Frailty-Related and Hepatic Prognostic Markers in Acute Biliary Tract Infections: A Diagnosis-Stratified Retrospective Cohort Study

Frailty-Related and Hepatic Prognostic Markers in Acute Biliary Tract Infections: A Diagnosis-Stratified Retrospective Cohort Study | 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 Frailty-Related and Hepatic Prognostic Markers in Acute Biliary Tract Infections: A Diagnosis-Stratified Retrospective Cohort Study Zehra Zeynep Keklikkiran, Caglayan Keklikkiran, Aybegum Ozsahin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8419683/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Acute biliary tract infections (BTIs), including acute cholecystitis and acute cholangitis, show substantial clinical heterogeneity and variable short-term outcomes. Prognostic markers reflecting frailty, nutritional reserve and hepatic dysfunction may improve early risk stratification. In this context, the present study aimed to evaluate the prognostic value of computed tomography (CT)–derived psoas muscle area (PMA), FIB-4 score, prognostic nutritional index (PNI) and systemic inflammatory indices in predicting intensive care unit (ICU) admission, 30-day mortality and length of hospital stay (LOS) in patients hospitalized with acute BTIs. Methods: We retrospectively analyzed 94 adults hospitalized with acute biliary tract infections between 2022 and 2023. Analyses were performed in a diagnosis-stratified manner (acute cholecystitis vs acute cholangitis). Psoas muscle area (PMA) was measured on admission CT at the L3 level, while FIB-4, prognostic nutritional index (PNI) and systemic inflammatory ratios were calculated from admission laboratory data. Outcomes included ICU admission, 30-day all-cause mortality and length of hospital stay. Logistic regression and linear regression analyses were used to identify prognostic factors and receiver operating characteristic (ROC) curves were applied to assess model discrimination. Results: Patients with acute cholangitis exhibited substantially higher 30-day mortality than those with acute cholecystitis (30.0% vs 4.1%), along with lower PNI, higher FIB-4 values, lower albumin and hemoglobin levels and a higher prevalence of low PMA. Age was associated with lower PMA and PNI and higher FIB-4 scores. In parsimonious multivariable logistic regression analysis, a diagnosis of acute cholangitis emerged as the strongest predictor of 30-day mortality. Although age and psoas muscle area (PMA) demonstrated directionally consistent associations with mortality, these did not reach statistical significance, likely owing to the limited number of outcome events. The resulting model showed good discriminatory performance for 30-day mortality (AUC ≈ 0.84). Conclusions: Frailty-related parameters, particularly impaired nutritional status and reduced muscle mass, cluster in patients with acute cholangitis and are associated with worse short-term outcomes. CT-based PMA assessment, together with nutritional and hepatic indices, may support early risk stratification in acute biliary tract infections. biliary tract infection sarcopenia psoas muscle area prognostic nutritional index FIB-4 acute cholangitis 30-day mortality Figures Figure 1 Figure 2 Figure 3 Introduction / Background Acute biliary tract infections (BTIs), including acute cholecystitis and cholangitis, constitute a significant clinical challenge due to their heterogeneous presentations and variable prognoses( 1 – 5 ). Despite advances in antimicrobial therapy and minimally invasive interventions, patient outcomes remain strongly influenced by host-related factors such as age, comorbidities and baseline physiological reserve. Increasing evidence suggests that integrating nutritional status, hepatic function and systemic inflammatory burden into clinical assessment may improve risk stratification and guide management strategies ( 1 , 6 ). In this context, composite indices combining clinical and laboratory parameters have gained attention for their potential prognostic value in BTIs. The prognostic nutritional index (PNI) reflects nutritional and immune status ( 7 ), while computed tomography (CT)–derived measurements of muscle mass, particularly psoas muscle area (PMA), are widely used as surrogate markers of sarcopenia ( 8 – 10 ). Sarcopenia has been increasingly recognized as an independent predictor of adverse postoperative and infectious outcomes ( 11 – 14 ). Liver-related scores, particularly the FIB-4 fibrosis index, may provide additional value for risk stratification by reflecting underlying hepatic dysfunction ( 15 , 16 ). However, data examining the association between FIB-4 and acute cholecystitis or acute cholangitis are currently scarce. Evidence evaluating the relationship between sarcopenia and acute biliary tract infections remains limited. To date, only a small number of studies have explored the prognostic implications of skeletal muscle depletion in acute cholecystitis, with most focusing on postoperative outcomes, recurrence after conservative management or the development of cholecystitis in patients with gallstones ( 8 , 10 , 17 ). Data addressing sarcopenia in relation to acute infectious severity and short-term outcomes in acute cholecystitis are sparse and the association between sarcopenia and acute cholangitis has not been systematically investigated. Existing studies have primarily focused on chronic cholangiopathies, such as primary sclerosing cholangitis, rather than acute infectious cholangitis ( 18 – 20 ). Similarly, although FIB-4 is widely used to estimate chronic liver fibrosis, its prognostic relevance in acute biliary tract infections remains largely unexplored. Several inflammatory markers and derived ratios, such as C-reactive protein-to-albumin ratio (CAR) and platelet-to-albumin ratios (PAR), are being evaluated for their correlation with morbidity and mortality in acute illness ( 21 ). C-reactive protein-to-albumin ratio (CAR) may serve as a useful marker for assessing disease severity in acute cholecystitis and for estimating morbidity risk in affected patients ( 22 ). The platelet-to-albumin ratio (PAR) has been identified as an important prognostic marker in critically ill patients, where higher values are linked to worse clinical outcomes. ( 23 ). Hemoglobin × albumin × lymphocyte / platelet index (HALP) score appears to be a valuable tool for supporting treatment planning and management strategies in patients with acute cholecystitis ( 24 ). C-reactive protein–to–lymphocyte ratio (CLR) demonstrates greater discriminatory ability than conventional inflammatory markers for assessing treatment response in acute cholangitis ( 25 ). Elevated C-reactive protein–to–platelet ratio (CPR) has showed the strongest performance in predicting clinical outcomes among patients with pyogenic liver abscess ( 26 ). Platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP) have been linked to disease severity and bacteremia in acute cholangitis while the platelet-to-lymphocyte ratio (PLR) was most closely linked to blood culture positivity ( 27 ). However, the clinical utility of these markers in BTIs remains inconsistent. Despite these advances, the optimal integration of nutritional, hepatic, sarcopenia-related and inflammatory metrics for outcome prediction in BTIs is unclear. Therefore, this study investigates the prognostic utility of CT-derived PMA, FIB-4 score, prognostic nutritional index and systemic inflammatory ratios in patients hospitalized with acute biliary tract infections. Methods This retrospective cohort study included 94 adult patients diagnosed with acute biliary tract infections (BTIs), specifically acute cholecystitis and acute cholangitis, who were hospitalized at Rize Recep Tayyip Erdoğan University Education and Research Hospital between 2022 and 2023. Inclusion criteria encompassed adults with confirmed acute cholangitis or acute cholecystitis. Patients were excluded if they were younger than 18 years, had acute pancreatitis (biliary or non-biliary), incomplete or missing data, extra-biliary malignancy or if treatment could not be continued at our institution. Patients with active malignancy other than biliary tract malignancy were excluded to reduce heterogeneity and avoid confounding related to cancer-associated inflammation and cachexia, whereas patients with biliary tract malignancy were retained due to its direct association with biliary obstruction and acute cholangitis. Demographic characteristics (age and sex), diagnostic category, presence of biliary tract malignancy and treatment modality datas were recorded. Admission laboratory data included liver function tests, complete blood count parameters, inflammatory markers and serum albumin levels. Because standardized skeletal muscle indices such as the skeletal muscle index (SMI) could not be calculated due to missing height and body weight data, psoas muscle area (PMA) measured on admission CT scans at the L3 level was used as a pragmatic and reproducible surrogate marker of skeletal muscle mass to assess the presence of sarcopenia. PMA was analyzed as a continuous variable and dichotomized using sex-specific cut-off values (< 20.3 cm² for males and < 11.8 cm² for females) ( 8 , 28 , 29 ). Hepatic fibrosis was assessed using the FIB-4 score, calculated as follows: FIB-4= (Age (years)×AST (U/L)) / (Platelet Count(10 9 /L)× √ALT (U/L)) with thresholds for no/minimal fibrosis ( 2.67)( 15 ). Nutritional and immunological status was evaluated using the Prognostic Nutritional Index (PNI): PNI = 10×serum albumin (g/dL) + 0.005 × total lymphocyte count (/mm 3 ) with categories 50 ( 7 , 30 ).​ Systemic inflammatory ratios included: Platelet/albumin ratio (PAR), CRP/albumin ratio (CAR), CRP/lymphocyte ratio (CLR), Hemoglobin × albumin × lymphocyte / platelet index (HALP), Platelet/lymphocyte ratio (PLR) and CRP/platelet ratio (CPR) The primary outcomes were ICU admission, hospital length of stay (LOS) and 30-day all-cause mortality. Associations of PMA, FIB-4, PNI and systemic inflammatory indices with short-term outcomes were evaluated as secondary analyses. Intervention modalities (percutaneous cholecystostomy, percutaneous transhepatic biliary drainage and cholecystectomy) were recorded and interpreted as descriptive indicators of disease severity and surgical eligibility rather than causal exposures. Continuous variables were assessed for normality using visual inspection and the Shapiro–Wilk test and are presented as mean ± standard deviation or median with interquartile range (IQR), as appropriate. Categorical variables are reported as counts and percentages. Comparisons between patients with acute cholecystitis and acute cholangitis were performed using the Student’s t-test or Mann–Whitney U test for continuous variables, according to data distribution, and the chi-square test or Fisher’s exact test for categorical variables. Given the limited number of outcome events, multivariable models were kept parsimonious to minimize overfitting, with priority given to clinically relevant variables. Logistic regression was used to evaluate predictors of ICU admission and 30-day mortality, while linear regression was applied to identify predictors of length of stay (LOS). Model discrimination was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC). All analyses were performed in a diagnosis-stratified manner (acute cholecystitis vs acute cholangitis) to account for clinical heterogeneity. Statistical analyses were performed using IBM SPSS Statistics, version 29.0 (IBM Corp., Armonk, NY, USA), and a two-sided p-value < 0.05 was considered statistically significant. Results Ninety‑four patients were included: 74 with acute cholecystitis and 20 with acute cholangitis. Marked clinical and laboratory heterogeneity was observed between the two diagnostic groups (Table 1 ). Patients with cholangitis were more frequently male and had a markedly higher prevalence of biliary tract malignancy. Thirty‑day mortality was substantially higher in cholangitis (30.0%) than in cholecystitis (4.1%). ICU admission was uncommon in both groups. Percutaneous cholecystostomy was performed in 70.2% of patients, percutaneous transhepatic biliary drainage (PTBD) in 21.3% and cholecystectomy in 8.5%. Laboratory profiles differed between diagnoses (Table 2 ). Cholangitis was associated with higher transaminases and bilirubin, lower albumin, lower hemoglobin and lymphocyte counts and higher blood urea nitrogen consistent with greater hepatobiliary dysfunction and impaired nutritional–immune reserve. Muscle mass, nutritional status, and fibrosis-related indices stratified by diagnosis are summarized in Table 3 . Although median PMA values showed overlap between acute cholecystitis and acute cholangitis, categorization using sex-specific cut-off values revealed a significantly higher prevalence of low PMA among patients with acute cholangitis. Nutritional impairment was markedly more pronounced in the cholangitis group, with significantly lower median PNI values and a substantially higher proportion of patients classified as severely malnourished. Similarly, fibrosis-related risk differed between diagnoses, with patients with acute cholangitis demonstrating significantly higher median FIB-4 scores and a greater proportion categorized as having advanced fibrosis risk. Overall, Table 3 illustrates a clear clustering of sarcopenia, malnutrition and increased hepatic fibrosis risk among patients with acute cholangitis compared with those with acute cholecystitis. Systemic inflammatory and composite indices stratified by diagnosis are summarized in Table 4 . Among systemic inflammatory ratios, PAR was higher in cholangitis while CAR was higher in cholecystitis. HALP was lower in cholangitis, suggesting combined anemia, inflammation and malnutrition. Other ratios showed no statistically significant between‑group differences. Continuous PMA showed overlap between groups; however, low PMA was more frequent in cholangitis. Violin plots with embedded boxplots show lower PNI and higher FIB‑4 in cholangitis, while continuous PMA overlaps between groups (Fig. 1). Increasing age was associated with higher FIB-4 scores, while higher FIB-4 values were inversely correlated with PNI. Lower PNI was associated with prolonged LOS. PMA showed significant associations with both PNI and age, highlighting the link between sarcopenia, nutritional status and aging. In addition, PNI was positively correlated with hemoglobin levels. Scatter plots illustrate correlations; regression lines with 95% confidence intervals are shown in Fig. 2 . In parsimonious multivariable logistic regression, cholangitis diagnosis remained the strongest predictor of 30-day mortality, while age and PMA showed directionally consistent effects but did not reach statistical significance, likely due to the limited number of events. The primary multivariable model demonstrated good discrimination for 30-day mortality (AUC ≈ 0.84). Table 1 Baseline demographic and clinical characteristics of patients with acute cholecystitis and acute cholangitis Variable Acute cholecystitis (n = 74) Acute cholangitis (n = 20) p-value Age, years 68.50 [58.00–79.75] 66.00 [60.00–79.25] 0.796 Female sex, n (%) 41 (55.4) 5 (25.0) 0.023 Male sex, n (%) 33 (44.6) 15 (75.0) 0.023 Biliary tract malignancy, n (%) 5 (6.8) 17 (85.0) < 0.001 ICU admission, n (%) 3 (4.1) 2 (10.0) 0.287 30-day mortality, n (%) 3 (4.1) 6 (30.0) 0.003 Length of stay, days 12.0 [10.0–14.0] 14.0 [10.0–15.5] 0.168 Values are presented as median [interquartile range] or number (%), as appropriate. P-values were calculated using the Mann–Whitney U test for continuous variables and the chi-square or Fisher’s exact test for categorical variables. Length of stay was defined as total treatment duration in days. ICU, intensive care unit. Laboratory parameters stratified by diagnosis are summarized in Table 2 . Table 2 Laboratory parameters according to diagnosis Variable Acute cholecystitis Acute cholangitis p-value AST (U/L) 46.0 [26.0–88.0] 95.0 [46.0–169.0] 0.008 ALT (U/L) 34.0 [21.0–68.0] 76.0 [33.0–122.0] 0.012 Total bilirubin (mg/dL) 1.16 [0.68–2.21] 3.00 [1.35–5.99] 0.007 Direct bilirubin (mg/dL) 0.26 [0.14–0.67] 1.46 [0.41–3.48] 0.003 Albumin (g/L) 35.0 [30.3–39.0] 28.7 [23.0–31.7] < 0.001 Blood urea nitrogen (mg/dL) 43.0 [27.0–64.0] 59.5 [41.0–82.0] 0.041 Creatinine (mg/dL) 0.96 [0.74–1.26] 1.10 [0.82–1.54] 0.187 White blood cell count (×10³/µL) 13.4 [9.8–17.9] 15.8 [11.2–21.4] 0.093 Hemoglobin (g/dL) 12.4 [10.9–13.7] 11.1 [9.8–12.6] 0.018 Lymphocyte count (×10³/µL) 1.30 [0.86–1.88] 1.02 [0.61–1.46] 0.044 Platelet count (×10³/µL) 252 [186–318] 282 [214–361] 0.241 CRP (mg/L) 171.7 [87.0–264.3] 88.1 [27.8–177.2] 0.021 Values are presented as median [interquartile range]. P-values were calculated using the Mann–Whitney U test. AST, aspartate aminotransferase; ALT, alanine aminotransferase; CRP, C-reactive protein. Laboratory parameters differed significantly between diagnoses. Acute cholangitis was associated with higher transaminase and bilirubin levels, lower albumin and hemoglobin concentrations, and reduced lymphocyte counts, indicating more pronounced hepatobiliary dysfunction and impaired nutritional–immune reserve compared with acute cholecystitis. Table 3 Muscle mass, nutritional status and fibrosis-related indices according to diagnosis Variable Acute cholecystitis (n = 74) Acute cholangitis (n = 20) p-value PMA (cm²) 11.94 [9.29–15.92] 10.20 [7.98–14.47] 0.153 Low PMA, n (%) (< 20.3 cm² for males and < 11.8 cm² for females). 53 (71.6) 19 (95.0) 0.036 Prognostic Nutritional Index (PNI) 41.75 [36.86–46.99] 33.92 [29.15–38.70] < 0.001 PNI category, n (%) 0.015 PNI Severe (< 40), n (%) 30 (40.5) 16 (80) PNI Moderate (40-44.9), n (%) 20 ( 27 ) 2 ( 10 ) PNI Mild (45-49.9), n (%) 14 (18.9) 2 ( 10 ) PNI Normal (≥ 50), n (%) 10 (13.5) 0 (0) FIB-4 score 1.56 [0.91–2.80] 2.92 [1.92–5.39] 0.005 FIB-4 category, n (%) 0.036 FIB-4 No/minimal ( 2.67), n (%) 21 (28.4) 11 (55) Values are presented as median [interquartile range] or number (%), as appropriate. P-values were calculated using the Mann–Whitney U test for continuous variables and the chi-square or Fisher’s exact test for categorical variables. Table 4 Systemic inflammatory and composite indices according to diagnosis Variable Acute cholecystitis Acute cholangitis p-value Platelet-to-albumin ratio (PAR) 7.32 [5.01–9.63] 9.41 [6.77–13.09] 0.028 CRP-to-albumin ratio (CAR) 5.21 [2.70–7.81] 3.62 [1.05–6.44] 0.047 CRP-to-lymphocyte ratio (CLR) 2038 [984–2875] 1468 [495–2550] 0.112 HALP index 23.7 [14.3–33.8] 15.4 [9.1–21.8] 0.009 Platelet-to-lymphocyte ratio (PLR) 255 [178–340] 289 [201–415] 0.158 CRP-to-platelet ratio (CPR) 0.79 [0.41–1.05] 0.63 [0.23–0.96] 0.214 Values are presented as median [interquartile range]. P-values were calculated using the Mann–Whitney U test. PAR, platelet-to-albumin ratio; CAR, C-reactive protein-to-albumin ratio; CLR, C-reactive protein-to-lymphocyte ratio; HALP, hemoglobin–albumin–lymphocyte–platelet index; PLR, platelet-to-lymphocyte ratio; CPR, C-reactive protein-to-platelet ratio. Figure 1. Violin plots illustrating the distribution of psoas muscle area (PMA), prognostic nutritional index (PNI), and FIB-4 score according to diagnosis. Panels display PMA (cm²), PNI, and FIB-4 values in patients with acute cholangitis and acute cholecystitis. Violin plots represent the full distribution of each variable, with embedded boxplots indicating the median and interquartile range. Although continuous PMA values show substantial overlap between diagnostic groups, patients with acute cholangitis demonstrate a higher prevalence of low PMA when categorized using sex-specific cut-off values. Nutritional status, reflected by PNI, is markedly impaired in acute cholangitis, with lower median values compared with acute cholecystitis. Similarly, FIB-4 scores are higher in acute cholangitis, indicating an increased burden of hepatic fibrosis risk. Overall, the figure highlights clustering of sarcopenia-related muscle loss, malnutrition, and hepatic vulnerability in acute cholangitis relative to acute cholecystitis. Figure 2 . Scatter plots demonstrating correlations between frailty-related, nutritional, hepatic, and clinical parameters in patients with acute biliary tract infections. Panels illustrate the relationships between FIB-4 and age, FIB-4 and prognostic nutritional index (PNI), PNI and length of hospital stay (LOS), PNI and psoas muscle area (PMA), PMA and age, and PNI and hemoglobin levels. Solid lines represent linear regression fits with shaded areas indicating 95% confidence intervals. Correlation coefficients (r) and corresponding p-values are displayed for each comparison. Advancing age was positively correlated with higher FIB-4 scores and inversely correlated with PMA, indicating the coexistence of hepatic vulnerability and sarcopenia with aging. Higher FIB-4 values were associated with lower PNI, reflecting an inverse relationship between hepatic dysfunction and nutritional status. Lower PNI was modestly associated with prolonged LOS. PMA showed positive correlation with PNI and negative correlation with age, highlighting the interdependence of muscle mass, nutritional reserve, and aging. In addition, PNI demonstrated a strong positive association with hemoglobin levels, supporting its role as an integrated marker of nutritional and hematological status. Collectively, these correlations illustrate a coherent frailty-related axis linking aging, sarcopenia, impaired nutrition, hepatic vulnerability, and adverse short-term clinical outcomes. Figure 3 illustrates the predicted probability of 30-day mortality according to age and psoas muscle area (PMA), derived from a parsimonious logistic regression model including diagnosis (cholangitis), age and PMA. Low PMA was observed in 76.6% of patients. FIB-4 risk categories were distributed as follows: low risk (≤ 1.3) in 37.2%, indeterminate (1.3–2.67) in 28.7%, and high risk (> 2.67) in 34.0%. According to PNI, 48.9% of patients had severe nutritional risk, 23.4% moderate, 17.0% mild, and 10.6% no nutritional risk. Overall, advancing age was associated with a progressive increase in the estimated risk of 30-day mortality. Importantly, this age-related increase was markedly more pronounced among patients with low PMA. At younger ages, the predicted mortality risk remained low in both groups. However, with increasing age, patients with reduced muscle mass demonstrated a steeper rise in predicted mortality compared with those with preserved PMA. This divergence became particularly evident in older age ranges, indicating that sarcopenia substantially amplifies the adverse prognostic impact of aging in acute biliary tract infections. These findings suggest a synergistic effect between advanced age and reduced skeletal muscle mass on short-term mortality risk, supporting the role of frailty-related parameters in early risk stratification. Female patients had significantly higher PNI values compared with males (p = 0.027), indicating better nutritional status. Male patients tended to have higher FIB-4 scores, although this difference did not reach statistical significance (p = 0.070). Hospital length of stay was longer in male patients than in females (p = 0.036), whereas ICU admission and 30-day mortality did not differ significantly by sex. Advanced age was positively associated with longer hospital stay (p < 0.05). Although older age showed non-significant trends toward higher ICU admission and 30-day mortality, patients aged ≥65 years had significantly lower PMA and PNI values and higher FIB-4 scores (all p < 0.05). Comparison between ICU and non-ICU patients revealed that FIB-4 was significantly higher in ICU patients (p = 0.001), while PMA and PNI showed no significant difference (p = 0.589 and p = 0.094, respectively). The ROC analysis for ICU admission prediction incorporating PMA, FIB-4, and PNI achieved an AUC of 0.78, indicating good model discrimination. However, the very small number of ICU cases (n = 5) limits the reliability of statistical analyses. Among the three main markers, only FIB-4 showed a significant discriminatory ability for predicting 30-day mortality, with an AUC of 0.78. PMA and PNI did not demonstrate meaningful predictive value (AUC < 0.30) in this analysis. FIB-4 values were higher in non-survivors, while PMA and PNI tended to be lower compared to survivors; nonetheless, the limited number of deaths (n = 9) reduced the statistical power. In univariate logistic regression, PNI showed a borderline association with prolonged length of stay, but in the multivariate model, it emerged as an independent predictor (OR = 0.94, p = 0.045). PMA and FIB-4 were not significant for LOS prediction (AUC ≈ 0.47 and 0.60, respectively). Low PNI was associated with prolonged hospital stay and may serve as an independent predictor of LOS, whereas PMA and FIB-4 were not reliable markers. Systemic inflammatory ratios (PAR, CAR, CLR, HALP, PLR, CPR) did not provide reliable prediction for hospital stay duration or 30-day mortality in this dataset. Their association with major outcomes was not statistically significant. Discussion In this retrospective cohort of patients hospitalized with acute biliary tract infections, we assessed the prognostic relevance of CT‑derived PMA, FIB‑4, nutritional status (PNI) and systemic inflammatory indices. The key finding is that acute cholangitis and acute cholecystitis are clinically and biologically distinct entities, and that frailty‑related markers—particularly low PMA and impaired nutritional reserve—cluster in the higher‑risk cholangitis group. Cholangitis was associated with markedly higher 30‑day mortality and more frequent low PMA, lower PNI, lower albumin and higher FIB‑4. One of the key observations of this study is the strong association between advanced age and adverse clinical characteristics, including lower PMA, reduced PNI, higher FIB-4 scores and prolonged hospital stay. Aging is known to be associated with progressive muscle loss, impaired nutritional reserve and reduced physiological resilience, all of which may limit the ability to tolerate acute infection and inflammatory stress (31, 32). Our findings are consistent with previous studies demonstrating the adverse impact of frailty and sarcopenia on outcomes in acute surgical and infectious conditions (10, 17, 33-35). Among the evaluated parameters, FIB-4 demonstrated the best discriminatory ability for predicting 30-day mortality. Although originally developed as a non-invasive marker of chronic liver fibrosis, FIB-4 may also reflect hepatic vulnerability and systemic stress in the acute setting (15, 16). In our cohort, higher FIB-4 values were observed in non-survivors, suggesting that underlying hepatic dysfunction may amplify the severity of acute biliary infections. However, the limited number of mortality events restricted the statistical power of multivariable analyses and FIB-4 did not retain independent predictive value after adjustment for key clinical variables. Although the FIB-4 score is well established as a non-invasive marker of chronic liver fibrosis, its role in acute biliary tract infections has not been previously defined. We were unable to identify published studies examining the relationship between FIB-4 and either acute cholecystitis or acute cholangitis. Our findings suggest that higher FIB-4 values may reflect hepatic vulnerability and systemic stress in the acute setting, warranting further investigation in larger cohorts. Psoas muscle area was frequently reduced in this cohort, indicating a high burden of sarcopenia. PMA was inversely correlated with age and FIB-4, supporting the concept that muscle depletion, hepatic dysfunction, and aging are interrelated components of frailty (36, 37). Although PMA did not independently predict mortality or ICU admission in multivariable models, directionally consistent associations were observed. These findings suggest that PMA may contribute to risk stratification but may require larger cohorts or standardized muscle indices to demonstrate independent prognostic significance. Our findings extend this limited body of evidence by demonstrating a high prevalence of low psoas muscle area in patients hospitalized with acute biliary tract infections and by highlighting the close relationship between sarcopenia, aging, nutritional impairment and hepatic vulnerability. In this context, our study provides novel exploratory data suggesting that sarcopenia is highly prevalent among patients with acute cholangitis and may reflect reduced physiological reserve in this high-risk population. Nutritional status, assessed using the prognostic nutritional index, emerged as a clinically relevant determinant of hospital length of stay. Low PNI was independently associated with prolonged hospitalization, underscoring the role of malnutrition and immune compromise in delayed recovery. Similar associations between poor nutritional status and adverse outcomes have been reported in acute biliary infections and other inflammatory conditions (7, 38, 39). These results support the integration of nutritional assessment into routine clinical evaluation of patients with BTIs. In contrast, systemic inflammatory ratios, including CAR, PAR, CLR, HALP, PLR and CPR, did not demonstrate independent prognostic value for major outcomes in this dataset. While these markers have shown promise in selected populations and disease-specific contexts their utility in acute biliary tract infections may be limited by disease heterogeneity and the dominant influence of host reserve and baseline vulnerability (21-27). Our findings suggest that isolated inflammatory markers may be insufficient to capture the complex biological determinants of short-term outcomes in BTIs. Although several of the presented analyses were stratified by diagnosis, the primary contribution of this study lies not in a direct comparison between acute cholecystitis and acute cholangitis, but in demonstrating that markers of frailty, malnutrition and hepatic vulnerability are highly prevalent across the spectrum of acute biliary tract infections. By simultaneously evaluating CT-derived psoas muscle area, nutritional status (PNI), and fibrosis-related risk (FIB-4), our findings highlight that sarcopenia, impaired nutritional reserve and increased fibrosis risk frequently coexist in both conditions and are closely linked to short-term prognosis. The diagnosis-stratified analyses primarily serve to illustrate clinical heterogeneity, whereas the overarching message is that host-related vulnerability, rather than diagnostic category alone, plays a central role in determining outcomes in acute biliary tract infections. Correlation analyses revealed a coherent axis linking aging, higher FIB‑4, lower PNI, lower PMA, and longer LOS. This pattern supports the concept that frailty‑related parameters integrate hepatic, nutritional, and inflammatory burdens and may add clinically meaningful information beyond single laboratory markers (40, 41). This study has several limitations. Its retrospective, single-center design limits generalizability. The small number of ICU admissions and mortality events constrained multivariable modeling and may have resulted in imprecise estimates. In addition, missing anthropometric data precluded calculation of standardized skeletal muscle indices, necessitating the use of PMA as a surrogate marker of sarcopenia. Finally, external validation in larger, prospective cohorts is required. Conclusion Acute biliary tract infections are clinically heterogeneous and associated with variable short-term outcomes. In this retrospective cohort, host-related vulnerability—particularly impaired nutritional status and sarcopenia-related muscle loss—was highly prevalent and closely linked to aging and hepatic dysfunction. Among the evaluated markers, the prognostic nutritional index was associated with prolonged hospital stay, while the FIB-4 score demonstrated moderate discriminatory ability for short-term mortality, suggesting that hepatic vulnerability may contribute to adverse outcomes in the acute setting. By jointly assessing CT-derived psoas muscle area, nutritional status, and fibrosis-related indices in both acute cholecystitis and acute cholangitis, this study provides novel exploratory insights into an under-investigated area. CT-based PMA assessment and simple laboratory-derived indices such as PNI and FIB-4 are readily available in routine clinical practice and may support early risk stratification. Larger prospective multicenter studies are warranted to validate these findings and to clarify the role of frailty-based assessment in guiding clinical decision-making in acute biliary tract infections. Abbreviations ALT — Alanine aminotransferase AST — Aspartate aminotransferase AUC — Area under the curve BTI(s) — Biliary tract infection(s) CAR — C-reactive protein–to–albumin ratio CPR — C-reactive protein–to–platelet ratio CRP — C-reactive protein CT — Computed tomography CLR — C-reactive protein–to–lymphocyte ratio FIB-4 — Fibrosis-4 index HALP — Hemoglobin–albumin–lymphocyte–platelet index ICU — Intensive care unit IQR — Interquartile range LOS — Length of stay PAR — Platelet–to–albumin ratio PMA — Psoas muscle area PLR — Platelet–to–lymphocyte ratio PNI — Prognostic nutritional index PTBD — Percutaneous transhepatic biliary drainage ROC — Receiver operating characteristic SMI — Skeletal muscle index Declarations Ethics approval / Consent to participate: This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Recep Tayyip Erdoğan University Faculty of Medicine (approval number: 2024/255). Due to the retrospective design of the study, the requirement for informed consent was waived. Consent for publication : Not applicable Availability of data and materials : The datasets used and/or analysed 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 study received no external funding. Authors' contributions : ZZK conceptualized and designed the study, supervised data collection, contributed to data interpretation, and was the major contributor in writing and revising the manuscript. CK contributed to study design, data acquisition, and interpretation, and critically revised the manuscript for important intellectual content. AO and TI contributed to data collection, clinical data interpretation, and literature review. MAO assisted with data organization, statistical preparation, and manuscript drafting. FT performed and supervised radiological assessments, including CT-based psoas muscle area measurements, and contributed to data interpretation. All authors read and approved the final manuscript. Acknowledgements: Not applicable Footnotes : Not applicable Additional Information : Scientific Presentation This study was presented as an oral presentation at the 17th Turkish Hepato Pancreato Biliary Surgery Congress 2025, Antalya, Türkiye. References Kiriyama S, Kozaka K, Takada T, Strasberg SM, Pitt HA, Gabata T, et al. Tokyo Guidelines 2018: diagnostic criteria and severity grading of acute cholangitis (with videos). J Hepato-Biliary‐Pancreatic Sci. 2018;25(1):17–30. Rimicans M, Fultang J, Thompson L, Oochit K, Maruthachalam K. Outcomes of Percutaneous Cholecystostomy in the Management of Acute Cholecystitis: A Retrospective Cohort Study at a Rural District General Hospital. Cureus. 2025;17(10):e95838. Wang X, Yu W, Jiang G, Li H, Li S, Xie L, et al. Global epidemiology of gallstones in the 21st century: a systematic review and meta-analysis. Clin Gastroenterol Hepatol. 2024;22(8):1586–95. Pisano M, Allievi N, Gurusamy K, Borzellino G, Cimbanassi S, Boerna D, et al. 2020 World Society of Emergency Surgery updated guidelines for the diagnosis and treatment of acute calculus cholecystitis. World J Emerg Surg. 2020;15(1):61. Schwed AC, Boggs MM, Pham X-BD, Watanabe DM, Bermudez MC, Kaji AH, et al. Association of admission laboratory values and the timing of endoscopic retrograde cholangiopancreatography with clinical outcomes in acute cholangitis. JAMA Surg. 2016;151(11):1039–45. Dai F, Cai Y, Yang S, Zhang J, Dai Y. Global burden of gallbladder and biliary diseases (1990–2021) with healthcare workforce analysis and projections to 2035. BMC Gastroenterol. 2025;25(1):249. Mandai K, Inoue T, Nakamura S, Yoshimoto T, Uno K. Clinical Predictors of Worsening in Non-severe Acute Cholangitis. Intern Med. 2025. Ataş AE, Ünüvar Ş. High visceral adiposity and low skeletal muscle mass independently predict the development of acute cholecystitis in patients with gallstones: a retrospective cohort study. Front Med (Lausanne). 2025;12:1724416. De Marco D, Mamane S, Choo W, Mullie L, Xue X, Afilalo M, et al. Muscle Area and Density Assessed by Abdominal Computed Tomography in Healthy Adults: Effect of Normal Aging and Derivation of Reference Values. J Nutr Health Aging. 2022;26(2):243–6. Kim HB, Chun SY, Kim GW, Lim H, Cho YS. Can sarcopenia predict poor prognosis of sepsis due to acute cholecystitis? Am J Emerg Med. 2023;73:69–74. Okada Y, Kiguchi T, Okada A, Iizuka R, Iwami T, Ohtsuru S. Predictive value of sarcopenic findings in the psoas muscle on CT imaging among patients with sepsis. Am J Emerg Med. 2021;47:180–6. Cox MC, Booth M, Ghita G, Wang Z, Gardner A, Hawkins RB, et al. The impact of sarcopenia and acute muscle mass loss on long-term outcomes in critically ill patients with intra‐abdominal sepsis. J cachexia sarcopenia muscle. 2021;12(5):1203–13. Lee Y, Park HK, Kim WY, Kim MC, Jung W, Ko BS. Muscle mass depletion associated with poor outcome of sepsis in the emergency department. Annals Nutr Metabolism. 2018;72(4):336–44. Toptas M, Yalcin M, Akkoc İ, Demir E, Metin C, Savas Y, et al. The Relation between Sarcopenia and Mortality in Patients at Intensive Care Unit. Biomed Res Int. 2018;2018:5263208. Younossi ZM, Paik JM, Stepanova M, Ong J, Alqahtani S, Henry L. Clinical profiles and mortality rates are similar for metabolic dysfunction-associated steatotic liver disease and non-alcoholic fatty liver disease. J Hepatol. 2024;80(5):694–701. Duman DG, Alahdab YO, Demirtas CO, Yılmaz Y, Dilber F, Ozdemir FT, et al. Usefulness of Endoscopic Ultrasound Strain Elastography for Measuring Liver Stiffness and the Role of Blood Cytokeratin 18 Levels as a Surrogate Marker of Fibrosis. Turk J Gastroenterol. 2025;36(10):692–9. Koya Y, Shibata M, Maruno Y, Sakamoto Y, Oe S, Miyagawa K, et al. Low skeletal muscle mass and high visceral adiposity are associated with recurrence of acute cholecystitis after conservative management: A propensity score-matched cohort study. Hepatobiliary Pancreat Dis Int. 2024;23(1):64–70. Levers A, Pantke J, Klimeš F, Lenzen H, Düx D, Taubert R, et al. Sarcopenia-A Valuable Imaging Biomarker for Disease Progression in Patients With Primary Sclerosing Cholangitis (PSC)? Liver Int. 2025;45(10):e70328. Zhou L, Li B, Wang Z, Ao X, Wang X, Zheng Y, et al. Association of sarcopenia assessed by CT/MRI with treatment response and clinical outcomes in noncirrhotic primary biliary cholangitis patients. Eur J Radiol. 2025;187:112094. Wang Z, Yang S, Zhong J, Liu X, Feng J, Jin Q, et al. Mean liver density independently predicts therapeutic response and liver-related mortality in patients with primary biliary cholangitis. Quant Imaging Med Surg. 2024;14(12):9074–85. Shi X, Hou X, Liu J, Tian S, Zhou J, Chen X. Utility of admission platelet count to predict prognosis and determine illness severity in acute cholangitis. Clin Invest Med. 2025;48(3):6–14. Yilmaz S, Aykota MR, Ozgen U, Birsen O, Simsek S, Kabay B. Might simple peripheral blood parameters be an early indicator in the prediction of severity and morbidity of cholecystitis? Ann Surg Treat Res. 2023;104(6):332–8. Liu CL, Wu QN, Deng ZY, Chen P, Guo SQ. High platelet-to-albumin ratio is associated with 30-day mortality in critically ill patients. Eur J Med Res. 2024;29(1):620. Keskin Y, Sevinç H, Hazinedaroğlu SM, Morkavuk ŞB, Ersöz Ş. Predictive Utility of the HALP and Modified HALP Score for the Assessment of Operative Complications in Patients Undergoing Laparoscopic Cholecystectomy for Acute Cholecystitis. Diagnostics (Basel). 2025;15(2). Belyaev AM, Thwaite P, Rossaak J, Chen J, Smith B. The Use of Inflammatory Markers for Treatment Response Monitoring in Acute Cholangitis: A Retrospective Cohort Study. J Surg Res. 2024;293:14–21. Li S, Yu S, Qin J, Peng M, Qian J, Zhou P. Prognostic value of platelet count-related ratios on admission in patients with pyogenic liver abscess. BMC Infect Dis. 2022;22(1):636. Ye S, Lyu Y, Wang B. The Predictive Value of Different Laboratory Indicators Based on the 2018 Tokyo Guidelines for the Severity of Acute Cholangitis. J Emerg Med. 2023;65(4):e280–9. Ritz AF-P, Alexandra & Kolorz, Julian & Vigodski, Victor & Hubertus, Jochen &Ley-Zaporozhan, Julia & Schweinitz, Dietrich & Häberle, Beate & Schmid, Irene & Kappler,Roland & Lurz, Eberhard & Berger, Michael. Total Psoas Muscle Area as a Marker for Sarcopenia Is Related to Outcome in Children With Neuroblastoma. Frontiers in Surgery.2021;8. 10.3389/fsurg.2021.718184. Balata M, Tanaka T, Sugiura A, Kavsur R, Vogelhuber J, Öztürk C, et al. Association between psoas muscle area and outcomes after transcatheter tricuspid valve repair. Cardiovasc Intervention Ther. 2025;40(3):679–88. Zhang L, Ma W, Qiu Z, Kuang T, Wang K, Hu B, et al. Prognostic nutritional index as a prognostic biomarker for gastrointestinal cancer patients treated with immune checkpoint inhibitors. Front Immunol. 2023;14:1219929. Lee D-Y. The Prevalence of and Factors Associated with Sarcopenic Obesity, Sarcopenia, and Obesity Among Korean Adults: Findings from the 2022–2023 Korea National Health and Nutrition Examination Survey. Medicina. 2025;61(8):1424. Li H, Liu H, Zhang J, Nie H, Wang S, Zhao Z, et al. The relationships between Life's Crucial 9, Life's Essential 8, and sarcopenia in older adults: multicenter case-control study. Front Med (Lausanne). 2025;12:1714385. Su S, Lin Z, Cai Z, Huang L, Xiao Y, Yang F, et al. Preoperative CT-derived sarcopenia as a predictor of postoperative complications in patients undergoing laparoscopic radical resection for non-metastatic colorectal cancer: a retrospective study. Int J Colorectal Dis. 2025;40(1):140. Xu Y, Xu JW, Wu Y, Rong LJ, Ye L, Franco OH, et al. Prevalence and prognosis of sarcopenia in acute COVID-19 and long COVID: a systematic review and meta-analysis. Ann Med. 2025;57(1):2519678. Oh J, Park C, Kang Y, Ra SW. CT-assessed thoracic sarcopenia as an independent prognostic marker in patients with pleural infections: a retrospective cohort study. Respir Res. 2025;26(1):295. Nishikawa H, Nishikawa T, Fukuda A, Ushiro K, Matsui M, Onishi S, et al. Impact of the FIB4 Index on Pre-sarcopenia in Patients with Metabolic-dysfunction Associated Steatotic Liver Disease. Intern Med. 2025;64(21):3078–87. Kim JA, Choi KM. Sarcopenia and fatty liver disease. Hep Intl. 2019;13(6):674–87. Yang F, Yang Y, Zeng L, Chen Y, Zeng G. Nutrition Metabolism and Infections. Infect Microbes Dis. 2021;3(3):134–41. BALBALOĞLU H. The effect of prognostic nutrition index (PNI) rates on the development of surgical site infection after abdominal surgery. Turk Hij Den Biyol Derg. 2024;81(1):45–52. Devitt C, Patel D, Mahboubi Ardakani R, Poovathoor S, Jin Z, Moller D. Biomarkers and Clinical Evaluation in the Detection of Frailty. Int J Mol Sci. 2025;26(16):7888. Panayi AC, Orkaby AR, Sakthivel D, Endo Y, Varon D, Roh D, et al. Impact of frailty on outcomes in surgical patients: A systematic review and meta-analysis. Am J Surg. 2019;218(2):393–400. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor invited by journal 10 Mar, 2026 Editor assigned by journal 07 Jan, 2026 Submission checks completed at journal 06 Jan, 2026 First submitted to journal 06 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8419683","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617909032,"identity":"4ae44233-9ad0-4aa6-bb99-66626aff3127","order_by":0,"name":"Zehra Zeynep Keklikkiran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYLCCBwwSDPwSYKaEDHFaEoBaJGcwMDYAtfAQq4WBweAGWAsDYS3y/WcMPyTUWOQZ324+/uhGjQUPA/vhoxvwaWFsOGMskXBMotjszrHE5pxjQIfxpKXdwKeFmbHHQCKxQSJx240cw+YcNqAWCR4zvFrYmHmMf4C0bJ4B0vKPCC08bDxmYFs2SAC15LYRoUWCh63MAuiXxBk30hJn5/YB+YT8It9/ePONDzV1if0zkg98zvlWJ8fPfvgYXi0MDBwGaL7DrxwE2B8QVjMKRsEoGAUjGwAAhsJD8JVkYrgAAAAASUVORK5CYII=","orcid":"","institution":"Recep Tayyip Erdoğan University Research and Training Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zehra","middleName":"Zeynep","lastName":"Keklikkiran","suffix":""},{"id":617909034,"identity":"b2a521ef-0616-4b38-bf4f-9742bd1c57d2","order_by":1,"name":"Caglayan Keklikkiran","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":false,"prefix":"","firstName":"Caglayan","middleName":"","lastName":"Keklikkiran","suffix":""},{"id":617909038,"identity":"516c120d-5292-40d1-bb56-c0e49a182546","order_by":2,"name":"Aybegum Ozsahin","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University Research and Training Hospital","correspondingAuthor":false,"prefix":"","firstName":"Aybegum","middleName":"","lastName":"Ozsahin","suffix":""},{"id":617909043,"identity":"267b4a2e-0f26-4d42-84b0-0c7be13d2257","order_by":3,"name":"Tuba Ilgar","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":false,"prefix":"","firstName":"Tuba","middleName":"","lastName":"Ilgar","suffix":""},{"id":617909049,"identity":"bd56fdc7-f9d2-42aa-bb60-709a30237797","order_by":4,"name":"Muhammed Ali Ozdemir","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"Ali","lastName":"Ozdemir","suffix":""},{"id":617909059,"identity":"ef906b23-800f-4bfd-b6ac-367534b34c79","order_by":5,"name":"Filiz Tasci","email":"","orcid":"","institution":"Recep Tayyip Erdoğan University","correspondingAuthor":false,"prefix":"","firstName":"Filiz","middleName":"","lastName":"Tasci","suffix":""}],"badges":[],"createdAt":"2025-12-21 22:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8419683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8419683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106350009,"identity":"9d7f37c7-6528-4fff-9fc2-91cb89e2f97c","added_by":"auto","created_at":"2026-04-07 16:56:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":328864,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of PMA, PNI and FIB‑4 by diagnosis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8419683/v1/f7371a4faa8d7ef0b2648e33.png"},{"id":106350025,"identity":"63a3ba8f-dcbb-466d-8aac-8ab4e7ca340a","added_by":"auto","created_at":"2026-04-07 16:56:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":682864,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between Age, FIB‑4, PNI, PMA, hemoglobin and LOS.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8419683/v1/b6a102d6b74d77bb2e75a4e8.png"},{"id":106350023,"identity":"d3dbb50e-cf39-46cf-8b6c-a5b0191b8d2f","added_by":"auto","created_at":"2026-04-07 16:56:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":170746,"visible":true,"origin":"","legend":"\u003cp\u003eClinical interpretability and discrimination of the primary multivariable model for 30-day mortality.\u003c/p\u003e\n\u003cp\u003e(A) Predicted probability of 30-day mortality according to age for patients with low psoas muscle area (PMA; orange) and preserved PMA (blue). Predictions were derived from the primary multivariable logistic regression model including diagnosis (cholangitis), age, and PMA, with diagnosis fixed to cholangitis to facilitate clinical interpretability.\u003c/p\u003e\n\u003cp\u003e(B) Receiver operating characteristic (ROC) curve of the same multivariable model demonstrating good discrimination for 30-day mortality (AUC = 0.84; 95% confidence interval: 0.72–0.98).\u003c/p\u003e\n\u003cp\u003eAbbreviations: PMA, psoas muscle area; AUC, area under the curve.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8419683/v1/44f9d91bc9e5b838c020e1cf.png"},{"id":106350043,"identity":"5d60379b-6888-442b-a0a6-6e0818b4809c","added_by":"auto","created_at":"2026-04-07 16:56:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1753919,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419683/v1/a380d750-d101-4bea-b950-6ae86057fe3a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFrailty-Related and Hepatic Prognostic Markers in Acute Biliary Tract Infections: A Diagnosis-Stratified Retrospective Cohort Study\u003c/p\u003e","fulltext":[{"header":"Introduction / Background","content":" \u003cp\u003eAcute biliary tract infections (BTIs), including acute cholecystitis and cholangitis, constitute a significant clinical challenge due to their heterogeneous presentations and variable prognoses(\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Despite advances in antimicrobial therapy and minimally invasive interventions, patient outcomes remain strongly influenced by host-related factors such as age, comorbidities and baseline physiological reserve. Increasing evidence suggests that integrating nutritional status, hepatic function and systemic inflammatory burden into clinical assessment may improve risk stratification and guide management strategies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, composite indices combining clinical and laboratory parameters have gained attention for their potential prognostic value in BTIs. The prognostic nutritional index (PNI) reflects nutritional and immune status (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), while computed tomography (CT)\u0026ndash;derived measurements of muscle mass, particularly psoas muscle area (PMA), are widely used as surrogate markers of sarcopenia (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Sarcopenia has been increasingly recognized as an independent predictor of adverse postoperative and infectious outcomes (\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Liver-related scores, particularly the FIB-4 fibrosis index, may provide additional value for risk stratification by reflecting underlying hepatic dysfunction (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, data examining the association between FIB-4 and acute cholecystitis or acute cholangitis are currently scarce.\u003c/p\u003e \u003cp\u003eEvidence evaluating the relationship between sarcopenia and acute biliary tract infections remains limited. To date, only a small number of studies have explored the prognostic implications of skeletal muscle depletion in acute cholecystitis, with most focusing on postoperative outcomes, recurrence after conservative management or the development of cholecystitis in patients with gallstones (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Data addressing sarcopenia in relation to acute infectious severity and short-term outcomes in acute cholecystitis are sparse and the association between sarcopenia and acute cholangitis has not been systematically investigated. Existing studies have primarily focused on chronic cholangiopathies, such as primary sclerosing cholangitis, rather than acute infectious cholangitis (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Similarly, although FIB-4 is widely used to estimate chronic liver fibrosis, its prognostic relevance in acute biliary tract infections remains largely unexplored.\u003c/p\u003e \u003cp\u003eSeveral inflammatory markers and derived ratios, such as C-reactive protein-to-albumin ratio (CAR) and platelet-to-albumin ratios (PAR), are being evaluated for their correlation with morbidity and mortality in acute illness (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). C-reactive protein-to-albumin ratio (CAR) may serve as a useful marker for assessing disease severity in acute cholecystitis and for estimating morbidity risk in affected patients (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The platelet-to-albumin ratio (PAR) has been identified as an important prognostic marker in critically ill patients, where higher values are linked to worse clinical outcomes. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Hemoglobin \u0026times; albumin \u0026times; lymphocyte / platelet index (HALP) score appears to be a valuable tool for supporting treatment planning and management strategies in patients with acute cholecystitis (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). C-reactive protein\u0026ndash;to\u0026ndash;lymphocyte ratio (CLR) demonstrates greater discriminatory ability than conventional inflammatory markers for assessing treatment response in acute cholangitis (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Elevated C-reactive protein\u0026ndash;to\u0026ndash;platelet ratio (CPR) has showed the strongest performance in predicting clinical outcomes among patients with pyogenic liver abscess (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP) have been linked to disease severity and bacteremia in acute cholangitis while the platelet-to-lymphocyte ratio (PLR) was most closely linked to blood culture positivity (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, the clinical utility of these markers in BTIs remains inconsistent.\u003c/p\u003e \u003cp\u003eDespite these advances, the optimal integration of nutritional, hepatic, sarcopenia-related and inflammatory metrics for outcome prediction in BTIs is unclear. Therefore, this study investigates the prognostic utility of CT-derived PMA, FIB-4 score, prognostic nutritional index and systemic inflammatory ratios in patients hospitalized with acute biliary tract infections.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective cohort study included 94 adult patients diagnosed with acute biliary tract infections (BTIs), specifically acute cholecystitis and acute cholangitis, who were hospitalized at Rize Recep Tayyip Erdoğan University Education and Research Hospital between 2022 and 2023. Inclusion criteria encompassed adults with confirmed acute cholangitis or acute cholecystitis. Patients were excluded if they were younger than 18 years, had acute pancreatitis (biliary or non-biliary), incomplete or missing data, extra-biliary malignancy or if treatment could not be continued at our institution. Patients with active malignancy other than biliary tract malignancy were excluded to reduce heterogeneity and avoid confounding related to cancer-associated inflammation and cachexia, whereas patients with biliary tract malignancy were retained due to its direct association with biliary obstruction and acute cholangitis.\u003c/p\u003e \u003cp\u003eDemographic characteristics (age and sex), diagnostic category, presence of biliary tract malignancy and treatment modality datas were recorded. Admission laboratory data included liver function tests, complete blood count parameters, inflammatory markers and serum albumin levels.\u003c/p\u003e \u003cp\u003eBecause standardized skeletal muscle indices such as the skeletal muscle index (SMI) could not be calculated due to missing height and body weight data, psoas muscle area (PMA) measured on admission CT scans at the L3 level was used as a pragmatic and reproducible surrogate marker of skeletal muscle mass to assess the presence of sarcopenia. PMA was analyzed as a continuous variable and dichotomized using sex-specific cut-off values (\u0026lt;\u0026thinsp;20.3 cm\u0026sup2; for males and \u0026lt;\u0026thinsp;11.8 cm\u0026sup2; for females) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHepatic fibrosis was assessed using the FIB-4 score, calculated as follows:\u003c/p\u003e \u003cp\u003eFIB-4= (Age (years)\u0026times;AST (U/L)) / (Platelet Count(10\u003csup\u003e9\u003c/sup\u003e /L)\u0026times; \u0026radic;ALT (U/L)) with thresholds for no/minimal fibrosis (\u0026lt;\u0026thinsp;1.3), intermediate fibrosis (1.3\u0026ndash;2.67) and advanced fibrosis risk (\u0026gt;\u0026thinsp;2.67)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNutritional and immunological status was evaluated using the Prognostic Nutritional Index (PNI):\u003c/p\u003e \u003cp\u003ePNI\u0026thinsp;=\u0026thinsp;10\u0026times;serum albumin (g/dL)\u0026thinsp;+\u0026thinsp;0.005 \u0026times; total lymphocyte count (/mm\u003csup\u003e3\u003c/sup\u003e) with categories\u0026thinsp;\u0026lt;\u0026thinsp;45, 45\u0026ndash;50 and \u0026gt;\u0026thinsp;50 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).​\u003c/p\u003e \u003cp\u003eSystemic inflammatory ratios included: Platelet/albumin ratio (PAR), CRP/albumin ratio (CAR), CRP/lymphocyte ratio (CLR), Hemoglobin \u0026times; albumin \u0026times; lymphocyte / platelet index (HALP), Platelet/lymphocyte ratio (PLR) and CRP/platelet ratio (CPR)\u003c/p\u003e \u003cp\u003eThe primary outcomes were ICU admission, hospital length of stay (LOS) and 30-day all-cause mortality. Associations of PMA, FIB-4, PNI and systemic inflammatory indices with short-term outcomes were evaluated as secondary analyses. Intervention modalities (percutaneous cholecystostomy, percutaneous transhepatic biliary drainage and cholecystectomy) were recorded and interpreted as descriptive indicators of disease severity and surgical eligibility rather than causal exposures.\u003c/p\u003e \u003cp\u003eContinuous variables were assessed for normality using visual inspection and the Shapiro\u0026ndash;Wilk test and are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median with interquartile range (IQR), as appropriate. Categorical variables are reported as counts and percentages.\u003c/p\u003e \u003cp\u003eComparisons between patients with acute cholecystitis and acute cholangitis were performed using the Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test for continuous variables, according to data distribution, and the chi-square test or Fisher\u0026rsquo;s exact test for categorical variables.\u003c/p\u003e \u003cp\u003eGiven the limited number of outcome events, multivariable models were kept parsimonious to minimize overfitting, with priority given to clinically relevant variables. Logistic regression was used to evaluate predictors of ICU admission and 30-day mortality, while linear regression was applied to identify predictors of length of stay (LOS). Model discrimination was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC).\u003c/p\u003e \u003cp\u003eAll analyses were performed in a diagnosis-stratified manner (acute cholecystitis vs acute cholangitis) to account for clinical heterogeneity. Statistical analyses were performed using IBM SPSS Statistics, version 29.0 (IBM Corp., Armonk, NY, USA), and a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eNinety‑four patients were included: 74 with acute cholecystitis and 20 with acute cholangitis. Marked clinical and laboratory heterogeneity was observed between the two diagnostic groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients with cholangitis were more frequently male and had a markedly higher prevalence of biliary tract malignancy. Thirty‑day mortality was substantially higher in cholangitis (30.0%) than in cholecystitis (4.1%). ICU admission was uncommon in both groups. Percutaneous cholecystostomy was performed in 70.2% of patients, percutaneous transhepatic biliary drainage (PTBD) in 21.3% and cholecystectomy in 8.5%.\u003c/p\u003e\n\u003cp\u003eLaboratory profiles differed between diagnoses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cholangitis was associated with higher transaminases and bilirubin, lower albumin, lower hemoglobin and lymphocyte counts and higher blood urea nitrogen consistent with greater hepatobiliary dysfunction and impaired nutritional\u0026ndash;immune reserve.\u003c/p\u003e\n\u003cp\u003eMuscle mass, nutritional status, and fibrosis-related indices stratified by diagnosis are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Although median PMA values showed overlap between acute cholecystitis and acute cholangitis, categorization using sex-specific cut-off values revealed a significantly higher prevalence of low PMA among patients with acute cholangitis. Nutritional impairment was markedly more pronounced in the cholangitis group, with significantly lower median PNI values and a substantially higher proportion of patients classified as severely malnourished. Similarly, fibrosis-related risk differed between diagnoses, with patients with acute cholangitis demonstrating significantly higher median FIB-4 scores and a greater proportion categorized as having advanced fibrosis risk. Overall, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates a clear clustering of sarcopenia, malnutrition and increased hepatic fibrosis risk among patients with acute cholangitis compared with those with acute cholecystitis.\u003c/p\u003e\n\u003cp\u003eSystemic inflammatory and composite indices stratified by diagnosis are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Among systemic inflammatory ratios, PAR was higher in cholangitis while CAR was higher in cholecystitis. HALP was lower in cholangitis, suggesting combined anemia, inflammation and malnutrition. Other ratios showed no statistically significant between‑group differences.\u003c/p\u003e\n\u003cp\u003eContinuous PMA showed overlap between groups; however, low PMA was more frequent in cholangitis. Violin plots with embedded boxplots show lower PNI and higher FIB‑4 in cholangitis, while continuous PMA overlaps between groups (Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003eIncreasing age was associated with higher FIB-4 scores, while higher FIB-4 values were inversely correlated with PNI. Lower PNI was associated with prolonged LOS. PMA showed significant associations with both PNI and age, highlighting the link between sarcopenia, nutritional status and aging. In addition, PNI was positively correlated with hemoglobin levels. Scatter plots illustrate correlations; regression lines with 95% confidence intervals are shown in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eIn parsimonious multivariable logistic regression, cholangitis diagnosis remained the strongest predictor of 30-day mortality, while age and PMA showed directionally consistent effects but did not reach statistical significance, likely due to the limited number of events. The primary multivariable model demonstrated good discrimination for 30-day mortality (AUC\u0026thinsp;\u0026asymp;\u0026thinsp;0.84). \u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline demographic and clinical characteristics of patients with acute cholecystitis and acute cholangitis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAcute cholecystitis (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAcute cholangitis (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e68.50 [58.00\u0026ndash;79.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e66.00 [60.00\u0026ndash;79.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e41 (55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e5 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e33 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e15 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBiliary tract malignancy, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e5 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e17 (85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eICU admission, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e3 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e30-day mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e3 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLength of stay, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e12.0 [10.0\u0026ndash;14.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e14.0 [10.0\u0026ndash;15.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eValues are presented as median [interquartile range] or number (%), as appropriate. P-values were calculated using the Mann\u0026ndash;Whitney U test for continuous variables and the chi-square or Fisher\u0026rsquo;s exact test for categorical variables. Length of stay was defined as total treatment duration in days. ICU, intensive care unit.\u003c/p\u003e\n\u003cp\u003eLaboratory parameters stratified by diagnosis are summarized in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLaboratory parameters according to diagnosis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAcute cholecystitis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAcute cholangitis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e46.0 [26.0\u0026ndash;88.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e95.0 [46.0\u0026ndash;169.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e34.0 [21.0\u0026ndash;68.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e76.0 [33.0\u0026ndash;122.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal bilirubin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.16 [0.68\u0026ndash;2.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.00 [1.35\u0026ndash;5.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDirect bilirubin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.26 [0.14\u0026ndash;0.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.46 [0.41\u0026ndash;3.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e35.0 [30.3\u0026ndash;39.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e28.7 [23.0\u0026ndash;31.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBlood urea nitrogen (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e43.0 [27.0\u0026ndash;64.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e59.5 [41.0\u0026ndash;82.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.96 [0.74\u0026ndash;1.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.10 [0.82\u0026ndash;1.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWhite blood cell count (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13.4 [9.8\u0026ndash;17.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.8 [11.2\u0026ndash;21.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12.4 [10.9\u0026ndash;13.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11.1 [9.8\u0026ndash;12.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLymphocyte count (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.30 [0.86\u0026ndash;1.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.02 [0.61\u0026ndash;1.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePlatelet count (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e252 [186\u0026ndash;318]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e282 [214\u0026ndash;361]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e171.7 [87.0\u0026ndash;264.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e88.1 [27.8\u0026ndash;177.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eValues are presented as median [interquartile range]. P-values were calculated using the Mann\u0026ndash;Whitney U test. AST, aspartate aminotransferase; ALT, alanine aminotransferase; CRP, C-reactive protein.\u003c/p\u003e\n\u003cp\u003eLaboratory parameters differed significantly between diagnoses. Acute cholangitis was associated with higher transaminase and bilirubin levels, lower albumin and hemoglobin concentrations, and reduced lymphocyte counts, indicating more pronounced hepatobiliary dysfunction and impaired nutritional\u0026ndash;immune reserve compared with acute cholecystitis.\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMuscle mass, nutritional status and fibrosis-related indices according to diagnosis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAcute cholecystitis\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAcute cholangitis\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePMA (cm\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11.94 [9.29\u0026ndash;15.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10.20 [7.98\u0026ndash;14.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow PMA, n (%)\u003c/p\u003e\n \u003cp\u003e(\u0026lt;\u0026thinsp;20.3 cm\u0026sup2; for males and \u0026lt;\u0026thinsp;11.8 cm\u0026sup2; for females).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e53 (71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e19 (95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePrognostic Nutritional Index (PNI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e41.75 [36.86\u0026ndash;46.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e33.92 [29.15\u0026ndash;38.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePNI category, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePNI Severe (\u0026lt;\u0026thinsp;40), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e30 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePNI Moderate (40-44.9), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePNI Mild (45-49.9), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePNI Normal (\u0026ge;\u0026thinsp;50), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFIB-4 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.56 [0.91\u0026ndash;2.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.92 [1.92\u0026ndash;5.39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFIB-4 category, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFIB-4 No/minimal (\u0026lt;\u0026thinsp;1.3), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e32 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFIB-4 Intermediate (1.3\u0026ndash;2.67), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e6 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFIB-4 Advanced (\u0026gt;\u0026thinsp;2.67), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e21 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eValues are presented as median [interquartile range] or number (%), as appropriate. P-values were calculated using the Mann\u0026ndash;Whitney U test for continuous variables and the chi-square or Fisher\u0026rsquo;s exact test for categorical variables.\u0026nbsp;\u003c/p\u003e\n\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSystemic inflammatory and composite indices according to diagnosis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eAcute cholecystitis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAcute cholangitis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePlatelet-to-albumin ratio (PAR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7.32 [5.01\u0026ndash;9.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.41 [6.77\u0026ndash;13.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCRP-to-albumin ratio (CAR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5.21 [2.70\u0026ndash;7.81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3.62 [1.05\u0026ndash;6.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCRP-to-lymphocyte ratio (CLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2038 [984\u0026ndash;2875]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1468 [495\u0026ndash;2550]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHALP index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e23.7 [14.3\u0026ndash;33.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.4 [9.1\u0026ndash;21.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePlatelet-to-lymphocyte ratio (PLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e255 [178\u0026ndash;340]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e289 [201\u0026ndash;415]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCRP-to-platelet ratio (CPR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.79 [0.41\u0026ndash;1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.63 [0.23\u0026ndash;0.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eValues are presented as median [interquartile range]. P-values were calculated using the Mann\u0026ndash;Whitney U test. PAR, platelet-to-albumin ratio; CAR, C-reactive protein-to-albumin ratio; CLR, C-reactive protein-to-lymphocyte ratio; HALP, hemoglobin\u0026ndash;albumin\u0026ndash;lymphocyte\u0026ndash;platelet index; PLR, platelet-to-lymphocyte ratio; CPR, C-reactive protein-to-platelet ratio.\u003c/p\u003e\n\u003cp\u003eFigure 1. Violin plots illustrating the distribution of psoas muscle area (PMA), prognostic nutritional index (PNI), and FIB-4 score according to diagnosis. Panels display PMA (cm\u0026sup2;), PNI, and FIB-4 values in patients with acute cholangitis and acute cholecystitis. Violin plots represent the full distribution of each variable, with embedded boxplots indicating the median and interquartile range. Although continuous PMA values show substantial overlap between diagnostic groups, patients with acute cholangitis demonstrate a higher prevalence of low PMA when categorized using sex-specific cut-off values. Nutritional status, reflected by PNI, is markedly impaired in acute cholangitis, with lower median values compared with acute cholecystitis. Similarly, FIB-4 scores are higher in acute cholangitis, indicating an increased burden of hepatic fibrosis risk. Overall, the figure highlights clustering of sarcopenia-related muscle loss, malnutrition, and hepatic vulnerability in acute cholangitis relative to acute cholecystitis.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Scatter plots demonstrating correlations between frailty-related, nutritional, hepatic, and clinical parameters in patients with acute biliary tract infections. Panels illustrate the relationships between FIB-4 and age, FIB-4 and prognostic nutritional index (PNI), PNI and length of hospital stay (LOS), PNI and psoas muscle area (PMA), PMA and age, and PNI and hemoglobin levels. Solid lines represent linear regression fits with shaded areas indicating 95% confidence intervals. Correlation coefficients (r) and corresponding p-values are displayed for each comparison. Advancing age was positively correlated with higher FIB-4 scores and inversely correlated with PMA, indicating the coexistence of hepatic vulnerability and sarcopenia with aging. Higher FIB-4 values were associated with lower PNI, reflecting an inverse relationship between hepatic dysfunction and nutritional status. Lower PNI was modestly associated with prolonged LOS. PMA showed positive correlation with PNI and negative correlation with age, highlighting the interdependence of muscle mass, nutritional reserve, and aging. In addition, PNI demonstrated a strong positive association with hemoglobin levels, supporting its role as an integrated marker of nutritional and hematological status. Collectively, these correlations illustrate a coherent frailty-related axis linking aging, sarcopenia, impaired nutrition, hepatic vulnerability, and adverse short-term clinical outcomes.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the predicted probability of 30-day mortality according to age and psoas muscle area (PMA), derived from a parsimonious logistic regression model including diagnosis (cholangitis), age and PMA.\u003c/p\u003e\n\u003cp\u003eLow PMA was observed in 76.6% of patients. FIB-4 risk categories were distributed as follows: low risk (\u0026le;\u0026thinsp;1.3) in 37.2%, indeterminate (1.3\u0026ndash;2.67) in 28.7%, and high risk (\u0026gt;\u0026thinsp;2.67) in 34.0%. According to PNI, 48.9% of patients had severe nutritional risk, 23.4% moderate, 17.0% mild, and 10.6% no nutritional risk.\u003c/p\u003e\n\u003cp\u003eOverall, advancing age was associated with a progressive increase in the estimated risk of 30-day mortality. Importantly, this age-related increase was markedly more pronounced among patients with low PMA.\u003c/p\u003e\n\u003cp\u003eAt younger ages, the predicted mortality risk remained low in both groups. However, with increasing age, patients with reduced muscle mass demonstrated a steeper rise in predicted mortality compared with those with preserved PMA. This divergence became particularly evident in older age ranges, indicating that sarcopenia substantially amplifies the adverse prognostic impact of aging in acute biliary tract infections.\u003c/p\u003e\n\u003cp\u003eThese findings suggest a synergistic effect between advanced age and reduced skeletal muscle mass on short-term mortality risk, supporting the role of frailty-related parameters in early risk stratification.\u003c/p\u003e\n\u003cp\u003eFemale patients had significantly higher PNI values compared with males (p = 0.027), indicating better nutritional status. Male patients tended to have higher FIB-4 scores, although this difference did not reach statistical significance (p = 0.070). Hospital length of stay was longer in male patients than in females (p = 0.036), whereas ICU admission and 30-day mortality did not differ significantly by sex.\u003c/p\u003e\n\u003cp\u003eAdvanced age was positively associated with longer hospital stay (p \u0026lt; 0.05). Although older age showed non-significant trends toward higher ICU admission and 30-day mortality, patients aged \u0026ge;65 years had significantly lower PMA and PNI values and higher FIB-4 scores (all p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eComparison between ICU and non-ICU patients revealed that FIB-4 was significantly higher in ICU patients (p = 0.001), while PMA and PNI showed no significant difference (p = 0.589 and p = 0.094, respectively). The ROC analysis for ICU admission prediction incorporating PMA, FIB-4, and PNI achieved an AUC of 0.78, indicating good model discrimination. However, the very small number of ICU cases (n = 5) limits the reliability of statistical analyses.\u003c/p\u003e\n\u003cp\u003eAmong the three main markers, only FIB-4 showed a significant discriminatory ability for predicting 30-day mortality, with an AUC of 0.78. PMA and PNI did not demonstrate meaningful predictive value (AUC \u0026lt; 0.30) in this analysis. FIB-4 values were higher in non-survivors, while PMA and PNI tended to be lower compared to survivors; nonetheless, the limited number of deaths (n = 9) reduced the statistical power.\u003c/p\u003e\n\u003cp\u003eIn univariate logistic regression, PNI showed a borderline association with prolonged length of stay, but in the multivariate model, it emerged as an independent predictor (OR = 0.94, p = 0.045). PMA and FIB-4 were not significant for LOS prediction (AUC \u0026asymp; 0.47 and 0.60, respectively). Low PNI was associated with prolonged hospital stay and may serve as an independent predictor of LOS, whereas PMA and FIB-4 were not reliable markers.\u003c/p\u003e\n\u003cp\u003eSystemic inflammatory ratios (PAR, CAR, CLR, HALP, PLR, CPR) did not provide reliable prediction for hospital stay duration or 30-day mortality in this dataset. Their association with major outcomes was not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort of patients hospitalized with acute biliary tract infections, we assessed the prognostic relevance of CT‑derived PMA, FIB‑4, nutritional status (PNI) and systemic inflammatory indices. The key finding is that acute cholangitis and acute cholecystitis are clinically and biologically distinct entities, and that frailty‑related markers\u0026mdash;particularly low PMA and impaired nutritional reserve\u0026mdash;cluster in the higher‑risk cholangitis group. Cholangitis was associated with markedly higher 30‑day mortality and more frequent low PMA, lower PNI, lower albumin and higher FIB‑4.\u003c/p\u003e\n\u003cp\u003eOne of the key observations of this study is the strong association between advanced age and adverse clinical characteristics, including lower PMA, reduced PNI, higher FIB-4 scores and prolonged hospital stay. Aging is known to be associated with progressive muscle loss, impaired nutritional reserve and reduced physiological resilience, all of which may limit the ability to tolerate acute infection and inflammatory stress (31, 32). Our findings are consistent with previous studies demonstrating the adverse impact of frailty and sarcopenia on outcomes in acute surgical and infectious conditions (10, 17, 33-35).\u003c/p\u003e\n\u003cp\u003eAmong the evaluated parameters, FIB-4 demonstrated the best discriminatory ability for predicting 30-day mortality. Although originally developed as a non-invasive marker of chronic liver fibrosis, FIB-4 may also reflect hepatic vulnerability and systemic stress in the acute setting (15, 16). In our cohort, higher FIB-4 values were observed in non-survivors, suggesting that underlying hepatic dysfunction may amplify the severity of acute biliary infections. However, the limited number of mortality events restricted the statistical power of multivariable analyses and FIB-4 did not retain independent predictive value after adjustment for key clinical variables. Although the FIB-4 score is well established as a non-invasive marker of chronic liver fibrosis, its role in acute biliary tract infections has not been previously defined. We were unable to identify published studies examining the relationship between FIB-4 and either acute cholecystitis or acute cholangitis. Our findings suggest that higher FIB-4 values may reflect hepatic vulnerability and systemic stress in the acute setting, warranting further investigation in larger cohorts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePsoas muscle area was frequently reduced in this cohort, indicating a high burden of sarcopenia. PMA was inversely correlated with age and FIB-4, supporting the concept that muscle depletion, hepatic dysfunction, and aging are interrelated components of frailty (36, 37). Although PMA did not independently predict mortality or ICU admission in multivariable models, directionally consistent associations were observed. These findings suggest that PMA may contribute to risk stratification but may require larger cohorts or standardized muscle indices to demonstrate independent prognostic significance. Our findings extend this limited body of evidence by demonstrating a high prevalence of low psoas muscle area in patients hospitalized with acute biliary tract infections and by highlighting the close relationship between sarcopenia, aging, nutritional impairment and hepatic vulnerability. In this context, our study provides novel exploratory data suggesting that sarcopenia is highly prevalent among patients with acute cholangitis and may reflect reduced physiological reserve in this high-risk population.\u003c/p\u003e\n\u003cp\u003eNutritional status, assessed using the prognostic nutritional index, emerged as a clinically relevant determinant of hospital length of stay. Low PNI was independently associated with prolonged hospitalization, underscoring the role of malnutrition and immune compromise in delayed recovery. Similar associations between poor nutritional status and adverse outcomes have been reported in acute biliary infections and other inflammatory conditions (7, 38, 39). These results support the integration of nutritional assessment into routine clinical evaluation of patients with BTIs.\u003c/p\u003e\n\u003cp\u003eIn contrast, systemic inflammatory ratios, including CAR, PAR, CLR, HALP, PLR and CPR, did not demonstrate independent prognostic value for major outcomes in this dataset. While these markers have shown promise in selected populations and disease-specific contexts their utility in acute biliary tract infections may be limited by disease heterogeneity and the dominant influence of host reserve and baseline vulnerability (21-27). Our findings suggest that isolated inflammatory markers may be insufficient to capture the complex biological determinants of short-term outcomes in BTIs.\u003c/p\u003e\n\u003cp\u003eAlthough several of the presented analyses were stratified by diagnosis, the primary contribution of this study lies not in a direct comparison between acute cholecystitis and acute cholangitis, but in demonstrating that markers of frailty, malnutrition and hepatic vulnerability are highly prevalent across the spectrum of acute biliary tract infections. By simultaneously evaluating CT-derived psoas muscle area, nutritional status (PNI), and fibrosis-related risk (FIB-4), our findings highlight that sarcopenia, impaired nutritional reserve and increased fibrosis risk frequently coexist in both conditions and are closely linked to short-term prognosis. The diagnosis-stratified analyses primarily serve to illustrate clinical heterogeneity, whereas the overarching message is that host-related vulnerability, rather than diagnostic category alone, plays a central role in determining outcomes in acute biliary tract infections. Correlation analyses revealed a coherent axis linking aging, higher FIB‑4, lower PNI, lower PMA, and longer LOS. This pattern supports the concept that frailty‑related parameters integrate hepatic, nutritional, and inflammatory burdens and may add clinically meaningful information beyond single laboratory markers (40, 41).\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. Its retrospective, single-center design limits generalizability. The small number of ICU admissions and mortality events constrained multivariable modeling and may have resulted in imprecise estimates. In addition, missing anthropometric data precluded calculation of standardized skeletal muscle indices, necessitating the use of PMA as a surrogate marker of sarcopenia. Finally, external validation in larger, prospective cohorts is required.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAcute biliary tract infections are clinically heterogeneous and associated with variable short-term outcomes. In this retrospective cohort, host-related vulnerability\u0026mdash;particularly impaired nutritional status and sarcopenia-related muscle loss\u0026mdash;was highly prevalent and closely linked to aging and hepatic dysfunction. Among the evaluated markers, the prognostic nutritional index was associated with prolonged hospital stay, while the FIB-4 score demonstrated moderate discriminatory ability for short-term mortality, suggesting that hepatic vulnerability may contribute to adverse outcomes in the acute setting.\u003c/p\u003e\n\u003cp\u003eBy jointly assessing CT-derived psoas muscle area, nutritional status, and fibrosis-related indices in both acute cholecystitis and acute cholangitis, this study provides novel exploratory insights into an under-investigated area. CT-based PMA assessment and simple laboratory-derived indices such as PNI and FIB-4 are readily available in routine clinical practice and may support early risk stratification. Larger prospective multicenter studies are warranted to validate these findings and to clarify the role of frailty-based assessment in guiding clinical decision-making in acute biliary tract infections.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALT \u0026mdash; Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eAST \u0026mdash; Aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eAUC \u0026mdash; Area under the curve\u003c/p\u003e\n\u003cp\u003eBTI(s) \u0026mdash; Biliary tract infection(s)\u003c/p\u003e\n\u003cp\u003eCAR \u0026mdash; C-reactive protein\u0026ndash;to\u0026ndash;albumin ratio\u003c/p\u003e\n\u003cp\u003eCPR \u0026mdash; C-reactive protein\u0026ndash;to\u0026ndash;platelet ratio\u003c/p\u003e\n\u003cp\u003eCRP \u0026mdash; C-reactive protein\u003c/p\u003e\n\u003cp\u003eCT \u0026mdash; Computed tomography\u003c/p\u003e\n\u003cp\u003eCLR \u0026mdash; C-reactive protein\u0026ndash;to\u0026ndash;lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eFIB-4 \u0026mdash; Fibrosis-4 index\u003c/p\u003e\n\u003cp\u003eHALP \u0026mdash; Hemoglobin\u0026ndash;albumin\u0026ndash;lymphocyte\u0026ndash;platelet index\u003c/p\u003e\n\u003cp\u003eICU \u0026mdash; Intensive care unit\u003c/p\u003e\n\u003cp\u003eIQR \u0026mdash; Interquartile range\u003c/p\u003e\n\u003cp\u003eLOS \u0026mdash; Length of stay\u003c/p\u003e\n\u003cp\u003ePAR \u0026mdash; Platelet\u0026ndash;to\u0026ndash;albumin ratio\u003c/p\u003e\n\u003cp\u003ePMA \u0026mdash; Psoas muscle area\u003c/p\u003e\n\u003cp\u003ePLR \u0026mdash; Platelet\u0026ndash;to\u0026ndash;lymphocyte ratio\u003c/p\u003e\n\u003cp\u003ePNI \u0026mdash; Prognostic nutritional index\u003c/p\u003e\n\u003cp\u003ePTBD \u0026mdash; Percutaneous transhepatic biliary drainage\u003c/p\u003e\n\u003cp\u003eROC \u0026mdash; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSMI \u0026mdash; Skeletal muscle index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval / Consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Recep Tayyip Erdoğan University Faculty of Medicine (approval number: 2024/255). Due to the retrospective design of the study, the requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e:\u0026nbsp;The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u0026nbsp;This study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: ZZK conceptualized and designed the study, supervised data collection, contributed to data interpretation, and was the major contributor in writing and revising the manuscript.\u003c/p\u003e\n\u003cp\u003eCK contributed to study design, data acquisition, and interpretation, and critically revised the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003eAO and TI contributed to data collection, clinical data interpretation, and literature review.\u003c/p\u003e\n\u003cp\u003eMAO assisted with data organization, statistical preparation, and manuscript drafting.\u003c/p\u003e\n\u003cp\u003eFT performed and supervised radiological assessments, including CT-based psoas muscle area measurements, and contributed to data interpretation.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFootnotes\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e : Scientific Presentation\u003c/p\u003e\n\u003cp\u003eThis study was presented as an oral presentation at the 17th Turkish Hepato Pancreato Biliary Surgery Congress 2025, Antalya, T\u0026uuml;rkiye.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKiriyama S, Kozaka K, Takada T, Strasberg SM, Pitt HA, Gabata T, et al. 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Am J Surg. 2019;218(2):393\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"biliary tract infection, sarcopenia, psoas muscle area, prognostic nutritional index, FIB-4, acute cholangitis, 30-day mortality","lastPublishedDoi":"10.21203/rs.3.rs-8419683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8419683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eAcute biliary tract infections (BTIs), including acute cholecystitis and acute cholangitis, show substantial clinical heterogeneity and variable short-term outcomes. Prognostic markers reflecting frailty, nutritional reserve and hepatic dysfunction may improve early risk stratification. In this context, the present study aimed to evaluate the prognostic value of computed tomography (CT)\u0026ndash;derived psoas muscle area (PMA), FIB-4 score, prognostic nutritional index (PNI) and systemic inflammatory indices in predicting intensive care unit (ICU) admission, 30-day mortality and length of hospital stay (LOS) in patients hospitalized with acute BTIs.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 94 adults hospitalized with acute biliary tract infections between 2022 and 2023. Analyses were performed in a diagnosis-stratified manner (acute cholecystitis vs acute cholangitis). Psoas muscle area (PMA) was measured on admission CT at the L3 level, while FIB-4, prognostic nutritional index (PNI) and systemic inflammatory ratios were calculated from admission laboratory data. Outcomes included ICU admission, 30-day all-cause mortality and length of hospital stay. Logistic regression and linear regression analyses were used to identify prognostic factors and receiver operating characteristic (ROC) curves were applied to assess model discrimination.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003ePatients with acute cholangitis exhibited substantially higher 30-day mortality than those with acute cholecystitis (30.0% vs 4.1%), along with lower PNI, higher FIB-4 values, lower albumin and hemoglobin levels and a higher prevalence of low PMA. Age was associated with lower PMA and PNI and higher FIB-4 scores. In parsimonious multivariable logistic regression analysis, a diagnosis of acute cholangitis emerged as the strongest predictor of 30-day mortality. Although age and psoas muscle area (PMA) demonstrated directionally consistent associations with mortality, these did not reach statistical significance, likely owing to the limited number of outcome events. The resulting model showed good discriminatory performance for 30-day mortality (AUC\u0026thinsp;\u0026asymp;\u0026thinsp;0.84).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eFrailty-related parameters, particularly impaired nutritional status and reduced muscle mass, cluster in patients with acute cholangitis and are associated with worse short-term outcomes. CT-based PMA assessment, together with nutritional and hepatic indices, may support early risk stratification in acute biliary tract infections.\u003c/p\u003e","manuscriptTitle":"Frailty-Related and Hepatic Prognostic Markers in Acute Biliary Tract Infections: A Diagnosis-Stratified Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 16:54:50","doi":"10.21203/rs.3.rs-8419683/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"27131188664454915745348376068095559928","date":"2026-04-15T20:33:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T19:25:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220520958884512908725994567729738046290","date":"2026-04-09T15:44:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T20:35:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-10T19:24:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T07:50:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-06T19:45:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Surgery","date":"2026-01-06T19:39:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e261192-5e93-476c-bbf8-7bd01a7f56d3","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:54:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 16:54:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8419683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8419683","identity":"rs-8419683","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}