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Abstract

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. References

The rationale for screening inflammatory serum biomarkers of the hepatic vein pressure gradient (HVPG) is based on the fact that portal hypertension is pathogenically related to liver injury and fibrosis, and that in turn these are associated with the activation of inflammatory pathways. This was a nested cohort study in the setting of a randomized, clinical trial to assess the development of gastroesophageal varices (GEV) (N Engl J Med 2005;353:2254). Patients had cirrhosis and portal hypertension but did not have GEV. A total of 90 patients who had baseline day-1 sera available were enrolled in the present study. The objective of this study was to determine whether inflammatory biomarkers in conjunction with clinical parameters could be used to develop a predictive paradigm for HVPG. The correlations between HVPG and interleukin (IL)-1β (P = 0.0052); IL-1R-α (P = 0.0085); Fas-R (P = 0.0354), and serum VCAM-1 (P = 0.0007) were highly significant. By using multivariate logistic regression analysis and selected parameters (transforming growth factor beta [TGFβ]; heat shock protein [HSP]-70; at-risk alcohol use; and Child class B) we could exclude HVPG ≥12 mmHg with 86% accuracy (95% confidence interval [CI]: 67.78 to 96.16%) and the sensitivity was 87.01% (95% CI: 69.68 to 96.34%). Therefore, the composite test could identify 86% of compensated cirrhosis patients with HVPG below 12 mmHg and prevent unnecessary esophagogastroduodenoscopy with its associated morbidity and costs in these patients. Our diagnostic test was not efficient in predicting HVPG ≥12 mmHg. Conclusion: A blood test for HVPG could be performed in cirrhosis patients to prevent unnecessary esophagogastroduodenoscopy. (Hepatology 2014;59:1052–1059)

Abbreviations
ALT

alanine aminotransferase

AST

aspartate aminotransferase

CPS

Child-Pugh score

EGD

esophagogastroduodenoscopy

GEV

gastroesophageal varices

HVPG

hepatic vein pressure gradient

IFN

interferon

IL-1α

interleukin-1α

LPB

LPS-binding protein

LS

liver stiffness

MELD

model for endstage liver disease

NPV

negative predictive value

PPV

positive predictive value

TLRs

Toll-like receptors

TNF

tumor necrosis factor

The majority of patients who die of cirrhosis die due to a complication of increased portal venous pressure, such as variceal hemorrhage, ascites, hepatic encephalopathy, hepatopulmonary syndrome, or hepatorenal syndrome.[1, 2] The hepatic vein pressure gradient (HVPG), an indirect measure of portal pressure,[3] is a prognostic indicator for long-term survival in cirrhosis[1, 2] Furthermore, HVPG can reflect progression of disease in the precirrhosis stage. There is an association between the severity of hepatic inflammation and fibrosis and the HVPG even before cirrhosis develops.[4] In addition, HVPG predicts the response to hepatitis C treatment among patients with cirrhosis.[5]

One of the most frequent severe complications of portal hypertension is hemorrhage from gastroesophageal varices (GEV), which is a significant cause of death in patients with cirrhosis. Reduction of the HVPG below 12 mmHg (normal is 0-5 mmHg), either through spontaneous reversion after the insult is resolved or with medical, radiological, or surgical interventions, effectively prevents recurrent bleeding.[3, 6-8] Currently, there is no established noninvasive test to predict portal pressure among patients who are treated medically and, thus, there is no way to predict either the response to standard of care or the complications of portal hypertension (including potentially lethal esophageal bleeding) other than performing screening esophagogastroduodenoscopy (EGD) with the added costs and morbidity of the procedure. Although transient elastography has a very good predictive value for clinically significant portal hypertension, there are some limitations of this technique in patients with chronic liver diseases and with obesity.[9]

The ability to predict portal pressure with a simple blood test would revolutionize clinical management of patients with chronic liver diseases, as well as aid in the design and performance of clinical research into the complications of cirrhosis.[1, 2] Given that liver inflammation due to liver injury and/or bacterial translocation occurs in liver cirrhosis with portal hypertension,[10-15] we postulated that some inflammatory biomarkers could serve as a noninvasive test to predict the presence of severe portal hypertension at levels associated with the presence variceal bleeding.[2]

Patients and Methods

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. References

The study was a nested cohort study in the setting of an investigator-initiated, prospective, randomized, double-blind, placebo-controlled, clinical trial designed to evaluate the efficacy of nonselective beta-blockers in preventing GEV and the usefulness of measuring HVPG sequentially. The complete description of the trial has been published elsewhere.[16] The protocol for conducting the current analysis of deidentified sera samples was approved by the University of California, San Diego (UCSD) Human Protection Program (Protocol #101569 on 8/16/2012), the Research and Development Committee, Veterans Affairs San Diego Healthcare System (VASDHS) (Project #1159016 on 11/6/2012), and the Yale Human Research Protection Program.

Patients

The patients were enrolled between August 1993 and March 1999. Eligible patients had cirrhosis and portal hypertension as defined by an HVPG of 6 mmHg or greater, did not have GEV, and were older than 18, and less than 75 years of age. Exclusion criteria included ascites requiring diuretics, hepatocellular carcinoma, splenic or portal vein thrombosis, concurrent illness expected to decrease life expectancy to less than 1 year, the use of any drug or procedure affecting the splanchnic hemodynamic or portal pressure, primary biliary cirrhosis or primary sclerosing cholangitis, or any contraindications to beta blocker therapy, pregnancy, and alcohol intake during the dose titration phase. A total of 90 of the 213 subjects (39 from the Connecticut Center, 26 from the London Center, and 25 from the Boston Center) who had baseline day-1 sera available prior to drug or placebo treatment were enrolled in the present study. Full details of the clinical trial have been previously published.[16]

Objectives

The objective of this study was to determine whether novel biomarkers of inflammation measured in conjunction with established demographic and clinical laboratory parameters could be used to develop a predictive paradigm for HVPG.

Outcomes

The primary outcome was the analysis of clinical parameters (age and model for endstage liver diseases [MELD]; Child-Pugh score [CPS] platelets; alanine aminotransferase [ALT]; aspartate aminotransferase [AST]); and novel inflammatory serum biomarkers with respect to any correlations with HVPG. Deidentified blood samples were then analyzed for novel inflammatory biomarkers. A multiplex peptide detection system (Human Sepsis Magnetic Bead Panels 1, 2, and 3; Millipore and Quansys Q-Plex Human Cytokine - Screen IR16-Plex; Quansys Biosciences) were utilized according to the manufacturer's protocol to determine inflammatory markers (interleukin [IL]-1α; IL-1β IL-2; IL-4; IL-5; IL-6; IL-8; IL-10; IL-12; IL-13; IL-15; IL-17; interferon [IFN]-γ; tumor necrosis factor [TNF]-α; TNFβ; CCL22/MDC; CCL-17/TARC; IL-Rα; and IL-1RA; elastase-2; lactoferrin; thrombospondin-1; MIF; ICAM-1; Fas-L; Fas-R; VCAM-1; tPAI-1; granzyme-B; heat shock protein [HSP]-70; MIP-1α; MIP-1β and MMP-8). Values were calculated from individual pixels using the MAGPIX analysis xPonent software and Q-View Imager system, respectively. An enzyme-linked immunoassay kit was used to determine serum lipopolysaccharide (LPS)-binding protein (LPB) and CD-163 according to the manufacturer's protocol (BioVision and Aviscera Bioscience, respectively)

Blinding

Individuals performing the laboratory tests were kept blinded to the subjects' demographics, clinical, and portal pressure data.

Statistical Methods

The exploratory correlations were assessed with Pearson's correlation coefficient with 95% confidence intervals (CI). We also used multiple linear regression analysis to test for predictive values of demographic, clinical laboratory, and novel inflammatory biomarkers to HVPG. The significance level was fixed at α = 5% for all tests. All analyses were performed using the Analyze-it program (www.Analyze-it. com).

Results

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. References
Baseline Demographic and Clinical Characteristics

As outlined in Table 1, most subjects were middle age (mean 50.5 ± 7 years; range 32 to 72 years), predominantly males (71%), and Caucasians (87%) with compensated cirrhosis (no ascites, no encephalopathy, no varices). Accordingly, the MELD score was low (9.5 ± 2.3; range 6.4 to 16.3) as was the CPS (5.5 ± 0.8; range 5.0 to 8.0) score. More than half of the subjects had chronic hepatitis C viral infection (55.6%) and 34% were at-risk alcohol use. At the time of HVPG measurement six patients had had a drink within the prior week but in the remaining 25 patients the last drink had been >1 month prior to HVPG measurements (with 12 having had the last drink >6 months prior to HVPG).

Table 1. Baseline Demographic and Clinical Characteristics of Subjects (N = 90)
ParametersNumbers (%)
  1. The parameters with their 95% confidence intervals and quartiles (when appropriate) are shown for the cohort (N = 90).

Sex (male)64 (71%)
Ethnicity 
Caucasians78 (87%)
Black4 (4%)
Hispanic4 (4%)
Others3 (3%)
Etiology 
Hepatitis C47 (52%)
Alcohol25 (28%)
Cryptogenic6 (7%)
Autoimmune5 (6%)
Hepatitis B4 (4%)
Others3 (3%)
ParametersMean (SD)95% CIPercentile (0th; 25th; 50th; 75th; 100th)
Age (years)50.5 (9.7)48.4 to 52.532; 44; 48; 57; 72
MELD9.5 (2.3)9.0 to 10.06.4; 7.5; 8.9; 10.9; 16.3
Child-Pugh (score)5.5 (0.8)5.3 to 5.65.0; 5.0; 5.0; 6.0; 8.0
ALT (IU/ml)95.1 (108.4)72.4 to 117.810.0; 33.8; 59.5; 97.1; 615.0
AST (IU/ml)84.8 (77.1)68.6 to 100.916.0; 40.8; 59.0; 97.3; 510.0
Platelets (x10−3 / μL)144.8 (73.9)129.4 to 160.315.0; 98.3; 138.0; 173.6; 559.0
HPVG10.9 (3.9)10.0 to 11.76.0; 8.0; 10.3; 12.7; 21.5
WHVP19.3 (5.2)18.2 to 20.47.0; 15;0; 18.7; 23.0; 30.0

The etiology of cirrhosis was in its majority attributed to chronic hepatitis C (52%) and alcohol (28%). Subjects had mild to severe degrees of liver injury, judging by the levels of ALT (95.1 ± 108.4; range 10 to 615 IU/mL) and AST (84.8 ± 77.1; range 16 to 510 IU/mL), which most likely reflects a mild to severe level of liver inflammation. As expected for a cirrhosis cohort the platelets were relatively low (median 138.0; range 15 to 559 × 103/mL). No hepatitis C viral load was measured at the time of enrollment.

Portal Pressure Measurements

As anticipated, in this cirrhosis population selected for the absence of GEV, the HVPG was 10.9 ± 3.9 mmHg (median: 10.3; range: 6.0 to 21.5 mmHg) (Table 1). The wedge hepatic vein pressure (WHVP, 19.3 ± 5.2; median: 18.7; range: 7.0 to 30.0 mmHg)[3] was on average 8.4 mmHg higher than the HVPG (Table 1). Thirty of the 90 subjects (33.3%) had HVPG ≥12 mmHg, a critical threshold for variceal bleeding of cirrhosis associated with portal hypertension[3] and 60 subjects (66.6%) had HVPG <12 mmHg.

Correlations Between HVPG and Clinical Indicators

As depicted in Table 2, we found that HVPG correlated positively with age (P = 0.0019); MELD (P < 0.0001); CPS (P = 0.0445); and platelets (P = 0.0154) but the linear regression correlation R2 was only 0.26 for age + MELD and lower for the other clinical indicators. There was no significant correlation between HVPG and either ALT or AST.

Table 2. Correlation of HVPG With Demographic and Clinical Characteristics of Subjects
ParametersPearson's P value
  1. Pearson's correlation is shown between these parameters and HVPG.

  2. a

    Statistical significance.

Age (years)0.0019a
MELD0.0001a
Age + MELD0.0001a
Child-Pugh (score)0.0445a
ALT (IU/ml)0.6123
AST (IU/ml)0.5134
Platelets (x10−3 /μL)0.0154a
Correlation Between HVPG and Inflammatory Biomarkers

The correlations between HVPG and IL-1β (P = 0.0052); IL-1Rα (P = 0.0085); Fas-R (P = 0.0354), and serum VCAM-1 (P = 0.0007) were highly significant. There were no significant correlations between HVPG and other inflammatory biomarkers (LBP, CD-163, IL-1α, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, Il-13, IL-15, IL-17, CCL-17, CCL-22, TNFα, TNFβ, elastase-2, lactoferrin, thrombospondin-1, N-Gal, resistin, MIF, ICAM, Fas-L, tPAI-1, Granzyme-B, MIP-1α, MIP-1β, and MMP-8) as assessed with Pearson's correlation coefficient (Table 3).

Table 3. Correlation Between HVPG and Inflammatory Biomarkers
Inflammatory BiomarkersPearson's P Value
  1. Pearson's correlation is shown between these inflammatory parameters and HVPG (N = 88).

  2. a

    Statistical significance.

VCAM-10.0007a
IL-1β0.0052a
IL-1Rα0.0085a
Fas-R0.0354a
ICAM-10.0609
CD-1630.0739
Thrombospondin-10.0950
Elastase-20.4105
Lactoferrin0.7008
LBP0.6297
IL-1α0.0772
IL-20.7130
IL-40.4357
IL-50.3703
IL-60.2943
IL-80.3585
IL-100.5814
IL-120.3990
IL-130.7905
IL-170.3132
IFN-γ0.5065
IL-1RA0.8545
CCL-220.0955
CCL-170.0905
TNF-α0.0955
TNF-β0.0905
Fas-L0.0894
Granzyme-B0.6713
HSP-700.0894
MIP-1α0.7681
MIP-1β0.1162
MMP-80.1183
N-Gal0.5171
Resistin0.3517
MIF0.1662
Resistin0.3517
MIF0.1662
Logistic Regression Analysis

Distribution analysis was performed for all of the variables by measuring skewness and kurtosis. Variables that did not have a normal distribution, judging by a skewness >0.5, were log transformed. All variables were analyzed by a two-sided t test or chi-square test. Univariate logistic regression was run for HVPG <12 mmHg or HVPG ≥12 mmHg (a clinically significant level of portal hypertension for variceal bleeding[36]) (Table 4). The four variables that were most significant (TNFβ [P = 0.019]; HSP-70 [P = 0.030]; at-risk alcohol use [P = 0.003]; and Child class B [P = 0.034]) were submitted to multivariate logistic regression with backward elimination of the variables that did not add to the model. The four variables remained. These four variables were combined by logistic regression to a synthetic composite. Receiver operating characteristic (ROC) curves were produced for the four variables and the composite (area 0.767 ± 0.057; asymptotic sigma P < 0.0001; 95% CI 0.656 to 0.879) (Fig. 1). A scatterplot was drawn and a cutpoint (CAT12) selected where probability of HVPG ≥12 based on a natural break in the scatterplot. CAT12 groups were compared to actual HVPG <12. The composite test was statistically significant using several chi-square tests (Pearson chi-square P = 0.017; Fisher's exact test P = 0.025; likelihood ratio P = 0.013; linear-by-linear association P = 0.018). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were computed for actual HVPG cut at 12 mmHg. The NPV was 86.21% (significant 95% CI: between 67.78 and 96.16%). Thus, if the equation predicts HVPG is <12 mmHg, then it will actually be <12 mmHg for 86% of the patients. The sensitivity was 87.09% (significant 95% CI: between 69.68 and 96.34%). However, both the PPV (45.76%; significant 95% CI: between 32.89 and 59.14%) and the specificity (43.86%; significant 95% CI: between 30.93 and 57.56%) were relatively low (Table 5).

Table 4. Logistic Regression Analysis of HVPG Biomarkers
 HVPG <12 mmHgHVPG ≥12 mmHg 
MeasureMean/NSD/%Mean/NSD/%P Values
  1. The analysis was performed as described in Methods (N = 88).

  2. a

    Statistical significance.

HSP704.231.123.641.320.030a
TNF-β412.08336.82594.58350.180.019a
At-risk alcohol use1933 %2166%0.003a
Child class B59 %825%0.034a
Table 5. Predictive Value of the Composite Test for HVPG Equal or > 12 mmHg
Test VariablePercent95% Confidence Intervals
  1. The sensitivity, negative predictive value, specificity, and positive predictive value and their 95% CIs are shown (N = 88).

Sensitivity87.0969.6896.34
Specificity43.8630.9357.56
Negative Predictive Value86.2167.7896.16
Positive Predictive Value45.7632.8959.14
image

Figure 1. ROC curve for the composite test. An ROC curve was produced for the composite (area 0.767 ± 0.057; asymptotic sigma P < 0.0001; 95% CI: 0.656 to 0.879). A scatterplot was drawn and a cutpoint (CAT12; arrow) selected where probability of HVPG ≥12 based on a natural break in the scatterplot. CAT12 groups were compared to actual HVPG <12. The composite test was statistically significant using several chi-square tests (Pearson chi-square P = 0.017; Fisher's exact test P = 0.025; likelihood ratio P = 0.013; linear-by-linear association P = 0.018).

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Discussion

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. References

In this study we found that the novel inflammatory biomarkers IL-1β, IL-1Rα, Fas-R, VCAM-1, TNFβ, and HSP-70 are significantly correlated with HVPG in a compensated cirrhosis cohort. Further, and as expected, some demographic and clinical parameters correlated significantly with HVPG, including age, MELD, CPS, platelets, and at-risk alcohol use.

The rationale for screening inflammatory serum biomarkers of HVPG is based on the fact that portal hypertension is pathogenically related to liver injury and fibrosis,[10-15] and that in turn these are associated with the activation of inflammatory pathways.[11, 12, 14, 15] Indeed, portal hypertension occurs in the presence of liver injury and inflammation even in the absence of liver fibrosis in fulminant acute liver failure and acute viral hepatitis,[17, 18] indicating that liver injury and inflammation can be sufficient and critical for the development of portal hypertension (with 50% of the patients having portal pressures >12 mmHg). In addition, patients with chronic alcoholic liver disease in the absence of cirrhosis may have HVPG >12 mmHg and develop esophageal varices, suggesting that in addition to and sometimes in the absence of liver fibrosis, hepatocyte injury and inflammation affect the portal pressure.[19]

Inflammatory pathways can be activated by bacterial translocation (or translocation of LPS and DNA) from the intestine to the portal vein circulation that occurs in patients with cirrhosis and portal hypertension.[10, 13, 14] Bacterial/LPS/DNA translocation leads to activation of Toll-like receptors (TLRs) and their induction of signaling pathways, resulting in the secretion of inflammatory mediators into the circulation.[12, 13] In support of our findings, the activation of these signaling inflammatory pathways may be clinically inconspicuous but could be detected by measuring hemodynamic effects or humoral mediators in blood.[10, 12, 13] The increase in HVPG after a meal significantly correlated with serum bacterial DNA concentration, suggesting a causal effect between HVPG and bacterial translocation.[10]

A critical inflammatory signaling pathway is the inflammasome. We have found that IL-1β, a critical cytokine product of the inflammasome, and its receptor IL-1Rα correlated significantly with HVPG.[20, 21] Active caspase-1 is essential for the cleavage of pro-IL-1β into its mature, biologically active form IL-1β.[20] Based on this rationale, anti-caspase drugs are being analyzed in clinical phase 2 studies to ameliorate hepatocyte injury.[22] Similarly, polymorphisms of the TLR-9, which initiates signals activating the Inflammasome,[23-25] have been implicated in rapidly progressing tissue fibrosis.[26] We have also found that TNFβ, a product of activated T and B lymphocytes and a member of the TNFα superfamily, correlates significantly with HVPG. TNFβ is secreted as a soluble inflammatory polypeptide that forms heterotrimers with lymphotoxin-β and mediates a large variety of inflammatory, immunostimulatory, and antiviral responses,[27] which are relevant to our cohort of cirrhosis patients etiologically linked to chronic HCV infection and alcohol use. In addition, the serum Fas-R, another member of the TNFα cell death receptor superfamily, which may be increased with liver injury and inflammation,[28, 29] also correlated with HVPG.

We also found a highly significant correlation between HVPG and serum VCAM-1, a product of endothelial cells.[30-32] The increase in circulating endothelial cells in cirrhosis patients is congruent with our findings.[33] In addition, bacterial DNA translocation is associated with intrahepatic endothelial dysfunction in patients with cirrhosis.[10] Hyaluronan, homocysteine, and angiotensin-II can induce the expression of VCAM-1 synthesis.[30-32] All of these factors are mechanistically related to cirrhosis. Serum hyaluronan and homocysteine are increased in liver fibrosis, while angiotensin-II stimulates liver fibrosis.[34-37] Therefore, in future studies we will analyze the relationship between hyaluronan, homocysteine, and angiotensin II with HVPG.

HSP-70 correlated significantly with HVPG in our logistic regression analysis. Of interest, glutamine, an amino acid induced in hepatic acinar zone 3 by hypoxia (characteristic of cirrhosis with portal hypertension), stimulates transcription of heat shock factor (HSF)-1, an inducer of HSP-70.[38, 39] Thus, glutamine and glutamine syntethase may also be biomarkers of HVPG.

We found a significant correlation using Pearson's test of HVPG with novel inflammatory biomarkers (IL-1β, IL-1Rα, Fas-R, and VCAM-1). By using multivariate logistic regression analysis and selected parameters (TNFβ, HSP-70, at-risk alcohol use, and Child class B) we can exclude HVPG ≥12 mmHg with 86% accuracy (significant 95% CI: between 67.78 and 96.16%) and the sensitivity was 87.09% (significant 95% CI: between 69.68 and 96.34%). Therefore, the composite test could identify 86% of compensated cirrhosis patients with HVPG below 12 mmHg and prevent unnecessary EGDs with their associated morbidity and costs in these patients. As is the case for estimating HVPG by measuring liver stiffness (LS) with transient elastography,[9] our diagnostic test was not efficient in predicting HVPG ≥12 mmHg (PPV: 45.76%; specificity: 43.86%). Therefore, as expected our ROC was only moderately accurate (area 0.767 ± 0.057; asymptotic sigma P < 0.0001; 95% CI: 0.656 to 0.879) and similar to the ROC curve (0.76 ± 0.07; 95% CI: 0.60-0.87) reported for the prediction of HVPG by LS-elastography for all cirrhosis patients in their cohort.[40]

Although LS has been proposed for predicting HVPG, the method as currently used has several technical and logistic limitations, making the measurement not interpretable in a large percentage of patients with cirrhosis.[41] The exclusion criteria for LS include obesity, ascites, congestive heart failure, extrahepatic cholestasis and severe liver inflammation related to HCV infection.[9, 42] Also, in cirrhosis patients LS values increased by 25% after a light meal, as compared with fasting patients, suggesting a spurious postprandial increase in the predicted HVPG in cirrhosis.[43]

Vizzutti et al.[40] reported a good correlation between LS and HVPG in the entire cohort (R2 = 0.61; P <0.0001) in 61 consecutive selected patients with HCV-related chronic liver disease. Although the correlation between LS and HVPG was very good for HVPG values less than 10 or 12 mmHg (R2 = 0.72, P = 0.0001 and R2 = 0.67 P <0.0001, respectively) it was poor for HVPG >10 mmHg and >12 mmHg (R2 = 0.35, P = 0.0001 and R2 = 0.17 P <0.02, respectively).[9] Berzigotti et al.[44] have shown that LS provides excellent results when combined with platelets count and spleen size (LSPS). Analyses of LSPS were effective in identifying patients with clinically significant HVPG; they correctly classified 83% of patients in the training set (N: 117) and 85% in the validation set (N: 56). Berzigotti et al.[45] also reported that obesity was present in 30% of a cohort of compensated cirrhosis patients. Thus, in evaluating HVPG by LS including all subjects (an “intention to diagnose” study), the 85% predictive accuracy of LSPS reported by Berzigotti et al.[44] would be applicable to only about 70% of those subjects, resulting in a correct classification of HVPG in about 60% of the patients (85% × 0.70).

Colecchia et al.[46] suggested using spleen stiffness (SS) measurement as a screening test for the indication of esophagogastroduodenoscopy. Using an intention-to-diagnose approach, only 7 of 113 (7.1%) screened patients would have wrongly avoided esophagogastroduodenoscopy. Similarly, Sharma et al.[47] found that SS ≥40.8 kPa had high sensitivity (94%), specificity (76%), PPV (91%), NPV (84%), and diagnostic accuracy (86%) for predicting EV. However, in the latter study, out of 270 patients SS was performed only in 174 patients since 70 patients were excluded before performing the SS measurement (due to ascites, alcohol abuse, and hepatitis reactivation) or in 26 of whom the SS measurement could not be obtained. Thus, the intention-to-treat would markedly reduce the sensitivity of the technique.[47]

Our study also has significant limitations: 1) the presence of only cirrhosis patients without esophageal varices in our cohort may not fully reflect other cirrhosis populations; 2) the lack of a validation cohort, as was the case with the studies using LS or SS by elastography[40, 46, 47]; and 3) the absence of a control group. Additional studies with a larger cohort of cirrhosis patients, including a significant percentage of cirrhosis patients with esophageal varices and a validation cohort, will be needed to confirm our findings. More important, a larger cohort, while introducing additional factors related to the selected members of the specific inflammatory signaling pathways, may allow a more precise prediction of HVPG in the future.

It remains to be established whether a blood test for HVPG would be effective in all patients, including those unsuitable for LS or SS measurements (e.g., patients with obesity, ascites, congestive heart failure, and extrahepatic cholestasis). Our present test has a similar accuracy in predicting HVPG to LS or SS and it may become more accurate with additional biomarkers. Thus, if a test based on blood biomarkers could be developed it would be more accessible worldwide due to low costs and ease of execution.

References

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. References
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