Prognostic Value of Interstitial Lung Abnormalities in Patients with Liver Cirrhosis: a Retrospective Cohort Study

Prognostic Value of Interstitial Lung Abnormalities in Patients with Liver Cirrhosis: a Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Value of Interstitial Lung Abnormalities in Patients with Liver Cirrhosis: a Retrospective Cohort Study Bo Yuan, Yu Jia, Min Zhu, Yiheng Zhou, Shanye Yi, Yanlin Xu, Aga Shama, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4522424/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cirrhosis is the end-stage liver fibrosis and leads to massive death worldwide. Interstitial lung abnormalities (ILAs) have received widespread attention because of their progression to pulmonary fibrosis and mortality. This study aimed to investigate whether the presence of ILAs is associated with elevated mortality in patients with cirrhosis. Methods: Patients diagnosed with cirrhosis between August 2011 and November 2023 were retrospectively included. Clinical data were collected from electronic records. ILAs were recorded by chest computed tomography. The proportion of ILAs and the associations between ILAs and all-cause mortality in cirrhosis were analyzed. Results: A total of 4,022 patients with cirrhosis were included, and 749 (18.6%) subjects were diagnosed with ILAs. During the median 2.1 (1.0-5.1) years of follow-up, patients with ILAs had higher mortality than those without (48.6% vs. 38.1%; P<0.001), ILAs significantly increased all-cause mortality (hazard ratio: 1.355; 95% confidence interval: 1.202-1.527; P<0.001). These associations remain significant in patients with viral, alcoholic, and primary biliary cirrhosis. Moreover, all the imaging features of the ILAs were positively related to mortality (P<0.05). According to the subgroup analysis, these associations were consistent across age and sex but were stronger in compensated cirrhosis than decompensation (P for interaction: 0.047). Conclusion: ILAs is high occurrence in patients with cirrhosis, is independently related to all-cause mortality in patients with cirrhosis, and strategies for risk stratification and prognosis assessment targeting ILA may yield clinical benefits. Liver cirrhosis Interstitial lung abnormalities Mortality Fibrosis Figures Figure 1 Figure 2 Figure 3 Introduction Liver cirrhosis is the end-stage of various chronic liver diseases characterized by liver tissue fibrosis and impaired liver function [ 1 ]. This condition has now become a substantial global health burden, with significant morbidity and mortality [1; 2; 3]. Given the seriousness of cirrhosis, it is important to identify the risk factors that influence the occurrence, development, and prognosis of cirrhosis. Fibrotic diseases are the results of recurrent tissue injury and aberrant repair, characterized by excessive extracellular matrix (ECM) deposition, disruption of normal tissue structure, and subsequent impairment of organ function [ 4 ]. These conditions encompass a wide spectrum of diseases including cirrhosis, pulmonary fibrosis, kidney fibrosis [4; 5]. Whether these diseases are systemic in nature and whether they share pathobiological mechanisms and interact with each other. The liver-lung axis theory suggests extensive interactions between the liver and lungs, with both organs exhibiting similar responses and lesions in reaction to stimuli and injuries [ 6 ]. Is there an association between cirrhosis and pulmonary fibrosis? Several clinical studies have reported that interstitial lung disease is a common and clinically significant complication of cirrhosis [7; 8; 9], indicating that the management of pulmonary fibrosis should be integrated into the overall management of cirrhosis. Recently, the concept of interstitial lung abnormalities (ILAs), which are considered an early stage of pulmonary fibrosis, has been proposed. and identified more frequently as CT screening becomes more common [ 10 ]. With the widespread use of CT scanning, ILAs are being identified with greater frequency [ 11 ]. Given that ILAs, as precursors to ILD, share a fibrotic nature with cirrhosis, and considering the documented association between ILDs and cirrhosis, there may be potential link between ILAs and cirrhosis, but it remains unexplored in the current literature. Therefore, the relationship between ILAs and liver cirrhosis deserves further investigation. In addition, studies have confirmed that the occurrence of ILAs is associated with respiratory symptoms, decreased lung function, mortality, multimorbidity and more [12; 13; 14; 15]. As an extrahepatic fibrotic disease, ILAs may be positively correlated with the severity of intrahepatic fibrosis. Therefore, exploring the relationship between ILAs and the prognosis of patients with cirrhosis may provide an early predictive marker of poor outcomes in patients with cirrhosis, and the evidence may support the pathological hypothesis that fibrotic diseases are systemic diseases. However, the prevalence and adverse outcomes of ILA in patients with cirrhosis are unknown. Overall, we hypothesized that there may be an association between ILAs and long-term mortality in the cirrhosis population. Therefore, in this study, we aimed to explore 1) The proportion of ILAs in the population with liver cirrhosis; 2) mortality of cirrhotic patients with or without ILAs; 3) whether the presence of ILAs is an independent predictor of mortality in patients with cirrhosis according to a retrospective cohort in China. Methods Study design and subjects A retrospective cohort study was designed to determine the proportion of ILAs in cirrhotic patients, and its impact on the severity and mortality of cirrhosis. This study complied with the Declaration of Helsinki and was approved by the Human Ethical Committee of Sichuan University West China Hospital. All patients signed informed consent to use their clinical data for scientific research upon admission. 4577 patients with cirrhosis who were hospitalized at West China Hospital of Sichuan University between August 2011 and November 2023, and underwent chest CT scans during hospitalization were retrospectively evaluated. Except for a small number of patients who undergo biopsy, most liver cirrhosis patients are diagnosed through noninvasive examinations according to domestic and international guidelines [ 16 ]. Liver stiffness ≥ 15 kPa, evaluated through vibration-controlled transient elastography, was used to confirm the presence of cirrhosis. Radiomorphological signs of cirrhosis were identified via cross-sectional imaging. Moreover, the incorporation of biochemical markers such as the INR, bilirubin, and albumin can reinforce the diagnosis of cirrhosis. In this study, we excluded patients who were < 18 years old (n = 55), who had suspected or confirmed ILD (n = 70), who had portal vein thrombosis or hepatocellular carcinoma (n = 101), who were lost to follow-up (n = 235), or who had missing covariates > 10% (n = 94). Finally, a total of 4022 patients with cirrhosis were included. Data collection and evaluation The demographic characteristics, smoking and drinking history, medical history, operation history, laboratory examinations and CT reports were extracted from the patients’ electronic health records, and two trained clinical research experts checked the data, especially for outliers. According to electronic medical records, we classified the causes of liver cirrhosis into viruses, alcohol, primary cholestasis, autoimmune diseases, and others. The Child‒Pugh stage was used to assess liver function and disease severity and was calculated by hepatic encephalopathy (none, grade 1–2, or grade 3–4), ascites degree (none, mild or moderate/severe), bilirubin ( 3 mg/ml), albumin (> 3.5 mg/ml, 2.8 to 3.5 mg/ml, or < 2.8 mg/ml), and prolonged prothrombin time ( 6 sec) according to reported standards [ 17 ]. Child‒Pugh class A indicates compensated cirrhosis, and Child‒Pugh class B or C indicates decompensated cirrhosis. The complete blood cell count was analyzed using a hematology analysis system (LH750; Beckman Coulter Inc., Brea, California). Blood biochemical data were analyzed using an Architect c16000 analyzer (Abbott Diagnostics, Dallas, Texas). Chest CT reporting and ILA diagnosis The supine position CT images were acquired using a low-dose protocol (tube voltage of 120 kVp and tube current ranging from 1–5 mHz) on a dual-slice CT scanner (Somatom Emotion Duo). Images were reconstructed with contiguous < 1 mm sections, and were independently evaluated and reported by two radiologists (one with at least 5 years of experience in thoracic radiology, and the other with at least 10 years of experience). Before the official report was released, the two experts agreed. Interobserver agreement was assessed. Patients with potential ILAs were firstly screened by key words related interstitial lung changes in chest CT scans reports. The imaging of selected patients was further independently validated by at least two well-trained readers with extensive experience in ILD, blinded to medical information and prior radiologic reports. The senior reviewer helped the two readers reach a final consensus on cases with inconsistent diagnoses or contradictions with CT reports. The assessment of ILA, and its features and subcategories were grounded in a comprehensive framework, drawing upon the Glossary of Terms for Thoracic Imaging and the position paper on ILAs [10; 18], as follows: ILA diagnosis was as follows: 1) features of ILA, including ground-glass abnormalities, reticular abnormalities, traction bronchiectasis, honeycombing, and nonemphysematous cysts; 2) involvement of at least 5% of the lung zone, delineated into upper, middle, and lower zones by the levels of the inferior aortic arch and right inferior pulmonary vein; and 3) interstitial lung disease was not suspected. ILA Subcategories: 1) Nonsubpleural ILAs: ILAs involve non-subpleural localization. 2) Subpleural nonfibrotic ILAs: ILAs with a predominant subpleural localization and lacking manifestations of fibrosis. 3) Subpleural fibrotic ILAs: ILAs with predominant subpleural localization and pulmonary fibrosis; fibrosis is characterized by architectural distortion accompanied by honeycombing and/or traction bronchiectasis. Follow-up and endpoints The final follow-up time was February 6, 2024. In this study, participants received a median of 2.3 (1.0-5.1) years of follow-up. Systematically trained staff conducted the follow-up by interviewing patients via telephone calls or questionnaires. The primary endpoint was long-term all-cause mortality, and the secondary endpoint was one-year all-cause mortality. Statistical analysis According to previous reports, missing data were addressed through multiple imputations using a chained equation approach. Following 10 iterations, 5 imputation datasets were generated. Ultimately, a predictive average-matching model was employed to assign values to each variable [ 19 ]. Participants were classified into groups according to their diagnosis of ILAs. Nonparametric variables are presented as medians (25th and 75th percentiles), parametric variables are presented as the means ± SDs, and categorical variables are presented as frequencies and percentages. The χ2 test or Fisher’s exact test for categorical variables and Student’s t test or one-way ANOVA for continuous variables were used to compare the differences between groups. The χ2 test, which depends on the Child‒Pugh stage and related parameters, was used to assess the associations between ILAs and the severity of cirrhosis. To explore the association between ILAs and one-year or long-term mortality in patients with cirrhosis, a Cox regression model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). In addition to univariable Model 1, Model 2 was adjusted for age, sex and BMI, while in fully adjusted Model 3, age, sex, BMI, smoking and drinking status, Child‒Pugh score, albumin, total bilirubin, creatinine, sodium, platelet count, alanine aminotransferase, glutamyl transpeptidase, aspartate aminotransferase and prolonged prothrombin time were adjusted. The associations between ILAs and mortality in patients with different types of cirrhosis, including alcoholic cirrhosis, viral hepatitis cirrhosis, primary biliary cirrhosis, and autoimmune cirrhosis, were also investigated. In addition, based on the above models, the associations between five features (ground-glass or reticular abnormalities, lung distortion, traction bronchiectasis, honeycombing, and nonemphysematous cysts) and three subcategories (nonsubpleural ILAs, subpleural fibrotic ILAs, subpleural nonfibrotic ILAs) of ILAs and mortality were explored, and forest plots and Kaplan‒Meier curves were drawn. Subgroup analyses stratified by age (55 < vs. ≥55 years), sex (female vs. male), and decompensation status (yes vs. no) were performed using the Cox regression model with the same covariates as above, and P for interaction was tested. In the sensitivity analysis, similar Cox regression models were used after excluding individuals with discordant ILA diagnoses. The significance of the P values was set to 0.05. Bonferroni correction was used to adjust for multiple comparisons. All the statistical analyses were performed using SPSS version 26.0 (IBM Corp, Armonk, NY, United States) and R software 3.5.0 (Vienna, Austria). Results ILAs proportion in cirrhosis and clinical characteristics In this study, a total of 4,022 patients with cirrhosis were included; these patients had an average age of 54 years, and 59% were male. 749 (18.6%) subjects were diagnosed with ILAs. Baseline characteristics were compared between the non-ILA group and the ILA group. Participants with ILAs were more likely to be older and have higher total bilirubin levels, prothrombin times, glutamyl transpeptidase levels, and aspartate aminotransferase levels. Additionally, higher rates of hepatic encephalopathy, autoimmune hepatitis and liver transplantation history were observed in the ILA group (Table 1 ). Moreover, patients with ILAs had a more advanced Child‒Pugh grade and a greater incidence of hepatic encephalopathy and hypoproteinaemia (P < 0.05) but not ascites, peritonitis, hypercholesterolaemia or coagulation disorders ( Supplementary Table 1 ). Similar finding were observed in nonsubpleural, subpleural nonfibroti, subpleural fibrotic ILAs. Table 1 Baseline characteristics of patients with liver cirrhosis. Variable Total Non-ILAs ILAs P value Sample size 4022 3273 (81.4%) 749 (18.6%) Age (year) 54.33 ± 13 53.15 ± 12.93 59.53 ± 12.36 < 0.001 Male, n(%) 2380 (59.1%) 1936 (59.2%) 444 (59.3%) 0.949 BMI (kg/m 2 ) 23.12 ± 3.67 23.16 ± 3.68 22.99 ± 3.56 0.275 Smoking status, n(%) Never 3017 (75.0%) 2472 (75.5%) 545 (72.8%) former 651 (16.1%) 521 (15.9%) 130 (17.4%) 0.270 Current 354 (8.8%) 280 (8.6%) 74 (9.9%) Alcohol status, n(%) Never 2920 (72.6%) 2376 (72.6%) 544 (72.6%) former 1069 (25.6%) 873 (26.7%) 196 (26.2%) 0.430 Current 33 (0.8%) 24 (0.7%) 9 (1.2%) Laboratory Examination Albumin 33.44 ± 6.98 33.66 ± 7.08 32.47 ± 6.42 < 0.001 Total bilirubin (umol/L) 25.65 (14.6, 50.23) 25.2 (14.5, 49.3) 27.5 (15.5, 55.1) 0.034 International standardized ratio 1.30 ± 0.36 1.30 ± 0.37 1.32 ± 0.34 0.085 Prothrombin time (S) 14.68 ± 4.08 14.62 ± 4.09 14.93 ± 4.00 0.031 Creatinine (ummol/L) 68.7 (56, 86) 68 (56, 86) 70 (56.6, 89.55) 0.192 Serum sodium (mmol/L) 138 ± 4.23 138.90 ± 4.23 138.69 ± 4.21 0.220 Platelet count (10 9 /L) 76 (47, 132) 77 (48, 134) 74 (44, 120) 0.015 Triglycerides (mmol/L) 0.96 (0.67, 1.48) 0.96 (0.67, 1.49) 0.95 (0.69, 1.40) 0.664 Cholesterol (mmol/L) 3.36 (2.56, 4.37) 3.38 (2.56, 4.43) 3.27 (2.53, 4.15) 0.017 Alanine aminotransferase (U/L) 29 (18, 55) 29 (18, 56) 29 (18, 52) 0.801 Glutamyl transpeptidase (U/L) 64 (29, 167) 62 (28, 164.5) 73 (33.5 ,176) 0.004 Aspartate aminotransferase (U/L) 46.5 (29, 93) 45 (28, 92) 51 (31, 96.5) 0.002 Causes of Liver cirrhosis Viral hepatitis 2952 (73.3%) 2442 (82.7%) 510 (68.1%) < 0.001 Alcoholic cirrhosis 628 (15.6%) 500 (15.3%) 128 (17.1%) 0.218 Primary biliary cirrhosis 426 (10.5%) 334 (10.2%) 92 (12.3%) 0.095 Autoimmune hepatitis 310 (7.7%) 237 (7.2%) 73 (9.7%) 0.020 Other causes 929 (23.1%) 429 (13.2%) 110 (14.7%) 0.252 Surgical history TIPS 312 (7.7%) 274 (8.4%) 38 (5.1%) 0.002 Liver transplantation 171 (4.2%) 120 (3.7%) 51 (6.8%) < 0.001 Hepatectomy 26 (0.6%) 24 (0.7%) 2 (0.3%) 0.151 Splenectomy 189 (4.6%) 155 (4.7%) 34 (4.5%) 0.819 BMI, body mass index; ILAs, interstitial lung abnormalitie Associations between ILAs and all-cause mortality During the 1 year of follow-up, 677 (16.5%) deaths were recorded, and patients with ILAs had higher mortality (20.8% vs. 15.6%; P < 0.001) than those without. According to the fully adjusted multivariate Cox regression models (Table 2 ), the hazard ratio (HR) of patients with ILAs was 1.251 (95% CI, 1.041–1.503; P = 0.017) compared with that of patients without ILAs. During long-term follow-up, the median observation period was 2.1 (1.0-5.1) years, and 1610 (40.0%) deaths were recorded. Similarly, patients with ILAs had higher mortality (48.6% vs. 38.1%; P < 0.001) than those without. Moreover, ILAs significantly elevated all-cause mortality (HR: 1.355, 95% CI: 1.202–1.527, P < 0.001). Notably, these associations remained significant in patients with viral cirrhosis, alcoholic cirrhosis, and primary biliary cirrhosis (P < 0.05) but not in those with autoimmune or other causes ( Supplementary Table 2 ). Table 2 Cox regression models were performed to analyze the association between ILAs and mortality in cirrhosis. Case/Total (%) Model 1 Model 2 Model 3 HR (95%Cl) P HR (95%Cl) P HR (95%Cl) P Total cirrhosis One-year mortality 667/4022 (16.5%) - - - - - - Non-ILAs 511/3273 (15.6%) Ref. - Ref. - Ref. - ILAs 156/749 (20.8%) 1.408 (1.177–1.685) < 0.001 1.338 (1.114–1.607) 0.002 1.251 (1.041–1.503) 0.017 Long-term mortality 1610 /4022 (40.0%) - - - - - - Non-ILAs 1246/3273 (38.1%) Ref. - Ref. - Ref. - ILAs 364/749 (48.6%) 1.532 (1.355–1.712) < 0.001 1.396 (1.239–1.547) < 0.001 1.355 (1.202–1.527) < 0.001 Model 1 is univariable Cox regression analysis. Mode 2 is adjusted by age, sex, and BMI. Model 3 is adjusted by age, sex, BMI, smoking and drinking status, Child-Pugh score, albumin, total bilirubin, creatinine, natrium, platelet count, alanine aminotransferase, glutamyl transpeptidase, aspartate aminotransferase and prolonged prothrombin time. ILAs, interstitial lung abnormalities. Associations between the features and subcategories of ILAs and mortality The present study revealed five features of ILAs, namely, ground-glass abnormalities, reticular abnormalities, traction bronchiectasis, honeycombing, and nonemphysematous cysts, and all of these features were positively related to all-cause mortality (P < 0.05, Fig. 1 ). Moreover, ILAs with honeycombing had the largest adjusted HR during the 1-year follow-up (HR 1.996, 95% CI 1.221–3.263, P = 0.006) and long-term follow-up (HR 1.601, 95% CI 1.149–2.231, P = 0.005). K‒M curve analysis (Fig. 2 ) revealed that among the subcategories of ILAs, subcategories of ILAs had a significantly lower cumulative survival rate than did non-ILAs at the 1-year follow-up (non-ILAs vs. nonsubpleural ILAs vs. subpleural nonfibrotic ILAs vs. subpleural fibrotic ILAs: 83.9% vs. 73.5% vs. 80.8% vs. 75.6%, log rank χ2: 19.373, P = 0.001) and long-term follow-up (non-ILAs vs. nonsubpleural ILAs vs. subpleural nonfibrotic ILAs vs. subpleural fibrotic ILAs: 45.5% vs. 27.7% vs. 28.3% vs. 24.6%, log rank χ2: 55.260, P 0.05). In addition, these associations were stronger in patients with compensated cirrhosis than in those with decompensated cirrhosis (P for interaction: 0.047). In this study, there were 210 inconsistent ILA diagnoses in the first round of inspection, and the kappa coefficient reached 0.829, which indicates that the diagnostic consistency of ILAs was almost perfect. Then, we removed samples from 210 inconsistent reports to conduct a sensitivity analysis, and the results were consistent in the absence of exclusion ( Supplementary Tables 3 and 4 ). Discussion This study represents the first instance where a relatively large sample size and adequate follow-up periods have been employed to explore the relationship between ILA and all-cause mortality in patients with cirrhosis. The present study demonstrated that the proportion of ILAs in patients with cirrhosis was 18.6%, and cirrhotic patients with ILA had 25.1% and 35.6% greater risks of one-year and long-term mortality, respectively, than those without ILA. These associations were significant in patients with different etiologies of liver cirrhosis, such as viruses, alcohols, and cholestasis, but not in patients with autoimmune cirrhosis. These results suggested that ILA was high occurrence in patients with cirrhosis and was a significant prognostic marker and even a risk factor among patients with cirrhosis. In our previous study involving a large sample of Chinese individuals undergoing periodic health check-up, the prevalence of ILAs was 2.1%, and the most common classification was subpleural nonfibrotic ILAs (81.7%) [ 20 ]. Notably, this study revealed that 18.6% of patients with cirrhosis had ILAs, and 56.4% had subpleural nonfibrotic ILAs. Compared to the general population, patients with cirrhosis had an 8-fold greater prevalence of ILA, and the proportions of nonsubpleural and fibrotic ILAs were greater. Therefore, cirrhosis and ILA are comorbidities worthy of attention. The Child‒Pugh score is the most common tool for assessing liver function, and we observed that cirrhotic patients with ILA were more likely to have advanced Child‒Pugh grade, indicating that ILA associated with the severity of cirrhosis. Importantly, after adjusting for confounders, such as demographic factors, smoking status, alcohol consumption status, liver enzymes, and medical history, ILAs were independently associated with all-cause mortality. Consistent with previous research reports, ILA not only is involved in the progression of ILD but also increases the risk of death in patients with cancer, and rheumatoid diseases [21; 22; 23; 24]. Importantly, according to the definition of ILAs, the incidence of ground-glass abnormalities, reticular abnormalities, traction bronchiectasis, honeycombing, and nonemphysematous cysts is associated with elevated mortality in patients with cirrhosis. Moreover, the results of the ILA classification showed that the associations between ILA and long-term mortality were consistent regardless of subpleural involvement or fibrotic manifestations. Furthermore, subgroup analysis revealed that this relationship remained significant in patients of different ages, sexes, and liver functions. All of these specific analyses emphasize that ILA may be a key risk factor for mortality in patients with cirrhosis. The association between ILA and adverse outcomes in patients with cirrhosis may be explained by several mechanisms. First, portal systemic venous shunting, vasodilation, and imbalanced ventilation/perfusion caused by liver dysfunction may all lead to pulmonary inflammation and fibrosis [ 25 ]. Moreover, as we previously reported, the liver serves as a multifunctional organ for immune, metabolic, endocrine, and other functions, and impaired liver function may respond to the respiratory system via oxidative stress and inflammation [ 26 ]. Second, based on the theory of the liver-lung axis, liver cirrhosis may interact with lung disease [ 27 ]. Thus, pulmonary abnormalities in patients with liver disease may have important implications for prognosis. Fibrosis is a common pathological manifestation of ILA and cirrhosis, and previous studies have reported common cellular dysfunction and signaling pathway activation. Studies have confirmed the presence of similar cellular responses, such as those involving macrophages and fibroblasts, as well as common signaling pathways, such as the JAK/STAT and TGF-β pathways, in liver and lung fibrosis [27; 28]. Telomere shortening has also been implicated in various liver and lung fibrosis (ref: pulmonary abnormalities in liver disease: relevance to transplantation and outcome). Therefore, the comorbidity of ILA and cirrhosis may indicate a stronger systemic fibrosis response and mediate poor individual prognosis. Fourth, liver cirrhosis and lung fibrosis are both aging-related diseases [ 29 ]. A previous study reported that telomere biology disorders could lead to ILD and cirrhosis [ 30 ]. Therefore, ILA may not only represent early manifestations of ILD but also be related to aging of liver function. There are several limitations that should be stressed. First, this was a single-center retrospective study, and inherent inaccuracies associated with the nature of the data collected through hospital electronic medical records are unavoidable. In addition, some clinical and pathological data on fibrosis assessment are not available. Second, the study population comprises Chinese individuals with a wide follow-up period. The extrapolation of these findings to different populations needs to be approached with caution. Third, we adjusted for covariates in the regression model as much as possible, but there are still potential confounding factors such as viral load, medication, and infection events that may have an impact. Fourth, this study only analyzes the association of ILAs and all-cause mortality, but not the cause-specific mortality, due to the study design. Future study should analyze the causes of death, eg. dying from respiratory causes or liver disease, to further explore the association. In conclusion, we observed a significant relationship between ILA and long-term mortality among patients with cirrhosis in a retrospective cohort from China. Importantly, patients with ILA who have an elevated mortality risk have different classifications of ILA and etiologies of liver cirrhosis. Therefore, targeting ILA and developing risk stratification, treatment evaluation, and prognosis assessment strategies for patients with liver cirrhosis may yield clinical benefits. Declarations Ethics approval and consent to participate Ethics approval for this study was obtained from the Human Ethical Committee of the West China Hospital of Sichuan University. The experimental protocols were established according to the ethical guidelines of the Helsinki Declaration. Written informed consent was obtained from the participants or their guardians. Competing interests The authors declare that they have no competing interests. Funding This work was supported financially by grants from the National Natural Science Foundation of China grant (No.32070764), Sichuan Science and Technology Program (No. 2023YFS0027, 2023YFS0240, 2023YFS0074, 2023NSFSC1652, 2022YFS0279, 2022JDRC0148, 2022-YF09-00003-SN), the Sichuan Provincial Health Commission (No. ZH2022-101), and 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC18021). Authors' contributions Dr. BY, YJ, MZ, and FL designed the research. Dr. BY, YJ, MZ, and YZ analyzed the data under the supervision of Dr. XL and FL. Dr. BY, YJ, and MZ wrote the first draft of the manuscript. Dr. YZ, SY, YX, AS, MY, XL, ZY, and XS reviewed the manuscript and provided critical scientific input. Dr. FL had the main responsibility for the final content of the manuscript. All the authors approved the final draft of the manuscript. Acknowledgments The authors appreciate the Yizhou Li, Jingxi Ma, Jia Feng, Jiawen Li, Yu Cheng, Yi Yao, and Jiaxin Bai on the CT report. Availability of data and material The data and materials can be requested from the corresponding author by mail. References The global. regional, and national burden of cirrhosis by cause in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol Hepatol. 2020;5:245–66. Huang DQ, Terrault NA, Tacke F, Gluud LL, Arrese M, Bugianesi E, Loomba R. Global epidemiology of cirrhosis - aetiology, trends and predictions. Nat Rev Gastroenterol Hepatol. 2023;20:388–98. Devarbhavi H, Asrani SK, Arab JP, Nartey YA, Pose E, Kamath PS. Global burden of liver disease: 2023 update. J Hepatol. 2023;79:516–37. Wynn TA, Ramalingam TR. Mechanisms of fibrosis: therapeutic translation for fibrotic disease. Nat Med. 2012;18:1028–40. Rosenbloom J, Macarak E, Piera-Velazquez S, Jimenez SA. 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Sanders JL, Axelsson G, Putman R, Menon A, Dupuis J, Xu H, Wang S, Murabito J, Vasan R, Araki T, Nishino M, Washko GR, Hatabu H, O'Connor G, Gudmundsson G, Gudnason V, Hunninghake GM. The relationship between interstitial lung abnormalities, mortality, and multimorbidity: a cohort study. Thorax. 2023;78:559–65. de Franchis R, Bosch J, Garcia-Tsao G, Reiberger T, Ripoll C. Baveno VII - Renewing consensus in portal hypertension. J Hepatol. 2022;76:959–74. El-Khateeb E, Darwich AS, Achour B, Athwal V, Rostami-Hodjegan A. Review article: time to revisit Child-Pugh score as the basis for predicting drug clearance in hepatic impairment. Aliment Pharmacol Ther. 2021;54:388–401. Bankier AA, MacMahon H, Colby T, Gevenois PA, Goo JM, Leung ANC, Lynch DA, Schaefer-Prokop CM, Tomiyama N, Travis WD, Verschakelen JA, White CS, Naidich DP. Fleischner Society: Glossary of Terms for Thoracic Imaging. Radiology 310 (2024) e232558. van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med. 1999;18:681–94. Zhang Y, Wan H, Richeldi L, Zhu M, Huang Y, Xiong X, Liao J, Zhu W, Mao L, Xu L, Ye D, Chen L, Liu J, Fu L, Li L, Lan L, Li P, Wang L, Tang X, Luo F. Reticulation Is a Risk Factor of Progressive Subpleural Nonfibrotic Interstitial Lung Abnormalities. Am J Respir Crit Care Med. 2022;206:178–85. Kadoch M, Kitich A, Alqalyoobi S, Lafond E, Foster E, Juarez M, Mendez C, Smith TW, Wong G, Boyd WD, Southard J, Oldham JM. Interstitial lung abnormality is prevalent and associated with worse outcome in patients undergoing transcatheter aortic valve replacement. Respir Med. 2018;137:55–60. Grant-Orser A, Min B, Elmrayed S, Podolanczuk AJ, Johannson KA. Prevalence, Risk Factors, and Outcomes of Adult Interstitial Lung Abnormalities: A Systematic Review and Meta-Analysis. Am J Respir Crit Care Med. 2023;208:695–708. Kim HJ, Jeong WG, Lee JY, Lee HJ, Lee BC, Lim HS, Kim YH. Pretreatment Interstitial Lung Abnormalities Detected on Abdominal Computed Tomography Scans in Prostate Cancer Patients. J Comput Assist Tomogr (2024). McDermott GC, Hayashi K, Yoshida K, Moll M, Cho MH, Doyle TJ, Kinney GL, Dellaripa PF, Putman RK, San Jose Estepar R, Hata A, Hino T, Hida T, Yanagawa M, Nishino M, Washko G, Regan EA, Hatabu H, Hunninghake GM, Silverman EK, Sparks JA. Prevalence and mortality associations of interstitial lung abnormalities in rheumatoid arthritis within a multicentre prospective cohort of smokers. Rheumatology (Oxford). 2023;62:Si286–95. Raevens S, Boret M, Fallon MB. Hepatopulmonary syndrome. JHEP Rep. 2022;4:100527. Jia Y, Li D, You Y, Yu J, Jiang W, Liu Y, Zeng R, Wan Z, Lei Y, Liao X. Multi-system diseases and death trajectory of metabolic dysfunction-associated fatty liver disease: findings from the UK Biobank. BMC Med. 2023;21:398. Arteel GE. Liver-lung axes in alcohol-related liver disease. Clin Mol Hepatol. 2020;26:670–6. Makarev E, Izumchenko E, Aihara F, Wysocki PT, Zhu Q, Buzdin A, Sidransky D, Zhavoronkov A, Atala A. Common pathway signature in lung and liver fibrosis. Cell Cycle. 2016;15:1667–73. Wei W, Li T, Chen J, Fan Z, Gao F, Yu Z, Jiang Y. SIRT3/6: an amazing challenge and opportunity in the fight against fibrosis and aging. Cell Mol Life Sci. 2024;81:69. Patnaik MM, Kamath PS, Simonetto DA. Hepatic manifestations of telomere biology disorders. J Hepatol. 2018;69:736–43. Supplementary Files supplementarytable.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4522424","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311966345,"identity":"75602310-c0d4-413f-b960-6fb4b9d969d5","order_by":0,"name":"Bo Yuan","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Yuan","suffix":""},{"id":311966346,"identity":"a38dd5e3-1f01-4406-88cb-a7a539583c9a","order_by":1,"name":"Yu Jia","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Jia","suffix":""},{"id":311966347,"identity":"425f237c-dc5c-4624-a63f-3945187b6310","order_by":2,"name":"Min Zhu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhu","suffix":""},{"id":311966348,"identity":"a4519a62-b2c9-4d3d-b6d2-c05d01342f7c","order_by":3,"name":"Yiheng Zhou","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yiheng","middleName":"","lastName":"Zhou","suffix":""},{"id":311966349,"identity":"08edb341-bd4c-4304-ac4d-0583866f6586","order_by":4,"name":"Shanye Yi","email":"","orcid":"","institution":"West China Hospital of Sichuan 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Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBAC9gYQacDAwMfAwPgAyJRhYGDDr4XnAFQLUB0zkDLgIVILA1gdmwRxWth7D7+6UXDHrk0i+VjFx7Y/PPzsbQkMPyq24dbCcy7NOsfgWXKbRFrazZltBjySPccOMPacuY1Ti71EjplxjsHhZDYg4zYvUIvBjfQGZsY23Fp45N8gtBQTp0WCx/gxUIsdSAszREvaAfxaeIAqgVoS2HieJUvOOGcM8kvCQXx+4WE/Y/w5589he3725IMfPpTJyQFDzPDBjwrcWhgg0cGQ2IAsdACfeiBg/gAk7AkoGgWjYBSMgpEMACojTy7YYuMPAAAAAElFTkSuQmCC","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Fengming","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-06-03 14:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4522424/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4522424/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59215559,"identity":"37a74516-6e07-4058-8b0f-f2d88d79ace7","added_by":"auto","created_at":"2024-06-27 18:59:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5146544,"visible":true,"origin":"","legend":"\u003cp\u003eCox regression models were used to analyze the associations between five features of interstitial lung abnormalities (ILAs) and mortality in patients with cirrhosis.\u003c/p\u003e","description":"","filename":"Figures1.png","url":"https://assets-eu.researchsquare.com/files/rs-4522424/v1/914be28da48b11d1402f3ff5.png"},{"id":59216456,"identity":"f12ae709-5136-4e9f-be62-2ae804cc0020","added_by":"auto","created_at":"2024-06-27 19:07:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":791571,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves were used to analyze the cumulative survival rate of patients with different interstitial lung abnormalities (ILAs) classification.\u003c/p\u003e","description":"","filename":"Figures2.png","url":"https://assets-eu.researchsquare.com/files/rs-4522424/v1/c149ce4ca34cd95fdf6b7129.png"},{"id":59216455,"identity":"3f110253-3289-4e0c-bb8a-3320a8aad5c1","added_by":"auto","created_at":"2024-06-27 19:07:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1202195,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between interstitial lung abnormalities (ILAs) and mortality in patients with cirrhosis. Age was classified as the median.\u003c/p\u003e","description":"","filename":"Figures3.png","url":"https://assets-eu.researchsquare.com/files/rs-4522424/v1/2767b15a6f7ad94f48e4e0f4.png"},{"id":59786099,"identity":"eee84f61-ced3-468a-820a-6104b4751a35","added_by":"auto","created_at":"2024-07-07 02:52:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6653542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4522424/v1/5722e561-1a84-4ec8-a386-884f0cb4fad6.pdf"},{"id":59215557,"identity":"ad92b395-4819-4433-a9ed-8f5355aae03b","added_by":"auto","created_at":"2024-06-27 18:59:04","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":45495,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4522424/v1/1548ee6b622584d0ecb27550.docx"}],"financialInterests":"","formattedTitle":"Prognostic Value of Interstitial Lung Abnormalities in Patients with Liver Cirrhosis: a Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cirrhosis is the end-stage of various chronic liver diseases characterized by liver tissue fibrosis and impaired liver function [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This condition has now become a substantial global health burden, with significant morbidity and mortality [1; 2; 3]. Given the seriousness of cirrhosis, it is important to identify the risk factors that influence the occurrence, development, and prognosis of cirrhosis.\u003c/p\u003e \u003cp\u003eFibrotic diseases are the results of recurrent tissue injury and aberrant repair, characterized by excessive extracellular matrix (ECM) deposition, disruption of normal tissue structure, and subsequent impairment of organ function [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These conditions encompass a wide spectrum of diseases including cirrhosis, pulmonary fibrosis, kidney fibrosis [4; 5]. Whether these diseases are systemic in nature and whether they share pathobiological mechanisms and interact with each other. The liver-lung axis theory suggests extensive interactions between the liver and lungs, with both organs exhibiting similar responses and lesions in reaction to stimuli and injuries [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Is there an association between cirrhosis and pulmonary fibrosis?\u003c/p\u003e \u003cp\u003eSeveral clinical studies have reported that interstitial lung disease is a common and clinically significant complication of cirrhosis [7; 8; 9], indicating that the management of pulmonary fibrosis should be integrated into the overall management of cirrhosis.\u003c/p\u003e \u003cp\u003eRecently, the concept of interstitial lung abnormalities (ILAs), which are considered an early stage of pulmonary fibrosis, has been proposed. and identified more frequently as CT screening becomes more common [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. With the widespread use of CT scanning, ILAs are being identified with greater frequency [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Given that ILAs, as precursors to ILD, share a fibrotic nature with cirrhosis, and considering the documented association between ILDs and cirrhosis, there may be potential link between ILAs and cirrhosis, but it remains unexplored in the current literature. Therefore, the relationship between ILAs and liver cirrhosis deserves further investigation. In addition, studies have confirmed that the occurrence of ILAs is associated with respiratory symptoms, decreased lung function, mortality, multimorbidity and more [12; 13; 14; 15]. As an extrahepatic fibrotic disease, ILAs may be positively correlated with the severity of intrahepatic fibrosis. Therefore, exploring the relationship between ILAs and the prognosis of patients with cirrhosis may provide an early predictive marker of poor outcomes in patients with cirrhosis, and the evidence may support the pathological hypothesis that fibrotic diseases are systemic diseases. However, the prevalence and adverse outcomes of ILA in patients with cirrhosis are unknown.\u003c/p\u003e \u003cp\u003eOverall, we hypothesized that there may be an association between ILAs and long-term mortality in the cirrhosis population. Therefore, in this study, we aimed to explore 1) The proportion of ILAs in the population with liver cirrhosis; 2) mortality of cirrhotic patients with or without ILAs; 3) whether the presence of ILAs is an independent predictor of mortality in patients with cirrhosis according to a retrospective cohort in China.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and subjects\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was designed to determine the proportion of ILAs in cirrhotic patients, and its impact on the severity and mortality of cirrhosis. This study complied with the Declaration of Helsinki and was approved by the Human Ethical Committee of Sichuan University West China Hospital. All patients signed informed consent to use their clinical data for scientific research upon admission.\u003c/p\u003e \u003cp\u003e4577 patients with cirrhosis who were hospitalized at West China Hospital of Sichuan University between August 2011 and November 2023, and underwent chest CT scans during hospitalization were retrospectively evaluated. Except for a small number of patients who undergo biopsy, most liver cirrhosis patients are diagnosed through noninvasive examinations according to domestic and international guidelines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Liver stiffness\u0026thinsp;\u0026ge;\u0026thinsp;15 kPa, evaluated through vibration-controlled transient elastography, was used to confirm the presence of cirrhosis. Radiomorphological signs of cirrhosis were identified via cross-sectional imaging. Moreover, the incorporation of biochemical markers such as the INR, bilirubin, and albumin can reinforce the diagnosis of cirrhosis.\u003c/p\u003e \u003cp\u003eIn this study, we excluded patients who were \u0026lt;\u0026thinsp;18 years old (n\u0026thinsp;=\u0026thinsp;55), who had suspected or confirmed ILD (n\u0026thinsp;=\u0026thinsp;70), who had portal vein thrombosis or hepatocellular carcinoma (n\u0026thinsp;=\u0026thinsp;101), who were lost to follow-up (n\u0026thinsp;=\u0026thinsp;235), or who had missing covariates\u0026thinsp;\u0026gt;\u0026thinsp;10% (n\u0026thinsp;=\u0026thinsp;94). Finally, a total of 4022 patients with cirrhosis were included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection and evaluation\u003c/h2\u003e \u003cp\u003eThe demographic characteristics, smoking and drinking history, medical history, operation history, laboratory examinations and CT reports were extracted from the patients\u0026rsquo; electronic health records, and two trained clinical research experts checked the data, especially for outliers. According to electronic medical records, we classified the causes of liver cirrhosis into viruses, alcohol, primary cholestasis, autoimmune diseases, and others. The Child‒Pugh stage was used to assess liver function and disease severity and was calculated by hepatic encephalopathy (none, grade 1\u0026ndash;2, or grade 3\u0026ndash;4), ascites degree (none, mild or moderate/severe), bilirubin (\u0026lt;\u0026thinsp;2 mg/ml, 2 to 3 mg/ml, or \u0026gt;\u0026thinsp;3 mg/ml), albumin (\u0026gt;\u0026thinsp;3.5 mg/ml, 2.8 to 3.5 mg/ml, or \u0026lt;\u0026thinsp;2.8 mg/ml), and prolonged prothrombin time (\u0026lt;\u0026thinsp;4 sec, 4 to 6 sec, \u0026gt;\u0026thinsp;6 sec) according to reported standards [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Child‒Pugh class A indicates compensated cirrhosis, and Child‒Pugh class B or C indicates decompensated cirrhosis. The complete blood cell count was analyzed using a hematology analysis system (LH750; Beckman Coulter Inc., Brea, California). Blood biochemical data were analyzed using an Architect c16000 analyzer (Abbott Diagnostics, Dallas, Texas).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eChest CT reporting and ILA diagnosis\u003c/h2\u003e \u003cp\u003eThe supine position CT images were acquired using a low-dose protocol (tube voltage of 120 kVp and tube current ranging from 1\u0026ndash;5 mHz) on a dual-slice CT scanner (Somatom Emotion Duo). Images were reconstructed with contiguous\u0026thinsp;\u0026lt;\u0026thinsp;1 mm sections, and were independently evaluated and reported by two radiologists (one with at least 5 years of experience in thoracic radiology, and the other with at least 10 years of experience). Before the official report was released, the two experts agreed. Interobserver agreement was assessed.\u003c/p\u003e \u003cp\u003ePatients with potential ILAs were firstly screened by key words related interstitial lung changes in chest CT scans reports. The imaging of selected patients was further independently validated by at least two well-trained readers with extensive experience in ILD, blinded to medical information and prior radiologic reports. The senior reviewer helped the two readers reach a final consensus on cases with inconsistent diagnoses or contradictions with CT reports. The assessment of ILA, and its features and subcategories were grounded in a comprehensive framework, drawing upon the Glossary of Terms for Thoracic Imaging and the position paper on ILAs [10; 18], as follows:\u003c/p\u003e \u003cp\u003eILA diagnosis was as follows: 1) features of ILA, including ground-glass abnormalities, reticular abnormalities, traction bronchiectasis, honeycombing, and nonemphysematous cysts; 2) involvement of at least 5% of the lung zone, delineated into upper, middle, and lower zones by the levels of the inferior aortic arch and right inferior pulmonary vein; and 3) interstitial lung disease was not suspected.\u003c/p\u003e \u003cp\u003eILA Subcategories: 1) Nonsubpleural ILAs: ILAs involve non-subpleural localization. 2) Subpleural nonfibrotic ILAs: ILAs with a predominant subpleural localization and lacking manifestations of fibrosis. 3) Subpleural fibrotic ILAs: ILAs with predominant subpleural localization and pulmonary fibrosis; fibrosis is characterized by architectural distortion accompanied by honeycombing and/or traction bronchiectasis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eFollow-up and endpoints\u003c/h2\u003e \u003cp\u003eThe final follow-up time was February 6, 2024. In this study, participants received a median of 2.3 (1.0-5.1) years of follow-up. Systematically trained staff conducted the follow-up by interviewing patients via telephone calls or questionnaires. The primary endpoint was long-term all-cause mortality, and the secondary endpoint was one-year all-cause mortality.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAccording to previous reports, missing data were addressed through multiple imputations using a chained equation approach. Following 10 iterations, 5 imputation datasets were generated. Ultimately, a predictive average-matching model was employed to assign values to each variable [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Participants were classified into groups according to their diagnosis of ILAs. Nonparametric variables are presented as medians (25th and 75th percentiles), parametric variables are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs, and categorical variables are presented as frequencies and percentages. The χ2 test or Fisher\u0026rsquo;s exact test for categorical variables and Student\u0026rsquo;s t test or one-way ANOVA for continuous variables were used to compare the differences between groups.\u003c/p\u003e \u003cp\u003eThe χ2 test, which depends on the Child‒Pugh stage and related parameters, was used to assess the associations between ILAs and the severity of cirrhosis. To explore the association between ILAs and one-year or long-term mortality in patients with cirrhosis, a Cox regression model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). In addition to univariable Model 1, Model 2 was adjusted for age, sex and BMI, while in fully adjusted Model 3, age, sex, BMI, smoking and drinking status, Child‒Pugh score, albumin, total bilirubin, creatinine, sodium, platelet count, alanine aminotransferase, glutamyl transpeptidase, aspartate aminotransferase and prolonged prothrombin time were adjusted. The associations between ILAs and mortality in patients with different types of cirrhosis, including alcoholic cirrhosis, viral hepatitis cirrhosis, primary biliary cirrhosis, and autoimmune cirrhosis, were also investigated.\u003c/p\u003e \u003cp\u003eIn addition, based on the above models, the associations between five features (ground-glass or reticular abnormalities, lung distortion, traction bronchiectasis, honeycombing, and nonemphysematous cysts) and three subcategories (nonsubpleural ILAs, subpleural fibrotic ILAs, subpleural nonfibrotic ILAs) of ILAs and mortality were explored, and forest plots and Kaplan‒Meier curves were drawn.\u003c/p\u003e \u003cp\u003eSubgroup analyses stratified by age (55\u0026thinsp;\u0026lt;\u0026thinsp;vs. \u0026ge;55 years), sex (female vs. male), and decompensation status (yes vs. no) were performed using the Cox regression model with the same covariates as above, and P for interaction was tested. In the sensitivity analysis, similar Cox regression models were used after excluding individuals with discordant ILA diagnoses.\u003c/p\u003e \u003cp\u003eThe significance of the P values was set to 0.05. Bonferroni correction was used to adjust for multiple comparisons. All the statistical analyses were performed using SPSS version 26.0 (IBM Corp, Armonk, NY, United States) and R software 3.5.0 (Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eILAs proportion in cirrhosis and clinical characteristics\u003c/p\u003e \u003cp\u003eIn this study, a total of 4,022 patients with cirrhosis were included; these patients had an average age of 54 years, and 59% were male. 749 (18.6%) subjects were diagnosed with ILAs. Baseline characteristics were compared between the non-ILA group and the ILA group. Participants with ILAs were more likely to be older and have higher total bilirubin levels, prothrombin times, glutamyl transpeptidase levels, and aspartate aminotransferase levels. Additionally, higher rates of hepatic encephalopathy, autoimmune hepatitis and liver transplantation history were observed in the ILA group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, patients with ILAs had a more advanced Child‒Pugh grade and a greater incidence of hepatic encephalopathy and hypoproteinaemia (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but not ascites, peritonitis, hypercholesterolaemia or coagulation disorders (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Similar finding were observed in nonsubpleural, subpleural nonfibroti, subpleural fibrotic ILAs.\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 patients with liver cirrhosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-ILAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eILAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3273 (81.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e749 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.33\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.15\u0026thinsp;\u0026plusmn;\u0026thinsp;12.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.53\u0026thinsp;\u0026plusmn;\u0026thinsp;12.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eMale, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2380 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1936 (59.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e444 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3017 (75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2472 (75.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545 (72.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e651 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e354 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol status, 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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2920 (72.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2376 (72.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e544 (72.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1069 (25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e873 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory Examination\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eTotal bilirubin (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.65 (14.6, 50.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.2 (14.5, 49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5 (15.5, 55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational standardized ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProthrombin time (S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.62\u0026thinsp;\u0026plusmn;\u0026thinsp;4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (ummol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.7 (56, 86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (56, 86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (56.6, 89.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum sodium (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.90\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (47, 132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (48, 134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (44, 120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.67, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.67, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.69, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36 (2.56, 4.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.38 (2.56, 4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.27 (2.53, 4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (18, 55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (18, 56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (18, 52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamyl transpeptidase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (29, 167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (28, 164.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (33.5 ,176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.5 (29, 93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (28, 92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (31, 96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCauses of Liver cirrhosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViral hepatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2952 (73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2442 (82.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e510 (68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eAlcoholic cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e628 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary biliary cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutoimmune hepatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther causes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e929 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e429 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver transplantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eHepatectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSplenectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; ILAs, interstitial lung abnormalitie\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between ILAs and all-cause mortality\u003c/h2\u003e \u003cp\u003eDuring the 1 year of follow-up, 677 (16.5%) deaths were recorded, and patients with ILAs had higher mortality (20.8% vs. 15.6%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those without. According to the fully adjusted multivariate Cox regression models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the hazard ratio (HR) of patients with ILAs was 1.251 (95% CI, 1.041\u0026ndash;1.503; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) compared with that of patients without ILAs. During long-term follow-up, the median observation period was 2.1 (1.0-5.1) years, and 1610 (40.0%) deaths were recorded. Similarly, patients with ILAs had higher mortality (48.6% vs. 38.1%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those without. Moreover, ILAs significantly elevated all-cause mortality (HR: 1.355, 95% CI: 1.202\u0026ndash;1.527, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, these associations remained significant in patients with viral cirrhosis, alcoholic cirrhosis, and primary biliary cirrhosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but not in those with autoimmune or other causes (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\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\u003eCox regression models were performed to analyze the association between ILAs and mortality in cirrhosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCase/Total (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR (95%Cl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cirrhosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOne-year mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e667/4022 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-ILAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e511/3273 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156/749 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.408 (1.177\u0026ndash;1.685)\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\u003e1.338 (1.114\u0026ndash;1.607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.251 (1.041\u0026ndash;1.503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLong-term mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1610 /4022 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-ILAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1246/3273 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364/749 (48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.532 (1.355\u0026ndash;1.712)\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\u003e1.396 (1.239\u0026ndash;1.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.355 (1.202\u0026ndash;1.527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1 is univariable Cox regression analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eMode 2 is adjusted by age, sex, and BMI.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 3 is adjusted by age, sex, BMI, smoking and drinking status, Child-Pugh score, albumin, total bilirubin, creatinine, natrium, platelet count, alanine aminotransferase, glutamyl transpeptidase, aspartate aminotransferase and prolonged prothrombin time.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eILAs, interstitial lung abnormalities.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between the features and subcategories of ILAs and mortality\u003c/h2\u003e \u003cp\u003eThe present study revealed five features of ILAs, namely, ground-glass abnormalities, reticular abnormalities, traction bronchiectasis, honeycombing, and nonemphysematous cysts, and all of these features were positively related to all-cause mortality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, ILAs with honeycombing had the largest adjusted HR during the 1-year follow-up (HR 1.996, 95% CI 1.221\u0026ndash;3.263, P\u0026thinsp;=\u0026thinsp;0.006) and long-term follow-up (HR 1.601, 95% CI 1.149\u0026ndash;2.231, P\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eK‒M curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that among the subcategories of ILAs, subcategories of ILAs had a significantly lower cumulative survival rate than did non-ILAs at the 1-year follow-up (non-ILAs vs. nonsubpleural ILAs vs. subpleural nonfibrotic ILAs vs. subpleural fibrotic ILAs: 83.9% vs. 73.5% vs. 80.8% vs. 75.6%, log rank χ2: 19.373, P\u0026thinsp;=\u0026thinsp;0.001) and long-term follow-up (non-ILAs vs. nonsubpleural ILAs vs. subpleural nonfibrotic ILAs vs. subpleural fibrotic ILAs: 45.5% vs. 27.7% vs. 28.3% vs. 24.6%, log rank χ2: 55.260, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and sensitivity analysis\u003c/h2\u003e \u003cp\u003eAmong the different age and sex subgroups in ILAs group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the associations between ILAs and mortality were consistent (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In addition, these associations were stronger in patients with compensated cirrhosis than in those with decompensated cirrhosis (P for interaction: 0.047).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, there were 210 inconsistent ILA diagnoses in the first round of inspection, and the kappa coefficient reached 0.829, which indicates that the diagnostic consistency of ILAs was almost perfect. Then, we removed samples from 210 inconsistent reports to conduct a sensitivity analysis, and the results were consistent in the absence of exclusion (\u003cb\u003eSupplementary Tables\u0026nbsp;3 and 4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents the first instance where a relatively large sample size and adequate follow-up periods have been employed to explore the relationship between ILA and all-cause mortality in patients with cirrhosis. The present study demonstrated that the proportion of ILAs in patients with cirrhosis was 18.6%, and cirrhotic patients with ILA had 25.1% and 35.6% greater risks of one-year and long-term mortality, respectively, than those without ILA. These associations were significant in patients with different etiologies of liver cirrhosis, such as viruses, alcohols, and cholestasis, but not in patients with autoimmune cirrhosis. These results suggested that ILA was high occurrence in patients with cirrhosis and was a significant prognostic marker and even a risk factor among patients with cirrhosis.\u003c/p\u003e \u003cp\u003eIn our previous study involving a large sample of Chinese individuals undergoing periodic health check-up, the prevalence of ILAs was 2.1%, and the most common classification was subpleural nonfibrotic ILAs (81.7%) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Notably, this study revealed that 18.6% of patients with cirrhosis had ILAs, and 56.4% had subpleural nonfibrotic ILAs. Compared to the general population, patients with cirrhosis had an 8-fold greater prevalence of ILA, and the proportions of nonsubpleural and fibrotic ILAs were greater. Therefore, cirrhosis and ILA are comorbidities worthy of attention.\u003c/p\u003e \u003cp\u003eThe Child‒Pugh score is the most common tool for assessing liver function, and we observed that cirrhotic patients with ILA were more likely to have advanced Child‒Pugh grade, indicating that ILA associated with the severity of cirrhosis. Importantly, after adjusting for confounders, such as demographic factors, smoking status, alcohol consumption status, liver enzymes, and medical history, ILAs were independently associated with all-cause mortality. Consistent with previous research reports, ILA not only is involved in the progression of ILD but also increases the risk of death in patients with cancer, and rheumatoid diseases [21; 22; 23; 24]. Importantly, according to the definition of ILAs, the incidence of ground-glass abnormalities, reticular abnormalities, traction bronchiectasis, honeycombing, and nonemphysematous cysts is associated with elevated mortality in patients with cirrhosis. Moreover, the results of the ILA classification showed that the associations between ILA and long-term mortality were consistent regardless of subpleural involvement or fibrotic manifestations. Furthermore, subgroup analysis revealed that this relationship remained significant in patients of different ages, sexes, and liver functions. All of these specific analyses emphasize that ILA may be a key risk factor for mortality in patients with cirrhosis.\u003c/p\u003e \u003cp\u003eThe association between ILA and adverse outcomes in patients with cirrhosis may be explained by several mechanisms. First, portal systemic venous shunting, vasodilation, and imbalanced ventilation/perfusion caused by liver dysfunction may all lead to pulmonary inflammation and fibrosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, as we previously reported, the liver serves as a multifunctional organ for immune, metabolic, endocrine, and other functions, and impaired liver function may respond to the respiratory system via oxidative stress and inflammation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Second, based on the theory of the liver-lung axis, liver cirrhosis may interact with lung disease [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thus, pulmonary abnormalities in patients with liver disease may have important implications for prognosis. Fibrosis is a common pathological manifestation of ILA and cirrhosis, and previous studies have reported common cellular dysfunction and signaling pathway activation. Studies have confirmed the presence of similar cellular responses, such as those involving macrophages and fibroblasts, as well as common signaling pathways, such as the JAK/STAT and TGF-β pathways, in liver and lung fibrosis [27; 28]. Telomere shortening has also been implicated in various liver and lung fibrosis (ref: pulmonary abnormalities in liver disease: relevance to transplantation and outcome). Therefore, the comorbidity of ILA and cirrhosis may indicate a stronger systemic fibrosis response and mediate poor individual prognosis. Fourth, liver cirrhosis and lung fibrosis are both aging-related diseases [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A previous study reported that telomere biology disorders could lead to ILD and cirrhosis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, ILA may not only represent early manifestations of ILD but also be related to aging of liver function.\u003c/p\u003e \u003cp\u003eThere are several limitations that should be stressed. First, this was a single-center retrospective study, and inherent inaccuracies associated with the nature of the data collected through hospital electronic medical records are unavoidable. In addition, some clinical and pathological data on fibrosis assessment are not available. Second, the study population comprises Chinese individuals with a wide follow-up period. The extrapolation of these findings to different populations needs to be approached with caution. Third, we adjusted for covariates in the regression model as much as possible, but there are still potential confounding factors such as viral load, medication, and infection events that may have an impact. Fourth, this study only analyzes the association of ILAs and all-cause mortality, but not the cause-specific mortality, due to the study design. Future study should analyze the causes of death, eg. dying from respiratory causes or liver disease, to further explore the association.\u003c/p\u003e \u003cp\u003eIn conclusion, we observed a significant relationship between ILA and long-term mortality among patients with cirrhosis in a retrospective cohort from China. Importantly, patients with ILA who have an elevated mortality risk have different classifications of ILA and etiologies of liver cirrhosis. Therefore, targeting ILA and developing risk stratification, treatment evaluation, and prognosis assessment strategies for patients with liver cirrhosis may yield clinical benefits.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethics approval for this study was obtained from the Human Ethical Committee of the West China Hospital of Sichuan University. The experimental protocols were established according to the ethical guidelines of the Helsinki Declaration. Written informed consent was obtained from the participants or their guardians.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported financially by grants from the National Natural Science Foundation of China grant (No.32070764), Sichuan Science and Technology Program (No. 2023YFS0027, 2023YFS0240, 2023YFS0074, 2023NSFSC1652, 2022YFS0279, 2022JDRC0148, 2022-YF09-00003-SN), the Sichuan Provincial Health Commission (No. ZH2022-101), and 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC18021).\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eDr. BY, YJ, MZ, and FL designed the research. Dr. BY, YJ, MZ, and YZ analyzed the data under the supervision of Dr. XL and FL. Dr. BY, YJ, and MZ wrote the first draft of the manuscript. Dr. YZ, SY, YX, AS, MY, XL, ZY, and XS reviewed the manuscript and provided critical scientific input. Dr. FL had the main responsibility for the final content of the manuscript. All the authors approved the final draft of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors appreciate the Yizhou Li, Jingxi Ma, Jia Feng, Jiawen Li, Yu Cheng, Yi Yao, and Jiaxin Bai on the CT report.\u003c/p\u003e\u003ch2\u003eAvailability of data and material\u003c/h2\u003e \u003cp\u003eThe data and materials can be requested from the corresponding author by mail.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe global. regional, and national burden of cirrhosis by cause in 195 countries and territories, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. 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Aliment Pharmacol Ther. 2021;54:388\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBankier AA, MacMahon H, Colby T, Gevenois PA, Goo JM, Leung ANC, Lynch DA, Schaefer-Prokop CM, Tomiyama N, Travis WD, Verschakelen JA, White CS, Naidich DP. Fleischner Society: Glossary of Terms for Thoracic Imaging. Radiology 310 (2024) e232558.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med. 1999;18:681\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Wan H, Richeldi L, Zhu M, Huang Y, Xiong X, Liao J, Zhu W, Mao L, Xu L, Ye D, Chen L, Liu J, Fu L, Li L, Lan L, Li P, Wang L, Tang X, Luo F. Reticulation Is a Risk Factor of Progressive Subpleural Nonfibrotic Interstitial Lung Abnormalities. Am J Respir Crit Care Med. 2022;206:178\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKadoch M, Kitich A, Alqalyoobi S, Lafond E, Foster E, Juarez M, Mendez C, Smith TW, Wong G, Boyd WD, Southard J, Oldham JM. Interstitial lung abnormality is prevalent and associated with worse outcome in patients undergoing transcatheter aortic valve replacement. Respir Med. 2018;137:55\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrant-Orser A, Min B, Elmrayed S, Podolanczuk AJ, Johannson KA. Prevalence, Risk Factors, and Outcomes of Adult Interstitial Lung Abnormalities: A Systematic Review and Meta-Analysis. Am J Respir Crit Care Med. 2023;208:695\u0026ndash;708.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim HJ, Jeong WG, Lee JY, Lee HJ, Lee BC, Lim HS, Kim YH. Pretreatment Interstitial Lung Abnormalities Detected on Abdominal Computed Tomography Scans in Prostate Cancer Patients. 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BMC Med. 2023;21:398.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArteel GE. Liver-lung axes in alcohol-related liver disease. Clin Mol Hepatol. 2020;26:670\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakarev E, Izumchenko E, Aihara F, Wysocki PT, Zhu Q, Buzdin A, Sidransky D, Zhavoronkov A, Atala A. Common pathway signature in lung and liver fibrosis. Cell Cycle. 2016;15:1667\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei W, Li T, Chen J, Fan Z, Gao F, Yu Z, Jiang Y. SIRT3/6: an amazing challenge and opportunity in the fight against fibrosis and aging. Cell Mol Life Sci. 2024;81:69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatnaik MM, Kamath PS, Simonetto DA. Hepatic manifestations of telomere biology disorders. J Hepatol. 2018;69:736\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Liver cirrhosis, Interstitial lung abnormalities, Mortality, Fibrosis","lastPublishedDoi":"10.21203/rs.3.rs-4522424/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4522424/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cirrhosis is the end-stage liver fibrosis and leads to massive death worldwide. Interstitial lung abnormalities (ILAs) have received widespread attention because of their progression to pulmonary fibrosis and mortality. This study aimed to investigate whether the presence of ILAs is associated with elevated mortality in patients with cirrhosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003ePatients diagnosed with cirrhosis between August 2011 and November 2023 were retrospectively included. Clinical data were collected from electronic records. ILAs were recorded by chest computed tomography. The proportion of ILAs and the associations between ILAs and all-cause mortality in cirrhosis were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 4,022 patients with cirrhosis were included, and 749 (18.6%) subjects were diagnosed with ILAs. During the median 2.1 (1.0-5.1) years of follow-up, patients with ILAs had higher mortality than those without (48.6% vs. 38.1%; P\u0026lt;0.001), ILAs significantly increased all-cause mortality (hazard ratio: 1.355; 95% confidence interval: 1.202-1.527; P\u0026lt;0.001). These associations remain significant in patients with viral, alcoholic, and primary biliary cirrhosis. Moreover, all the imaging features of the ILAs were positively related to mortality (P\u0026lt;0.05). According to the subgroup analysis, these associations were consistent across age and sex but were stronger in compensated cirrhosis than decompensation (P for interaction: 0.047).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eILAs is high occurrence in patients with cirrhosis, is independently related to all-cause mortality in patients with cirrhosis, and strategies for risk stratification and prognosis assessment targeting ILA may yield clinical benefits.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Interstitial Lung Abnormalities in Patients with Liver Cirrhosis: a Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-27 18:58:59","doi":"10.21203/rs.3.rs-4522424/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d81a37ff-fa4f-4d16-b795-9a54069d1291","owner":[],"postedDate":"June 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-07T02:44:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-27 18:58:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4522424","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4522424","identity":"rs-4522424","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}