003e\n\u003cp\u003eNo significant multicollinearity was observed among the predictor variables, as all variance inflation factors were below 10 (range: 1.30-2.31).The results of the multivariable logistic regression analysis are presented in Table 3 and illustrated in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2. Multivariable Predictors of Blood Culture Leakage\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Forest plot showing adjusted odds ratios (aOR) with 95% confidence intervals. Protective factors (aOR \u0026lt;1) are shown in blue, while risk factors (aOR \u0026gt;1) are in red. Antibiotic use was associated with a 2.08-fold increased risk of leakage (\u003cem\u003eP\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e=0.006). Fever (aOR\u0026nbsp;=0.45, \u003cem\u003eP\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e=0.005), presence of \u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003ein bile (aOR\u0026nbsp;=0.50, P\u0026nbsp;=0.007), and polymicrobial infection (aOR\u0026nbsp;=0.50, \u003cem\u003eP\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e=0.006) were protective factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Clinical and microbiological characteristics across bile and blood culture concordance groups.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeakage (n=234)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-leakage (n=99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDouble-negative (n=55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood-only (n=10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eFever, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e126 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e66 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e21 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e4 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePCT, ng/mL, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.2 (0.3-8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5.3 (0.5-31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.7 (0.3-5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e11.0 (0.7-16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrobiological features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eBile \u003cem\u003eE. coli\u003c/em\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e102 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e64 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003ePolymicrobial infection, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e82 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e53 (53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eAntibiotic use, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e132 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e42 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e34 (61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData are presented as median (interquartile range) for continuous variables and n (%) for categorical variables. PCT, procalcitonin.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e*Post-hoc pairwise comparisons with Bonferroni correction revealed significant differences (P \u0026lt; 0.05) between the Non-leakage and Double-negative groups for fever, and between the Non-leakage and Leakage groups for bile E. coli and polymicrobial infection.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Factors Associated with Blood Culture Leakage in Multivariable Logistic Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable aOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable \u003cem\u003eP\u003c/em\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.58 (0.36-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.45 (0.26-0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eBile \u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.42 (0.26-0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.50 (0.30-0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003ePolymicrobial infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.47 (0.29-0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.50 (0.30-0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eAntibiotic use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1.76 (1.09-2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2.08 (1.23-3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough paired microbiological analysis of 398 patients with biliary obstruction, this study reveals the existence of a highly variable and pathogen-selective \"microbial barrier\" between the bile and the circulatory system. We confirmed a high rate of blood culture under-detection (70.27%) in biliary-derived bacteremia and, for the first time, systematically demonstrated that this phenomenon is non-random and strongly associated with specific pathogens, particularly \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, and identifiable clinical scenarios such as antibiotic use, absence of fever, and monomicrobial infection. Based on these findings, we developed a clinical prediction model with moderate discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.693). These insights have profound implications for both the clinical management of biliary infections and fundamental research on host\u0026ndash;pathogen interactions.\u003c/p\u003e\u003cp\u003eOur study indicates that bile culture provides richer and more direct microbiological information than blood culture in diagnosing biliary infections. With a high positivity rate of 83.67% and a polymicrobial infection rate of 40.54%, bile culture accurately reflects the complexity of bacterial infections in the biliary tract, consistent with previous studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Notably, polymicrobial infections, associated with worse clinical outcomes in both this study and the literature [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], were also identified as a protective factor against blood culture leakage (aOR\u0026thinsp;=\u0026thinsp;0.50). This may reflect more severe barrier disruption and higher bacterial load, closely related to the activation of systemic inflammatory response syndrome (SIRS).\u003c/p\u003e\u003cp\u003eA key contribution of this study is providing a contextual framework for interpreting blood culture results. We found that the sensitivity of blood culture highly depends on clinical context. Blood culture is more likely to be positive in patients presenting with fever, elevated inflammatory markers like PCT, presence of \u003cem\u003eEscherichia coli\u003c/em\u003e in bile, or polymicrobial infection. These factors collectively indicate a state of strong SIRS, which often implies greater bacterial translocation or impaired host clearance capacity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, blood culture offers the highest diagnostic value in such patients. Conversely, in afebrile patients, those who have received antibiotic therapy, or those with \u003cem\u003eEnterococcus\u003c/em\u003e-dominated infections, clinicians should maintain a high index of suspicion regarding negative blood culture results. Treatment decisions should primarily rely on bile culture and susceptibility results to avoid undertreatment. This finding aligns with a recent Japanese study emphasizing that blood cultures should be routinely collected regardless of cholangitis severity, as a considerable proportion of mild cases also carry a risk of bacteremia [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe significant variation in leakage rates among pathogens observed in this study carries important clinical implications. Gram-negative bacilli such as \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eK. pneumoniae\u003c/em\u003e showed relatively high concordance rates (31.93% and 25.88%, respectively) attributable to their ability to produce potent inflammatory mediators like lipopolysaccharide (LPS). LPS can trigger a vigorous systemic inflammatory response (SIRS) by activating innate immune responses such as the TLR4 pathway. This inflammation increases vascular permeability, disrupts endothelial barriers, and promotes bacterial dissemination from the primary infection site into the bloodstream, making these pathogens more likely to be detected by blood culture [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eE. faecalis\u003c/em\u003e exhibited an extremely high blood culture leakage rate (94.23%), a finding of major clinical significance. Several mechanisms may underlie this phenomenon. First, regarding pathogenic mode, unlike Gram-negative bacteria that induce a \"cytokine storm,\" enterococci may favor a \"latent\" or \"chronic\" mode of infection. Their strong adhesins and biofilm-forming capacity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] enable firm colonization on mucosal surfaces, leading to continuous low-level antigen release without causing massive bacteremia. Second, immune evasion and intracellular persistence: evidence suggests that \u003cem\u003eE. faecalis\u003c/em\u003e can be phagocytosed by immune cells like macrophages but survives intracellularly through various mechanisms, evading extracellular bactericidal drugs and immune clearance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This intracellular persistence may allow it to exist with low bacteremic load. Furthermore, as Gram-positive bacteria, enterococci weakly induce potent inflammatory cytokines like TNF-α, and their complement resistance may lead to rapid clearance from the bloodstream, making them difficult to capture by conventional blood culture [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These findings suggest that in patients at risk factors for \u003cem\u003eEnterococcus\u003c/em\u003e infection (e.g., history of endoscopic sphincterotomy (EST), biliary stent placement, cholangiocarcinoma) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], even with negative blood cultures, if bile culture indicates \u003cem\u003eEnterococcus\u003c/em\u003e as the predominant pathogen, empirical therapy should carefully consider covering enterococci.\u003c/p\u003e\u003cp\u003eMultivariable analysis firmly established prior antibiotic use as an independent risk factor for blood culture leakage (aOR\u0026thinsp;=\u0026thinsp;2.08), consistent with classical principles and recent studies in infectious diseases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Antibiotics can significantly reduce circulating bacterial loads below the detection threshold of cultures within hours. This finding strongly reaffirms the absolute necessity of routinely collecting blood cultures prior to initiating empirical antibiotic therapy.\u003c/p\u003e\u003cp\u003eThe results of this study have direct implications for clinical practice and Antimicrobial Stewardship Programs (ASP). First, bile culture should be elevated as the core of the diagnostic workflow for biliary infections, with results should serve as the primary basis for guiding targeted therapy, especially when blood cultures are negative. Second, empirical therapy should be optimized with pathogen-specific considerations.\u003c/p\u003e\u003cp\u003eFor patients at high risk for \u003cem\u003eEnterococcus\u003c/em\u003e infection, empirical regimens should avoid relying solely on third-generation cephalosporins. Based on this study and recent literature [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], piperacillin-tazobactam or carbapenems may be preferable choices due to their coverage against enterococci and most Gram-negative bacteria. In regions with high VRE prevalence or in high-risk patients, early consideration of vancomycin or daptomycin may be necessary. Third, blood culture results require careful contextual interpretation. In patients identified by our model as high-risk for leakage, a negative blood culture must not justify the discontinuation or de-escalation of targeted therapy against pathogens isolated from bile.\u003c/p\u003e\u003cp\u003eWe honestly acknowledge several limitations. First, its single-center retrospective design may introduce selection bias, as all included patients underwent invasive procedures for bile collection, potentially representing a more severely ill population than general biliary infection patients. Second, although pathogen concordance was performed at the species level, the absence of molecular typing (e.g., MLST or WGS) prevents confirmation of clonal identity between bile and blood isolates, leaving open the possibility of multi-strain infections. Third, we were unable to comprehensively analyze all potential confounding factors, such as the host's detailed immune status, specific antibiotic types, and timing of administration. These limitations precisely highlight directions for future research: 1) conduct prospective multicenter studies to validate this prediction model and incorporate more variables; 2) perform whole-genome sequencing on paired isolates to precisely trace bacterial dissemination pathways at the molecular level; 3) integrate rapid molecular diagnostic technologies (e.g., rapid MALDI-TOF MS identification from positive blood culture bottles, direct nanopore sequencing or mNGS on bile samples) [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] to obtain comprehensive pathogen and resistance information within hours rather than days, thereby revolutionizing the diagnostic paradigm for biliary infections and enabling truly precise anti-infective therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBlood culture\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eaOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdjusted odds ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eERCP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEndoscopic retrograde cholangiopancreatography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePTBD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePercutaneous transhepatic biliary drainage\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProcalcitonin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIRS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic inflammatory response syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLipopolysaccharide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEndoscopic sphincterotomy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAntimicrobial Stewardship Program\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVRE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVancomycin-resistant enterococci\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Ethics Committee of Shandong Provincial Third Hospital (Approval No. KYLL-2024064). The need for informed consent was waived by the ethics committee due to the retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the Shandong Provincial Public Health Association [Grant Number SDPHA202424]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXZ contributed to the study conception and design. Material preparation, data collection and analysis were performed by XZ, XB and CB. The first draft of the manuscript was written by XZ and all authors commented on previous versions of the manuscript. XZ and YZ supervised the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMiura F, et al. Guidelines 2018: initial management of acute biliary infection and flowchart for acute cholangitis. J Hepatobiliary Pancreat Sci. 2018;25(1):31\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith SE. Management of Acute Cholangitis and Choledocholithiasis. Surg Clin North Am. 2024;104(6):1175\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNve E et al. Early Management of Severe Biliary Infection in the Era of the Tokyo Guidelines. J Clin Med, 2023. 12(14).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, et al. Global burden of gallbladder and biliary diseases: A systematic analysis for the Global Burden of Disease Study 2019. J Gastroenterol Hepatol. 2022;37(7):1389\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTazuma S. Gallstone disease: Epidemiology, pathogenesis, and classification of biliary stones (common bile duct and intrahepatic). Best Pract Res Clin Gastroenterol. 2006;20(6):1075\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGassiep I, Edwards F, Laupland KB. Epidemiology of biliary tract-associated bloodstream infections and adequacy of empiric therapy: an Australian population-based study. Eur J Clin Microbiol Infect Dis. 2024;43(9):1753\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrtega M, et al. Epidemiology and prognostic determinants of bacteraemic biliary tract infection. J Antimicrob Chemother. 2012;67(6):1508\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvans L, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181\u0026ndash;247.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGauer R, Forbes D, Boyer N. Sepsis: Diagnosis and Management. Am Fam Physician. 2020;101(7):409\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoseph B, et al. Evaluating the Relevance of the 2013 Tokyo Guidelines for the Diagnosis and Management of Cholecystitis. J Am Coll Surg. 2018;227(1):38\u0026ndash;e431.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYokoe M, et al. 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Nat Rev Microbiol. 2019;17(2):82\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStewart L et al. Cholangitis: bacterial virulence factors that facilitate cholangiovenous reflux and tumor necrosis factor-alpha production. J Gastrointest Surg, 2003. 7(2): pp. 191-8; discussion 198-9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMussa M, et al. Risk Factors and Predictive Score for Bacteremic Biliary Tract Infections Due to Enterococcus faecalis and Enterococcus faecium: a Multicenter Cohort Study from the PROBAC Project. Microbiol Spectr. 2022;10(4):e0005122.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu Y, et al. Diagnostic performance of metagenomic next-generation sequencing among hematological malignancy patients with bloodstream infections after antimicrobial therapy. J Infect. 2025;90(2):106395.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu Y, et al. Role of plasma and blood-cell co-metagenomic sequencing in precise diagnosis and severity evaluation of sepsis, a prospective cohort study in sepsis patients. J Infect. 2025;90(3):106434.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhittle E, et al. Optimizing Nanopore Sequencing for Rapid Detection of Microbial Species and Antimicrobial Resistance in Patients at Risk of Surgical Site Infections. mSphere. 2022;7(1):e0096421.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaradag D, Ergon MC. Investigation of different methods in rapid microbial identification directly from positive blood culture bottles by MALDI-TOF MS. Microbiol Spectr. 2024;12(8):e0063824.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute biliary tract infection, Bacteremia, Bile culture, Blood culture, Under-detection","lastPublishedDoi":"10.21203/rs.3.rs-7821955/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7821955/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBlood culture (BC) is considered the gold standard for diagnosing bacteremia; however, its sensitivity is notably limited in cases of acute biliary tract infections, often resulting in under-detection. Despite this, the patterns and contributing factors leading to such under-detection have not been systematically investigated. This study aims to quantify the discrepancies in pathogen profiles between bile and paired blood cultures, and to identify pathogen-specific under-detection rates and independent predictors of blood culture leakage.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective study included 398 patients with radiologically confirmed biliary obstruction who underwent concurrent bile and blood culture collection between January 2017 and December 2024. Patients were categorized into four groups based on culture results: leakage (bile+/blood\u0026thinsp;\u0026minus;\u0026thinsp;OR bile+/blood\u0026thinsp;+\u0026thinsp;without identical pathogen) non-leakage (bile+/blood\u0026thinsp;+\u0026thinsp;with \u0026ge;\u0026thinsp;1 identical pathogen), double-negative, and blood-only-positive. Multivariable logistic regression was used to identify independent factors associated with blood culture leakage.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe positivity rate of bile cultures (83.67%, 333/398) was significantly higher than that of blood cultures (36.18%, 144/398) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The overall leakage rate was 70.27% (234/333). Significant differences in leakage rates were observed among different pathogens (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with \u003cem\u003eEnterococcus faecalis\u003c/em\u003e exhibiting the highest leakage rate (94.23%), while \u003cem\u003eEscherichia coli\u003c/em\u003e (31.93%) and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (25.88%) showed higher concordance. Multivariable analysis identified fever (aOR\u0026thinsp;=\u0026thinsp;0.45, 95% CI: 0.26\u0026ndash;0.79, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), presence of E. coli in bile (aOR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.30\u0026ndash;0.82, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), and polymicrobial infection (aOR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.30\u0026ndash;0.82, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) as protective factors, whereas antibiotic use (aOR\u0026thinsp;=\u0026thinsp;2.08, 95% CI: 1.23\u0026ndash;3.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) was an independent risk factor. The predictive model exhibited moderate discriminative capacity with an AUC of 0.693.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study confirms that the under-detection of blood cultures in biliary tract infections is highly pathogen-specific, with \u003cem\u003eE. faecalis\u003c/em\u003e being most frequently missed. A clinical prediction model based on fever, antibiotic use, \u003cem\u003eE. coli\u003c/em\u003e colonization, and infection complexity was developed. These findings emphasize that in afebrile patients, those receiving antibiotic therapy, or those with \u003cem\u003eEnterococcus\u003c/em\u003e-dominated bile cultures, clinical decision-making should rely more heavily on bile culture\u003c/p\u003e","manuscriptTitle":"Bile Over Blood: A Predictive Model for Guiding Antimicrobial Therapy When Blood Cultures Miss the Pathogen in Biliary Infections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 16:19:40","doi":"10.21203/rs.3.rs-7821955/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"836bdf92-c5db-434e-af9c-0a4ee23d4972","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T08:57:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 16:19:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7821955","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7821955","identity":"rs-7821955","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}