Neutrophil Percentage-to-Albumin Ratio as an Early Prognostic Marker in ICU Patients with Acute Pancreatitis: A 12-Month Cohort Study

Neutrophil Percentage-to-Albumin Ratio as an Early Prognostic Marker in ICU Patients with Acute Pancreatitis: A 12-Month 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 Article Neutrophil Percentage-to-Albumin Ratio as an Early Prognostic Marker in ICU Patients with Acute Pancreatitis: A 12-Month Cohort Study Tien Manh Huynh, Thanh Chi Phan Nguyen, Phuc-loi Luu, Doan Thi Nha Nguyen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8851627/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: Acute pancreatitis (AP) is a leading cause of ICU admission with high mortality, yet long-term prognostic tools remain limited. This study evaluated the neutrophil percentage-to-albumin ratio (NPAR), a marker of inflammation and nutrition, in predicting short-term (7, 28 days) and long-term (90 days, 1 year) mortality in ICU-admitted AP patients. Methods: A retrospective cohort of 628 ICU-AP patients was extracted from the MIMIC-IV database. NPAR was calculated at admission and categorized into quartiles. All-cause mortality was assessed using Kaplan–Meier analysis, Cox regression, restricted cubic splines, and sensitivity analyses. NPAR was compared to other inflammatory markers and the BISAP score. A decision tree model combining NPAR and BISAP was also developed. Results: Higher admission NPAR was associated with progressively increased mortality across all time points; patients in the highest quartile had 7-day, 28-day, 90-day, and 1-year mortality of 12.1%, 31.2%, 40.1%, and 43.9%, respectively, versus 3.2%, 11.4%, 17.7%, and 25.3% in the lowest quartile. After adjustment for demographics, illness-severity scores, acute kidney injury, and vasopressor use, elevated NPAR remained independently associated with 28-day, 90-day, and 1-year mortality, with adjusted hazard ratios per unit increase ranging from approximately 1.03 to 1.05. Restricted cubic splines suggested a mainly linear association below prespecified NPAR thresholds. NPAR provided discrimination comparable to BISAP for 7-day mortality (AUC 0.72 vs. 0.69) and outperformed most other inflammatory indices. A simple decision-tree using NPAR together with BISAP improved classification of high-risk patients. Conclusions: NPAR is associated with a consistent mortality risk over 12 months in critically ill patients with AP, contributing to early risk stratification and long-term management. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Acute pancreatitis Intensive care unit Neutrophil Percentage to Albumin Ratio Prognosis Mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Acute pancreatitis (AP) is a leading cause of gastrointestinal hospitalization worldwide, with an estimated 2.81 million cases and over 115,000 deaths in 2019. 1 About one-fifth of patients progress to moderate to severe pancreatitis, and one-tenth require intensive care, with a mortality rate of approximately 20–40%. 2 Despite medical advances, no therapies currently modify the disease course. Early management focuses on supportive care, including risk stratification, fluid resuscitation, nutritional support, infection control, and pain or necrosis management. 3 Commonly used prognostic scoring systems—Ranson criteria, APACHE, and the Bedside Index for Severity in Acute Pancreatitis (BISAP)—have limitations, including low positive predictive value and the need for 24–48 hours for accurate assessment, delaying timely intervention. 4 , 5 Moreover, mortality within 90 days post-discharge is nearly equal to in-hospital mortality, with 22.5% of deaths due to pancreatitis-related sepsis, 22.5% from heart failure, and 15% from other sepsis. 6 These tools often overlook long-term prognosis.A simple, early biomarker for both short- and long-term outcomes is needed. Albumin, a negative acute-phase reactant, reflects inflammatory burden and is prognostically relevant in AP. 7 Neutrophil counts and related indices are also strongly associated with AP severity. 8 , 9 The neutrophil percentage-to-albumin ratio (NPAR) combines markers of acute inflammation (neutrophils) and chronic inflammatory status or nutritional compromise (albumin). Elevated NPAR levels typically reflect more severe inflammation. 10 Recently, several studies have reported that NPAR may serve as the prognostic indicator in patients with cardiongenic shock 11 , ischemic stroke 12 , atrial fibrillation 13 , palliative pancreatic cancer 14 , acute kidney injury 15 , myocardial infarction 16 . Given the strong association of AP with inflammation, fluid imbalance, nutritional deficits, and multi-organ dysfunction, we hypothesize that NPAR could serve as a prognostic marker for AP outcomes. However, its specific prognostic relevance in AP remains underexplored. This study aims to assess the prognostic value of NPAR in predicting short-term (7- and 28-day) and long-term (90-day and 1-year) mortality in patients with AP, and to evaluate its combined use with the BISAP score. Material and methods Data source This was a retrospective cohort study using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, version 3.1, which includes de-identified information on over 364,627 unique individuals admissions from 2008 to 2022 at Beth Israel Deaconess Medical Center, USA. 17 , 18 All data were de-identified to protect patient privacy. The study was approved by the institutional review board, with a waiver of informed consent due to its retrospective design. The first author (TMH) completed the Collaborative Institutional Training Initiative (CITI) courses on 'Data or Specimens Only Research' and 'Conflicts of Interest' (ID: 67841419) to access the database. The study complied with the Declaration of Helsinki. Data are available through the MIMIC-IV database via PhysioNet, subject to access approval. This retrospective cohort study followed the STROBE guidelines for observational studies. A completed STROBE checklist is provided in the Supplementary Material. 19 Criteria for participants selection ICU admission data for patients with AP were retrieved using the International Classification of Diseases, 9th Revision (ICD-9) code 577.0, and the 10th Revision (ICD-10) codes K85.0-K85.92. The following patients were excluded from this study: (1) those who were not first-time ICU admissions, (2) those younger than 18 years old, (3) those with ICU stays shorter than 24 h, and (4) those whose NPAR data were not recorded within 24 h of ICU admission. Ultimately, a total of 628 patients were included in the study (see Fig. S1 ). Data extraction All variables were extracted from the MIMIC-IV database using Structured Query Language (SQL) with PostgreSQL. For each ICU admission, NPAR and other laboratory variables were calculated from the worst values recorded within the first 24 hours after ICU admission. Extracted variables included: (1) demographics: sex, age; (2) vital signs: temperature, heart rate, mean blood pressure (MBP), respiratory rate (RR); (3) laboratory parameters: glucose, hemoglobin, hematocrit, white blood cell count, neutrophils, monocytes, lymphocytes, anion gap, bicarbonate, calcium, chloride, creatinine, sodium, potassium, total bilirubin, platelets, prothrombin time (PT), AST, ALT, cholesterol, and triglycerides; (4) severity scores: Acute Physiology Score III (APS III), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), and Charlson Comorbidity Index (CCI); (5) comorbidities: chronic heart failure, liver disease, diabetes, renal disease, sepsis, and acute kidney injury (AKI) within 48 hours; (6) treatments: mechanical ventilation, continuous renal replacement therapy (CRRT), vasopressors, endoscopic retrograde cholangiopancreatography (ERCP), and pancreatic drainage. The worst values of vital signs and laboratory results within the first 24 hours of ICU admission were used. Sepsis was defined as suspected or confirmed infection with an acute increase in SOFA score ≥ 2. 20 ICU admission was defined pragmatically as any recorded ICU stay in MIMIC-IV, no external triage criteria were applied. This reflects real-world clinical practice and aligns with the database structure. 17 Definitions of inflammatory indices and BISAP The NPAR was calculated as neutrophil percentage (%) divided by serum albumin (g/dL). NPAR reflects acute inflammatory response (via neutrophils) and nutritional/inflammatory status (via albumin), providing a combined marker of inflammation and hypoalbuminemia in AP. 22 Other indices for comparison included: systemic immune-inflammation index (SII = [neutrophil × platelet] / lymphocyte), neutrophil-to-lymphocyte ratio (NLR = neutrophil / lymphocyte), platelet-to-lymphocyte ratio (PLR = platelet / lymphocyte), lymphocyte-to-monocyte ratio (LMR = lymphocyte / monocyte), neutrophil-to-platelet ratio (NPR = [neutrophil × 1000] / platelet), and systemic inflammation response index (SIRI = [neutrophil × monocyte] / lymphocyte) 23 . All cell counts were in ×10⁹/L. The BISAP score includes five criteria, each worth one point: BUN > 25 mg/dL, GCS 60, and pleural effusion on imaging. 24 BISAP components were also assessed during this initial 24-hour window, and the score was calculated using the earliest available measurements within this period. Endpoint events The primary outcome was all-cause mortality at 7, 28, and 90 days, and at 1 year. Patients in the MIMIC-IV database were passively followed for up to 1 year after hospital discharge using hospital records and state death registries. Deaths occurring within this period were recorded; those beyond 1 year were censored in accordance with de-identification protocols. Time-to-event was measured from ICU admission to death or censoring. Missing data and covariates selection To mitigate potential bias, variables with more than 20% missing values were excluded 22 , including C-reactive protein, triglycerides, serum lactate dehydrogenase, PaO2, pH, lactate. Given the low rates of missing data (PT 4.87%, total bilirubin 2.3%, calcium 0.9%, body temperature 0.64%, and glucose 0.26%), simple median substitution was performed for the missing values. Confounders were selected for the adjusted regression models based on having a plausible relationship with the dependent variable according to clinical judgment or if they led to a change in the effect estimate greater than 10%. 25 Meanwhile, multicollinearity was assessed among confounders using the variance inflation factor (VIF), with a threshold of 5 to identify potential issues. All included variables had VIF values below the specified threshold, indicating minimal risk of multicollinearity. Statistical analysis Sample size and power: As this was a retrospective cohort study using a fixed dataset, no formal sample size calculation was feasible. Instead, we included all eligible cases to ensure adequate statistical power and estimation precision. The normality of continuous variables was assessed using the Kolmogorov–Smirnov test. Normally distributed continuous variables were reported as mean ± standard deviation (SD), and non-normally distributed variables as median with interquartile range (IQR). Categorical variables were presented as counts and percentages. Baseline characteristics were compared using t-tests or one-way ANOVA for continuous variables and Pearson’s chi-square or Fisher’s exact tests for categorical variables. Patients were stratified into four groups based on quartiles of the NPAR (Q1–Q4). Kaplan–Meier survival curves were plotted to assess survival differences across NPAR quartiles, with statistical significance evaluated using log-rank tests. Restricted cubic spline (RCS) analysis explored non-linear associations between NPAR and all-cause mortality. 26 Univariate Cox regression identified potential risk factors for mortality, with variables having p < 0.1 included in multivariable Cox regression models. Proportional hazards assumptions were verified using Schoenfeld residuals. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated, adjusting for confounders (age, sex, body temperature, hemoglobin, serum creatinine, prothrombin time, APS III, CC, AKI, vasopressors). Subgroup analyses examined interactions between NPAR and baseline characteristics (age, sex, diabetes, liver disease, renal disease, malignant disease) using likelihood ratio tests, with results displayed as forest plots. Sensitivity analyses excluded patients with liver, kidney diseases, malignant disease to assess robustness, as these conditions may affect neutrophil counts and albumin levels. Missing data (< 5% for included variables) were handled using multiple imputation with 10 replications, pooling results from 10 complete datasets. 27 To address unmeasured confounding, E-value analysis quantified the minimum strength of association required for unmeasured confounders to nullify the observed NPAR–mortality relationship 28 . For mortality at 7, 28, and 90 days, and 1 year, ROC analyses were performed for NPAR, SIRI, SII, NPR, NLR, PLR, LMR, and BISAP. Because early deaths were infrequent, SMOTE (Synthetic Minority Oversampling Technique) was applied. 29 AUCs with 95% CIs were estimated by DeLong’s method, pairwise AUC comparisons used bootstrap-DeLong (2,000 resamples) with NPAR as reference. 30 Optimal cut points were chosen by Youden’s J, reporting F1, sensitivity, specificity, and accuracy at those thresholds. To combine NPAR and BISAP, we used QUEST, selecting splits by one-way ANOVA / Student’s t test (continuous) and χ² (categorical) with Bonferroni-adjusted p, binary splits, and stopping at α = 0.05, minimum node size, or max depth. 31 Performance is reported as AUC, F1, sensitivity, specificity, and accuracy. Two-sided p < 0.05 was considered significant. Analyses were performed in Python, using libraries such as SciPy, Statsmodels, lifelines, sklearn, imblearn, and matplotlib/seaborn and R 3.4.3 with custom code adapted from MIT-LCP/mimic-code. Result Baseline demographic and clinical characteristics A total of 628 patients with acute pancreatitis (AP) admitted to the ICU were included from the MIMIC-IV database. The mean age was 57.6 years (SD 17.2), and 56.8% were male (Table 1 ). Patients were stratified into quartiles (Q1–Q4) based on the NPAR. No significant differences in age (p = 0.08) or sex (p = 0.092) were observed across quartiles. Higher NPAR quartiles were associated with elevated heart rate, respiratory rate, and severity scores (SOFA, SAPS II, APS III; all p < 0.001), along with lower mean blood pressure and body temperature (both p < 0.001). Laboratory findings in higher NPAR quartiles included increased white blood cell count, blood urea nitrogen, potassium, creatinine (p = 0.05), and total bilirubin (p = 0.035), and decreased hemoglobin and calcium levels (all p < 0.001). Patients in the highest quartile (Q4) required more intensive interventions, including mechanical ventilation (61.8% vs. 36.1% in Q1; p < 0.001), continuous renal replacement therapy (22.3% vs. 10.1%; p = 0.006), and vasopressors (40.1% vs. 16.5%; p < 0.001). Median pre-ICU length of stay increased from 8.0 days in Q1 to 17.0 days in Q4 (p < 0.001), and ICU stay ranged from 3.0 to 4.0 days (p = 0.002). In-hospital mortality rose from 9.5% (Q1) to 28.7% (Q4), with consistent trends across timepoints: 7-day (3.2% vs. 12.1%), 28-day (11.4% vs. 31.2%), 90-day (17.7% vs. 40.1%), and 1-year mortality (25.3% vs. 43.9%) (all p < 0.001). Table 1 Baseline characteristics of ICU-AP patients by NPAR quartiles Characteritis All (n = 628) NPAR p Q1 Q2 Q3 Q4 Demographic Age (year) 57.6 ± 17.2 55.2 ± 16.9 59.3 ± 17.3 56.6 ± 18.2 59.4 ± 16.3 0.08 Gender (n,%) 0.092 Male 357(56.8) 95 (60.1) 97 (62.2) 77 (49.0) 88 (56.1) Female 271(43.2) 63 (39.9) 59 (37.8) 80 (51.0) 69 (43.9) Race (n,%) < 0.001 White people (n,%) 386(61.5) 91 (57.6) 106 (67.9) 94 (59.9) 95 (60.5) Black people (n,%) 62(9.9) 31 (19.6) 10 (6.4) 9 (5.7) 12 (7.6) Other (n,%) 180(28.7) 36 (22.8) 40 (25.6) 54 (34.4) 50 (31.8) BMI (median, IQR) 28.5 (24.9, 33.4) 28.3 (24.4, 31.8) 27.5 (24.4, 33.4) 28.4 (24.7, 33.4) 29.3 (26.6, 34.1) 0.053 Vital sign Heart rate, beats/minute (mean ± SD) 96.5 ± 18.1 94.1 ± 18.1 93.1 ± 17.2 100.6 ± 17.5 98.3 ± 18.6 < 0.001 MBP, mmHg (mean ± SD) 81.4 ± 13.3 86.9 ± 14.4 80.2 ± 13.5 80.6 ± 11.7 77.8 ± 11.6 < 0.001 RR, breaths/minute (mean ± SD) 21.1 ± 4.3 19.8 ± 4.0 20.9 ± 4.2 22.0 ± 4.5 21.7 ± 4.4 < 0.001 Body temperature, ℃ (mean ± SD) 37.0 ± 0.6 36.9 ± 0.5 37.1 ± 0.7 37.1 ± 0.6 36.8 ± 0.7 < 0.001 SOFA (median, IQR) 6.5(3.0, 10.0) 5.0 (2.0–8.0) 6.0 (3.0–10) 7.0 (4.0–10.0) 8.0 (5–11) < 0.001 CCI (median, IQR) 4.0(2.0, 6.0) 3.0 (1.0–5.0) 4.0 (2.0–6.0) 3.0 (2.0–5.0) 4.0 (3.0–6.0) 0.04 SAPSII (median, IQR) 37.0 (27.0, 50.0) 32.0 (23.0–44.0) 35.0 (27.0–46.0) 38.0 (28.0–50.0) 44.0 (32.0–57.0) < 0.001 APS III score (median, IQR) 53.0 (40.0, 75.0) 45.0 (34.2–59.8) 48.0 (38.0-65.2) 54.0 (42.0–69.0) 73.0 (53.0–90.0) < 0.001 Comorbidites Chronic heart failure(n, %) 125(19.9) 29 (18.4%) 36 (23.1) 31 (19.7) 29 (18.5) 0.699 Liver disease (n, %) 98(15.6) 19 (12.0%) 21 (13.5) 20 (12.7) 38 (24.2) 0.008 Chronic kidney disease(n, %) 114(18.2) 25 (15.8) 37 (23.7) 23 (14.6) 29 (18.5) 0.162 Diabetes (n, %) 32(5.1) 14 (8.9) 4 (2.6) 8 (5.1) 6 (3.8) 0.065 Malignant disease (n, %) 48(7.6) 14 (8.9) 10 (6.4) 8 (5.1) 16 (10.2) 0.314 Sepsis (n, %) 346(55.1) 78 (49.4) 93 (59.6) 85 (54.1) 90 (57.3) 0.289 Laboratory parameter WBC (10 9 /mL) (median, IQR) 14.7(10.5, 21.3) 11.9 (8.4–17.4) 13.0 (10.2–18.9) 16.4 (11.6–21.8) 17.5 (13.1–24.1) < 0.001 Plt (10 9 /mL) (median, IQR) 154.0 (102.0, 221.2) 152.0 (98.0-218.8) 164.0 (108.5-221.8) 143.0 (108.0-226.0) 155.0 (99.0-219.0) 0.522 Hb (g/dL) (mean ± SD) 10.2 ± 2.4 10.8 ± 2.5 10.7 ± 2.2 10.2 ± 2.2 9.3 ± 2.2 < 0.001 Hct (%) (mean ± SD) 36.1 ± 7.6 37.9 ± 8.0 36.4 ± 7.1 35.7 ± 7.4 34.4 ± 7.3 < 0.001 PT (s) (median, IQR) 15.0(13.2, 19.4) 13.5 (12.1–16.0) 14.6 (13.1–19.2) 15.5 (13.8–19.3) 16.7 (14.3–22.3) < 0.001 Creatinine (mg/dL) (median, IQR) 1.4(0.9, 2.7) 1.2 (0.8–2.2) 1.3 (0.9–2.5) 1.3 (0.8–2.5) 1.8 (1.0-3.5) 0.05 BUN (mg/dL) (median, IQR) 20.0(11.0, 34.0) 14.5 (9.0-30.8) 21.0 (13.0–32.0) 20.0 (11.0–32.0) 24.0 (14.0–41.0) < 0.001 Glucose (mg/dL) (mean ± SD) 217.7 ± 243.7 242.9 ± 231.4 202.8 ± 135.8 202.4 ± 173.0 222.6 ± 368.1 0.399 Calcium (mg/dL) (median, IQR) 7.6(6.9, 8.2) 8.0 (7.4–8.6) 7.7 (6.9–8.5) 7.6 (7.0–8.0) 7.1 (6.4–7.6) < 0.001 Potassium (mmol/L) (mean ± SD) 4.7 ± 1.0 4.9 ± 1.2 4.6 ± 0.9 4.6 ± 1.1 4.7 ± 0.9 0.039 Sodium (mmol/L) (mean ± SD) 140.1 ± 6.1 140.4 ± 4.9 139.9 ± 5.2 140.8 ± 7.5 139.3 ± 6.4 0.157 ALP (U/L) (median, IQR) 112.0(75.0, 188.0) 103.5 (73.0-148.0) 106.0 (73.5–173.0) 116.5 (75.5–200.0) 132.5 (85.8-235.8) 0.01 ALT (U/L) (median, IQR) 60.0(27.0, 185.8) 55.5 (28.8–131.0) 81.5 (31.5-234.5) 56.0 (25.8–213.0) 54.5 (25.0-176.0) 0.205 AST (U/L) (median, IQR) 95.0(41.0, 262.0) 80.0 (39.0-188.0) 107.5 (49.8–271.0) 98.5 (37.8-271.8) 92.0 (43.8–313.0) 0.34 Total bilirubin (mg/dL) (median, IQR) 1.4(0.6, 4.0) 1.1 (0.6–2.9) 1.5 (0.6-4.0) 1.4 (0.6-5.0) 1.8 (0.8–4.7) 0.035 Lipase (U/L) (median, IQR) 457.0(139.0, 1164.0) 544.0 (140.5-1101.8) 562.0 (210.8-1467.2) 439.5 (118.8-1178.5) 365.0 (119.0-858.0) 0.071 Amylase (U/L) (median, IQR) 273.0(106.8, 627.0) 326.0 (133.0-998.0) 332.5 (137.0-744.5) 221.0 (102.5-584.5) 188.5 (66.0-417.5) 0.070 Clinical treatments Mechanical ventilation (n, %) 326(51.9) 57 (36.1) 85 (54.5) 87 (55.4) 97 (61.8) < 0.001 CRRT (%) (n, %) 92(14.6) 16 (10.1) 16 (10.3) 25 (15.9) 35 (22.3) 0.006 Vasopressor (n, %) 165(26.3) 26 (16.5%) 36 (23.1) 40 (25.5) 63 (40.1) < 0.001 ERCP 70(11.1) 20 (12.7) 26 (16.7) 13 (8.3) 11 (7.0) 0.027 Outcome LOS ICU (day) (median, IQR) 3.0(2.0, 8.0) 3.0 (1.0–6.0) 3.0 (2.0–8.0) 4.0 (2.0–9.0) 4.0 (2.0–12.0) 0.002 ICU mortality(n, %) 77(12.3) 12 (7.6) 14 (9.0) 18 (11.5) 33 (21.0) 0.001 In-hospital mortality (n, %) 105(16.7) 15 (9.5) 17 (10.9) 28 (17.8) 45 (28.7) < 0.001 7-day mortality (n, %) 39(6.2) 5 (3.2) 6 (3.8) 9 (5.7) 19 (12.1) 0.004 28-day mortality (n, %) 108(17.2) 18 (11.4) 15 (9.6) 26 (16.6) 49 (31.2) < 0.001 90-day mortality (n, %) 156(24.8) 28 (17.7) 28 (17.9) 37 (23.6) 63 (40.1) < 0.001 1-year mortality (n, %) 187(29.8) 40 (25.3) 37 (23.7) 41 (26.1) 69 (43.9) < 0.001 (*)Other included: Asian, Latino,American Indian, Native Hawaiian or Other Pacific Islander. NPAR , Neutrophil Percentage-to-albumin Ratio, SOFA, Sequential Organ Failure Assessment; GCS, Glasgow Coma Scale; MV, mechanical ventilation; CRRT, continuous renal replacement treatment; ERCP, Endoscopic Retrograde Cholangiopancreatography; AKI, acute kidney injury; RF, respiratory failure; HF, heart failure; WBC, white blood cell count; RBC, red blood cell count; Plt, platelet; Hb, hemoglobin; Hct, hematocrit; PT, prothrombin time; Cr, creatinine; BUN, blood urea nitrogen; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; LOS ICU, length of ICU stay; LOS Hospital, length of hospital stay, MBP mean blood pressure, RR respiratory rate, APS III, acute physiology score III; CCI, Charlson comorbidity index. Kaplan–Meier curve and restricted cubic spline Kaplan-Meier survival curves showed significant mortality differences across NPAR quartiles, with the highest mortality in Q4 (log-rank p = 0.016, < 0.001, 0.005, 0.035 for 7, 28, 90 days, and 1 year, respectively) (Fig. 1 ). RCS analysis confirmed non-linear associations between NPAR and mortality, with significant risk increases at NPAR thresholds of 40.3–48.2 (p = 0.043 for 7 days, p < 0.001 for 28 days, p = 0.008 for 90 days, p = 0.038 for 1 year) (Fig. 2 ). Threshold effect analysis revealed a linear association for 7-day mortality (adjusted HR 1.048, 95% CI 1.007–1.090, p = 0.020 for NPAR < 48.07), with no further risk increase beyond 48.07 (p = 0.768). Similar patterns were observed for 28-day (adjusted HR 1.043, 95% CI 1.014–1.074, p = 0.004 for NPAR < 40.32) and 90-day mortality (adjusted HR 1.030, 95% CI 1.008–1.053, p = 0.008 for NPAR < 43.14) (Table S1 ). Association Between NPAR and Mortality: Cox Regression Analysis Univariate Cox regression identified age (HR 1.036–1.040, p < 0.001), mean blood pressure (HR 0.921–0.977, p < 0.001), and NPAR (HR 1.017–1.045, p < 0.05) as significant predictors of all-cause mortality at all time points (Table S2). In multivariable Cox regression, higher NPAR quartiles were consistently associated with increased mortality risk (Table 2 ). At 7 days, patients in Q4 had a crude hazard ratio (HR) of 4.37 (95% CI 1.89–10.09; p < 0.001) compared with Q1. After full adjustment (Model 3: age, sex, body temperature, hemoglobin, creatinine, prothrombin time, APS III, Charlson Comorbidity Index, AKI, and vasopressor use), the association was attenuated and became non-significant (adjusted HR 2.31; 95% CI 0.50–3.40; p = 0.584).At 28 days, the crude HR was 2.76 (95% CI 1.59–4.80; p < 0.001) and remained significant in Model 3 (adjusted HR 1.80; 95% CI 1.02–3.19; p = 0.044). At 90 days, Q4 remained a significant predictor (crude HR 2.07; 95% CI 1.34–3.18; p = 0.001; adjusted HR 1.77; 95% CI 1.10–2.84; p = 0.019), as did 1-year mortality (crude HR 1.65; 95% CI 1.11–2.46; p = 0.015; adjusted HR 1.56; 95% CI 1.01–2.41; p = 0.048). Trend analyses across quartiles were statistically significant in most models (p < 0.05). Table 2 Multivariable Cox regression models of NPAR ratio and all-cause mortality Variable Crude Model 1 Model 2 Model 3 HR (95%CI) p HR (95%CI) p HR (95%CI) p HR (95%CI) p 7 days NPAR 1.05 (1.02–1.07) 0.002 1.04 (1.01–1.07) 0.005 1.03 (1.00-1.06) 0.094 1.01 (0.98–1.04) 0.645 NPAR Q1 1 (Ref) 1 (Ref) 1 (Ref) 1 (Ref) NPAR Q2 1.70 (0.68–4.23) 0.254 1.78 (0.71–4.44) 0.218 2.19 (0.84–5.70) 0.108 1.76 (0.64–4.82) 0.274 NPAR Q3 2.45 (1.01–5.90) 0.047 2.47 (1.02–5.97) 0.044 1.77 (0.68–4.61) 0.241 1.77 (0.69–4.51) 0.234 NPAR Q4 4.37 (1.89–10.09) < 0.001 4.00 (1.73–9.24) 0.001 3.07 (1.20–7.85) 0.019 2.31 (0.50–3.40) 0.584 p for trend < 0.001 < 0.001 0.027 0.048 28 days NPAR 1.02 (1.01–1.04) 0.008 1.03 (1.01–1.04) 0.004 1.02 (1.00-1.03) 0.062 1.01 (0.99–1.03) 0.310 NPAR Q1 1 (Ref) 1 (Ref) 1 (Ref) 1 (Ref) NPAR Q2 1.39 (0.82–2.35) 0.222 1.69 (0.99–2.89) 0.053 1.87 (1.07–3.26) 0.029 1.94 (1.11–3.38) 0.020 NPAR Q3 2.17 (1.31–3.59) 0.003 2.21 (1.33–3.66) 0.002 2.04 (1.20–3.46) 0.008 2.05 (1.20–3.47) 0.008 NPAR Q4 2.76 (1.63–4.69) < 0.001 2.88 (1.69–4.89) < 0.001 2.37 (1.35–4.14) 0.002 1.80 (1.02–3.19) 0.044 p for trend < 0.001 < 0.001 < 0.001 0.013 90 days NPAR 1.02 (1.01–1.04) 0.005 1.02 (1.01–1.04) 0.002 1.02 (1.00-1.03) 0.028 1.01 (1.00-1.03) 0.049 NPAR Q1 1 (Ref) 1 (Ref) 1 (Ref) 1 (Ref) NPAR Q2 1.14 (0.74–1.76) 0.543 1.38 (0.89–2.14) 0.152 1.52 (0.96–2.40) 0.075 1.61 (1.02–2.54) 0.041 NPAR Q3 1.59 (1.04–2.42) 0.032 1.65 (1.08–2.52) 0.021 1.51 (0.96–2.38) 0.074 1.65 (1.04–2.61) 0.033 NPAR Q4 2.07 (1.33–3.22) 0.001 2.20 (1.41–3.43) < 0.001 1.88 (1.18-3.00) 0.008 1.77 (1.10–2.87) 0.019 p for trend < 0.001 < 0.001 0.006 0.008 1- Year NPAR 1.02 (1.00-1.03) 0.035 1.02 (1.00-1.03) 0.010 1.01 (1.00-1.02) 0.094 1.01 (1.00-1.03) 0.052 NPAR Q1 1 (Ref) 1 (Ref) 1 (Ref) 1 (Ref) NPAR Q2 0.92 (0.62–1.37) 0.673 1.10 (0.74–1.65) 0.633 1.17 (0.77–1.78) 0.463 1.30 (0.85–1.98) 0.223 NPAR Q3 1.26 (0.86–1.87) 0.239 1.32 (0.89–1.95) 0.164 1.24 (0.82–1.88) 0.308 1.48 (0.97–2.26) 0.071 NPAR Q4 1.65 (1.10–2.48) 0.015 1.75 (1.16–2.63) 0.007 1.45 (0.95–2.22) 0.086 1.56 (1.00-2.44) 0.048 p for trend 0.016 0.007 0.076 0.023 HR hazard ratio, CI confidence interval, NPAR: Neutrophil Percentage-to-Albumin Ratio, Q , Quartile Crude: non-adjusted; Model 1: adjusted for age, sex; Model 2: adjusted model 1 + body temperature, hemoglobin, serum creatinine, prothrombin time; Model 3: adjusted for model 2 + acute physiology score III, Charlson comorbidity index, vasopressor. Subgroup analyses of NPAR and all-cause mortality To examine whether the association between NPAR and mortality was consistent across subgroups, we conducted stratified and interaction analyses (Fig. 4). At 7 days, a significant positive association was observed in patients with malignant disease (HR = 1.60, p-interaction < 0.001). At 28 days, age (< 60 years), liver disease (HR = 1.37, p-interaction = 0.001), and renal disease (HR = 1.19, p-interaction < 0.001) showed stronger associations. At 90 days, significant interactions were observed for malignant disease (HR = 1.77, p-interaction < 0.001) and renal disease (HR = 1.60, p-interaction < 0.001). At 1-year mortality, the malignant disease subgroup (HR = 1.56, p-interaction < 0.001) and renal disease (HR = 1.48, p-interaction = 0.004) showed the strongest associations. Sensitivity analyses for the NPAR on clinical outcomes in patients with AP Sensitivity analyses, excluding patients with renal disease (n = 114), liver disease (n = 98), or sepsis (n = 225), confirmed the robustness of the association between NPAR and mortality. For 28-day mortality, the adjusted HR per unit increase in NPAR was 1.043 (95% CI 1.014–1.074; p = 0.004), and for 1-year mortality it was 1.018 (95% CI 0.999–1.037; p = 0.049) (Table 5 and Table S1 ). Multiple imputation for missing data (< 5% for included variables) and E-value analysis (E-values 2.97–3.64 for unmeasured confounders) supported the reliability of these findings (Figures S1 and S2). Prognostic utility of inflammatory biomarkers and decision tree modeling Table 3 Prognostic performance of biomarkers for all-cause mortality in acute pancreatitis Index Cut-off AUC (95%CI) F1 Sens Spec Acc p 7 days NPAR 35.2 0.70(0.66–0.72) 0.64 0.69 0.65 0.66 Ref SII 24959.2 0.59(0.56–0.62) 0.61 0.48 0.71 0.54 < 0.001 SIRI 173.05 0.49(0.45–0.52) 0.62 0.75 0.33 0.53 < 0.001 NLR 137.92 0.55(0.51–0.58) 0.6 0.37 0.71 0.53 < 0.001 PLR 1482.04 0.62(0.59–0.65) 0.59 0.37 0.86 0.55 0.004 LMR 46.82 0.46(0.42–0.49) 0.59 0.82 0.2 0.5 < 0.001 NPR 426.56 0.65(0.62–0.68) 0.53 0.41 0.86 0.61 0.088 BISAP 2 0.69(0.66–0.72) 0.64 0.94 0.53 0.61 0.944 28 days NPAR 36.13 0.68(0.65–0.71) 0.65 0.66 0.67 0.66 Ref SII 24405.2 0.50(0.46–0.54) 0.41 0.21 0.87 0.52 < 0.001 SIRI 165.07 0.60(0.56–0.63) 0.45 0.74 0.44 0.56 < 0.001 NLR 133.63 0.57(0.53–0.60) 0.45 0.49 0.64 0.54 < 0.001 PLR 1361.89 0.56(0.53–0.60) 0.57 0.45 0.69 0.52 < 0.001 LMR 45.79 0.47(0.43–0.50) 0.29 0.95 0.08 0.48 < 0.001 NPR 408.56 0.58(0.54–0.62) 0.47 0.47 0.69 0.57 < 0.001 BISAP 2 0.67(0.64–0.70) 0.64 0.82 0.57 0.62 0.599 90 days NPAR 35.01 0.65(0.62–0.69) 0.6 0.65 0.61 0.62 Ref SII 24631.74 0.51(0.47–0.54) 0.41 0.25 0.85 0.52 < 0.001 SIRI 171.66 0.57(0.53–0.60) 0.43 0.39 0.73 0.55 < 0.001 NLR 134.4 0.54(0.50–0.58) 0.42 0.42 0.66 0.52 < 0.001 PLR 1303.8 0.56(0.52–0.60) 0.59 0.46 0.69 0.51 0.001 LMR 47.76 0.48(0.45–0.52) 0.33 0.05 0.97 0.49 < 0.001 NPR 411.5 0.57(0.53–0.61) 0.45 0.46 0.69 0.55 0.002 BISAP 2 0.70(0.67–0.73) 0.67 0.82 0.6 0.65 0.066 1 year NPAR 36.21 0.61(0.57–0.64) 0.55 0.56 0.62 0.58 Ref SII 24947.1 0.52(0.48–0.56) 0.41 0.24 0.85 0.53 < 0.001 SIRI 174.85 0.57(0.53–0.60) 0.43 0.31 0.81 0.55 0.080 NLR 133.63 0.56(0.52–0.60) 0.43 0.42 0.68 0.53 0.027 PLR 1302.13 0.49(0.45–0.53) 0.42 0.34 0.73 0.52 < 0.001 LMR 47.41 0.46(0.43–0.50) 0.33 0.04 0.97 0.48 < 0.001 NPR 432.22 0.52(0.48–0.56) 0.42 0.23 0.86 0.53 0.002 BISAP 2 0.71(0.68–0.74) 0.67 0.81 0.62 0.65 < 0.001 Sens: Sensitivity, Spec: Specficity, AUC-95: Area Under the Curve (95% Confidence Interval), SII: Systemic Immune-Inflammation Index, SIRI: Systemic Inflammation Response Index. BISAP: Bedside Index of Severity in Acute Pancreatitis, NPAR: Neutrophil Percentage-to-Albumin Ratio, NPR: Neutrophil-to-Platelet Ratio, NLR: Neutrophil-to-Lymphocyte Ratio, PLR: Platelet-to-Lymphocyte Ratio, LMR: Lymphocyte-to- Monocyte-Ratio, p-De long test Among single predictors, NPAR emerged as the leading inflammatory biomarker for mortality risk stratification across all horizons, with its strongest discrimination in the early window. At 7 days, NPAR showed an AUC 0.70 (95% CI, 0.66–0.72) with balanced performance (F1 0.64; sensitivity 0.69; specificity 0.65) at a cut-off of 35.2. By DeLong testing, SII, SIRI, NLR, PLR, and LMR were all inferior to NPAR (all p < 0.001), whereas NPR was comparable (AUC 0.65; p = 0.088) and BISAP performed similarly (AUC 0.69; p = 0.944). NPAR maintained the best performance among inflammatory indices at later timepoints, with AUCs of 0.68 (28 days; cut-off 36.13), 0.65 (90 days; 35.01), and 0.61 (1 year; 36.21)—each outperforming SII, NLR, PLR, LMR, and NPR (all p ≤ 0.002, except SIRI at 1 year: p = 0.080). Although BISAP exceeded NPAR beyond the acute window (AUC 0.67 at 28 days, 0.70 at 90 days, 0.71 at 1 year; p = 0.599, 0.066, and < 0.001, respectively), NPAR remained the top inflammatory marker at every horizon. Optimal cutoffs were NPAR ~ 35–36 and BISAP ≥ 2. Combining NPAR with BISAP in a decision-tree (Fig. 5) improved discrimination across all time points: 7 days AUC 0.768 (95% CI 0.699–0.837; F1 0.843; sensitivity 0.667; specificity 0.744; accuracy 0.739); 28 days AUC 0.738 (0.692–0.784; F1 0.667; sensitivity 0.815; specificity 0.520; accuracy 0.571); 90 days AUC 0.743 (0.704–0.783; F1 0.566; sensitivity 0.917; specificity 0.406; accuracy 0.533); 1 year AUC 0.717 (0.680–0.755; F1 0.638; sensitivity 0.850; specificity 0.498; accuracy 0.603); all P < 0.001. These trees therefore enhance sensitivity—especially at 90 days—at the cost of specificity. Discussion This is the first study to demonstrate that NPAR is independently associated with both short- and long-term all-cause mortality in ICU-admitted patients with AP. First, NPAR was independently associated with mortality across all timepoints, showing a stepwise increase in risk across quartiles. Restricted cubic spline and Kaplan–Meier analyses revealed a non-linear relationship, with an inflection point between NPAR values of 40.3 and 48.1. Second, as a single inflammatory biomarker, NPAR consistently outperformed other indices—including NLR, SII, SIRI, NPR, PLR, and LMR—at all timepoints (AUC 0.61–0.72 vs. 0.49–0.65). Third, integration of NPAR with the BISAP score in a decision tree model enhanced discriminative performance over either marker alone, with AUCs ranging from 0.74 to 0.77 for 7- to 90-day mortality, and 0.72 for 1-year mortality. This combined model also yielded higher sensitivity (85–91%) for longer-term outcomes. The development of AP is closely tied to systemic inflammation, sepsis, and organ failure, with prognosis depending on inflammation severity, nutrition, nitrogen balance, and functional reserves. 5 , 32 Neutrophils play a key role in AP by migrating to the pancreas and releasing enzymes, reactive oxygen species, cytokines, and NETs, exacerbating local and systemic damage 33 , 34 . Neutrophil depletion reduces damage and mortality in animal models, supporting their pathogenic role. 9 Clinically, various neutrophil-based indices, such as NLR, SII, SIRI, LMR and NPR have shown promising prognostic potential. 8 , 23 Neutrophil-based indices like NPAR predict severity, organ failure, and mortality in AP, aiding early risk assessment and treatment decisions. 23 Albumin, the most abundant plasma protein, regulates oncotic pressure, circulation, and has anti-inflammatory, antioxidant, and endothelial-stabilizing properties. Hypoalbuminemia, common in acute pancreatitis (AP), is associated with increased disease severity, organ failure, and mortality. Patients with serum albumin < 25 g/L face significantly higher mortality risks. 35 – 37 However, using albumin levels alone for prognosis has limitations, as chronic inflammation or nutritional status also affects albumin levels. 37 While the benefits of albumin supplementation remain unclear, some studies suggest it may improve outcomes in severe hypoalbuminemia, though more research is needed. 35 NPAR is a simple and readily available biomarker that demonstrates strong utility for early risk stratification in ICU patients with acute pancreatitis. Its clinical value lies in two key features: (i) a stable optimal threshold (~ 35–36) consistently observed across timepoints, which facilitates bedside application, and (ii) a favorable early balance between sensitivity and specificity, enabling effective triage in the acute phase where modest over-triage is acceptable. In this context, NPAR emerges as the preferred early inflammatory prognostic index, whereas BISAP provides superior long-term predictive performance and complements NPAR in extended follow-up. The moderate AUC values observed for NPAR likely reflect its threshold-dependent and non-linear behavior, which may be underestimated by conventional ROC analysis. Indeed, restricted cubic spline modeling revealed a non-linear association between NPAR and mortality, with inflection points at approximately 40.3–48.2. Mortality risk increased linearly below this range but plateaued at higher levels, possibly due to survivor bias—where only a select subgroup of critically ill patients persists—or a biological ceiling effect, in which additional inflammatory burden no longer translates into higher risk. Importantly, although NPAR does not account for evolving organ dysfunction or comorbidities, its integration with BISAP in decision-tree models enhanced prognostic accuracy, underscoring the potential of a multimodal approach to mortality prediction in ICU patients with acute pancreatitis. This study has strengths. First, it leverages a large, diverse cohort of ICU-admitted patients with acute pancreatitis, enhancing generalizability and statistical power. Despite its retrospective nature, all eligible cases were included to ensure comprehensive analysis. Advanced statistical approaches, including Kaplan–Meier survival curves, Cox proportional hazards models, and restricted cubic spline regression, were applied to examine mortality risk and detect potential non-linear associations. Sensitivity and subgroup analyses—excluding key comorbidities—confirmed the robustness of findings. Multiple imputation was used to address missing data, and E-value analysis was conducted to assess the impact of unmeasured confounders. Second, the integration of NPAR with the BISAP score using decision tree analysis and SMOTE-based balancing improved prognostic performance, Third, NPAR, a readily available and non-invasive marker, independently associated both short- and long-term mortality, offering practical value for early risk assessment. Lastly, its potential utility in resource-limited ICU settings—where complex scoring systems like APACHE may be impractical—further supports its clinical relevance. However, this study also has limitations. First, its retrospective design precludes causal inference between NPAR and mortality. Second, missing data on key covariates—including comorbidities and pancreatitis etiology may have introduced residual bias, despite multiple imputation. Third, the single-center, U.S.-based cohort raises concerns of external validity, particularly given the racial distribution (61.5% White) and potential practice-pattern differences in non-Western populations. Fourth, long-term follow-up was limited to 1 year and dependent on registry data; censoring was not systematically addressed, which may have influenced survival estimates. Fifth, unmeasured variability in management strategies, such as albumin administration or early intervention timing, could have confounded the observed associations. Sixth, the absence of serial NPAR measurements restricted evaluation of its temporal dynamics. Finally, as the cohort was limited to ICU-admitted patients, findings may not be generalizable to those with mild or moderate acute pancreatitis. Moreover, cause-specific mortality data were unavailable, preventing differentiation between AP-related and non–AP-related deaths. Prospective, multi-center validation with detailed etiologic and longitudinal follow-up data will be needed to confirm these findings. In conclusion , NPAR showed a consistent and clinically relevant association with both short- and long-term mortality among ICU-admitted patients with acute pancreatitis. When combined with BISAP in a decision-tree model, its prognostic performance improved, suggesting potential value as a complementary tool for early clinical decision-making. Abbreviations AKI, Acute Kidney Injury; ALT, Alanine Aminotransferase; AP, Acute Pancreatitis; APACHE, Acute Physiology and Chronic Health Evaluation; APS III, Acute Physiology Score III; AST, Aspartate Aminotransferase; AUC, Area Under the Curve; BISAP, Bedside Index for Severity in Acute Pancreatitis; CCI, Charlson Comorbidity Index; CI, Confidence Interval; CRRT, Continuous Renal Replacement Therapy; HR, Hazard Ratio; ICU, Intensive Care Unit; KM, Kaplan–Meier; LMR, Lymphocyte-to-Monocyte Ratio; MBP, Mean Blood Pressure; MIMIC-IV, Medical Information Mart for Intensive Care IV; NPAR, Neutrophil Percentage-to-Albumin Ratio; NLR, Neutrophil-to-Lymphocyte Ratio; NPR, Neutrophil-to-Platelet Ratio; PLR, Platelet-to-Lymphocyte Ratio; PT, Prothrombin Time; RCS, Restricted Cubic Spline; SAPS II, Simplified Acute Physiology Score II; SD, Standard Deviation; SII, Systemic Immune-Inflammation Index; SIRI, Systemic Inflammatory Response Index; SMOTE, Synthetic Minority Over-sampling Technique; SOFA, Sequential Organ Failure Assessment; WBC, White Blood Cell Count. Declarations Ethics approval and consent to participate This retrospective cohort study used de-identified data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. All data in MIMIC-IV are fully de-identified and publicly available; therefore, individual informed consent was waived. Given the use of anonymized, pre-existing data, our institutional IRB (School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City) waived additional review and informed consent. Access to the MIMIC-IV database was granted to the first author (Tien Manh Huynh) after completion of the required Collaborative Institutional Training Initiative (CITI) training (Record ID: 67841419). Consent for publication Not applicable. Availability of data and materials The datasets analyzed during the current study are available in the MIMIC-IV database via PhysioNet (https://mimic-iv.mit.edu), subject to credentialed access approval. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions Conceptualization and supervision: Tien Manh Huynh, Duc Trong Quach, and Phuc-Loi Luu. Methodology: Tien Manh Huynh, Thanh Chi Phan Nguyen. Data analysis: Tien Manh Huynh, Thanh Chi Phan Nguyen, Phuc-Loi Luu, and Duc Trong Quach. Data curation and writing – original draft preparation: Tien Manh Huynh, Thanh Chi Phan Nguyen, and Duc Trong Quach. Writing – review and editing: Tien Manh Huynh, Duc Trong Quach, Phuc-Loi Luu, and Doan Thi Nha Nguyen. Project administration: Duc Trong Quach and Phuc-Loi Luu. All authors read and approved the final manuscript. Acknowledgements We would like to thank the PhysioNet platform and the Massachusetts Institute of Technology Laboratory for Computational Physiology for providing and maintaining the MIMIC-IV database, as well as Beth Israel Deaconess Medical Center for contributing the original clinical data. AI use statement During the preparation of this manuscript, Grammarly was used solely for spelling, grammar, and style correction. The authors reviewed and take full responsibility for the content. Authors’ information Tien Manh Huynh, MD, is a gastroenterologist and clinical researcher at the Department of Internal Medicine, School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam. 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Serum albumin in health and disease: From comparative biochemistry to translational medicine p. 13725 (MDPI, 2023). Ocskay, K. et al. Hypoalbuminemia affects one third of acute pancreatitis patients and is independently associated with severity and mortality. Scientific Reports . /12/17 2021;11(1):24158. (2021). 10.1038/s41598-021-03449-8 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8851627","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":630557197,"identity":"c73faa2b-f557-4099-a9c1-a7e6bc271a80","order_by":0,"name":"Tien Manh Huynh","email":"","orcid":"","institution":"University of Medicine and Pharmacy at Ho Chi Minh City","correspondingAuthor":false,"prefix":"","firstName":"Tien","middleName":"Manh","lastName":"Huynh","suffix":""},{"id":630557198,"identity":"6ebffda3-690e-4a77-b8d7-e84c17d6ab61","order_by":1,"name":"Thanh Chi Phan Nguyen","email":"","orcid":"","institution":"Ho Chi Minh City University of Science","correspondingAuthor":false,"prefix":"","firstName":"Thanh","middleName":"Chi Phan","lastName":"Nguyen","suffix":""},{"id":630557199,"identity":"e976d099-1dc5-419d-94ee-bf1a89149c8a","order_by":2,"name":"Phuc-loi Luu","email":"","orcid":"","institution":"Thong Nhat Hospital","correspondingAuthor":false,"prefix":"","firstName":"Phuc-loi","middleName":"","lastName":"Luu","suffix":""},{"id":630557200,"identity":"cb005f57-d6b9-4ca7-b091-4def0c1cf288","order_by":3,"name":"Doan Thi Nha Nguyen","email":"","orcid":"","institution":"University of Medicine and Pharmacy at Ho Chi Minh City","correspondingAuthor":false,"prefix":"","firstName":"Doan","middleName":"Thi Nha","lastName":"Nguyen","suffix":""},{"id":630557201,"identity":"23b76c02-e4ac-45a0-a3f7-73e58f1fa01b","order_by":4,"name":"Duc Trong Quach","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDCCAzDyeAOYxdhArBYJhjMHSNZyI4FILXy3D2+T+FFzp47v5hvTzQUMNrIbDvCYPcCnRfJcWplkz7FnEpK3c8xuz2BIMwZqMTfAp8XgDI+ZBG/DYQkDkBYehsOJGw6wpUkQ0iL5F6Tl5hmQlv/EaZEG23KDB6TlAFAL8zG8WiTPsBVbyxw7LDnzTFrZ7RkGycYzDxPQwneGeePNNzWH+fmOH952u6DCTrbveGMbXi0gt8FZzGA2MwH1aFpGwSgYBaNgFGABABMBUDmlki3IAAAAAElFTkSuQmCC","orcid":"","institution":"University of Medicine and Pharmacy at Ho Chi Minh City","correspondingAuthor":true,"prefix":"","firstName":"Duc","middleName":"Trong","lastName":"Quach","suffix":""}],"badges":[],"createdAt":"2026-02-11 12:23:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8851627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8851627/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108492702,"identity":"a04a7e86-5cf2-4c44-bc18-6bdab54e0ee9","added_by":"auto","created_at":"2026-05-05 09:58:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier Survival Curves for Mortality by NPAR Quartiles. \u003c/strong\u003eNPAR\u003cstrong\u003e, \u003c/strong\u003eneutrophil percentage-to-albumin ratio\u003c/p\u003e","description":"","filename":"floatimage120.png","url":"https://assets-eu.researchsquare.com/files/rs-8851627/v1/bcac7f52ca68c1d27cf4c1e9.png"},{"id":108383789,"identity":"eef17d6a-905e-484d-81bb-29d63f78e008","added_by":"auto","created_at":"2026-05-04 05:48:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":704957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between NPAR and all-cause mortality at 7, 28, 90 days, and 1 year. (A) 7-day, 28-day, and 90-day mortality. (B) 1-year mortality. \u003c/strong\u003eNPAR,\u003cstrong\u003e \u003c/strong\u003eneutrophil percentage-to-albumin ratio\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8851627/v1/92fbcb4141d975614431829d.png"},{"id":108493804,"identity":"f75bd40b-9229-4122-a819-a8e6ee524ee7","added_by":"auto","created_at":"2026-05-05 10:01:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":918848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of the relationship between NPAR and 7-day, 28-day, 90-dayand 1-year (B) mortality\u003c/strong\u003e. \u003cem\u003eHR hazard ratio, MV mechanical ventilation, CRRT continuous renal replacement therapy. Red diamonds indicate the overall HR values for both unadjusted and fully adjusted models. Green squares denote HR values for subgroups, while blue lines represent the 95% confidence intervals.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8851627/v1/215d3634eb934a8315266be7.png"},{"id":108492376,"identity":"6492dcce-27f7-4ac5-a32b-86180650e8e8","added_by":"auto","created_at":"2026-05-05 09:57:37","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144239,"visible":true,"origin":"","legend":"\u003cp\u003eDecision trees combining NPAR and BISAP to mortality prognosis at 7 days, 28 days, 90 days, and 1 year. \u003cem\u003eNPAR, neutrophil-to-albumin ratio; BISAP, bedside index for severity in acute pancreatitis.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8851627/v1/9cf43950cb5e3a2c59ed86ce.jpeg"},{"id":108804627,"identity":"1b1b69b0-2413-4518-a9e4-f062a2187a6e","added_by":"auto","created_at":"2026-05-08 15:22:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2720251,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8851627/v1/58ce5779-7da5-406d-a77a-bf68e7806302.pdf"},{"id":108383788,"identity":"be6371dc-acfd-4b79-84ea-26611103bc09","added_by":"auto","created_at":"2026-05-04 05:48:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":369223,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYFILES.docx","url":"https://assets-eu.researchsquare.com/files/rs-8851627/v1/66fb083e1f2c88a36e600a8f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neutrophil Percentage-to-Albumin Ratio as an Early Prognostic Marker in ICU Patients with Acute Pancreatitis: A 12-Month Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute pancreatitis (AP) is a leading cause of gastrointestinal hospitalization worldwide, with an estimated 2.81\u0026nbsp;million cases and over 115,000 deaths in 2019.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e About one-fifth of patients progress to moderate to severe pancreatitis, and one-tenth require intensive care, with a mortality rate of approximately 20\u0026ndash;40%.\u003csup\u003e2\u003c/sup\u003e Despite medical advances, no therapies currently modify the disease course. Early management focuses on supportive care, including risk stratification, fluid resuscitation, nutritional support, infection control, and pain or necrosis management.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Commonly used prognostic scoring systems\u0026mdash;Ranson criteria, APACHE, and the Bedside Index for Severity in Acute Pancreatitis (BISAP)\u0026mdash;have limitations, including low positive predictive value and the need for 24\u0026ndash;48 hours for accurate assessment, delaying timely intervention.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Moreover, mortality within 90 days post-discharge is nearly equal to in-hospital mortality, with 22.5% of deaths due to pancreatitis-related sepsis, 22.5% from heart failure, and 15% from other sepsis.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e These tools often overlook long-term prognosis.A simple, early biomarker for both short- and long-term outcomes is needed. Albumin, a negative acute-phase reactant, reflects inflammatory burden and is prognostically relevant in AP.\u003csup\u003e7\u003c/sup\u003e Neutrophil counts and related indices are also strongly associated with AP severity.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The neutrophil percentage-to-albumin ratio (NPAR) combines markers of acute inflammation (neutrophils) and chronic inflammatory status or nutritional compromise (albumin). Elevated NPAR levels typically reflect more severe inflammation.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Recently, several studies have reported that NPAR may serve as the prognostic indicator in patients with cardiongenic shock\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, ischemic stroke\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, atrial fibrillation\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, palliative pancreatic cancer\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, acute kidney injury\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, myocardial infarction\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Given the strong association of AP with inflammation, fluid imbalance, nutritional deficits, and multi-organ dysfunction, we hypothesize that NPAR could serve as a prognostic marker for AP outcomes. However, its specific prognostic relevance in AP remains underexplored. This study aims to assess the prognostic value of NPAR in predicting short-term (7- and 28-day) and long-term (90-day and 1-year) mortality in patients with AP, and to evaluate its combined use with the BISAP score.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThis was a retrospective cohort study using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, version 3.1, which includes de-identified information on over 364,627 unique individuals admissions from 2008 to 2022 at Beth Israel Deaconess Medical Center, USA.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e All data were de-identified to protect patient privacy. The study was approved by the institutional review board, with a waiver of informed consent due to its retrospective design. The first author (TMH) completed the Collaborative Institutional Training Initiative (CITI) courses on 'Data or Specimens Only Research' and 'Conflicts of Interest' (ID: 67841419) to access the database. The study complied with the Declaration of Helsinki. Data are available through the MIMIC-IV database via PhysioNet, subject to access approval. This retrospective cohort study followed the STROBE guidelines for observational studies. A completed STROBE checklist is provided in the Supplementary Material.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCriteria for participants selection\u003c/h3\u003e\n\u003cp\u003eICU admission data for patients with AP were retrieved using the International Classification of Diseases, 9th Revision (ICD-9) code 577.0, and the 10th Revision (ICD-10) codes K85.0-K85.92. The following patients were excluded from this study: (1) those who were not first-time ICU admissions, (2) those younger than 18 years old, (3) those with ICU stays shorter than 24 h, and (4) those whose NPAR data were not recorded within 24 h of ICU admission. Ultimately, a total of 628 patients were included in the study (see Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eAll variables were extracted from the MIMIC-IV database using Structured Query Language (SQL) with PostgreSQL. For each ICU admission, NPAR and other laboratory variables were calculated from the worst values recorded within the first 24 hours after ICU admission. Extracted variables included: (1) demographics: sex, age; (2) vital signs: temperature, heart rate, mean blood pressure (MBP), respiratory rate (RR); (3) laboratory parameters: glucose, hemoglobin, hematocrit, white blood cell count, neutrophils, monocytes, lymphocytes, anion gap, bicarbonate, calcium, chloride, creatinine, sodium, potassium, total bilirubin, platelets, prothrombin time (PT), AST, ALT, cholesterol, and triglycerides; (4) severity scores: Acute Physiology Score III (APS III), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), and Charlson Comorbidity Index (CCI); (5) comorbidities: chronic heart failure, liver disease, diabetes, renal disease, sepsis, and acute kidney injury (AKI) within 48 hours; (6) treatments: mechanical ventilation, continuous renal replacement therapy (CRRT), vasopressors, endoscopic retrograde cholangiopancreatography (ERCP), and pancreatic drainage. The worst values of vital signs and laboratory results within the first 24 hours of ICU admission were used. Sepsis was defined as suspected or confirmed infection with an acute increase in SOFA score\u0026thinsp;\u0026ge;\u0026thinsp;2.\u003csup\u003e20\u003c/sup\u003e ICU admission was defined pragmatically as any recorded ICU stay in MIMIC-IV, no external triage criteria were applied. This reflects real-world clinical practice and aligns with the database structure.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eDefinitions of inflammatory indices and BISAP\u003c/h3\u003e\n\u003cp\u003eThe NPAR was calculated as neutrophil percentage (%) divided by serum albumin (g/dL). NPAR reflects acute inflammatory response (via neutrophils) and nutritional/inflammatory status (via albumin), providing a combined marker of inflammation and hypoalbuminemia in AP.\u003csup\u003e22\u003c/sup\u003e Other indices for comparison included: systemic immune-inflammation index (SII = [neutrophil \u0026times; platelet] / lymphocyte), neutrophil-to-lymphocyte ratio (NLR\u0026thinsp;=\u0026thinsp;neutrophil / lymphocyte), platelet-to-lymphocyte ratio (PLR\u0026thinsp;=\u0026thinsp;platelet / lymphocyte), lymphocyte-to-monocyte ratio (LMR\u0026thinsp;=\u0026thinsp;lymphocyte / monocyte), neutrophil-to-platelet ratio (NPR = [neutrophil \u0026times; 1000] / platelet), and systemic inflammation response index (SIRI = [neutrophil \u0026times; monocyte] / lymphocyte)\u003csup\u003e23\u003c/sup\u003e. All cell counts were in \u0026times;10⁹/L.\u003c/p\u003e \u003cp\u003eThe BISAP score includes five criteria, each worth one point: BUN\u0026thinsp;\u0026gt;\u0026thinsp;25 mg/dL, GCS\u0026thinsp;\u0026lt;\u0026thinsp;15, SIRS\u0026thinsp;\u0026ge;\u0026thinsp;2, age\u0026thinsp;\u0026gt;\u0026thinsp;60, and pleural effusion on imaging. \u003csup\u003e24\u003c/sup\u003e BISAP components were also assessed during this initial 24-hour window, and the score was calculated using the earliest available measurements within this period.\u003c/p\u003e\n\u003ch3\u003eEndpoint events\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was all-cause mortality at 7, 28, and 90 days, and at 1 year. Patients in the MIMIC-IV database were passively followed for up to 1 year after hospital discharge using hospital records and state death registries. Deaths occurring within this period were recorded; those beyond 1 year were censored in accordance with de-identification protocols. Time-to-event was measured from ICU admission to death or censoring.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMissing data and covariates selection\u003c/h2\u003e \u003cp\u003eTo mitigate potential bias, variables with more than 20% missing values were excluded \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, including C-reactive protein, triglycerides, serum lactate dehydrogenase, PaO2, pH, lactate. Given the low rates of missing data (PT 4.87%, total bilirubin 2.3%, calcium 0.9%, body temperature 0.64%, and glucose 0.26%), simple median substitution was performed for the missing values. Confounders were selected for the adjusted regression models based on having a plausible relationship with the dependent variable according to clinical judgment or if they led to a change in the effect estimate greater than 10%. \u003csup\u003e25\u003c/sup\u003e Meanwhile, multicollinearity was assessed among confounders using the variance inflation factor (VIF), with a threshold of 5 to identify potential issues. All included variables had VIF values below the specified threshold, indicating minimal risk of multicollinearity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSample size and power: As this was a retrospective cohort study using a fixed dataset, no formal sample size calculation was feasible. Instead, we included all eligible cases to ensure adequate statistical power and estimation precision.\u003c/p\u003e \u003cp\u003eThe normality of continuous variables was assessed using the Kolmogorov\u0026ndash;Smirnov test. Normally distributed continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and non-normally distributed variables as median with interquartile range (IQR). Categorical variables were presented as counts and percentages. Baseline characteristics were compared using t-tests or one-way ANOVA for continuous variables and Pearson\u0026rsquo;s chi-square or Fisher\u0026rsquo;s exact tests for categorical variables. Patients were stratified into four groups based on quartiles of the NPAR (Q1\u0026ndash;Q4).\u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival curves were plotted to assess survival differences across NPAR quartiles, with statistical significance evaluated using log-rank tests. Restricted cubic spline (RCS) analysis explored non-linear associations between NPAR and all-cause mortality.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Univariate Cox regression identified potential risk factors for mortality, with variables having p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 included in multivariable Cox regression models. Proportional hazards assumptions were verified using Schoenfeld residuals. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated, adjusting for confounders (age, sex, body temperature, hemoglobin, serum creatinine, prothrombin time, APS III, CC, AKI, vasopressors). Subgroup analyses examined interactions between NPAR and baseline characteristics (age, sex, diabetes, liver disease, renal disease, malignant disease) using likelihood ratio tests, with results displayed as forest plots. Sensitivity analyses excluded patients with liver, kidney diseases, malignant disease to assess robustness, as these conditions may affect neutrophil counts and albumin levels. Missing data (\u0026lt;\u0026thinsp;5% for included variables) were handled using multiple imputation with 10 replications, pooling results from 10 complete datasets.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e To address unmeasured confounding, E-value analysis quantified the minimum strength of association required for unmeasured confounders to nullify the observed NPAR\u0026ndash;mortality relationship\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor mortality at 7, 28, and 90 days, and 1 year, ROC analyses were performed for NPAR, SIRI, SII, NPR, NLR, PLR, LMR, and BISAP. Because early deaths were infrequent, SMOTE (Synthetic Minority Oversampling Technique) was applied.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e AUCs with 95% CIs were estimated by DeLong\u0026rsquo;s method, pairwise AUC comparisons used bootstrap-DeLong (2,000 resamples) with NPAR as reference.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Optimal cut points were chosen by Youden\u0026rsquo;s J, reporting F1, sensitivity, specificity, and accuracy at those thresholds. To combine NPAR and BISAP, we used QUEST, selecting splits by one-way ANOVA / Student\u0026rsquo;s t test (continuous) and χ\u0026sup2; (categorical) with Bonferroni-adjusted p, binary splits, and stopping at α\u0026thinsp;=\u0026thinsp;0.05, minimum node size, or max depth.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Performance is reported as AUC, F1, sensitivity, specificity, and accuracy. Two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Analyses were performed in Python, using libraries such as SciPy, Statsmodels, lifelines, sklearn, imblearn, and matplotlib/seaborn and R 3.4.3 with custom code adapted from MIT-LCP/mimic-code.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline demographic and clinical characteristics\u003c/h2\u003e \u003cp\u003eA total of 628 patients with acute pancreatitis (AP) admitted to the ICU were included from the MIMIC-IV database. The mean age was 57.6 years (SD 17.2), and 56.8% were male (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients were stratified into quartiles (Q1\u0026ndash;Q4) based on the NPAR. No significant differences in age (p\u0026thinsp;=\u0026thinsp;0.08) or sex (p\u0026thinsp;=\u0026thinsp;0.092) were observed across quartiles. Higher NPAR quartiles were associated with elevated heart rate, respiratory rate, and severity scores (SOFA, SAPS II, APS III; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), along with lower mean blood pressure and body temperature (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eLaboratory findings in higher NPAR quartiles included increased white blood cell count, blood urea nitrogen, potassium, creatinine (p\u0026thinsp;=\u0026thinsp;0.05), and total bilirubin (p\u0026thinsp;=\u0026thinsp;0.035), and decreased hemoglobin and calcium levels (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients in the highest quartile (Q4) required more intensive interventions, including mechanical ventilation (61.8% vs. 36.1% in Q1; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), continuous renal replacement therapy (22.3% vs. 10.1%; p\u0026thinsp;=\u0026thinsp;0.006), and vasopressors (40.1% vs. 16.5%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eMedian pre-ICU length of stay increased from 8.0 days in Q1 to 17.0 days in Q4 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ICU stay ranged from 3.0 to 4.0 days (p\u0026thinsp;=\u0026thinsp;0.002). In-hospital mortality rose from 9.5% (Q1) to 28.7% (Q4), with consistent trends across timepoints: 7-day (3.2% vs. 12.1%), 28-day (11.4% vs. 31.2%), 90-day (17.7% vs. 40.1%), and 1-year mortality (25.3% vs. 43.9%) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of ICU-AP patients by NPAR quartiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteritis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;628)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eNPAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e357(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite people (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e386(61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlack people (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62(9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5 (24.9, 33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003cp\u003e(24.4, 31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003cp\u003e(24.4, 33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.4 (24.7, 33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.3 (26.6, 34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVital sign\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart rate, beats/minute (mean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.1\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMBP, mmHg (mean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRR, breaths/minute\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody temperature, ℃\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFA (median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5(3.0, 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (2.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.0 (4.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.0 (5\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI (median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0(2.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (1.0\u0026ndash;5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (2.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0 (3.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSAPSII (median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003cp\u003e(27.0, 50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003cp\u003e(23.0\u0026ndash;44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003cp\u003e(27.0\u0026ndash;46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003cp\u003e(28.0\u0026ndash;50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003cp\u003e(32.0\u0026ndash;57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPS III score\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003cp\u003e(40.0, 75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003cp\u003e(34.2\u0026ndash;59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.0\u003c/p\u003e \u003cp\u003e(38.0-65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003cp\u003e(42.0\u0026ndash;69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.0\u003c/p\u003e \u003cp\u003e(53.0\u0026ndash;90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidites\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic heart failure(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver disease (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic kidney disease(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMalignant disease\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSepsis\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory parameter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/mL)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7(10.5, 21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9 (8.4\u0026ndash;17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.0 (10.2\u0026ndash;18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.4 (11.6\u0026ndash;21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.5 (13.1\u0026ndash;24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlt (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sup\u003e \u003cb\u003e/mL)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154.0\u003c/p\u003e \u003cp\u003e(102.0, 221.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152.0 (98.0-218.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164.0 (108.5-221.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143.0 (108.0-226.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e155.0 (99.0-219.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHb (g/dL) (mean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHct (%) (mean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePT (s)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0(13.2, 19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5 (12.1\u0026ndash;16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.6 (13.1\u0026ndash;19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.5 (13.8\u0026ndash;19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.7 (14.3\u0026ndash;22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4(0.9, 2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.8\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3 (0.9\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3 (0.8\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8 (1.0-3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBUN (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.0(11.0, 34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5 (9.0-30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.0 (13.0\u0026ndash;32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.0 (11.0\u0026ndash;32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.0 (14.0\u0026ndash;41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose (mg/dL) (mean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217.7\u0026thinsp;\u0026plusmn;\u0026thinsp;243.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242.9\u0026thinsp;\u0026plusmn;\u0026thinsp;231.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202.8\u0026thinsp;\u0026plusmn;\u0026thinsp;135.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202.4\u0026thinsp;\u0026plusmn;\u0026thinsp;173.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e222.6\u0026thinsp;\u0026plusmn;\u0026thinsp;368.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalcium (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6(6.9, 8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (7.4\u0026ndash;8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7 (6.9\u0026ndash;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.6 (7.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.1 (6.4\u0026ndash;7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePotassium (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSodium (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mean\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALP (U/L)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.0(75.0, 188.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.5 (73.0-148.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.0 (73.5\u0026ndash;173.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.5 (75.5\u0026ndash;200.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e132.5 (85.8-235.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT (U/L)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.0(27.0, 185.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.5 (28.8\u0026ndash;131.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.5 (31.5-234.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.0 (25.8\u0026ndash;213.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.5 (25.0-176.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST (U/L)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.0(41.0, 262.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0 (39.0-188.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107.5 (49.8\u0026ndash;271.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.5 (37.8-271.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.0 (43.8\u0026ndash;313.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal bilirubin\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(mg/dL)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4(0.6, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.6\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (0.6-4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4 (0.6-5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003cp\u003e(0.8\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipase (U/L)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e457.0(139.0, 1164.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e544.0 (140.5-1101.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e562.0 (210.8-1467.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e439.5 (118.8-1178.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e365.0 (119.0-858.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmylase (U/L)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e273.0(106.8, 627.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326.0 (133.0-998.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332.5 (137.0-744.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e221.0 (102.5-584.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e188.5 (66.0-417.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical treatments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMechanical ventilation\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326(51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97 (61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRRT (%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVasopressor\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eERCP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLOS ICU (day)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0(2.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (1.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0 (2.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0 (2.0\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU mortality(n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIn-hospital mortality (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7-day mortality (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e28-day mortality (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108(17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e90-day mortality (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156(24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1-year mortality (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e(*)Other included: Asian, Latino,American Indian, Native Hawaiian or Other Pacific Islander. NPAR\u003c/em\u003e, Neutrophil Percentage-to-albumin Ratio, \u003cem\u003eSOFA, Sequential Organ Failure Assessment; GCS, Glasgow Coma Scale; MV, mechanical ventilation; CRRT, continuous renal replacement treatment; ERCP, Endoscopic Retrograde Cholangiopancreatography; AKI, acute kidney injury; RF, respiratory failure; HF, heart failure; WBC, white blood cell count; RBC, red blood cell count; Plt, platelet; Hb, hemoglobin; Hct, hematocrit; PT, prothrombin time; Cr, creatinine; BUN, blood urea nitrogen; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; LOS ICU, length of ICU stay; LOS Hospital, length of hospital stay, MBP mean blood pressure, RR respiratory rate, APS III, acute physiology score III; CCI, Charlson comorbidity index.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eKaplan\u0026ndash;Meier curve and restricted cubic spline\u003c/h2\u003e \u003cp\u003eKaplan-Meier survival curves showed significant mortality differences across NPAR quartiles, with the highest mortality in Q4 (log-rank p\u0026thinsp;=\u0026thinsp;0.016, \u0026lt;\u0026thinsp;0.001, 0.005, 0.035 for 7, 28, 90 days, and 1 year, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). RCS analysis confirmed non-linear associations between NPAR and mortality, with significant risk increases at NPAR thresholds of 40.3\u0026ndash;48.2 (p\u0026thinsp;=\u0026thinsp;0.043 for 7 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for 28 days, p\u0026thinsp;=\u0026thinsp;0.008 for 90 days, p\u0026thinsp;=\u0026thinsp;0.038 for 1 year) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Threshold effect analysis revealed a linear association for 7-day mortality (adjusted HR 1.048, 95% CI 1.007\u0026ndash;1.090, p\u0026thinsp;=\u0026thinsp;0.020 for NPAR\u0026thinsp;\u0026lt;\u0026thinsp;48.07), with no further risk increase beyond 48.07 (p\u0026thinsp;=\u0026thinsp;0.768). Similar patterns were observed for 28-day (adjusted HR 1.043, 95% CI 1.014\u0026ndash;1.074, p\u0026thinsp;=\u0026thinsp;0.004 for NPAR\u0026thinsp;\u0026lt;\u0026thinsp;40.32) and 90-day mortality (adjusted HR 1.030, 95% CI 1.008\u0026ndash;1.053, p\u0026thinsp;=\u0026thinsp;0.008 for NPAR\u0026thinsp;\u0026lt;\u0026thinsp;43.14) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between NPAR and Mortality: Cox Regression Analysis\u003c/h2\u003e \u003cp\u003eUnivariate Cox regression identified age (HR 1.036\u0026ndash;1.040, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), mean blood pressure (HR 0.921\u0026ndash;0.977, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and NPAR (HR 1.017\u0026ndash;1.045, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as significant predictors of all-cause mortality at all time points (Table S2). In multivariable Cox regression, higher NPAR quartiles were consistently associated with increased mortality risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At 7 days, patients in Q4 had a crude hazard ratio (HR) of 4.37 (95% CI 1.89\u0026ndash;10.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with Q1. After full adjustment (Model 3: age, sex, body temperature, hemoglobin, creatinine, prothrombin time, APS III, Charlson Comorbidity Index, AKI, and vasopressor use), the association was attenuated and became non-significant (adjusted HR 2.31; 95% CI 0.50\u0026ndash;3.40; p\u0026thinsp;=\u0026thinsp;0.584).At 28 days, the crude HR was 2.76 (95% CI 1.59\u0026ndash;4.80; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and remained significant in Model 3 (adjusted HR 1.80; 95% CI 1.02\u0026ndash;3.19; p\u0026thinsp;=\u0026thinsp;0.044). At 90 days, Q4 remained a significant predictor (crude HR 2.07; 95% CI 1.34\u0026ndash;3.18; p\u0026thinsp;=\u0026thinsp;0.001; adjusted HR 1.77; 95% CI 1.10\u0026ndash;2.84; p\u0026thinsp;=\u0026thinsp;0.019), as did 1-year mortality (crude HR 1.65; 95% CI 1.11\u0026ndash;2.46; p\u0026thinsp;=\u0026thinsp;0.015; adjusted HR 1.56; 95% CI 1.01\u0026ndash;2.41; p\u0026thinsp;=\u0026thinsp;0.048). Trend analyses across quartiles were statistically significant in most models (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Cox regression models of NPAR ratio and all-cause mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e7 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.02\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (1.01\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03 (1.00-1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70 (0.68\u0026ndash;4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78 (0.71\u0026ndash;4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.19 (0.84\u0026ndash;5.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.76 (0.64\u0026ndash;4.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45 (1.01\u0026ndash;5.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47 (1.02\u0026ndash;5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.77 (0.68\u0026ndash;4.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.77 (0.69\u0026ndash;4.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.37 (1.89\u0026ndash;10.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 (1.73\u0026ndash;9.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.07 (1.20\u0026ndash;7.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.31 (0.50\u0026ndash;3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e28 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (1.00-1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39 (0.82\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69 (0.99\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.87 (1.07\u0026ndash;3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.94 (1.11\u0026ndash;3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17 (1.31\u0026ndash;3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21 (1.33\u0026ndash;3.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.04 (1.20\u0026ndash;3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.05 (1.20\u0026ndash;3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.76 (1.63\u0026ndash;4.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.88 (1.69\u0026ndash;4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.37 (1.35\u0026ndash;4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.80 (1.02\u0026ndash;3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e90 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (1.00-1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (1.00-1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14 (0.74\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38 (0.89\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52 (0.96\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.61 (1.02\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 (1.04\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65 (1.08\u0026ndash;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51 (0.96\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65 (1.04\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.07 (1.33\u0026ndash;3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20 (1.41\u0026ndash;3.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.88 (1.18-3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.77 (1.10\u0026ndash;2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e1- Year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.00-1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.00-1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (1.00-1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.62\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.74\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17 (0.77\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.30 (0.85\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (0.86\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32 (0.89\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24 (0.82\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.48 (0.97\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65 (1.10\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75 (1.16\u0026ndash;2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45 (0.95\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.56 (1.00-2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ep for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eHR hazard ratio, CI confidence interval, NPAR: Neutrophil Percentage-to-Albumin Ratio, Q\u003c/em\u003e, Quartile\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCrude: non-adjusted; Model 1: adjusted for age, sex; Model 2: adjusted model 1\u0026thinsp;+\u0026thinsp;body temperature, hemoglobin, serum creatinine, prothrombin time; Model 3: adjusted for model 2\u0026thinsp;+\u0026thinsp;acute physiology score III, Charlson comorbidity index, vasopressor.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses of NPAR and all-cause mortality\u003c/h2\u003e \u003cp\u003eTo examine whether the association between NPAR and mortality was consistent across subgroups, we conducted stratified and interaction analyses (Fig.\u0026nbsp;4). At 7 days, a significant positive association was observed in patients with malignant disease (HR\u0026thinsp;=\u0026thinsp;1.60, p-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At 28 days, age (\u0026lt;\u0026thinsp;60 years), liver disease (HR\u0026thinsp;=\u0026thinsp;1.37, p-interaction\u0026thinsp;=\u0026thinsp;0.001), and renal disease (HR\u0026thinsp;=\u0026thinsp;1.19, p-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed stronger associations. At 90 days, significant interactions were observed for malignant disease (HR\u0026thinsp;=\u0026thinsp;1.77, p-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and renal disease (HR\u0026thinsp;=\u0026thinsp;1.60, p-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At 1-year mortality, the malignant disease subgroup (HR\u0026thinsp;=\u0026thinsp;1.56, p-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and renal disease (HR\u0026thinsp;=\u0026thinsp;1.48, p-interaction\u0026thinsp;=\u0026thinsp;0.004) showed the strongest associations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eSensitivity analyses for the NPAR on clinical outcomes in patients with AP\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eSensitivity analyses, excluding patients with renal disease (n\u0026thinsp;=\u0026thinsp;114), liver disease (n\u0026thinsp;=\u0026thinsp;98), or sepsis (n\u0026thinsp;=\u0026thinsp;225), confirmed the robustness of the association between NPAR and mortality. For 28-day mortality, the adjusted HR per unit increase in NPAR was 1.043 (95% CI 1.014\u0026ndash;1.074; p\u0026thinsp;=\u0026thinsp;0.004), and for 1-year mortality it was 1.018 (95% CI 0.999\u0026ndash;1.037; p\u0026thinsp;=\u0026thinsp;0.049) (Table\u0026nbsp;5 and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Multiple imputation for missing data (\u0026lt;\u0026thinsp;5% for included variables) and E-value analysis (E-values 2.97\u0026ndash;3.64 for unmeasured confounders) supported the reliability of these findings (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic utility of inflammatory biomarkers and decision tree modeling\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrognostic performance of biomarkers for all-cause mortality in acute pancreatitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSens\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAcc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70(0.66\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24959.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59(0.56\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49(0.45\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55(0.51\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46(0.42\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65(0.62\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBISAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69(0.66\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68(0.65\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57(0.53\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd 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\u003cp\u003e47.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46(0.43\u0026ndash;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e432.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52(0.48\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBISAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71(0.68\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eSens: Sensitivity, Spec: Specficity, AUC-95: Area Under the Curve (95% Confidence Interval), SII: Systemic Immune-Inflammation Index, SIRI: Systemic Inflammation Response Index. BISAP: Bedside Index of Severity in Acute Pancreatitis, NPAR: Neutrophil Percentage-to-Albumin Ratio, NPR: Neutrophil-to-Platelet Ratio, NLR: Neutrophil-to-Lymphocyte Ratio, PLR: Platelet-to-Lymphocyte Ratio, LMR: Lymphocyte-to- Monocyte-Ratio, p-De long test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong single predictors, NPAR emerged as the leading inflammatory biomarker for mortality risk stratification across all horizons, with its strongest discrimination in the early window. At 7 days, NPAR showed an AUC 0.70 (95% CI, 0.66\u0026ndash;0.72) with balanced performance (F1 0.64; sensitivity 0.69; specificity 0.65) at a cut-off of 35.2. By DeLong testing, SII, SIRI, NLR, PLR, and LMR were all inferior to NPAR (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas NPR was comparable (AUC 0.65; p\u0026thinsp;=\u0026thinsp;0.088) and BISAP performed similarly (AUC 0.69; p\u0026thinsp;=\u0026thinsp;0.944). NPAR maintained the best performance among inflammatory indices at later timepoints, with AUCs of 0.68 (28 days; cut-off 36.13), 0.65 (90 days; 35.01), and 0.61 (1 year; 36.21)\u0026mdash;each outperforming SII, NLR, PLR, LMR, and NPR (all p\u0026thinsp;\u0026le;\u0026thinsp;0.002, except SIRI at 1 year: p\u0026thinsp;=\u0026thinsp;0.080). Although BISAP exceeded NPAR beyond the acute window (AUC 0.67 at 28 days, 0.70 at 90 days, 0.71 at 1 year; p\u0026thinsp;=\u0026thinsp;0.599, 0.066, and \u0026lt;\u0026thinsp;0.001, respectively), NPAR remained the top inflammatory marker at every horizon. Optimal cutoffs were NPAR\u0026thinsp;~\u0026thinsp;35\u0026ndash;36 and BISAP\u0026thinsp;\u0026ge;\u0026thinsp;2.\u003c/p\u003e \u003cp\u003eCombining NPAR with BISAP in a decision-tree (Fig.\u0026nbsp;5) improved discrimination across all time points: 7 days AUC 0.768 (95% CI 0.699\u0026ndash;0.837; F1 0.843; sensitivity 0.667; specificity 0.744; accuracy 0.739); 28 days AUC 0.738 (0.692\u0026ndash;0.784; F1 0.667; sensitivity 0.815; specificity 0.520; accuracy 0.571); 90 days AUC 0.743 (0.704\u0026ndash;0.783; F1 0.566; sensitivity 0.917; specificity 0.406; accuracy 0.533); 1 year AUC 0.717 (0.680\u0026ndash;0.755; F1 0.638; sensitivity 0.850; specificity 0.498; accuracy 0.603); all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. These trees therefore enhance sensitivity\u0026mdash;especially at 90 days\u0026mdash;at the cost of specificity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to demonstrate that NPAR is independently associated with both short- and long-term all-cause mortality in ICU-admitted patients with AP. First, NPAR was independently associated with mortality across all timepoints, showing a stepwise increase in risk across quartiles. Restricted cubic spline and Kaplan\u0026ndash;Meier analyses revealed a non-linear relationship, with an inflection point between NPAR values of 40.3 and 48.1. Second, as a single inflammatory biomarker, NPAR consistently outperformed other indices\u0026mdash;including NLR, SII, SIRI, NPR, PLR, and LMR\u0026mdash;at all timepoints (AUC 0.61\u0026ndash;0.72 vs. 0.49\u0026ndash;0.65). Third, integration of NPAR with the BISAP score in a decision tree model enhanced discriminative performance over either marker alone, with AUCs ranging from 0.74 to 0.77 for 7- to 90-day mortality, and 0.72 for 1-year mortality. This combined model also yielded higher sensitivity (85\u0026ndash;91%) for longer-term outcomes.\u003c/p\u003e \u003cp\u003eThe development of AP is closely tied to systemic inflammation, sepsis, and organ failure, with prognosis depending on inflammation severity, nutrition, nitrogen balance, and functional reserves.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Neutrophils play a key role in AP by migrating to the pancreas and releasing enzymes, reactive oxygen species, cytokines, and NETs, exacerbating local and systemic damage\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Neutrophil depletion reduces damage and mortality in animal models, supporting their pathogenic role.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Clinically, various neutrophil-based indices, such as NLR, SII, SIRI, LMR and NPR have shown promising prognostic potential.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Neutrophil-based indices like NPAR predict severity, organ failure, and mortality in AP, aiding early risk assessment and treatment decisions.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Albumin, the most abundant plasma protein, regulates oncotic pressure, circulation, and has anti-inflammatory, antioxidant, and endothelial-stabilizing properties. Hypoalbuminemia, common in acute pancreatitis (AP), is associated with increased disease severity, organ failure, and mortality. Patients with serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;25 g/L face significantly higher mortality risks.\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e However, using albumin levels alone for prognosis has limitations, as chronic inflammation or nutritional status also affects albumin levels.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e While the benefits of albumin supplementation remain unclear, some studies suggest it may improve outcomes in severe hypoalbuminemia, though more research is needed.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNPAR is a simple and readily available biomarker that demonstrates strong utility for early risk stratification in ICU patients with acute pancreatitis. Its clinical value lies in two key features: (i) a stable optimal threshold (~\u0026thinsp;35\u0026ndash;36) consistently observed across timepoints, which facilitates bedside application, and (ii) a favorable early balance between sensitivity and specificity, enabling effective triage in the acute phase where modest over-triage is acceptable. In this context, NPAR emerges as the preferred early inflammatory prognostic index, whereas BISAP provides superior long-term predictive performance and complements NPAR in extended follow-up. The moderate AUC values observed for NPAR likely reflect its threshold-dependent and non-linear behavior, which may be underestimated by conventional ROC analysis. Indeed, restricted cubic spline modeling revealed a non-linear association between NPAR and mortality, with inflection points at approximately 40.3\u0026ndash;48.2. Mortality risk increased linearly below this range but plateaued at higher levels, possibly due to survivor bias\u0026mdash;where only a select subgroup of critically ill patients persists\u0026mdash;or a biological ceiling effect, in which additional inflammatory burden no longer translates into higher risk. Importantly, although NPAR does not account for evolving organ dysfunction or comorbidities, its integration with BISAP in decision-tree models enhanced prognostic accuracy, underscoring the potential of a multimodal approach to mortality prediction in ICU patients with acute pancreatitis.\u003c/p\u003e \u003cp\u003eThis study has strengths. First, it leverages a large, diverse cohort of ICU-admitted patients with acute pancreatitis, enhancing generalizability and statistical power. Despite its retrospective nature, all eligible cases were included to ensure comprehensive analysis. Advanced statistical approaches, including Kaplan\u0026ndash;Meier survival curves, Cox proportional hazards models, and restricted cubic spline regression, were applied to examine mortality risk and detect potential non-linear associations. Sensitivity and subgroup analyses\u0026mdash;excluding key comorbidities\u0026mdash;confirmed the robustness of findings. Multiple imputation was used to address missing data, and E-value analysis was conducted to assess the impact of unmeasured confounders. Second, the integration of NPAR with the BISAP score using decision tree analysis and SMOTE-based balancing improved prognostic performance, Third, NPAR, a readily available and non-invasive marker, independently associated both short- and long-term mortality, offering practical value for early risk assessment. Lastly, its potential utility in resource-limited ICU settings\u0026mdash;where complex scoring systems like APACHE may be impractical\u0026mdash;further supports its clinical relevance.\u003c/p\u003e \u003cp\u003eHowever, this study also has limitations. First, its retrospective design precludes causal inference between NPAR and mortality. Second, missing data on key covariates\u0026mdash;including comorbidities and pancreatitis etiology may have introduced residual bias, despite multiple imputation. Third, the single-center, U.S.-based cohort raises concerns of external validity, particularly given the racial distribution (61.5% White) and potential practice-pattern differences in non-Western populations. Fourth, long-term follow-up was limited to 1 year and dependent on registry data; censoring was not systematically addressed, which may have influenced survival estimates. Fifth, unmeasured variability in management strategies, such as albumin administration or early intervention timing, could have confounded the observed associations. Sixth, the absence of serial NPAR measurements restricted evaluation of its temporal dynamics. Finally, as the cohort was limited to ICU-admitted patients, findings may not be generalizable to those with mild or moderate acute pancreatitis. Moreover, cause-specific mortality data were unavailable, preventing differentiation between AP-related and non\u0026ndash;AP-related deaths. Prospective, multi-center validation with detailed etiologic and longitudinal follow-up data will be needed to confirm these findings.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn conclusion\u003c/b\u003e, \u003c/\u003eNPAR showed a consistent and clinically relevant association with both short- and long-term mortality among ICU-admitted patients with acute pancreatitis. When combined with BISAP in a decision-tree model, its prognostic performance improved, suggesting potential value as a complementary tool for early clinical decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eAKI, Acute Kidney Injury; ALT, Alanine Aminotransferase; AP, Acute Pancreatitis; APACHE, Acute Physiology and Chronic Health Evaluation; APS III, Acute Physiology Score III; AST, Aspartate Aminotransferase; AUC, Area Under the Curve; BISAP, Bedside Index for Severity in Acute Pancreatitis; CCI, Charlson Comorbidity Index; CI, Confidence Interval; CRRT, Continuous Renal Replacement Therapy; HR, Hazard Ratio; ICU, Intensive Care Unit; KM, Kaplan\u0026ndash;Meier; LMR, Lymphocyte-to-Monocyte Ratio; MBP, Mean Blood Pressure; MIMIC-IV, Medical Information Mart for Intensive Care IV; NPAR, Neutrophil Percentage-to-Albumin Ratio; NLR, Neutrophil-to-Lymphocyte Ratio; NPR, Neutrophil-to-Platelet Ratio; PLR, Platelet-to-Lymphocyte Ratio; PT, Prothrombin Time; RCS, Restricted Cubic Spline; SAPS II, Simplified Acute Physiology Score II; SD, Standard Deviation; SII, Systemic Immune-Inflammation Index; SIRI, Systemic Inflammatory Response Index; SMOTE, Synthetic Minority Over-sampling Technique; SOFA, Sequential Organ Failure Assessment; WBC, White Blood Cell Count.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study used de-identified data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. All data in MIMIC-IV are fully de-identified and publicly available; therefore, individual informed consent was waived. Given the use of anonymized, pre-existing data, our institutional IRB (School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City) waived additional review and informed consent. Access to the MIMIC-IV database was granted to the first author (Tien Manh Huynh) after completion of the required Collaborative Institutional Training Initiative (CITI) training (Record ID: 67841419).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the MIMIC-IV database via PhysioNet (https://mimic-iv.mit.edu), subject to credentialed access approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and supervision: Tien Manh Huynh, Duc Trong Quach, and Phuc-Loi Luu. Methodology: Tien Manh Huynh, Thanh Chi Phan Nguyen. Data analysis: Tien Manh Huynh, Thanh Chi Phan Nguyen, Phuc-Loi Luu, and Duc Trong Quach. Data curation and writing \u0026ndash; original draft preparation: Tien Manh Huynh, Thanh Chi Phan Nguyen, and Duc Trong Quach. Writing \u0026ndash; review and editing: Tien Manh Huynh, Duc Trong Quach, Phuc-Loi Luu, and Doan Thi Nha Nguyen. Project administration: Duc Trong Quach and Phuc-Loi Luu. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the PhysioNet platform and the Massachusetts Institute of Technology Laboratory for Computational Physiology for providing and maintaining the MIMIC-IV database, as well as Beth Israel Deaconess Medical Center for contributing the original clinical data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI use statement\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this manuscript, Grammarly was used solely for spelling, grammar, and style correction. The authors reviewed and take full responsibility for the content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTien Manh Huynh, MD, is a gastroenterologist and clinical researcher at the Department of Internal Medicine, School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam. Associate Prof Duc Trong Quach, MD, PhD, is a gastroenterologist at the Department of Internal Medicine, School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, and the Department of Gastroenterology, Nhan Dan Gia Dinh Hospital, Ho Chi Minh City, Vietnam.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi, C., Jiang, M., Pan, C., Li, J. \u0026amp; Xu, L. 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(2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12876-024-03314-8\u003c/span\u003e\u003cspan address=\"10.1186/s12876-024-03314-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelinskaia, D. A., Jenkins, R. O. \u0026amp; Goncharov, N. V. \u003cem\u003eSerum albumin in health and disease: From comparative biochemistry to translational medicine\u003c/em\u003e p. 13725 (MDPI, 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOcskay, K. et al. Hypoalbuminemia affects one third of acute pancreatitis patients and is independently associated with severity and mortality. \u003cem\u003eScientific Reports\u003c/em\u003e. /12/17 2021;11(1):24158. (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-03449-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-03449-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute pancreatitis, Intensive care unit, Neutrophil Percentage to Albumin Ratio, Prognosis, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-8851627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8851627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eAcute pancreatitis (AP) is a leading cause of ICU admission with high mortality, yet long-term prognostic tools remain limited. This study evaluated the neutrophil percentage-to-albumin ratio (NPAR), a marker of inflammation and nutrition, in predicting short-term (7, 28 days) and long-term (90 days, 1 year) mortality in ICU-admitted AP patients.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA retrospective cohort of 628 ICU-AP patients was extracted from the MIMIC-IV database. NPAR was calculated at admission and categorized into quartiles. All-cause mortality was assessed using Kaplan\u0026ndash;Meier analysis, Cox regression, restricted cubic splines, and sensitivity analyses. NPAR was compared to other inflammatory markers and the BISAP score. A decision tree model combining NPAR and BISAP was also developed.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eHigher admission NPAR was associated with progressively increased mortality across all time points; patients in the highest quartile had 7-day, 28-day, 90-day, and 1-year mortality of 12.1%, 31.2%, 40.1%, and 43.9%, respectively, versus 3.2%, 11.4%, 17.7%, and 25.3% in the lowest quartile. After adjustment for demographics, illness-severity scores, acute kidney injury, and vasopressor use, elevated NPAR remained independently associated with 28-day, 90-day, and 1-year mortality, with adjusted hazard ratios per unit increase ranging from approximately 1.03 to 1.05. Restricted cubic splines suggested a mainly linear association below prespecified NPAR thresholds. NPAR provided discrimination comparable to BISAP for 7-day mortality (AUC 0.72 vs. 0.69) and outperformed most other inflammatory indices. A simple decision-tree using NPAR together with BISAP improved classification of high-risk patients.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eNPAR is associated with a consistent mortality risk over 12 months in critically ill patients with AP, contributing to early risk stratification and long-term management.\u003c/p\u003e","manuscriptTitle":"Neutrophil Percentage-to-Albumin Ratio as an Early Prognostic Marker in ICU Patients with Acute Pancreatitis: A 12-Month Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 05:48:24","doi":"10.21203/rs.3.rs-8851627/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T08:10:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T04:05:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T09:12:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286079688406378773719679225643170480224","date":"2026-04-26T03:38:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110934258773224964745050329551325650901","date":"2026-04-25T05:49:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68685910829095173085612140615155377465","date":"2026-04-24T13:40:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136676069654684182828687922703185396038","date":"2026-04-21T21:11:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308933977057711753841509865388551957985","date":"2026-04-21T06:30:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T19:12:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-17T06:43:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T10:53:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-12T10:50:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-11T12:07:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbefee25-3e82-4fa7-88c0-461e595ad5cd","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T08:10:50+00:00","index":102,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T04:05:23+00:00","index":101,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T09:12:49+00:00","index":100,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67113450,"name":"Health sciences/Biomarkers"},{"id":67113451,"name":"Health sciences/Diseases"},{"id":67113452,"name":"Health sciences/Medical research"},{"id":67113453,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-04T05:48:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 05:48:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8851627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8851627","identity":"rs-8851627","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}