Wu ZP, Wang YF, Shi FW, Cao WH, Sun J, Yang L, Ding FP, Hu CX, Kang WW, Han J, Yang RH, Song QK, Jin JW, Shi HB, Ma YM. Predictive models and clinical manifestations of intrapulmonary vascular dilatation and hepatopulmonary syndrome in patients with cirrhosis: Prospective comparative study. World J Gastroenterol 2025; 31(15): 105720 [DOI: 10.3748/wjg.v31.i15.105720]
Corresponding Author of This Article
Ying-Min Ma, MD, Chief Physician, Full Professor, Beijing Institute of Hepatology, Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, No. 8 Xi Tou Tiao, Youanmenwai Street, Fengtai District, Beijing 100069, China. ma.yingmin@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Prospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Apr 21, 2025; 31(15): 105720 Published online Apr 21, 2025. doi: 10.3748/wjg.v31.i15.105720
Predictive models and clinical manifestations of intrapulmonary vascular dilatation and hepatopulmonary syndrome in patients with cirrhosis: Prospective comparative study
Zhi-Peng Wu, Ying-Fei Wang, Feng-Wei Shi, Wen-Hui Cao, Rong-Hui Yang, Hong-Bo Shi, Beijing Institute of Hepatology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
Jie Sun, Department of Respiratory and Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China
Liu Yang, Fang-Ping Ding, Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
Cai-Xia Hu, Hepatic Disease and Tumor Interventional Treatment Center, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
Wei-Wei Kang, Fourth Department of Liver Disease, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
Jing Han, Ultrasound and Functional Diagnosis Center, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
Qing-Kun Song, Division of Clinical Epidemiology and Evidence-Based Medicine, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
Jia-Wei Jin, Department of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100043, China
Ying-Min Ma, Beijing Institute of Hepatology, Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, Beijing 100069, Beijing, China
Co-corresponding authors: Hong-Bo Shi and Ying-Min Ma.
Author contributions: Ma YM, Shi HB, Wu ZP, Wang YF, and Yang L contributed to the conceptualization of the study; Sun J, Shi FW, Cao WH, Hui CX, Kang WW, Han J, and Ding FP contributed to the data and blood sample collection; Wu ZP, Wang YF, Yang RH, and Song QK contributed to the formal analyses; Ding FP, Wang YF, Yang L, and Cao WH contributed to ELISA testing; Ma YM, Shi HB, and Yang RH contributed to the funding acquisition; Ma YM, Shi HB, and Jin JW contributed to the investigation; Wu ZP, Wang YF, and Song QK contributed to the methodology; Ma YM, Shi HB, and Song QK contributed to the supervision and validation; Wu ZP, Wang YF, and Ding FP contributed to the visualization; Wu ZP, Wang YF, and Shi HB contributed to writing the original draft; Ma YM, Shi HB, and Jin JW contributed to revising the manuscript; All authors read and approved the final manuscript.
Supported by the National Key Research and Development Program of China, No. 2022YFC2305002; Beijing Natural Science Foundation, No. 7232079; Middle-aged and Young Talent Incubation Programs (Clinical Research) of Beijing Youan Hospital, No. BJYAYY-YN2022-12, No. BJYAYY-YN2022-13, and No. BJYAYY-YN2022-01; and the China Postdoctoral Science Foundation, No. 2023M732410 and No. 2024T170595.
Institutional review board statement: This study was reviewed and approved by the Ethical Committee of Beijing Youan Hospital (No. LL-2022-141-K).
Clinical trial registration statement: This study is registered at ClinicalTrials.gov under registration identification number NCT05932927.
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment, and patient information was anonymized.
Conflict-of-interest statement: Ma YM has received research funding from Beijing Natural Science Foundation, No. 7232079.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ying-Min Ma, MD, Chief Physician, Full Professor, Beijing Institute of Hepatology, Department of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, No. 8 Xi Tou Tiao, Youanmenwai Street, Fengtai District, Beijing 100069, China. ma.yingmin@163.com
Received: February 12, 2025 Revised: March 13, 2025 Accepted: March 26, 2025 Published online: April 21, 2025 Processing time: 64 Days and 23.6 Hours
Abstract
BACKGROUND
Patients with cirrhosis with hepatopulmonary syndrome (HPS) have a poorer prognosis. The disease has a subtle onset, symptoms are easily masked, clinical attention is insufficient, and misdiagnosis rates are high.
AIM
To compare the clinical characteristics of patients with cirrhosis, cirrhosis combined with intrapulmonary vascular dilatation (IPVD), and HPS, and to establish predictive models for IPVD and HPS.
METHODS
Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital. Clinical information and blood samples were collected, and biomarker levels in blood samples were measured. Patients with cirrhosis were divided into three groups: Those with pure cirrhosis, those with combined IPVD, and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values. Univariate logistic regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods were utilized to identify risk factors for IPVD and HPS, and nomograms were constructed to predict IPVD and HPS.
RESULTS
A total of 320 patients were analyzed, with 101 diagnosed with IPVD, of whom 54 were diagnosed with HPS. There were statistically significant differences in clinical parameters among these three groups of patients. Among the tested biomarkers, sphingosine 1 phosphate, angiopoietin-2, and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis. Following LASSO logistic regression screening, prediction models for IPVD and HPS were established. The area under the receiver operating characteristic curve for IPVD prediction was 0.792 (95% confidence interval [CI]: 0.737-0.847), and for HPS prediction was 0.891 (95%CI: 0.848-0.934).
CONCLUSION
This study systematically compared the clinical characteristics of patients with cirrhosis, IPVD, and HPS, and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators. These models showed good predictive value for IPVD and HPS in patients with cirrhosis. They can assist clinicians in the early prognosis assessment of patients with cirrhosis, ultimately benefiting the patients.
Core Tip: This study included 320 patients with liver cirrhosis, among whom 101 were diagnosed with intrapulmonary vascular dilatation (IPVD), and of these, 54 were diagnosed with hepatopulmonary syndrome (HPS). Nine potential biomarkers possibly related to HPS were tested, and the clinical parameters and biomarker levels of three patient groups were compared. Predictive models for IPVD and HPS were built respectively based on patients’ clinical information and three biomarkers: sphingosine 1 phosphate, angiopoietin-2, and platelet-derived growth factor BB.
Citation: Wu ZP, Wang YF, Shi FW, Cao WH, Sun J, Yang L, Ding FP, Hu CX, Kang WW, Han J, Yang RH, Song QK, Jin JW, Shi HB, Ma YM. Predictive models and clinical manifestations of intrapulmonary vascular dilatation and hepatopulmonary syndrome in patients with cirrhosis: Prospective comparative study. World J Gastroenterol 2025; 31(15): 105720
Hepatopulmonary syndrome (HPS) is characterized by hypoxemia and a constellation of pathophysiological alterations and clinical manifestations resulting from intrapulmonary vascular dilatation (IPVD) and impaired arterial oxygenation in the context of chronic liver disease and/or portal hypertension[1,2]. The prevalence of HPS among patients with cirrhosis varies considerably, ranging from 4% to 47%, with pulmonary vasodilation detectable in 13% to 80% of liver transplant candidates[3]. A prospective study found that patients with HPS had poorer quality of life in certain domains compared to those without HPS. Additionally, patients with HPS had a higher risk of death, even after adjusting for age, sex, and race/ethnicity[4].
Although the presence of HPS in patients with cirrhosis affects patient prognosis, clinical attention to HPS remains inadequate[2,5]. A diagnostic study of HPS in a large integrated healthcare system found that only a small fraction of patients with cirrhosis were diagnosed with HPS through International Classification of Diseases coding within the vast patient population. Following diagnosis, only a minority of patients met the diagnostic criteria[6]. The current gold standard for diagnosing HPS involves a combination of contrast-enhanced transthoracic echocardiography (CE-TTE) and assessment of the pulmonary alveolar-arterial oxygen gradient (P(A-a)O2)[3,7]. However, the specificity of CE-TTE in diagnosing HPS is low. Many patients with cirrhosis may have some degree of intrapulmonary shunting, but they may not meet the diagnostic criteria for HPS due to the lack of hypoxemia[8]. Additionally, CE-TTE has drawbacks such as being invasive and costly, making it difficult to be widely used in clinical settings. While identifying patients with low oxygen levels based on arterial blood gas is straightforward, patients with cirrhosis may experience respiratory difficulties due to conditions like ascites, leading to hypoxemia. Furthermore, it has poor sensitivity. Therefore, research on biomarkers for IPVD and HPS is of significant importance[9,10].
Several molecular markers of endothelial dysfunction and angiogenesis, which correlate with IPVD in HPS, have emerged as potential discriminators between patients with cirrhosis with and without HPS. Basic scientific research has implicated various cytokines and growth factors as potential therapeutic targets in HPS[11]. Li et al[12] identified soluble vascular endothelial growth factor receptor 1 (VEGFR1) and the ratio of soluble VEGFR1 to placental growth factor as key variables for distinguishing between patients with and without HPS. Recent investigations have also suggested that serum levels of vascular cellular adhesion molecule 1 (VCAM1), von Willebrand factor (vWF), and bone morphogenetic protein-9 may serve as diagnostic markers for HPS in patients with cirrhosis[13-15].
In this study, we relied on a large cohort of patients with cirrhosis to analyze clinical parameters, common laboratory indices, and levels of nine potential biomarkers including sphingosine 1 phosphate (S1P), angiopoietin-2, and vWF, aiming to identify the clinical characteristics of patients with cirrhosis with IPVD and HPS. Ultimately, we constructed predictive models for IPVD and HPS in patients with cirrhosis through univariate logistic regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods. By focusing on the predictive capabilities of specific biomarkers and integrating comprehensive patient data, we aim to provide valuable insights to guide clinical decision-making and improve patient outcomes.
MATERIALS AND METHODS
Participants
A total of 465 patients with liver cirrhosis admitted to Youan Hospital, Capital Medical University (Beijing, China) between April 2023 and May 2024 were included for screening. Demographic data, clinical information, and laboratory indices of the patients were collected from the electronic medical record system. Blood samples were retained from eligible patients for biomarker testing. Inclusion criteria were: (1) Patients with liver cirrhosis due to various reasons; (2) Age ≥ 18 years; 3) Availability of CE-TTE results and arterial blood gas analysis; and (4) Patient consent for participation in the study. Exclusion criteria were: (1) Concomitant primary lung diseases such as pneumonia, pulmonary vascular diseases, interstitial lung diseases, chronic obstructive pulmonary disease, bronchial asthma, and lung cancer; (2) Acute upper gastrointestinal bleeding; and (3) Refusal to participate in the study. The study was approved by the Hospital Ethics Committee (No. LL-2022-141-K) and registered in the ClinicalTrials.gov database under identifier number NCT05932927. All patients provided informed consent, and patient information was anonymized. Finally, 145 patients were excluded, primarily due to refusal to participate in the study and lack of CE-TTE results and arterial blood gas analysis. In the study, of 320 individuals, 101 were diagnosed with IPVD, and ultimately 54 individuals were diagnosed with HPS. Refer to Figure 1 for details.
Diagnostic criteria for HPS: The gold standard for HPS diagnosis is the combination of CE-TTE and P(A-a)O2. The diagnostic criteria proposed in the 2016 “International Liver Transplantation Society Practice Guidelines: Diagnosis and Treatment of Hepatopulmonary Syndrome and Portal Hypertension” are used for diagnosis[1]: (1) Presence of liver disease (typically cirrhosis with portal hypertension); (2) Positive CE-TTE results; and (3) Abnormal arterial blood gas results: P(A-a)O2 ≥ 15 mmHg when the patient is breathing room air in an upright position (1 mmHg = 0.133 kPa, if age > 64, then ≥ 20 mmHg). Patients diagnosed with HPS are classified based on severity using arterial oxygen pressure (PaO2): mild if PaO2 ≥ 80 mmHg, moderate if PaO2 ≥ 60 mmHg and < 80 mmHg, severe if PaO2 ≥ 50 mmHg and < 60 mmHg, and very severe if PaO2 < 50 mmHg.
Definition of P(A-a)O2: P(A-a)O2 is the most sensitive indicator for monitoring early lung ventilation abnormalities[16]. Patients are in an upright position, breathing room air, and undergo arterial blood gas testing. The formula for P(A-a)O2 is: P (alveolar) O2 - P (arterial) O2 = [FiO2 (Patm - PH2O) - PaCO2/0.8] - PaO2, where FiO2 represents the inspired oxygen concentration, Patm denotes atmospheric pressure, PH2O indicates water vapor pressure, PaCO2 stands for arterial carbon dioxide partial pressure, and PaO2 represents arterial oxygen partial pressure.
Introduction to CE-TTE: CE-TTE is the preferred and cornerstone examination method for diagnosing IPVD[17,18]. A mixture of 10 mL of 0.5 g sodium bicarbonate injection and 300 mg of vitamin B6 injection is agitated in a syringe to form microbubbles with a diameter > 10 μm, which are then injected into a peripheral vein. In a normal human body, these bubbles are absorbed by the pulmonary capillary bed (diameter < 8-15 μm) when they return to the right heart via the systemic circulation and enter the pulmonary circulation, not reaching the left heart, thus only visible in the right heart. In patients with HPS, microbubbles flow back through dilated capillary beds or arteriovenous shunts and can appear in the left atrium after 3-6 cardiac cycles, showing a misty shadow on echocardiography. Appearance of microbubbles in the left atrium within three cardiac cycles suggests intracardiac shunting. If microbubbles are seen in the left atrium within 3-6 cardiac cycles after injection, the CE-TTE result is considered positive; the absence of microbubbles in the left atrium is considered a negative CE-TTE result.
Data collection
The following data were collected for all patients: Demographic information (e.g., sex, age, height, weight), etiology of liver cirrhosis (e.g., alcoholic hepatitis, hepatitis B virus, hepatitis C virus), comorbidities (e.g., history of hypertension and diabetes), assessment of disease severity (Child-Pugh classification and Model for End-Stage Liver Disease [MELD] score), arterial blood gas results, laboratory indices (e.g., complete blood count, liver function tests, renal function tests, coagulation function, infection markers), and the results of cardiac ultrasound examination. Refer to Table 1 for details.
Table 1 Baseline characteristics and clinical data after hospitalization of patients with cirrhosis with and without intrapulmonary vascular dilatation, n (%).
A total of 10 mL of whole blood was drawn from patients who consented to participate in the study and placed into ethylene diamine tetraacetic acid-anticoagulated tubes. Plasma was separated by centrifugation at 2000 g for 10 minutes and stored at -80 °C in a freezer until uniform retrieval for the detection of plasma cytokine levels. Three biomarkers, S1P, angiopoietin-2, and vWF, were measured using enzyme-linked immunosorbent assay (ELISA) methods. Specifically, the following ELISA kits were used: Human S1P ELISA Kit (Catalog No. EKF58355), Angiopoietin-2 Human ELISA Kit (Catalog No. KHC1641), and Human vWF ELISA Kit (Catalog No. KHC1641). The levels of the additional six factors, sE-selectin, intercellular adhesion molecule-1 (ICAM-1), platelet-derived growth factor BB (PDGF-BB), tumor necrosis factor alpha (TNF-α), VCAM-1, and VEGF-A, were measured using the custom ProcartaPlex 6-Plex Assay (Thermo Fisher Scientific, Waltham, MA, United States), and the data were read on the Luminex 200 Instrument (Luminex Co., Austin, TX, United States). All procedures were strictly conducted following the manufacturers’ instructions.
Statistical analyses
Statistical analyses were performed using SPSS 22.0 software and R language (version 4.0.3; IBM SPSS Statistics, Armonk, NY, United States). GraphPad Prism 9 (GraphPad Software, Inc., La Jolla, CA, United States) was used for graphing. Continuous variables are expressed as the median (mean) and interquartile range, while categorical variables are expressed as the number (n) and percentage (%). χ2 test or Fisher’s exact test was used for comparing categorical variables, t-test or Mann-Whitney U test was used for comparing continuous variables between two groups, and grouped F-test or the Kruskal-Wallis rank sum test was used for comparing continuous variables among multiple groups. Binary logistic regression models were used for univariate analysis of the association between variables and HPS, with results expressed as the odds ratio and 95% confidence interval (95%CI). LASSO regression was adopted to screen modeling factors, effectively avoiding overfitting, with 10-fold cross-validation set to select the appropriate parameter (λ). Receiver operating characteristic (ROC) curves were used to evaluate the classification and diagnostic efficacy of variables for HPS, with performance quantified by the area under the curve (AUC), and the optimal cut-off value determined by the Youden index. Cut-off value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were also presented. Heatmaps were used to display correlation analysis between different clinical parameters and biomarkers. The larger the absolute value of the correlation coefficient r, the higher the correlation; a positive r value indicates a positive correlation, while a negative value indicates a negative correlation. Nomograms are constructed by integrating the identified predictive factors. The accuracy of these predictive tools was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots. To evaluate their practical value in clinical settings, decision curve analysis was employed.
RESULTS
Clinical characteristics of patients with cirrhosis without IPVD, with IPVD, and with HPS showed statistically significant differences
After screening, a total of 320 patients were included in the analysis, with 101 patients meeting the diagnosis of IPVD, of whom 54 were diagnosed with HPS (Figure 1). The demographic data of these three patient groups, including age, sex, and weight, showed statistical differences. Differences were observed in etiology, including viral hepatitis B and autoimmune hepatitis. There were statistical differences in the proportions of comorbidities such as hypertension, diabetes, liver cancer, bleeding, and ascites. The severity of the diseases, as indicated by factors like MELD scores, and arterial blood gas analysis parameters such as oxygen partial pressure, also exhibited statistical differences. Laboratory markers such as procalcitonin, hemoglobin (HGB), and platelet count showed statistical variances. Additionally, significant statistical differences were observed in the levels of S1P, angiopoietin-2, ICAM-1, PDGF-BB, and VEGF-A measured in this study (Supplementary Table 1). Patients were further categorized into groups with and without IPVD (Table 1) and undiagnosed HPS and diagnosed HPS groups (Table 2).
Table 2 Baseline characteristics and clinical data after hospitalization of patients with cirrhosis with and without hepatopulmonary syndrome, n (%).
Selection of variables to predict IPVD and HPS using univariate logistic regression and LASSO logistic regression
Univariate logistic regression was conducted on variables showing significant statistical differences between the IPVD and non-IPVD groups, revealing that the majority of variables with statistically significant differences among groups also displayed statistical differences in univariate regression analysis (Table 3). With the exception of body mass index, neutrophil count, systemic inflammation index, glucose, D-dimer, and ICAM-1, variables with statistically significant differences identified in univariate regression were included in the LASSO logistic regression, resulting in a final selection of seven factors: Age, hypertension, MELD score, P(A-a)O2, S1P, angiopoietin-2, and PDGF-BB (Supplementary Figure 1). The results of univariate logistic regression predicting the relationship between variables and HPS are shown in Table 4, while the results of LASSO logistic regression are presented in Supplementary Figure 2. In the model, two clinical indicators, age and P(A-a)O2, along with six laboratory markers, HGB, total bilirubin (TBIL), albumin, S1P, angiopoietin-2, and PDGF-BB, were included. Comparison of the levels of the three biomarkers such as S1P, angiopoietin-2, and PDGF-BB in individual patients among the three groups is shown in Figure 2. Areas under the ROC curves for predicting IPVD and HPS in patients with cirrhosis using these identified numerical factors are respectively illustrated in Figure 3. The predicted value information for different parameters is detailed in Table 5.
Figure 3 Receiver operating characteristic curves for predicting intrapulmonary vascular dilatation and hepatopulmonary syndrome by different parameters.
A: Predicting intrapulmonary vascular dilatation (IPVD) in patients with cirrhosis. Area under the curve (AUC) for: Age was 0.613 (95% confidence interval [CI]: 0.546-0.679), Model for End-Stage Liver Disease (MELD) (score) 0.691 (95%CI: 0.629-0.753), arterial oxygen pressure 0.565 (95%CI: 0.496-0.633), reticulocalbin 3 0.587 (95%CI: 0.518-0.656), sphingosine 1 phosphate (S1P) 0.649 (95%CI: 0.584-0.714), angiopoietin-2 0.699 (95%CI: 0.638-0.760), and platelet-derived growth factor BB (PDGF-BB) 0.647 (95%CI: 0.583-0.711); B: Predicting hepatopulmonary syndrome (HPS) in patients with cirrhosis. AUC for age was 0.618 (95%CI: 0.542-0.693), pulmonary alveolar-arterial oxygen gradient (P(A-a)O2) 0.837 (95%CI: 0.790-0.885), hemoglobin (HGB) 0.674 (95%CI: 0.600-0.749), total bilirubin (TBIL) 0.749 (95%CI: 0.681-0.818), albumin (ALB) 0.734 (95%CI: 0.667-0.801), S1P 0.635 (95%CI: 0.556-0.713); angiopoietin-2 0.726 (95%CI: 0.652-0.800), and PDGF-BB 0.599 (95%CI: 0.519-0.679). FPR: False-positive rate; TPR: True-positive rat.
Table 3 Univariable logistic regression analysis for the predictors of intrapulmonary vascular dilatation in patients with cirrhosis.
Variables
Univariate analysis
OR (95%CI)
P value
Sex, male
0.475 (0.277-0.812)
0.007
Age, years
0.957 (0.934-0.981)
< 0.001
Weight, kg
0.979 (0.961-0.998)
0.027
BMI, kg/m2
0.946 (0.893-1.003)
0.064
Hepatitis B
0.472 (0.292-0.762)
0.002
Autoimmune hepatitis
2.776 (1.234-6.244)
0.014
Hypertension
0.347 (0.190-0.634)
< 0.001
Diabetes
0.434 (0.242-0.776)
0.005
Liver cancer
0.532 (0.329-0.858)
0.010
Ascites
2.196 (1.291-3.738)
0.004
Hepatic encephalopathy
1.756 (1.060-2.907)
0.029
Palmar erythema
1.822 (1.026-3.236)
0.040
MELD score
1.018 (1.007-1.028)
< 0.001
Child-Pugh classification
I
II
4.352 (2.269-8.345)
< 0.001
III
7.559 (3.554-16.078)
< 0.001
Decompensated stage
2.798 (1.486-5.269)
0.001
PH
12245 (13-11971501)
0.007
P(A-a)O2
1.028 (1.008-1.048)
0.006
PaCO2
0.915 (0.870-0.962)
< 0.001
HGB, g/L
0.974 (0.964-0.983)
< 0.001
Platelet count, × 109/L
0.991 (0.986-0.996)
< 0.001
Neutrophil count, × 109/L
0.942 (0.855-1.038)
0.228
Lymphocyte count, × 109/L
0.472 (0.291-0.766)
0.002
SII
1.000 (0.999-1.000)
0.136
Albumin, g/L
0.880 (0.838-0.923)
< 0.001
DBIL, μmol/L
1.004 (1.001-1.007)
0.006
Glucose, mg/dL
0.870 (0.754-1.003)
0.055
INR
6.529 (2.710-15.729)
< 0.001
D-dimer, mg/L
1.000 (1.000-1.001)
0.346
Prothrombin time, seconds
1.164 (1.074-1.261)
< 0.001
Prothrombin time activity, %
0.953 (0.938-0.968)
< 0.001
Cardiac output, L/minute
1.251 (1.059-1.478)
0.009
Effective ejection fraction, %
1.140 (1.028-1.264)
0.013
Cardiac index, L/minute/m²
1.525 (1.128-2.061)
0.006
S1P, pg/mL
0.992 (0.986-0.997)
0.005
Angiopoietin-2, pg/mL
1.000 (1.000-1.000)
< 0.001
VWF, ng/mL
1.000 (1.000-1.000)
0.556
sE-selectin, ng/mL
1.000 (1.000-1.000)
0.909
ICAM-1, ng/mL
1.000 (1.000-1.000)
0.089
PDGF-BB, pg/mL
0.992 (0.988-0.997)
< 0.001
TNF alpha, pg/mL
0.996 (0.982-1.011)
0.600
VCAM-1, ng/mL
1.000 (1.000-1.000)
0.257
VEGF-A, pg/mL
1.001 (1.000-1.001)
0.195
Table 4 Univariable logistic regression analysis for the predictors of hepatopulmonary syndrome in patients with cirrhosis.
Variables
Univariate analysis
OR (95%CI)
P value
Age, years
0.959 (0.931-0.988)
0.006
Hepatitis B
0.408 (0.224-0.740)
0.003
Autoimmune hepatitis
2.929 (1.230-6.979)
0.015
Liver cancer
0.459 (0.254-0.830)
0.010
Ascites
2.780 (1.340-5.767)
0.006
MELD score
1.019 (1.008-1.029)
< 0.001
Child-Pugh classification
I
II
9.402 (2.790-31.687)
< 0.001
III
19.744 (5.580-69.859)
< 0.001
Decompensated stage
3.200 (1.315-7.788)
0.010
PH
2123 (0.517-8719201)
0.071
P(A-a)O2, mmHg
1.095 (1.065-1.126)
< 0.001
PaO2, mmHg
0.921 (0.897-0.945)
< 0.001
PaCO2, mmHg
0.864 (0.808-0.923)
< 0.001
SpO2, %
0.726 (0.612-0.862)
< 0.001
PCT, ng/mL
1.133 (0.979-1.312)
0.094
HGB, g/L
0.975 (0.964-0.987)
< 0.001
Platelet count, × 109/L
0.995 (0.989-1.001)
0.105
NLR
1.052 (1.001-1.106)
0.044
Albumin, g/L
0.847 (0.797-0.901)
< 0.001
AST, U/L
1.003 (0.999-1.007)
0.127
TBIL, μmol/L
1.004 (1.002-1.007)
< 0.001
DBIL, μmol/L
1.005 (1.002-1.009)
< 0.001
INR
3.457 (1.694-7.055)
< 0.001
Prothrombin time, seconds
1.098 (1.028-1.172)
0.005
Prothrombin time activity, %
0.960 (0.943-0.978)
< 0.001
Sodium, mmol/L
0.894 (0.840-0.952)
< 0.001
Cardiac output, L/minute
1.346 (1.108-1.635)
0.003
Cardiac index, L/minute/m²
1.118 (0.929-1.347)
0.238
S1P
0.991 (0.983-0.999)
0.029
Angiopoietin-2
1.000 (1.000-1.000)
< 0.001
vWF
1.000 (1.000-1.000)
0.762
sE-selectin, pg/mL
1.000 (1.000-1.000)
0.546
ICAM-1, pg/mL
1.000 (1.000-1.000)
0.225
PDGF-BB, pg/mL
0.994 (0.988-0.999)
0.027
TNF alpha, pg/mL
1.000 (0.988-1.012)
0.951
VCAM-1, pg/mL
1.000 (1.000-1.000)
0.133
VEGF-A, pg/mL
1.001 (1.000-1.002)
0.051
Table 5 Predicted value information of different parameters and the combined model for intrapulmonary vascular dilatation and hepatopulmonary syndrome in patients with cirrhosis.
Variables
Cut off value
Sensitivity
Specificity
Accuracy
PPV
NPV
Youden index
Predicting IPVD
Age, years
60
0.68
0.49
0.55
0.38
0.77
0.17
MELD score
9
0.78
0.55
0.63
0.45
0.85
0.33
P(A-a)O2, mmHg
24
0.52
0.72
0.65
0.49
0.73
0.23
S1P, pg/mL
36
0.58
0.69
0.66
0.46
0.78
0.27
Angiopoietin-2, pg/mL
2922
0.85
0.48
0.60
0.42
0.88
0.33
PDGF-BB, pg/mL
79
0.78
0.46
0.56
0.40
0.82
0.24
The combined model
0.27
0.87
0.59
0.69
0.52
0.90
0.47
Predicting HPS
Age, years
60
0.76
0.47
0.52
0.22
0.91
0.23
P(A-a)O2, mmHg,
18
0.96
0.64
0.71
0.41
0.99
0.60
HGB, g/L
115
0.72
0.55
0.58
0.25
0.91
0.27
Albumin, g/L
35
0.89
0.45
0.53
0.25
0.95
0.34
TBIL, μmol/L
29
0.81
0.61
0.64
0.30
0.94
0.42
S1P, pg/mL
36
0.61
0.65
0.64
0.26
0.89
0.26
Angiopoietin-2, pg/mL
4373
0.73
0.62
0.64
0.27
0.92
0.35
PDGF-BB, pg/mL
79
0.78
0.41
0.48
0.21
0.90
0.19
Combined model
0.14
0.94
0.74
0.78
0.47
0.98
0.68
Heatmap of correlation analysis between different clinical parameters and biomarkers
Pairwise comparisons of correlations were performed for all variables that showed statistically significant differences in univariate logistic regression (Figure 4). Figure 4A shows the correlation coefficient r of Spearman’s rank correlation between each pair of parameters, while Figure 4B shows the corresponding P value for each correlation coefficient r. We can see that many parameters have statistically significant correlations with each other. For example, age is correlated with international normalized ratio, prothrombin time, and prothrombin time activity (r values are -0.229, -0.231, and 0.231, respectively; all P < 0.001). We also observed that the correlations among the six numerical parameters included in the study were not statistically significant, suggesting that there is no collinearity among these parameters. This indicates that the combination of these parameters may better predict the presence of HPS in patients with cirrhosis. For instance, the P values of the correlation coefficients between age and the other five parameters are all greater than 0.05.
Figure 4 Heatmap depicting the correlation between clinical parameters and biomarkers.
A: The values are presented as Spearman’s correlation coefficient (r) for a sample of 320 runners. The colormap ranges from 1 to -1, with blue indicating the highest value and red indicating the lowest value; B: Heatmap of corresponding P values. The colormap ranges from 0 to 1, with blue representing the largest value and white representing the smallest value. White cells without numerical values indicate that the P value is smaller than 0.001, indicating a highly significant correlation. DBIL: Direct bilirubin; ICAM-1: Intercellular adhesion molecule-1; INR: International normalized ratio; MELD: Model for End-Stage Liver Disease; P(A-a)O2: Alveolar-arterial oxygen gradient; PaO2: Partial pressure of oxygen in arterial blood; PaCO2: Partial pressure of carbon dioxide in arterial blood; PDGF-BB: Platelet-derived growth factor BB; PH: Potential of hydrogen; PT: Prothrombin time; PTA: Prothrombin time activity; S1P: Sphingosine 1 phosphate; SpO2: Peripheral oxygen saturation; TBIL: Total bilirubin; TNF: Tumor necrosis factor; VCAM-1: Vascular cell adhesion molecule-1; VEGF-A: Vascular endothelial growth factor A; vWF: Von Willebrand factor.
Establishment of prediction models to predict IPVD and HPS in patients with cirrhosis
Based on LASSO regression, we developed a prediction model for IPVD in patients with cirrhosis using seven parameters (Figure 5A). The ROC curve (Figure 5B) for this model’s predictions showed an AUC of 0.792 (95%CI: 0.737-0.847). The calibration curves of the model (Figure 5C) suggested that the constructed model has good simulation performance (P = 0.153). The decision curve also indicates that this clinical prediction model will effectively assist clinicians in identifying patients with cirrhosis with HPS (Figure 5D). The model for predicting HPS is presented in Figure 6A, with the area under the ROC curve of this model being 0.891 (95%CI: 0.848-0.934) (Figure 6B). The calibration curves depicted in Figure 6C indicate the well-performing simulation of the constructed model (P = 0.548). Furthermore, the decision curve in Figure 6D suggests that this clinical prediction model will effectively aid clinicians in identifying patients with cirrhosis with HPS.
Figure 5 Predictive model for intrapulmonary vascular dilatation combining clinical parameters and biomarkers.
A: Nomogram of the predictive model for intrapulmonary vascular dilatation (IPVD) in patients with cirrhosis. This model integrates four clinical parameters and three biomarkers; B: Receiver operating characteristic (ROC) curve predicting IPVD in patients with cirrhosis. Area under the curve (AUC) was 0.792 (95% confidence interval [CI]: 0.737-0.847); C: Calibration curves; D: Decision curve analysis of the nomogram for the prediction of IPVD. FPR: False-positive rate; MELD: Model for End-Stage Liver Disease; P(A-a)O2: Alveolar-arterial oxygen gradient; PDGF-BB: Platelet-derived growth factor BB; S1P: Sphingosine 1 phosphate; TPR: True-positive rate.
Figure 6 Predictive model for hepatopulmonary syndrome combining clinical parameters and biomarkers.
A: Nomogram of the predictive model for hepatopulmonary syndrome (HPS) in patients with cirrhosis. This model integrates two clinical parameters and six biomarkers; B: Receiver operating characteristic (ROC) curve predicting HPS in patients with cirrhosis. Area under the curve (AUC) was 0.891 (95% confidence interval [CI]: 0.848-0.934); C: Calibration curves; D: Decision curve analysis of the nomogram for the prediction of HPS. ALB: Albumin; HGB: Hemoglobin; FPR: False-positive rate; P(A-a)O2: Alveolar-arterial oxygen gradient; PDGF-BB: Platelet-derived growth factor BB; S1P: Sphingosine 1 phosphate; TBIL: Total bilirubin; TPR: True-positive rate.
DISCUSSION
HPS is a serious complication associated with liver cirrhosis, characterized by the triad of liver disease, pulmonary gas exchange abnormalities, and IPVD. This syndrome significantly impacts patients’ respiratory function and overall quality of life, leading to increased morbidity and mortality among those with cirrhosis. The underlying pathophysiological mechanisms of HPS involve alterations in pulmonary vascular tone and architecture, which can result in hypoxemia due to significant shunting and ventilation-perfusion mismatch[19,20]. Currently, there is insufficient attention to HPS in clinical practice, and the diagnostic rate is not high. Many patients with cirrhosis may have HPS but remain undiagnosed. The proportion of patients undergoing CE-TTE examination is not sufficiently high, and relying solely on low arterial blood gas analysis of oxygen partial pressure is also insufficient for diagnosing HPS, as some of these patients may not have IPVD.
This study classified patients with cirrhosis into three groups: Those without IPVD, those with isolated IPVD, and those with both IPVD and hypoxemia (HPS group). A comparison of the clinical characteristics and common laboratory indices among these three groups revealed statistically significant differences. The study also found significant statistical differences in the levels of S1P, angiopoietin-2, and PDGF-BB among these groups. By combining these three biomarkers with clinical parameters, a clinical prediction model effectively predicted IPVD and HPS in patients with cirrhosis. The AUC for predicting IPVD was 0.792 (95%CI: 0.737-0.847), while for predicting HPS, the AUC was 0.891 (95%CI: 0.848-0.934).
Our research found that there were significant differences in clinical characteristics between patients with IPVD or HPS. For example, there were noticeable distinctions in factors such as bleeding, ascites, and the severity of the patients’ conditions. Studies have shown that patients with HPS may not exhibit obvious respiratory symptoms in the early stages, only showing manifestations related to liver damage. As the disease progresses into the intermediate stage, patients may display varying degrees of hypoxia symptoms such as difficulty breathing, cyanosis, and clubbed fingers[2,21]. Regarding the severity of the disease, there is still debate on the relationship between the presence or severity of HPS and the severity of the patients’ liver disease. Prospective studies have indicated that the occurrence of HPS is related to the severity of liver disease, but the conclusions of these studies have not been consistent. Some studies suggest that HPS may be associated with either the MELD score or the Child-Pugh score but not both[22,23], while others indicate correlations with both the MELD score and the Child-Pugh score[24-26]. However, previous studies have not consistently demonstrated differences in the severity of liver function between patients with and without HPS[4,18,27].
The pathogenesis of HPS remains incompletely elucidated. Current understanding posits that portal hypertension and hepatic dysfunction impair the liver’s capacity to clear circulating pulmonary vasodilators (e.g., nitric oxide) and modulate cytokine release (e.g., TNF-α), leading to pulmonary vascular dilation and angiogenesis, ultimately culminating in HPS[28-30]. Guided by this pathophysiological framework, we collected comprehensive clinical data, with particular emphasis on biomarkers associated with vascular dilation, angiogenesis, and inflammation (including S1P, angiopoietin-2, vWF, sE-selectin, ICAM-1, PDGF-BB, TNF-α, VCAM-1, and VEGF-A) to develop a model for differentiating between patients with cirrhosis with and without HPS.
Experimental models of HPS have consistently demonstrated increased pulmonary angiogenesis[31,32]. Elevated angiopoietin-2 levels have been correlated with enhanced pathological angiogenesis in various studies of patients with liver disease. Kawut et al[23] reported that patients with HPS exhibited higher circulating levels of angiopoietin-2, VCAM-1, and vWF, and lower E-selectin levels compared to patients without HPS, suggesting that HPS is characterized by elevated proangiogenic biomarkers. Baweja et al[33] observed significantly lower plasma S1P levels in patients with HPS compared to those without HPS, with decreased S1P correlating with higher mortality in patients with HPS, indicating its potential as a diagnostic and prognostic biomarker for HPS. Consistent with these findings, our study demonstrated increased angiopoietin-2 levels and decreased S1P and PDGF-BB levels in patients with HPS compared to those without HPS.
To distinguish between IPVD and non-IPVD, S1P, angiopoietin-2, and PDGF-BB exhibited AUCs of 0.649, 0.699, and 0.647, respectively. To distinguish between HPS and non-HPS, S1P, angiopoietin-2, and PDGF-BB exhibited AUCs of 0.635, 0.726, and 0.599, respectively. The inclusion of vascular-related indicators in our model underscores the central role of vascular abnormalities in HPS pathogenesis and diagnosis.
Our study aligns with previous research. For instance, a 2022 study demonstrated a notable difference in vWF levels between the HPS and non-HPS groups[23]. However, our investigation did not reveal a statistically significant distinction between these cohorts. Nevertheless, our findings indicated a discernible trend towards divergence, with a P value of 0.054, marginally approaching the significance threshold of 0.05. Intriguingly, subgroup analysis indicated no significant disparity between IPVD and non-IPVD groups, with a P value of 0.312, suggesting that vWF may signal hypoxia more than vasodilation. Similarly, a previous study examined PDGF-BB, where although statistical significance was lacking, a trend was observed, with a P value of 0.08, nearing the 0.05 threshold[23]. By contrast, our study identified a statistical contrast in this marker between HPS and non-HPS groups, bolstering the credibility of our findings.
The innovative aspect of this study lies in the systematic comparison of the clinical characteristics of patients with cirrhosis, cirrhosis combined with IPVD, and HPS, and establishment of the development of comprehensive diagnostic models for IPVD and HPS incorporating age, hepatic function, respiratory function, and vascular function. This multifactorial approach, considering various pathogenic elements of HPS, may enhance diagnostic accuracy, potentially increasing HPS detection rates and reducing missed or misdiagnosed cases. Furthermore, the identification of PDGF-BB as a novel biomarker for distinguishing HPS in liver cirrhosis represents a significant contribution to the field, as it has not been previously reported in current literature[34,35]. A limitation of this study is the relatively small cohort of HPS cases, precluding detailed stratified analysis and external model validation. Future research will focus on expanding the case series to further validate our model and refine diagnostic methodologies for patients with HPS.
CONCLUSION
This study systematically compared the clinical characteristics of patients with cirrhosis, IPVD, and HPS, and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators. These models have shown good predictive value for IPVD and HPS in patients with cirrhosis. They can assist clinicians in the early prognosis assessment of patients with cirrhosis, ultimately benefiting the patients.
ACKNOWLEDGEMENTS
We would like to express our gratitude to Beijing Youan Hospital for granting access to the clinical data of the patients.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B
Novelty: Grade B, Grade B
Creativity or Innovation: Grade C, Grade C
Scientific Significance: Grade C, Grade C
P-Reviewer: Abdu S; Reese K S-Editor: Fan M L-Editor: Filipodia P-Editor: Zhao S
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