Abstract
Background
The diagnostic performance of biochemical scores and artificial neural network models
for portal hypertension and cirrhosis is not well established.
Aims
To assess diagnostic accuracy of six serum scores, artificial neural networks and
liver stiffness measured by transient elastography, for diagnosing cirrhosis, clinically
significant portal hypertension and oesophageal varices.
Methods
202 consecutive compensated patients requiring liver biopsy and hepatic venous pressure
gradient measurement were included. Several serum tests (alone and combined into scores)
and liver stiffness were measured. Artificial neural networks containing or not liver
stiffness as input variable were also created.
Results
The best non-invasive method for diagnosing cirrhosis, portal hypertension and oesophageal
varices was liver stiffness (C-statistics = 0.93, 0.94, and 0.90, respectively). Among serum tests/scores the best for diagnosing
cirrhosis and portal hypertension and oesophageal varices were, respectively, Fibrosis-4,
and Lok score. Artificial neural networks including liver stiffness had high diagnostic
performance for cirrhosis, portal hypertension and oesophageal varices (accuracy > 80%), but were not statistically superior to liver stiffness alone.
Conclusions
Liver stiffness was the best non-invasive method to assess the presence of cirrhosis,
portal hypertension and oesophageal varices. The use of artificial neural networks
integrating different non-invasive tests did not increase the diagnostic accuracy
of liver stiffness alone.
Keywords
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References
- The clinical use of HVPG measurements in chronic liver disease.Nature Reviews Gastroenterology & Hepatology. 2009; 6: 573-582
- Biopsy and non-invasive methods for the diagnosis of liver fibrosis: does it take two to tango.Gut. 2010; 59: 861-866
- Now there are many (stages) where before there was one: In search of a pathophysiological classification of cirrhosis.Hepatology. 2010; 51: 1445-1449
- Assessing portal hypertension in liver disease.Expert Review of Gastroenterology & Hepatology. 2013; 7: 141-155
- Hepatic venous pressure gradient predicts clinical decompensation in patients with compensated cirrhosis.Gastroenterology. 2007; 133: 481-488
- Non invasive evaluation of portal hypertension using transient elastography.Journal of Hepatology. 2012; 56: 696-703
- Evolving consensus in portal hypertension. Report of the Baveno IV consensus workshop on methodology of diagnosis and therapy in portal hypertension.Journal of Hepatology. 2005; 43: 167-176
- Revising consensus in portal hypertension: report of the Baveno V consensus workshop on methodology of diagnosis and therapy in portal hypertension.Journal of Hepatology. 2010; 53: 762-768
- Prediction of oesophageal varices in hepatic cirrhosis by simple serum non-invasive markers: results of a multicenter, large-scale study.Journal of Hepatology. 2010; 53: 630-638
- Non-invasive diagnosis of large oesophageal varices with FibroTest in patients with cirrhosis: a preliminary retrospective study.Liver International. 2006; 26: 271-278
- Platelet count/spleen diameter ratio for the noninvasive diagnosis of oesophageal varices: results of a multicenter, prospective, validation study.American Journal of Gastroenterology. 2006; 101: 2511-2519
- Application of artificial neural networks to clinical medicine.Lancet. 1995; 346: 1135-1138
- Introduction to neural networks.Lancet. 1995; 346: 1075-1079
- Prognostic models including the Child–Pugh, MELD and Mayo risk scores – where are we and where should we go.Journal of Hepatology. 2004; 41: 344-350
- A comparison of Cox proportional hazards and artificial neural network models for medical prognosis.Computers in Biology and Medicine. 1997; 27: 55-65
- Modeling medical prognosis: survival analysis techniques.Journal of Biomedical Informatics. 2001; 34: 428-439
- An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group.Hepatology. 1996; 24: 289-293
- Validity and clinical utility of the aspartate aminotransferase–alanine aminotransferase ratio in assessing disease severity and prognosis in patients with hepatitis C virus-related chronic liver disease.Archives of Internal Medicine. 2003; 163: 218-224
- A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C.Hepatology. 2003; 38: 518-526
- Predicting cirrhosis in patients with hepatitis C based on standard laboratory tests: results of the HALT-C cohort.Hepatology. 2005; 42: 282-292
- FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection. Comparison with liver biopsy and fibrotest.Hepatology. 2007; 46: 32-36
- Cirrhosis in hepatitis C virus-infected patients can be excluded using an index of standard biochemical serum markers.Scandinavian Journal of Gastroenterology. 2005; 40: 867-872
- Non-invasive model predicting clinically-significant portal hypertension in patients with advanced fibrosis.Journal of Gastroenterology and Hepatology. 2009; 24: 1289-1293
- Transient elastography accurately predicts presence of significant portal hypertension in patients with chronic liver disease.Alimentary Pharmacology and Therapeutics. 2008; 27: 1261-1268
- Liver stiffness accurately predicts portal hypertension related complications in patients with chronic liver disease: a prospective study.Journal of Hepatology. 2011; 55: 1017-1024
- Non-invasive evaluation of liver fibrosis using transient elastography.Journal of Hepatology. 2008; 48: 835-847
- Performance of transient elastography for the staging of liver fibrosis: a meta-analysis.Gastroenterology. 2008; 134: 960-974
- Liver stiffness measurement predicts severe portal hypertension in patients with HCV-related cirrhosis.Hepatology. 2007; 45: 1290-1297
- Learning representations by back-propagating errors.Nature. 1986; 323: 533-536
- A new and simple algorithm for the noninvasive assessment of oesophageal varices in cirrhotic patients using serum fibrosis markers and transient elastography.Journal of Gastrointestinal and Liver Diseases. 2011; 20: 57-64
- SAFE biopsy: a validated method for large-scale staging of liver fibrosis in chronic hepatitis C.Hepatology. 2009; 49: 1821-1827
- Stepwise combination algorithms of non-invasive markers to diagnose significant fibrosis in chronic hepatitis C.Journal of Hepatology. 2006; 44: 686-693
- Comparison of eight diagnostic algorithms for liver fibrosis in hepatitis C: new algorithms are more precise and entirely noninvasive.Hepatology. 2012; 55: 58-67
- Liver stiffness is an unreliable marker of liver fibrosis in patients with cardiac insufficiency.Hepatology. 2008; 48: 2089
- Liver stiffness is directly influenced by central venous pressure.Journal of Hepatology. 2010; 52: 206-210
- Extrahepatic cholestasis increases liver stiffness (FibroScan) irrespective of fibrosis.Hepatology. 2008; 48: 1718-1723
- Acute viral hepatitis increases liver stiffness values measured by transient elastography.Hepatology. 2008; 47: 380-384
- Transient elastography: a new surrogate marker of liver fibrosis influenced by major changes of transaminases.Journal of Viral Hepatitis. 2007; 14: 360-369
- Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review.Hepatology International. 2008; 2: 31-38
- Predicting mortality in patients with cirrhosis of liver with application of neural network technology.Journal of Gastroenterology and Hepatology. 2003; 18: 1054-1060
- Artificial neural network is superior to MELD in predicting mortality of patients with end-stage liver disease.Gut. 2007; 56: 253-258
- Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study.Journal of Hepatology. 2010; 52: 880-888
- Elastography, spleen size, and platelet count identify portal hypertension in patients with compensated cirrhosis.Gastroenterology. 2013; 144: 102-111
- Ultrasonographic evaluation of the liver surface and transient elastography in clinically doubtful cirrhosis.Journal of Hepatology. 2010; 52: 846-853
Article info
Publication history
Published online: February 26, 2015
Accepted:
February 2,
2015
Received:
September 25,
2014
Identification
Copyright
© 2015 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Inc. All rights reserved.
