Aspirin reduces the risk of death in patients with cirrhosis: a propensity-matched retrospective analysis of the MIMIC-IV database

Aspirin reduces the risk of death in patients with cirrhosis: a propensity-matched retrospective analysis of the MIMIC-IV database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Aspirin reduces the risk of death in patients with cirrhosis: a propensity-matched retrospective analysis of the MIMIC-IV database Yu Yi, Yinghua Chen, Yawen Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7556874/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2025 Read the published version in BMC Gastroenterology → Version 1 posted 11 You are reading this latest preprint version Abstract Background Cirrhosis of the liver not only leads to high mortality rates but also carries a huge economic burden and health losses. Aspirin is a drug with potential liver disease indications. However, the benefits of aspirin in cirrhosis remain controversial. We sought to determine whether aspirin therapy has a protective effect on outcomes in patients with cirrhosis. Methods We selected patients with cirrhosis from the Medical Information Marketplace in Intensive Care IV (MIMIC-IV) database. Propensity score matching (PSM) balanced baseline differences. Multivariate Cox regression models assessed the association between aspirin therapy and 30- and 90-day mortality, and multifactorial logistic regression models assessed the association between aspirin therapy and hospital mortality. Results We included a total of 3,105 patients, of whom 346 received aspirin therapy and 2,759 did not. Following PSM, there were 321 matched pairs. Aspirin users had a 30-day mortality rate of 15.26% and a 90-day mortality rate of 16.82%, both lower than nonusers. Multifactorial Cox regression analysis indicated that aspirin use was associated with reduced 30-day mortality (HR 0.66, 95% CI 0.44–0.98) and 90-day mortality (HR 0.61, 95% CI 0.42–0.89). Multifactorial logistic regression analysis indicated that aspirin use was associated with reduced in-hospital mortality (OR 0.64, 95% CI 0.42–0.97). No significant differences were found in ICU length of stay. Conclusion Aspirin is associated with reduced 30-day, 90-day, and hospitalization mortality in cirrhotic patients. MIMIC-IV database aspirin cirrhosis intensive care unit mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Liver disease is one of the major public health challenges globally, causing well over 2 million deaths annually and accounting for 4% of all deaths globally. 1. Although liver disease is currently ranked as the 11th leading cause of death globally, its actual impact is likely to be underestimated. 2. Cirrhosis is the 10th leading cause of death in Africa (up 3 places from 2015), the 9th leading cause of death in Southeast Asia and Europe, and as high as the 5th leading cause of death in the Eastern Mediterranean region. 3. In addition, cirrhosis contributes significantly to global healthy life expectancy lost (DALY), ranking 15th globally, and disproportionately affects the younger population aged 25–49 years, being the 12th leading cause of DALY in this age group,⁴ with potential life expectancy loss likely to be even higher in Europe. 5, 6 On an economic level, cirrhosis imposes a heavy healthcare burden. In the United States, for example, liver-related healthcare expenditures amounted to $ 32.5 billion in 2016, two-thirds of which was spent on inpatient and emergency care, and related expenditures have increased by 4% per year over the past 20 years. 7. In 2017, there were 10.6 million cases of decompensated cirrhosis and 112 million cases of compensated cirrhosis globally. 4. Patients with compensated cirrhosis and decompensated cirrhosis have a 5-fold and 10-fold increased risk of death, respectively, compared to the general population. 8 The 1- and 5-year survival rates for patients with compensated cirrhosis have been reported to be 87% and 67%, respectively, whereas the rates for patients with decompensated cirrhosis drop significantly to 75% and 45%. 8 Cirrhosis not only leads to high mortality rates but also carries a significant economic burden and health loss. Aspirin (acetylsalicylic acid) is a widely used medicine that helps reduce inflammation, fight tumors, and affect certain fats in the body by blocking the action of the pro-inflammatory cyclooxygenase-2 (COX-2) and platelet-derived growth factor (PDGF) pathways. 9–12 Studies have shown that aspirin can be used for many purposes, such as pain relief, reducing fever, managing heart and brain diseases, treating joint diseases, helping during pregnancy, and preventing cancer. 13–15 Cirrhosis is a disease characterized by chronic inflammation accompanied by multiple complications and a high mortality rate. Clinical treatment is still based on symptomatic therapy. In studies with rats that have cirrhosis, various medications such as the COX-2 inhibitor celecoxib, aspirin, curcumin, carvacrol, hexoketone cacodylate, diosmin, statins, emricasan, and silymarin have shown potential in lowering inflammation and combating oxidative stress. 16 Of these, aspirin has shown strong effects in stopping scarring and cell growth in early studies, likely by slowing down liver fibrosis and lowering the chance of liver cancer (HCC). 17 Observational studies back this assertion up: in patients with metabolic dysfunction-associated steatohepatopathy (MASLD), using aspirin was linked to slower progression of severe liver scarring, fewer cases of liver cancer (HCC), and lower death rates related to liver issues. 18–21 However, patients with advanced cirrhosis may experience an attenuated protective effect [ 22 ], and the results of available meta-analyses are controversial. 23 Notably, the use of aspirin in patients with cirrhosis needs to be weighed against safety. Although patients with portal hypertension are often comorbid with thrombocytopenia, which may increase the risk of bleeding, 24–25 retrospective studies have indicated that the use of aspirin for cardiovascular indications in patients with cirrhosis does not significantly increase the number of major bleeding events. 26–28 A recent study involving 587 patients showed that taking aspirin after a transjugular intrahepatic portosystemic shunt (TIPS) greatly helped patients with severe fluid buildup live longer without needing another procedure after 12 months, but it didn't significantly help those with bleeding from varices, indicating that aspirin might work better for certain groups of patients. Whether aspirin use is associated with lower mortality in cirrhotic patients admitted to the ICU remains controversial. Therefore, we designed this observational study to examine the potential beneficial effects of aspirin in patients with cirrhosis. 2 Materials and methods 2.1 Data sources This study examines data from the MIMIC-IV database version 3.1, which contains anonymous clinical information about patients in the Emergency Department and Intensive Care Unit (ICU) at Beth Israel Deaconess Medical Center (BIDMC) in the United States from 2008 to 2022, including details like patient demographics, vital signs, lab tests, medication records, and diagnostic codes. The construction of the database was reviewed by the BIDMC Institutional Review Board (IRB), which waived informed consent and approved data sharing, so no additional ethical approval was required for this study. The first author (Yu Yi) has completed the CITI program training (certification ID: 68122805) and passed the data use compliance test and was granted access to the MIMIC-IV data. 2.2 Study participants The cohort study evaluated cirrhotic patients admitted to the ICU, and it identified 7,830 cirrhotic patients using International Classification of Diseases (ICD) codes. This analysis was done by excluding 4,725 patients who were younger than 18 years of age, admitted to the hospital for a non-first admission or non-first ICU admission, and had an ICU stay shorter than 24 hours. The final cohort consisted of 3,105 patients, 346 aspirin users, and 2,759 nonusers. After propensity score matching (PSM), 321 pairs of patients were matched (Fig. 1 ). 2.3 Aspirin exposure Aspirin exposure was defined as a prescription for oral aspirin within the first 72 hours of ICU admission. 2.4 Data extraction We extracted the following clinical data from the database: (1) Demographic data such as age, gender, and ethnicity were collected. (2) Vital signs measured on admission included heart rate, respiratory rate, and blood oxygen saturation (SpO2). (3) Disease severity was assessed using the model for end-stage liver disease (MELD), Sequential Organ Failure Assessment (SOFA), and Charlson comorbidity index. (4) Laboratory tests were first measured at admission, including white blood cells (WBC), hemoglobin, platelet count, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, albumin (ALB), creatinine, lactate, international normalized ratio (INR), prothrombin time (PT), and partial thromboplastin time (PTT). (5) Complications or comorbidities: ascites, hepatic encephalopathy, gastrointestinal bleeding, hepatorenal syndrome, liver transplantation, hypertension, type 2 diabetes mellitus (T2DM), cerebral infarction, myocardial infarction, chronic obstructive pulmonary disease (COPD), sepsis. (6) Therapeutic interventions included continuous renal replacement therapy (CRRT) and mechanical ventilation. Measured outcomes included 30- and 90-day survival, in-hospital mortality, and ICU length of stay. 2.5 Primary and Secondary Outcomes The primary outcomes of this study were 30-day and 90-day mortality. Secondary outcomes were in-hospital mortality and length of stay in the ICU. 2.6 Statistical analysi s Decision Link. The software version 1.0 aims to conduct statistical analysis (20). The platform incorporates various programming language environments and simplifies the data processing and analysis process through a graphical interface. In our study, all variables exhibited less than 20% missing data, and multiple interpolation was used to fill in the missing data. Continuous variables were expressed as mean ± standard deviation for normally distributed data and median (IQR) for non-normally distributed data. Categorical variables were expressed as numbers and percentages. The t-test or Wilcoxon rank sum test was used for continuous variables, and the Pearson chi-square (χ²) test was used for categorical variables to compare baseline data between the aspirin and non-aspirin groups. We applied a calliper width of 0.05 logits standard deviation to PSM to mitigate baseline imbalance. Cohorts were paired in a 1:1 ratio using the nearest-neighbor matching technique. The efficacy of PSM was assessed by a standardized mean difference (SMD), with SMD ≤ 0.1 indicating a balanced model of initial characteristics. We used Kaplan-Meier (KM) survival analyses and log-rank tests to compare 30- and 90-day total mortality in patients with and without aspirin. We calculated variance inflation factors (VIF) for all covariates in the model. All VIF values were less than 5, indicating that multicollinearity was within acceptable limits (Supplementary Table S1 ). We developed four models: model 1 is unadjusted. Model 2 was adjusted for age, gender, and ethnicity. Model 3 was further adjusted for ascites, hepatic encephalopathy, gastrointestinal bleeding, hepatorenal syndrome, liver transplantation, hypertension, T2DM, cerebral infarction, myocardial infarction, COPD, and sepsis. Model 4 was updated from model 3 by including additional factors such as heart rate, respiratory rate, SpO₂, WBC, hemoglobin, platelets, ALT, AST, total bilirubin, ALB, creatinine, lactate, INR, PT, PPT, MELD score, SOFA score, Charlson comorbidity index, mechanical ventilation, and CRRT. The results were expressed as hazard ratios (HR) or odds ratios (OR) with 95% confidence intervals (CI). In addition, subgroups were categorized according to ethnicity, age, gender, and comorbidity (including cerebral infarction, hypertension, T2DM, myocardial infarction, COPD, and sepsis). These subgroup analyses were designed to verify the consistency and robustness of our results. Interactions between subgroups were also investigated using the variance ratio test, with statistical significance set at p < 0.05. 3 Results 3.1 Patient characteristics This study is a retrospective cohort study, which may be confounded by confounders. To reduce bias, we used the propensity score matching (PSM) method to balance confounding variables between groups. The study included 3105 patients with cirrhosis, of whom 346 (11.10%) were treated with aspirin and 2759 (88.90%) were untreated. As shown in Table 1 , patients in the pre-PSM aspirin group had the following characteristics: (1) Higher age and proportion of males. (2) The disease was more severe, as shown by a higher Charlson comorbidity index (median [IQR]: 7 [ 5 – 9 ] compared to 5 [ 4 – 7 ]), but the MELD score (16.97 [11.88–22.81] compared to 19.88 [14.08–27.68]) and SOFA score (7 [ 4 – 10 ] compared to 8 [ 5 – 11 ]) were lower (both p < 0.05). (3) Laboratory parameters: higher hemoglobin and ALT, lower AST, total bilirubin, lactate, INR, PT, and PPT (p < 0.05). (4) Complication spectrum: The prevalence of T2DM (42.49% vs. 27.11%), myocardial infarction (13.29% vs. 3.19%), and COPD (16.47% vs. 12.32%) and the rate of liver transplantation (4.30% vs. 0.94%) were higher (all p < 0.01), and the incidence of liver-related complications such as ascites and hepatic encephalopathy was lower. A 1:1 nearest-neighbor matching method with a caliper value of 0.05 was used to successfully match 321 pairs of patients with a standard mean difference of < 0.1 for all baseline characteristics after PSM (Table 1 ; Fig. 2 ). Table 1 Baseline characteristics of cirrhosis patients before and after PSM. Variables Before PSM After PSM Total Non aspirin Aspirin P-value SMD Total Non aspirin Aspirin P-value SMD (n = 3,105) (n = 2,759) (n = 346) (n = 642) (n = 321) (n = 321) Age 60 (53–68) 59 (52–67) 66 (59–74) < 0.001 0.541 65 (58–73) 65 (58–73) 65 (58–74) 0.846 0.015 Gender, n (%) < 0.001 0.213 0.788 0.028 Female 1088 (35.04) 997 (36.14) 91 (26.30) 170 (26.48) 83 (25.86) 87 (27.10) Male 2017 (64.96) 1762 (63.86) 255 (73.70) 472 (73.52) 238 (74.14) 234 (72.90) Ethnicity, n (%) 0.328 0.084 0.772 0.057 White 2024 (65.19) 1794 (65.02) 230 (66.47) 425 (66.20) 214 (66.67) 211 (65.73) Black 225 (7.25) 195 (7.07) 30 (8.67) 53 (8.26) 24 (7.48) 29 (9.03) Other/Unknown 856 (27.57) 770 (27.91) 86 (24.86) 164 (25.55) 83 (25.86) 81 (25.23) Vital signs Heart rate (beats/min) 90 (78–105) 91 (78–105) 85 (74-98.75) < 0.001 0.238 86.5 (74–100) 89 (75–101) 85 (74–100) 0.273 0.087 Respiratory rate (beats/min) 19 (15–23) 19 (15–23) 18 (16–22) 0.087 0.105 18 (16–22) 18 (15–22) 18 (16–22) 0.379 0.07 SpO 2 (%) 98 (95–100) 98 (95–100) 99 (96–100) 0.001 0.093 98 (96–100) 98 (96–100) 98 (96–100) 0.942 0.006 Severity of illness MELD score 19.51(13.73–27.15) 19.88(14.08–27.68) 16.97(11.88–22.81) < 0.001 0.369 16.81 (11.79–23.22) 16.53 (11.80-23.55) 17.30(11.78–23.07) 0.848 0.015 SOFA score 8 (5–11) 8 (5–11) 7 (4–10) < 0.001 0.279 7 (4–9) 7 (4–9) 7 (4–9) 0.879 0.012 Charlson comorbidity index 6 (4–8) 5 (4–7) 7 (5–9) < 0.001 0.389 7 (5–9) 6 (5–9) 7 (5–9) 0.889 0.011 Laboratory measurements WBC (K/uL) 9.6 (6.20–14.7) 9.6 (6.10–14.80) 9.95 (6.63–14.50) 0.763 0.019 9.9 (6.50-14.58) 10 (6.40–15.5) 9.9 (6.60–14.40) 0.415 0.064 Hemoglobin (g/dL) 9.4 (8.10–10.90) 9.4 (8-10.80) 10 (8.60-11.38) < 0.001 0.3 10 (8.63–11.50) 10.1 (8.80–11.70) 10 (8.60–11.30) 0.461 0.058 Platelet (K/uL) 104 (67–158) 102 (65–157) 120 (81–171) < 0.001 0.206 118 (79–174) 118 (78–177) 119 (80–171) 0.747 0.026 ALT (U/dL) 34 (20–73) 33 (20–70) 38 (19.25–177) < 0.001 0.269 34 (19-114.75) 31 (18–89) 38 (20–165) 0.666 0.034 AST (U/dL) 69 (39–156) 69 (40–149) 66.5 (35-327.75) 0.003 0.158 62 (34.25–205.5) 57 (34–159) 66 (35–306) 0.816 0.018 Total bilirubin (mg/dL) 2.7 (1.2–6.9) 2.9 (1.3–7.5) 1.5 (1–4) < 0.001 0.417 1.7 (1-3.85) 1.8 (1-3.5) 1.5 (1-4.2) 0.561 0.046 ALB (g/dL) 2.9 (2.5–3.4) 2.9 (2.5–3.4) 2.95 (2.6–3.3) 0.846 0.012 3 (2.6–3.4) 3 (2.6–3.4) 2.9 (2.6–3.3) 0.528 0.05 Creatinine (mg/dL) 1.2 (1-2.1) 1.2 (1-2.1) 1.1 (1–2) 0.126 0.089 1.1 (1–2) 1.2 (1-1.9) 1.1 (1–2) 0.721 0.028 Lactate (mmol/L) 2.2 (1.5–3.5) 2.2 (1.6–3.5) 2.1 (1.5-3.175) 0.007 0.174 2.1 (1.4-3) 2.1 (1.4-3) 2.1 (1.4-3) 0.236 0.094 INR 1.7 (1.4–2.1) 1.7 (1.4–2.1) 1.5 (1.3–1.9) 0.002 0.176 1.5 (1.3–1.9) 1.5 (1.3–1.9) 1.5 (1.3–1.9) 0.81 0.019 PT (sec) 18.1 (15-22.7) 18.2 (15.2–23) 16.7 (14.4-20.575) 0.003 0.178 16.5 (14.325–20.6) 16.2 (14.2–20.2) 16.7 (14.5–20.7) 0.835 0.016 PPT (sec) 36.6 (31.4–46.2) 36.8 (31.5–46.4) 35.5 (30.7–44.1) 0.081 0.086 34.85 (30.425–42.9) 34.5 (30.1–42.9) 35.2 (30.7–42.8) 0.85 0.015 Complications or comorbidities Ascites, n (%) 1491 (48.02) 1376 (49.87) 115 (33.24) < 0.001 0.342 218 (33.96) 111 (34.58) 107 (33.33) 0.803 0.026 Hepatic encephalopathy, n (%) 304 (9.79) 293 (10.62) 11 (3.18) < 0.001 0.297 23 (3.58) 12 (3.74) 11 (3.43) 1 0.017 Gastrointestinal bleeding, n (%) 98 (3.16) 93 (3.37) 5 (1.45) 0.077 0.126 9 (1.40) 4 (1.25) 5 (1.56) 1 0.026 Hepatorenal syndrome, n (%) 447 (14.40) 430 (15.59) 17 (4.91) < 0.001 0.357 36 (5.61) 19 (5.92) 17 (5.30) 0.864 0.027 Liver transplantation, n (%) 41 (1.32) 26 (0.94) 15 (4.34) < 0.001 0.213 24 (3.74) 11 (3.43) 13 (4.05) 0.835 0.033 Hypertension, n (%) 991 (31.92) 877 (31.79) 114 (32.95) 0.707 0.025 216 (33.64) 107 (33.33) 109 (33.96) 0.933 0.013 T2DM, n (%) 895 (28.82) 748 (27.11) 147 (42.49) < 0.001 0.327 269 (41.90) 135 (42.06) 134 (41.74) 1 0.006 Cerebral infarction, n (%) 123 (3.96) 103 (3.73) 20 (5.78) 0.09 0.096 36 (5.61) 16 (4.98) 20 (6.23) 0.607 0.054 Myocardial infarction, n (%) 134 (4.32) 88 (3.19) 46 (13.29) < 0.001 0.374 68 (10.59) 34 (10.59) 34 (10.59) 1 < 0.001 COPD, n (%) 397 (12.79) 340 (12.32) 57 (16.47) 0.036 0.118 101 (15.73) 52 (16.20) 49 (15.26) 0.828 0.026 Sepsis, n (%) 2282 (73.49) 2046 (74.16) 236 (68.21) 0.022 0.132 439 (68.38) 221 (68.85) 218 (67.91) 0.865 0.02 Treatments CRRT, n (%) 433 (13.95) 398 (14.43) 35 (10.12) 0.036 0.132 60 (9.35) 28 (8.72) 32 (9.97) 0.684 0.043 Mechanical ventilation, n (%) 2479 (79.84) 2190 (79.38) 289 (83.53) 0.081 0.107 534 (83.18) 268 (83.49) 266 (82.87) 0.916 0.017 3.2 Aspirin and primary outcomes Aspirin users had a 30-day mortality rate of 15.26% and a 90-day mortality rate of 16.82%, both of which were lower than those of non-users (Table 2 ). Kaplan-Meier analyses showed that people taking aspirin had better chances of surviving for 30 and 90 days compared to those not taking it, both before and after matching the groups (Fig. 3 ). We developed four models using multivariate Cox regression to assess the independent effect of aspirin treatment on 30- and 90-day mortality. In the group before matching, the chances of dying at 30 days (HR 0.74, 95% CI 0.55–0.99) and 90 days (HR 0.70, 95% CI 0.53–0.93) were significantly lower; in the group after matching, the chances of dying at 30 days (HR 0.66, 95% CI 0.44–0.98) and 90 days (HR 0.61, 95% CI 0.42–0.89) were even lower (Table 3 ). Table 2 Association between aspirin and clinical outcomes in cirrhosis. Variables Total Non aspirin Aspirin P-value HR/OR(95%CI) Before PSM n = 3,105 n = 2,759 n = 346 Primary outcomes 30-day mortality, n(%) 757(24.38) 704(25.52) 53(15.32) < 0.001 0.57(0.43–0.75) 90-day mortality, n(%) 853(24.47) 795(28.81) 58(16.76) < 0.001 0.55(0.42–0.71) Secondary outcomes Hospital mortality, n(%) 1052(33.88) 972(35.23) 80(23.12) < 0.001 0.55(0.42–0.72) ICU stay(days) 3.02 (1.82–5.96) 3.06 (1.83–6.15) 2.77 (1.64–4.943) 0.013 After PSM n = 642 n = 321 n = 321 Primary outcomes 30-day mortality, n(%) 122(19) 73(22.74) 49(15.26) 0.018 0.65(0.45–0.93) 90-day mortality, n(%) 139(21.65) 85(26.48) 54(16.82) 0.004 0.61(0.43–0.85) Secondary outcomes Hospital mortality, n(%) 173(26.95) 98(30.53) 75(23.36) 0.041 0.69(0.49–0.98) ICU stay(days) 2.81 (1.77–5.188) 2.95 (1.86–5.37) 2.69 (1.64–4.9) 0.442 Table 3 Association of aspirin and the risk of 30-day and 90-day mortality and hospital mortality. Models Before PSM After PSM HR/OR 95%CI P-value HR/OR 95%CI P-value 30-day mortality Model 1 0.57 (0.43–0.75) < 0.001 0.65 (0.45–0.93) 0.018 Model 2 0.54 (0.41–0.72) < 0.001 0.63 (0.44–0.91) 0.013 Model 3 0.61 (0.46–0.82) 0.001 0.63 (0.44–0.91) 0.014 Model 4 0.74 (0.55–0.99) 0.048 0.66 (0.44–0.98) 0.041 90-day mortality Model 1 0.55 (0.42–0.71) < 0.001 0.61 (0.43–0.85) 0.004 Model 2 0.53 (0.41–0.69) < 0.001 0.60 (0.42–0.84) 0.003 Model 3 0.59 (0.45–0.78) < 0.001 0.59 (0.42–0.83) 0.003 Model 4 0.70 (0.53–0.93) 0.013 0.61 (0.42–0.89) 0.01 Hospital mortality Model 1 0.55 (0.42–0.72) < 0.001 0.69 (0.49–0.98) 0.041 Model 2 0.54 (0.41–0.70) < 0.001 0.69 (0.48–0.98) 0.039 Model 3 0.59 (0.44–0.78) < 0.001 0.68 (0.46–0.98) 0.042 Model 4 0.73 (0.54–0.99) 0.044 0.64 (0.42–0.97) 0.036 Model 1: unadjusted. Model 2: adjusted for age, gender, and ethnicity. Model 3: adjusted for age, gender, ethnicity, ascites, hepatic encephalopathy, gastrointestinal bleeding, hepatorenal syndrome, liver transplantation, hypertension, T2DM, cerebral infarction, myocardial infarction, COPD, and sepsis. Model 4: adjusted for age, gender, ethnicity, ascites, hepatic encephalopathy, gastrointestinal bleeding, hepatorenal syndrome, liver transplantation, hypertension, T2DM, cerebral infarction, myocardial infarction, COPD, sepsis, heart rate, respiratory rate, SpO2, WBC, hemoglobin, platelet, ALT, AST, total bilirubin, ALB, creatinine, lactate, INR, PT, PPT, MELD score, SOFA score, Charlson comorbidity index, mechanical ventilation, and CRRT. 3.3 Aspirin and secondary outcomes Before PSM, both in-hospital mortality and ICU length of stay were lower in the aspirin group compared to the non-aspirin group. The difference in in-hospital mortality only remained significant after PSM (23.36% vs. 30.53%, p = 0.041). The multifactorial logistic regression analysis indicated that aspirin treatment was a separate protective factor for in-hospital mortality on its own, both before and after PSM. 3.4 Subgroup analysis In our detailed analysis, we discovered that using aspirin did not have important differences based on ethnicity, age, gender, cerebral infarction, hypertension, T2DM, myocardial infarction, COPD, and sepsis groups (Fig. 4 ). 4 Discussion Current research on the use of aspirin in patients with cirrhosis suggests potential benefits, particularly in terms of inhibiting the progression of liver fibrosis and reducing the risk of hepatocellular carcinoma (HCC). Our observational study using the MIMIC IV database further confirmed the benefits of aspirin in the management of cirrhosis. We found that aspirin therapy was associated with improved 30-day, 90-day, and hospitalized survival in a propensity score-weighted model. Aspirin exerts chemo preventive effects on the liver through a multi-target mechanism, and its protective effects are mainly reflected in four aspects: in antitumor, it reduces the glucose uptake of hepatocellular carcinoma cells through inhibition of the NF-κB signaling pathway and down-regulation of glucose transporter protein 1 (GLUT1) to inhibit tumor proliferation,1 while enhancing antitumor drug sensitivity,2–4 and through antiplatelet effects to inhibit tumor cell adhesion and pro-metastatic microenvironment formation;5 in terms of anti-fibrosis, thioacetamide (TAA) and CCl4-induced hepatic fibrosis was significantly ameliorated by modulating the TGF-β1/Smad pathway and inhibiting the activity of COX-2; for cirrhosis and portal hypertension, aspirin improved hepatic sinusoidal endothelial dysfunction by modulating the COX-1/TXA2 pathway,6 and simultaneously reduced inflammatory cell infiltration7 by inhibiting NF-κ B reduces inflammatory cell infiltration7 and enhances the antitumor effect of interferon-α. 8 In terms of anti-inflammatory and antioxidant effects, aspirin reduces inflammation by inhibiting the NF-κB signaling pathway and, at the same time, reduces the levels of reactive oxygen species (ROS) and reactive nitrogen species (RNS), blocking the activation of hepatic stellate cells and thus breaking the vicious cycle of inflammation andoxidative stress. 9 These multifaceted mechanisms of action suggest that aspirin may become an important strategy for the prevention and treatment of liver diseases. This could serve as a crucial strategy for both disease prevention and treatment. Additional clinical studies have further validated the efficacy of aspirin in patients with cirrhosis. A large study using data from the CGRD database in Taiwan found that taking low-dose aspirin every day for 10 years was linked to a lower chance of developing HCC in people with cirrhosis (three-year HR 0.57, 95% CI 0.37–0.87; p = 0.0091, five-year HR 0.63, 95% CI 0.45–0.88; p = 0.0072). Overall mortality was significantly lower in aspirin users with cirrhosis compared to untreated controls (three-year HR 0.43 (0.33–0.57); five-year HR 0.51 (0.42–0.63)). Notably, there was also no increased risk of gastrointestinal bleeding in APA users compared with untreated patients (3-year: corrected HR 0.71; 95% CI 0.53–0.94; 5-year: corrected HR 0.66; 95% CI 0.51–0.84). This safety profile provides key support for its clinical use. Another study found 11 that in a single-center cirrhosis cohort, aspirin users demonstrated a reduced risk of HCC and had a significantly lower risk of the composite outcome of hepatic encephalopathy and ascites, suggesting that aspirin may delay hepatic dysfunction through anti-inflammatory or antifibrotic mechanisms. Also, in patients being checked for liver transplantation, using both aspirin and statins together showed better results: those on the combination had better INR (1.17 vs 1.52), total bilirubin (1.33 vs 5.61 mg/dL), and MELD scores (11.67 vs 19.66) compared to those not on the combination (all P < 0.001). There was a higher risk of liver problems and fluid buildup in patients checked for liver transplantation compared to those using only aspirin (23.5% vs 76.5%, P = 0.023) or only statins (23.5% vs 76.5%, P = 0.021), and patients using both aspirin and statins had a much lower death rate compared to those using just aspirin (23.5% vs 76.5%, P = 0.021). 12 Overall, these results indicate that aspirin might offer cirrhotic patients several health advantages, including preventing liver cancer and slowing down the progression of the disease, all without raising the risk of bleeding. We discovered that patients with cirrhosis in ICUs who took aspirin had much higher rates of combined T2DM (42.49% vs. 27.11%), myocardial infarction (13.29% vs. 3.19%), COPD (16.47% vs. 12.32%), and liver transplantation (4.30% vs. 0.94%) compared to those who did not take aspirin (p < 0.01). This difference may result from the clinical dosing profile of aspirin and its potential protective effects: patients with myocardial infarction usually require long-term aspirin for secondary prevention due to elevated cardiovascular risk; patients with T2DM may benefit from aspirin's improved insulin sensitivity and glycemic modulation;13 and patients with COPD may be more likely to be on aspirin due to its anti-inflammatory effects. 14 Also, the higher rate of liver transplants might be linked to the various protective benefits of aspirin, which studies have shown can not only help improve outcomes by lowering the chances of portal hypertension and bleeding but may also help transplant candidates live longer by decreasing the occurrence of HCC, a key reason for needing a liver transplant. 15 However, our study has some limitations. First, our study relied on the MIMIC database, which has some limitations and lacks information for the etiologic diagnosis of cirrhosis to obtain a specific etiologic diagnosis, so the etiology of cirrhosis was not included in this study. Second, the gastrointestinal bleeding extracted from the MIMIC database based on ICD codes could not be determined to have been caused by cirrhosis itself or by aspirin use, so it was not included as one of the outcome indicators in this study. Future studies could conduct further prospective studies to validate our observations in a more diverse cohort to more fully assess the effect of aspirin on the prognosis of patients with cirrhosis. Third, even though we used propensity score matching (PSM) to account for known factors, we should consider how unknown factors that weren't recorded might affect the study results. These variables, which were not documented in the database, may have limited our ability to clarify the causal relationship between aspirin use and mortality reduction. In conclusion, aspirin use was associated with reduced 30- and 90-day mortality, as well as hospitalization mortality. Conclusion Aspirin is associated with reduced 30-day, 90-day, and hospitalization mortality in cirrhotic patients. Abbreviations ALT Alanine Aminotransferase AST Aspartate Aminotransferase ALB Serum Albumin COPD Chronic Obstructive Pulmonary Disease CRRT Continuous Renal Replacement Therapy HR Hazard Ratio INR International Normalized Ratio ICU Intensive Care Unit MELD Model For End-stage Liver Disease MIMIC-IV Medical Information Mart for Intensive Care-IV OR Odds Ratio PT Prothrombin Time PTT Partial Thromboplastin Time PSM Propensity Score Matching SpO 2 Blood Oxygen Saturation SMD Standardized Mean Difference SOFA Sequential Organ Failure Assessment T2DM Type 2 Diabetes Mellitus WBC White Blood Cell Count Declarations Ethics approval and consent to participate Not Applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Yu Yi designed the research; Yinghua Chen extracted and analyzed the data; Yu Yi wrote the manuscript; Yu Yi collated and interpreted the data; and Yinghua Chen modified the manuscript and interpreted the analysis. Yawen Luo reviewed the manuscript. All authors contributed to the article and approved the final submission. Acknowledgments We are grateful to all the participants for their valuable contributions. Data Availability Publicly available datasets were analyzed in this study. The datasets generated and analyzed during the current study are available in the MIMIC-IV database (https://physionet.org/content/mimiciv/3.1/). References Asrani SK, Devarbhavi H, Eaton J, Kamath PS. Burden of liver diseases in the world. J Hepatol. 2019;70(1):151–71. 10.1016/j.jhep.2018.09 . Epub 2018/09/30. Griffin C, Agbim U, Ramani A, Shankar N, Kanwal F, Asrani SK. Underestimation of Cirrhosis-Related Mortality in the Medicare Eligible Population, 1999–2018. 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J Hepatol. 2024;80:S237. 10.1016/S0168-8278(24)00914-0 . Patel AK, Fazir A, Cooper K, Devuni D. S2001 Effect of Aspirin and Statin on Liver Markers and Mortality in Patients With Cirrhosis. Official J Am Coll Gastroenterol | ACG. 2024;119(10S):S1431–2. 10.14309/01.ajg.0001037372.00517 . .29. PubMed PMID: 00000434-202410001-02002. Cazzola M, Rogliani P, Ora J, Calzetta L, Lauro D, Matera MG. Hyperglycaemia and Chronic Obstructive Pulmonary Disease. Diagnostics (Basel). 2023;13(21). 10.3390/diagnostics13213362 . PubMed PMID: 37958258; PubMed Central PMCID: PMCPMC10650064. Epub 2023/11/14. Abdelmalak J, Tan N, Con D, Eslick G, Majeed A, Kemp W, et al. The Effect of Aspirin Use on Incident Hepatocellular Carcinoma-An Updated Systematic Review and Meta-Analysis. Cancers (Basel). 2023;15(13). 10.3390/cancers15133518 . Epub 2023/07/14. Additional Declarations No competing interests reported. Supplementary Files SupportingInformationS1Table.docx Supplementary information S1 Table. Variance inflation factors (VIF) for all covariates in the model. All VIF values were less than 5, indicating that multicollinearity was within acceptable limits. Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2025 Read the published version in BMC Gastroenterology → Version 1 posted Editorial decision: Revision requested 27 Oct, 2025 Reviews received at journal 26 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Editor invited by journal 16 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 15 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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23:27:56","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171391,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7556874/v1/70b4be9d3c209830dac333f0.html"},{"id":94049147,"identity":"ca78fc5d-0a71-4e24-a636-931e80c4c189","added_by":"auto","created_at":"2025-10-21 23:27:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118617,"visible":true,"origin":"","legend":"\u003cp\u003eScreening admissions for inclusion.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7556874/v1/c72729504da0855cf5700c50.png"},{"id":94049429,"identity":"64ff60df-31f6-436a-acf6-2e6c5bf2a6b3","added_by":"auto","created_at":"2025-10-21 23:35:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109415,"visible":true,"origin":"","legend":"\u003cp\u003eSMD between the aspirin nonusers group and aspirin users group in each cohort.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7556874/v1/5ce5d17061e5af4624261603.png"},{"id":94049146,"identity":"f863dabb-f4cc-4952-94b9-66ee64b9fc55","added_by":"auto","created_at":"2025-10-21 23:27:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100885,"visible":true,"origin":"","legend":"\u003cp\u003eK-M curves were used to compare the 30-day and 90-day mortality of patients with cirrhosis between the aspirin nonusers group and aspirin users group in each cohort (A; B, in the original cohort; C; D, in the matched cohort).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7556874/v1/6223d175b274ed95e322d95e.png"},{"id":94049156,"identity":"26ee2f88-3b42-426b-992d-66b30d8dd7a3","added_by":"auto","created_at":"2025-10-21 23:27:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163638,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between aspirin use and mortality risk of cirrhosis patients (A, 30-day mortality; B, 90-day mortality).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7556874/v1/6940382e9c9f346194011776.png"},{"id":98814086,"identity":"d714b269-5026-445f-be8e-d8334e6f7776","added_by":"auto","created_at":"2025-12-22 16:11:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1763438,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7556874/v1/190953c9-f303-490a-8592-0d3c500d9d70.pdf"},{"id":94049151,"identity":"c1e35c2e-1e95-48d6-82da-303bb981256a","added_by":"auto","created_at":"2025-10-21 23:27:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS1 Table. Variance inflation factors (VIF) for all covariates in the model. \u003c/strong\u003eAll VIF values were less than 5, indicating that multicollinearity was within acceptable limits.\u003c/p\u003e","description":"","filename":"SupportingInformationS1Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-7556874/v1/22b27372ed454ad6d57ec2fe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Aspirin reduces the risk of death in patients with cirrhosis: a propensity-matched retrospective analysis of the MIMIC-IV database","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLiver disease is one of the major public health challenges globally, causing well over 2\u0026nbsp;million deaths annually and accounting for 4% of all deaths globally. 1. Although liver disease is currently ranked as the 11th leading cause of death globally, its actual impact is likely to be underestimated. 2. Cirrhosis is the 10th leading cause of death in Africa (up 3 places from 2015), the 9th leading cause of death in Southeast Asia and Europe, and as high as the 5th leading cause of death in the Eastern Mediterranean region. 3. In addition, cirrhosis contributes significantly to global healthy life expectancy lost (DALY), ranking 15th globally, and disproportionately affects the younger population aged 25\u0026ndash;49 years, being the 12th leading cause of DALY in this age group,⁴ with potential life expectancy loss likely to be even higher in Europe. 5, 6 On an economic level, cirrhosis imposes a heavy healthcare burden. In the United States, for example, liver-related healthcare expenditures amounted to \u003cspan\u003e$\u003c/span\u003e32.5\u0026nbsp;billion in 2016, two-thirds of which was spent on inpatient and emergency care, and related expenditures have increased by 4% per year over the past 20 years. 7. In 2017, there were 10.6\u0026nbsp;million cases of decompensated cirrhosis and 112\u0026nbsp;million cases of compensated cirrhosis globally. 4. Patients with compensated cirrhosis and decompensated cirrhosis have a 5-fold and 10-fold increased risk of death, respectively, compared to the general population. 8 The 1- and 5-year survival rates for patients with compensated cirrhosis have been reported to be 87% and 67%, respectively, whereas the rates for patients with decompensated cirrhosis drop significantly to 75% and 45%. 8 Cirrhosis not only leads to high mortality rates but also carries a significant economic burden and health loss.\u003c/p\u003e\u003cp\u003eAspirin (acetylsalicylic acid) is a widely used medicine that helps reduce inflammation, fight tumors, and affect certain fats in the body by blocking the action of the pro-inflammatory cyclooxygenase-2 (COX-2) and platelet-derived growth factor (PDGF) pathways. 9\u0026ndash;12 Studies have shown that aspirin can be used for many purposes, such as pain relief, reducing fever, managing heart and brain diseases, treating joint diseases, helping during pregnancy, and preventing cancer. 13\u0026ndash;15\u003c/p\u003e\u003cp\u003eCirrhosis is a disease characterized by chronic inflammation accompanied by multiple complications and a high mortality rate. Clinical treatment is still based on symptomatic therapy. In studies with rats that have cirrhosis, various medications such as the COX-2 inhibitor celecoxib, aspirin, curcumin, carvacrol, hexoketone cacodylate, diosmin, statins, emricasan, and silymarin have shown potential in lowering inflammation and combating oxidative stress. 16 Of these, aspirin has shown strong effects in stopping scarring and cell growth in early studies, likely by slowing down liver fibrosis and lowering the chance of liver cancer (HCC). 17 Observational studies back this assertion up: in patients with metabolic dysfunction-associated steatohepatopathy (MASLD), using aspirin was linked to slower progression of severe liver scarring, fewer cases of liver cancer (HCC), and lower death rates related to liver issues. 18\u0026ndash;21 However, patients with advanced cirrhosis may experience an attenuated protective effect [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and the results of available meta-analyses are controversial. 23 Notably, the use of aspirin in patients with cirrhosis needs to be weighed against safety. Although patients with portal hypertension are often comorbid with thrombocytopenia, which may increase the risk of bleeding, 24\u0026ndash;25 retrospective studies have indicated that the use of aspirin for cardiovascular indications in patients with cirrhosis does not significantly increase the number of major bleeding events. 26\u0026ndash;28 A recent study involving 587 patients showed that taking aspirin after a transjugular intrahepatic portosystemic shunt (TIPS) greatly helped patients with severe fluid buildup live longer without needing another procedure after 12 months, but it didn't significantly help those with bleeding from varices, indicating that aspirin might work better for certain groups of patients.\u003c/p\u003e\u003cp\u003eWhether aspirin use is associated with lower mortality in cirrhotic patients admitted to the ICU remains controversial. Therefore, we designed this observational study to examine the potential beneficial effects of aspirin in patients with cirrhosis.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data sources\u003c/h2\u003e\u003cp\u003e This study examines data from the MIMIC-IV database version 3.1, which contains anonymous clinical information about patients in the Emergency Department and Intensive Care Unit (ICU) at Beth Israel Deaconess Medical Center (BIDMC) in the United States from 2008 to 2022, including details like patient demographics, vital signs, lab tests, medication records, and diagnostic codes. The construction of the database was reviewed by the BIDMC Institutional Review Board (IRB), which waived informed consent and approved data sharing, so no additional ethical approval was required for this study. The first author (Yu Yi) has completed the CITI program training (certification ID: 68122805) and passed the data use compliance test and was granted access to the MIMIC-IV data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study participants\u003c/h2\u003e\u003cp\u003eThe cohort study evaluated cirrhotic patients admitted to the ICU, and it identified 7,830 cirrhotic patients using International Classification of Diseases (ICD) codes. This analysis was done by excluding 4,725 patients who were younger than 18 years of age, admitted to the hospital for a non-first admission or non-first ICU admission, and had an ICU stay shorter than 24 hours. The final cohort consisted of 3,105 patients, 346 aspirin users, and 2,759 nonusers. After propensity score matching (PSM), 321 pairs of patients were matched (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Aspirin exposure\u003c/h2\u003e\u003cp\u003eAspirin exposure was defined as a prescription for oral aspirin within the first 72 hours of ICU admission.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data extraction\u003c/h2\u003e\u003cp\u003eWe extracted the following clinical data from the database: (1) Demographic data such as age, gender, and ethnicity were collected. (2) Vital signs measured on admission included heart rate, respiratory rate, and blood oxygen saturation (SpO2). (3) Disease severity was assessed using the model for end-stage liver disease (MELD), Sequential Organ Failure Assessment (SOFA), and Charlson comorbidity index. (4) Laboratory tests were first measured at admission, including white blood cells (WBC), hemoglobin, platelet count, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, albumin (ALB), creatinine, lactate, international normalized ratio (INR), prothrombin time (PT), and partial thromboplastin time (PTT). (5) Complications or comorbidities: ascites, hepatic encephalopathy, gastrointestinal bleeding, hepatorenal syndrome, liver transplantation, hypertension, type 2 diabetes mellitus (T2DM), cerebral infarction, myocardial infarction, chronic obstructive pulmonary disease (COPD), sepsis. (6) Therapeutic interventions included continuous renal replacement therapy (CRRT) and mechanical ventilation. Measured outcomes included 30- and 90-day survival, in-hospital mortality, and ICU length of stay.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Primary and Secondary Outcomes\u003c/h2\u003e\u003cp\u003eThe primary outcomes of this study were 30-day and 90-day mortality. Secondary outcomes were in-hospital mortality and length of stay in the ICU.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.6 Statistical analysi\u003c/b\u003es\u003c/h2\u003e\u003cp\u003eDecision Link. The software version 1.0 aims to conduct statistical analysis (20). The platform incorporates various programming language environments and simplifies the data processing and analysis process through a graphical interface. In our study, all variables exhibited less than 20% missing data, and multiple interpolation was used to fill in the missing data. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed data and median (IQR) for non-normally distributed data. Categorical variables were expressed as numbers and percentages. The t-test or Wilcoxon rank sum test was used for continuous variables, and the Pearson chi-square (χ\u0026sup2;) test was used for categorical variables to compare baseline data between the aspirin and non-aspirin groups.\u003c/p\u003e\u003cp\u003eWe applied a calliper width of 0.05 logits standard deviation to PSM to mitigate baseline imbalance. Cohorts were paired in a 1:1 ratio using the nearest-neighbor matching technique. The efficacy of PSM was assessed by a standardized mean difference (SMD), with SMD\u0026thinsp;\u0026le;\u0026thinsp;0.1 indicating a balanced model of initial characteristics.\u003c/p\u003e\u003cp\u003eWe used Kaplan-Meier (KM) survival analyses and log-rank tests to compare 30- and 90-day total mortality in patients with and without aspirin. We calculated variance inflation factors (VIF) for all covariates in the model. All VIF values were less than 5, indicating that multicollinearity was within acceptable limits (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We developed four models: model 1 is unadjusted. Model 2 was adjusted for age, gender, and ethnicity. Model 3 was further adjusted for ascites, hepatic encephalopathy, gastrointestinal bleeding, hepatorenal syndrome, liver transplantation, hypertension, T2DM, cerebral infarction, myocardial infarction, COPD, and sepsis. Model 4 was updated from model 3 by including additional factors such as heart rate, respiratory rate, SpO₂, WBC, hemoglobin, platelets, ALT, AST, total bilirubin, ALB, creatinine, lactate, INR, PT, PPT, MELD score, SOFA score, Charlson comorbidity index, mechanical ventilation, and CRRT. The results were expressed as hazard ratios (HR) or odds ratios (OR) with 95% confidence intervals (CI). In addition, subgroups were categorized according to ethnicity, age, gender, and comorbidity (including cerebral infarction, hypertension, T2DM, myocardial infarction, COPD, and sepsis). These subgroup analyses were designed to verify the consistency and robustness of our results. Interactions between subgroups were also investigated using the variance ratio test, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Patient characteristics\u003c/h2\u003e\u003cp\u003eThis study is a retrospective cohort study, which may be confounded by confounders. To reduce bias, we used the propensity score matching (PSM) method to balance confounding variables between groups. The study included 3105 patients with cirrhosis, of whom 346 (11.10%) were treated with aspirin and 2759 (88.90%) were untreated. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, patients in the pre-PSM aspirin group had the following characteristics: (1) Higher age and proportion of males. (2) The disease was more severe, as shown by a higher Charlson comorbidity index (median [IQR]: 7 [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] compared to 5 [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]), but the MELD score (16.97 [11.88\u0026ndash;22.81] compared to 19.88 [14.08\u0026ndash;27.68]) and SOFA score (7 [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] compared to 8 [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]) were lower (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (3) Laboratory parameters: higher hemoglobin and ALT, lower AST, total bilirubin, lactate, INR, PT, and PPT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (4) Complication spectrum: The prevalence of T2DM (42.49% vs. 27.11%), myocardial infarction (13.29% vs. 3.19%), and COPD (16.47% vs. 12.32%) and the rate of liver transplantation (4.30% vs. 0.94%) were higher (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the incidence of liver-related complications such as ascites and hepatic encephalopathy was lower. A 1:1 nearest-neighbor matching method with a caliper value of 0.05 was used to successfully match 321 pairs of patients with a standard mean difference of \u0026lt;\u0026thinsp;0.1 for all baseline characteristics after PSM (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of cirrhosis patients before and after PSM.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eBefore PSM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e\u003cp\u003eAfter PSM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon aspirin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAspirin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSMD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNon aspirin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAspirin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSMD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,105)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,759)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;346)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;642)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;321)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;321)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60 (53\u0026ndash;68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (52\u0026ndash;67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (59\u0026ndash;74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65 (58\u0026ndash;73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65 (58\u0026ndash;73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e65 (58\u0026ndash;74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1088 (35.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e997 (36.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91 (26.30)\u003c/p\u003e\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\u003e170 (26.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83 (25.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e87 (27.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2017 (64.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1762 (63.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e255 (73.70)\u003c/p\u003e\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\u003e472 (73.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e238 (74.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e234 (72.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2024 (65.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1794 (65.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e230 (66.47)\u003c/p\u003e\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\u003e425 (66.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e214 (66.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e211 (65.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225 (7.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195 (7.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (8.67)\u003c/p\u003e\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\u003e53 (8.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24 (7.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e29 (9.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther/Unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e856 (27.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e770 (27.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (24.86)\u003c/p\u003e\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\u003e164 (25.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83 (25.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e81 (25.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital signs\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate (beats/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (78\u0026ndash;105)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (78\u0026ndash;105)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85 (74-98.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e86.5 (74\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e89 (75\u0026ndash;101)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e85 (74\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory rate (beats/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (15\u0026ndash;23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (15\u0026ndash;23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (16\u0026ndash;22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18 (16\u0026ndash;22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18 (15\u0026ndash;22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e18 (16\u0026ndash;22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (95\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (95\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99 (96\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e98 (96\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98 (96\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e98 (96\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSeverity of illness\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMELD score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.51(13.73\u0026ndash;27.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.88(14.08\u0026ndash;27.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.97(11.88\u0026ndash;22.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.81 (11.79\u0026ndash;23.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.53 (11.80-23.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17.30(11.78\u0026ndash;23.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOFA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (5\u0026ndash;11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (5\u0026ndash;11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (4\u0026ndash;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7 (4\u0026ndash;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7 (4\u0026ndash;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7 (4\u0026ndash;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson comorbidity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (4\u0026ndash;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (4\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (5\u0026ndash;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7 (5\u0026ndash;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6 (5\u0026ndash;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7 (5\u0026ndash;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory measurements\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.6 (6.20\u0026ndash;14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.6 (6.10\u0026ndash;14.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.95 (6.63\u0026ndash;14.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.9 (6.50-14.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10 (6.40\u0026ndash;15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.9 (6.60\u0026ndash;14.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.4 (8.10\u0026ndash;10.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.4 (8-10.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (8.60-11.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10 (8.63\u0026ndash;11.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.1 (8.80\u0026ndash;11.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10 (8.60\u0026ndash;11.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet (K/uL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (67\u0026ndash;158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (65\u0026ndash;157)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e120 (81\u0026ndash;171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e118 (79\u0026ndash;174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e118 (78\u0026ndash;177)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e119 (80\u0026ndash;171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (20\u0026ndash;73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (20\u0026ndash;70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (19.25\u0026ndash;177)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e34 (19-114.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31 (18\u0026ndash;89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38 (20\u0026ndash;165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (39\u0026ndash;156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (40\u0026ndash;149)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.5 (35-327.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62 (34.25\u0026ndash;205.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e57 (34\u0026ndash;159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e66 (35\u0026ndash;306)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bilirubin (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.7 (1.2\u0026ndash;6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9 (1.3\u0026ndash;7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5 (1\u0026ndash;4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.7 (1-3.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.8 (1-3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.5 (1-4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.9 (2.5\u0026ndash;3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9 (2.5\u0026ndash;3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.95 (2.6\u0026ndash;3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (2.6\u0026ndash;3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3 (2.6\u0026ndash;3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.9 (2.6\u0026ndash;3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 (1-2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2 (1-2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1 (1\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.1 (1\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.2 (1-1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.1 (1\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactate (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2 (1.5\u0026ndash;3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.2 (1.6\u0026ndash;3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.1 (1.5-3.175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1 (1.4-3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1 (1.4-3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.1 (1.4-3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.7 (1.4\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.7 (1.4\u0026ndash;2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5 (1.3\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.5 (1.3\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.5 (1.3\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.5 (1.3\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT (sec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.1 (15-22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.2 (15.2\u0026ndash;23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.7 (14.4-20.575)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.5 (14.325\u0026ndash;20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.2 (14.2\u0026ndash;20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e16.7 (14.5\u0026ndash;20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPT (sec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.6 (31.4\u0026ndash;46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.8 (31.5\u0026ndash;46.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.5 (30.7\u0026ndash;44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e34.85 (30.425\u0026ndash;42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34.5 (30.1\u0026ndash;42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e35.2 (30.7\u0026ndash;42.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComplications or comorbidities\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAscites, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1491 (48.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1376 (49.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115 (33.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e218 (33.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e111 (34.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e107 (33.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatic encephalopathy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304 (9.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e293 (10.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (3.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23 (3.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12 (3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11 (3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal bleeding, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (3.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (3.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9 (1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4 (1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5 (1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatorenal syndrome, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e447 (14.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e430 (15.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (4.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36 (5.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19 (5.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17 (5.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver transplantation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (4.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24 (3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11 (3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13 (4.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e991 (31.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e877 (31.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (32.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e216 (33.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e107 (33.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e109 (33.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2DM, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e895 (28.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e748 (27.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147 (42.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e269 (41.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e135 (42.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e134 (41.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebral infarction, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e123 (3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103 (3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (5.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e36 (5.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16 (4.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20 (6.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyocardial infarction, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (4.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (3.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (13.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68 (10.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34 (10.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34 (10.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\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\u003eCOPD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e397 (12.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e340 (12.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (16.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e101 (15.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e52 (16.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e49 (15.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2282 (73.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2046 (74.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e236 (68.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e439 (68.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e221 (68.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e218 (67.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatments\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\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRRT, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e433 (13.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e398 (14.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (10.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60 (9.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28 (8.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32 (9.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2479 (79.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2190 (79.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e289 (83.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e534 (83.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e268 (83.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e266 (82.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.017\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\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Aspirin and primary outcomes\u003c/h2\u003e\u003cp\u003eAspirin users had a 30-day mortality rate of 15.26% and a 90-day mortality rate of 16.82%, both of which were lower than those of non-users (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Kaplan-Meier analyses showed that people taking aspirin had better chances of surviving for 30 and 90 days compared to those not taking it, both before and after matching the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We developed four models using multivariate Cox regression to assess the independent effect of aspirin treatment on 30- and 90-day mortality. In the group before matching, the chances of dying at 30 days (HR 0.74, 95% CI 0.55\u0026ndash;0.99) and 90 days (HR 0.70, 95% CI 0.53\u0026ndash;0.93) were significantly lower; in the group after matching, the chances of dying at 30 days (HR 0.66, 95% CI 0.44\u0026ndash;0.98) and 90 days (HR 0.61, 95% CI 0.42\u0026ndash;0.89) were even lower (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between aspirin and clinical outcomes in cirrhosis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon aspirin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAspirin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR/OR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBefore PSM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;3,105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;2,759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrimary outcomes\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30-day mortality, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e757(24.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e704(25.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53(15.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.57(0.43\u0026ndash;0.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90-day mortality, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e853(24.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e795(28.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58(16.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55(0.42\u0026ndash;0.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSecondary outcomes\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital mortality, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1052(33.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e972(35.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80(23.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55(0.42\u0026ndash;0.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU stay(days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.02 (1.82\u0026ndash;5.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.06 (1.83\u0026ndash;6.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.77 (1.64\u0026ndash;4.943)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfter PSM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrimary outcomes\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30-day mortality, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122(19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73(22.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49(15.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65(0.45\u0026ndash;0.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90-day mortality, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139(21.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85(26.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54(16.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.61(0.43\u0026ndash;0.85)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSecondary outcomes\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital mortality, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173(26.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98(30.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75(23.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.69(0.49\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU stay(days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.81 (1.77\u0026ndash;5.188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.95 (1.86\u0026ndash;5.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.69 (1.64\u0026ndash;4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of aspirin and the risk of 30-day and 90-day mortality\u0026nbsp;and hospital mortality.\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\u003eModels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBefore PSM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eAfter PSM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR/OR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR/OR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e30-day mortality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.43\u0026ndash;0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.45\u0026ndash;0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.41\u0026ndash;0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.44\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.46\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.44\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.55\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048\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\u003e(0.44\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e90-day mortality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.42\u0026ndash;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.43\u0026ndash;0.85)\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\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.41\u0026ndash;0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.42\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.45\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.42\u0026ndash;0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.53\u0026ndash;0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.42\u0026ndash;0.89)\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\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eHospital mortality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.42\u0026ndash;0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e(0.49\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.41\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e(0.48\u0026ndash;0.98)\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\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.44\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.46\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.54\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.42\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eModel 1: unadjusted.\u003c/p\u003e\u003cp\u003eModel 2: adjusted for age, gender, and ethnicity.\u003c/p\u003e\u003cp\u003eModel 3: adjusted for age, gender,\u0026nbsp;ethnicity, ascites, hepatic encephalopathy, gastrointestinal bleeding, hepatorenal syndrome, liver transplantation, hypertension, T2DM, cerebral infarction, myocardial infarction, COPD, and sepsis.\u003c/p\u003e\u003cp\u003eModel 4: adjusted for age, gender,\u0026nbsp;ethnicity, ascites,\u0026nbsp;hepatic encephalopathy, gastrointestinal bleeding, hepatorenal syndrome, liver transplantation, hypertension, T2DM, cerebral infarction, myocardial infarction, COPD, sepsis, heart\u0026nbsp;rate, respiratory\u0026nbsp;rate, SpO2, WBC, hemoglobin, platelet, ALT,\u0026nbsp;AST, total bilirubin, ALB, creatinine, lactate, INR, PT, PPT, MELD score, SOFA score, Charlson comorbidity index, mechanical ventilation, and CRRT.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Aspirin and secondary outcomes\u003c/h2\u003e\u003cp\u003eBefore PSM, both in-hospital mortality and ICU length of stay were lower in the aspirin group compared to the non-aspirin group. The difference in in-hospital mortality only remained significant after PSM (23.36% vs. 30.53%, p\u0026thinsp;=\u0026thinsp;0.041). The multifactorial logistic regression analysis indicated that aspirin treatment was a separate protective factor for in-hospital mortality on its own, both before and after PSM.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Subgroup analysis\u003c/h2\u003e\u003cp\u003eIn our detailed analysis, we discovered that using aspirin did not have important differences based on ethnicity, age, gender, cerebral infarction, hypertension, T2DM, myocardial infarction, COPD, and sepsis groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eCurrent research on the use of aspirin in patients with cirrhosis suggests potential benefits, particularly in terms of inhibiting the progression of liver fibrosis and reducing the risk of hepatocellular carcinoma (HCC). Our observational study using the MIMIC IV database further confirmed the benefits of aspirin in the management of cirrhosis. We found that aspirin therapy was associated with improved 30-day, 90-day, and hospitalized survival in a propensity score-weighted model.\u003c/p\u003e\u003cp\u003eAspirin exerts chemo preventive effects on the liver through a multi-target mechanism, and its protective effects are mainly reflected in four aspects: in antitumor, it reduces the glucose uptake of hepatocellular carcinoma cells through inhibition of the NF-κB signaling pathway and down-regulation of glucose transporter protein 1 (GLUT1) to inhibit tumor proliferation,1 while enhancing antitumor drug sensitivity,2\u0026ndash;4 and through antiplatelet effects to inhibit tumor cell adhesion and pro-metastatic microenvironment formation;5 in terms of anti-fibrosis, thioacetamide (TAA) and CCl4-induced hepatic fibrosis was significantly ameliorated by modulating the TGF-β1/Smad pathway and inhibiting the activity of COX-2; for cirrhosis and portal hypertension, aspirin improved hepatic sinusoidal endothelial dysfunction by modulating the COX-1/TXA2 pathway,6 and simultaneously reduced inflammatory cell infiltration7 by inhibiting NF-κ B reduces inflammatory cell infiltration7 and enhances the antitumor effect of interferon-α. 8 In terms of anti-inflammatory and antioxidant effects, aspirin reduces inflammation by inhibiting the NF-κB signaling pathway and, at the same time, reduces the levels of reactive oxygen species (ROS) and reactive nitrogen species (RNS), blocking the activation of hepatic stellate cells and thus breaking the vicious cycle of inflammation andoxidative stress. 9 These multifaceted mechanisms of action suggest that aspirin may become an important strategy for the prevention and treatment of liver diseases. This could serve as a crucial strategy for both disease prevention and treatment.\u003c/p\u003e\u003cp\u003eAdditional clinical studies have further validated the efficacy of aspirin in patients with cirrhosis. A large study using data from the CGRD database in Taiwan found that taking low-dose aspirin every day for 10 years was linked to a lower chance of developing HCC in people with cirrhosis (three-year HR 0.57, 95% CI 0.37\u0026ndash;0.87; p\u0026thinsp;=\u0026thinsp;0.0091, five-year HR 0.63, 95% CI 0.45\u0026ndash;0.88; p\u0026thinsp;=\u0026thinsp;0.0072). Overall mortality was significantly lower in aspirin users with cirrhosis compared to untreated controls (three-year HR 0.43 (0.33\u0026ndash;0.57); five-year HR 0.51 (0.42\u0026ndash;0.63)). Notably, there was also no increased risk of gastrointestinal bleeding in APA users compared with untreated patients (3-year: corrected HR 0.71; 95% CI 0.53\u0026ndash;0.94; 5-year: corrected HR 0.66; 95% CI 0.51\u0026ndash;0.84). This safety profile provides key support for its clinical use. Another study found 11 that in a single-center cirrhosis cohort, aspirin users demonstrated a reduced risk of HCC and had a significantly lower risk of the composite outcome of hepatic encephalopathy and ascites, suggesting that aspirin may delay hepatic dysfunction through anti-inflammatory or antifibrotic mechanisms. Also, in patients being checked for liver transplantation, using both aspirin and statins together showed better results: those on the combination had better INR (1.17 vs 1.52), total bilirubin (1.33 vs 5.61 mg/dL), and MELD scores (11.67 vs 19.66) compared to those not on the combination (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was a higher risk of liver problems and fluid buildup in patients checked for liver transplantation compared to those using only aspirin (23.5% vs 76.5%, P\u0026thinsp;=\u0026thinsp;0.023) or only statins (23.5% vs 76.5%, P\u0026thinsp;=\u0026thinsp;0.021), and patients using both aspirin and statins had a much lower death rate compared to those using just aspirin (23.5% vs 76.5%, P\u0026thinsp;=\u0026thinsp;0.021). 12 Overall, these results indicate that aspirin might offer cirrhotic patients several health advantages, including preventing liver cancer and slowing down the progression of the disease, all without raising the risk of bleeding.\u003c/p\u003e\u003cp\u003eWe discovered that patients with cirrhosis in ICUs who took aspirin had much higher rates of combined T2DM (42.49% vs. 27.11%), myocardial infarction (13.29% vs. 3.19%), COPD (16.47% vs. 12.32%), and liver transplantation (4.30% vs. 0.94%) compared to those who did not take aspirin (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This difference may result from the clinical dosing profile of aspirin and its potential protective effects: patients with myocardial infarction usually require long-term aspirin for secondary prevention due to elevated cardiovascular risk; patients with T2DM may benefit from aspirin's improved insulin sensitivity and glycemic modulation;13 and patients with COPD may be more likely to be on aspirin due to its anti-inflammatory effects. 14 Also, the higher rate of liver transplants might be linked to the various protective benefits of aspirin, which studies have shown can not only help improve outcomes by lowering the chances of portal hypertension and bleeding but may also help transplant candidates live longer by decreasing the occurrence of HCC, a key reason for needing a liver transplant. 15\u003c/p\u003e\u003cp\u003eHowever, our study has some limitations. First, our study relied on the MIMIC database, which has some limitations and lacks information for the etiologic diagnosis of cirrhosis to obtain a specific etiologic diagnosis, so the etiology of cirrhosis was not included in this study. Second, the gastrointestinal bleeding extracted from the MIMIC database based on ICD codes could not be determined to have been caused by cirrhosis itself or by aspirin use, so it was not included as one of the outcome indicators in this study. Future studies could conduct further prospective studies to validate our observations in a more diverse cohort to more fully assess the effect of aspirin on the prognosis of patients with cirrhosis. Third, even though we used propensity score matching (PSM) to account for known factors, we should consider how unknown factors that weren't recorded might affect the study results. These variables, which were not documented in the database, may have limited our ability to clarify the causal relationship between aspirin use and mortality reduction. In conclusion, aspirin use was associated with reduced 30- and 90-day mortality, as well as hospitalization mortality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAspirin is associated with reduced 30-day, 90-day, and hospitalization mortality in cirrhotic patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALT Alanine Aminotransferase\u003c/p\u003e\u003cp\u003eAST Aspartate Aminotransferase\u003c/p\u003e\u003cp\u003eALB Serum Albumin\u003c/p\u003e\u003cp\u003eCOPD Chronic Obstructive Pulmonary Disease\u003c/p\u003e\u003cp\u003eCRRT Continuous Renal Replacement Therapy\u003c/p\u003e\u003cp\u003eHR Hazard Ratio\u003c/p\u003e\u003cp\u003eINR International Normalized Ratio\u003c/p\u003e\u003cp\u003eICU Intensive Care Unit\u003c/p\u003e\u003cp\u003eMELD Model For End-stage Liver Disease\u003c/p\u003e\u003cp\u003eMIMIC-IV Medical Information Mart for Intensive Care-IV\u003c/p\u003e\u003cp\u003eOR Odds Ratio\u003c/p\u003e\u003cp\u003ePT Prothrombin Time\u003c/p\u003e\u003cp\u003ePTT Partial Thromboplastin Time\u003c/p\u003e\u003cp\u003ePSM Propensity Score Matching\u003c/p\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e Blood Oxygen Saturation\u003c/p\u003e\u003cp\u003eSMD Standardized Mean Difference\u003c/p\u003e\u003cp\u003eSOFA Sequential Organ Failure Assessment\u003c/p\u003e\u003cp\u003eT2DM Type 2 Diabetes Mellitus\u003c/p\u003e\u003cp\u003eWBC 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\u003cp\u003eNot Applicable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYu Yi designed the research; Yinghua Chen extracted and analyzed the data; Yu Yi wrote the manuscript; Yu Yi collated and interpreted the data; and Yinghua Chen modified the manuscript and interpreted the analysis. Yawen Luo reviewed the manuscript. All authors contributed to the article and approved the final submission.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eWe are grateful to all the participants for their valuable contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePublicly available datasets were analyzed in this study. The datasets generated and analyzed during the current study are available in the MIMIC-IV database (https://physionet.org/content/mimiciv/3.1/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAsrani SK, Devarbhavi H, Eaton J, Kamath PS. Burden of liver diseases in the world. J Hepatol. 2019;70(1):151\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2018.09\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2018.09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Epub 2023/07/14.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MIMIC-IV database, aspirin, cirrhosis, intensive care unit, mortality","lastPublishedDoi":"10.21203/rs.3.rs-7556874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7556874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCirrhosis of the liver not only leads to high mortality rates but also carries a huge economic burden and health losses. Aspirin is a drug with potential liver disease indications. However, the benefits of aspirin in cirrhosis remain controversial. We sought to determine whether aspirin therapy has a protective effect on outcomes in patients with cirrhosis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe selected patients with cirrhosis from the Medical Information Marketplace in Intensive Care IV (MIMIC-IV) database. Propensity score matching (PSM) balanced baseline differences. Multivariate Cox regression models assessed the association between aspirin therapy and 30- and 90-day mortality, and multifactorial logistic regression models assessed the association between aspirin therapy and hospital mortality.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe included a total of 3,105 patients, of whom 346 received aspirin therapy and 2,759 did not. Following PSM, there were 321 matched pairs. Aspirin users had a 30-day mortality rate of 15.26% and a 90-day mortality rate of 16.82%, both lower than nonusers. Multifactorial Cox regression analysis indicated that aspirin use was associated with reduced 30-day mortality (HR 0.66, 95% CI 0.44\u0026ndash;0.98) and 90-day mortality (HR 0.61, 95% CI 0.42\u0026ndash;0.89). Multifactorial logistic regression analysis indicated that aspirin use was associated with reduced in-hospital mortality (OR 0.64, 95% CI 0.42\u0026ndash;0.97). No significant differences were found in ICU length of stay.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAspirin is associated with reduced 30-day, 90-day, and hospitalization mortality in cirrhotic patients.\u003c/p\u003e","manuscriptTitle":"Aspirin reduces the risk of death in patients with cirrhosis: a propensity-matched retrospective analysis of the MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 23:27:51","doi":"10.21203/rs.3.rs-7556874/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-27T07:47:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T07:57:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T18:24:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219280033151452812715581627689143074158","date":"2025-10-14T00:12:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142657760787668877144003444023468574903","date":"2025-10-11T02:40:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338820945237705217259592494962317722442","date":"2025-10-08T16:12:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T15:15:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-07T09:26:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-16T08:48:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T12:31:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2025-09-15T12:12:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e6b443e-a656-4c9a-8977-543beeb9c1e4","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:05:27+00:00","versionOfRecord":{"articleIdentity":"rs-7556874","link":"https://doi.org/10.1186/s12876-025-04499-2","journal":{"identity":"bmc-gastroenterology","isVorOnly":false,"title":"BMC Gastroenterology"},"publishedOn":"2025-12-18 15:58:05","publishedOnDateReadable":"December 18th, 2025"},"versionCreatedAt":"2025-10-21 23:27:51","video":"","vorDoi":"10.1186/s12876-025-04499-2","vorDoiUrl":"https://doi.org/10.1186/s12876-025-04499-2","workflowStages":[]},"version":"v1","identity":"rs-7556874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7556874","identity":"rs-7556874","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}