Castera du pitie 17 janvier 2012 selection

Laurent CASTERA

Service d’Hépatologie, Hôpital Beaujon, Université Paris VII

Alternatives à la PBH : mesure de l’élasticité

hépatique

DU Hépatites Virales Cytokines et Antiviraux

Pitie, Paris, 17 Janvier 2012

Méthodes non invasives disponibles 2 approches différentes mais complémentaires

Biomarqueurs

Approche « biologique »

Castera & Pinzani. Lancet 2010; 375: 419-20

Approche « physique »

Elasticité hépatique

Castera, Forns & Alberti. J Hepatol 2008; 48: 835-47

Elasticité hépatique FibroScan

Sandrin et al. UMB 2003; 29: 1705-13

Friedrich-Rust et al. Radiology 2009 ; 252: 595-604

Elasticité hépatique Acoustic Radiation Force Impulse Imaging (ARFI)

Nightingale et al. UMB 2002 ; 28: 227-35

-90

0

+90

Dis

plac

emen

t (µ

m)

Shea

r Stif

fnes

s (k

Pa)

0

10

4

6

8

2

Elastogram

Elasticité hépatique Elasto-IRM

Muthupillai et al. Science 1995; 269: 1854-7 Huwart et al. Gastroenterology 2008; 135: 32-40

Elasticité hépatique Supersonic shear Imaging

Muller et al. UMB 2009; 35: 219-29

Bavu et al. UMB 2011;37: 1361-73

Contrary to FS, as vibration induced by the radiationforce creates a short transient excitation, the frequencybandwidth of the generated shear wave is large, typicallyranging from 60 to 600 Hz (Fig. 3). Such wideband‘‘shear wave spectroscopy’’ can give a refined analysisof the complex mechanical behavior of tissue. As shownin Figure 3, the shear wave dispersion law can be assessedfrom displacement movies in the region-of-interest.

Thus, the global elasticity imaged by SSI makes useof higher frequency content and is also influenced by thedispersive properties of the liver tissues because it aver-ages the full mechanical response of the liver tissuesover a large bandwidth. In parallel, SWS providesa refined analysis in a larger box of these dispersive prop-erties of tissues by estimating frequency dependence ofthe shear wave speed.

Statistical methodsThe diagnosis performance of FS and SSI are

compared by using receiver operating characteristic(ROC) curves and box-and-whisker curves on the samecohort. A patient was assessed as positive or negative ac-cording to whether the noninvasive marker value wasgreater than or less than to a given cutoff value, respec-tively. Connected with any cutoff value is the probabilityof a true positive (sensitivity) and the probability of a truenegative (specificity). The ROC curve is a plot ofsensitivity vs. (1-specificity) for all possible cutoff values.The most commonly used index of accuracy is the areaunder the ROC curve (AUROC), with values close to1.0 indicating high diagnosis accuracy. Optimal cutoffvalues for liver stiffness were chosen to maximize thesum of sensitivity and specificity and positive and nega-tive predictive values were computed for these cutoffvalues. By using these cutoff values, the agreementbetween FS and SSI was evaluated. Statistical analyseswere performed with Matlab R2007a software (Math-works, Natick, MA, USA) using the statistical analysistoolbox and Medcalc software (Mariakerke, Belgium).

RESULTS

Liver stiffness mapping using SSIThe Young’s modulus corresponding to the stiffness

of the liver tissues are presented for 4 patients in Figure 4.The elasticity mapping is superimposed with the corre-sponding B-mode images on which the fat and muscleregion are well differentiated from the liver region andthe elasticity is mapped only in the liver region.Figure 4a, b, c and d show the elasticity mapping forpatients who have been classified as predicted fibrosislevels F1, F2, F3 and F4, respectively.

The median elasticity derived from these maps areequal to 4.786 0.83 kPa for the patient with F1, 10.646

Fig. 4. Bidimensional liver elasticity maps assessed using thesupersonic shear imaging (SSI) technique superimposed tothe corresponding B-scan. The Young’s modulus representingthe liver stiffness is represented in color levels. (a): patient59 - F1. E5 4.786 0.83 kPa (b): patient 51 - F2. E5 10.6461.10 kPa (c): patient 39 - F3. E5 14.526 2.20 kPa (d): patient

22 - F4. E 5 27.43 6 2.64 kPa.

1366 Ultrasound in Medicine and Biology Volume 37, Number 9, 2011

Contrary to FS, as vibration induced by the radiationforce creates a short transient excitation, the frequencybandwidth of the generated shear wave is large, typicallyranging from 60 to 600 Hz (Fig. 3). Such wideband‘‘shear wave spectroscopy’’ can give a refined analysisof the complex mechanical behavior of tissue. As shownin Figure 3, the shear wave dispersion law can be assessedfrom displacement movies in the region-of-interest.

Thus, the global elasticity imaged by SSI makes useof higher frequency content and is also influenced by thedispersive properties of the liver tissues because it aver-ages the full mechanical response of the liver tissuesover a large bandwidth. In parallel, SWS providesa refined analysis in a larger box of these dispersive prop-erties of tissues by estimating frequency dependence ofthe shear wave speed.

Statistical methodsThe diagnosis performance of FS and SSI are

compared by using receiver operating characteristic(ROC) curves and box-and-whisker curves on the samecohort. A patient was assessed as positive or negative ac-cording to whether the noninvasive marker value wasgreater than or less than to a given cutoff value, respec-tively. Connected with any cutoff value is the probabilityof a true positive (sensitivity) and the probability of a truenegative (specificity). The ROC curve is a plot ofsensitivity vs. (1-specificity) for all possible cutoff values.The most commonly used index of accuracy is the areaunder the ROC curve (AUROC), with values close to1.0 indicating high diagnosis accuracy. Optimal cutoffvalues for liver stiffness were chosen to maximize thesum of sensitivity and specificity and positive and nega-tive predictive values were computed for these cutoffvalues. By using these cutoff values, the agreementbetween FS and SSI was evaluated. Statistical analyseswere performed with Matlab R2007a software (Math-works, Natick, MA, USA) using the statistical analysistoolbox and Medcalc software (Mariakerke, Belgium).

RESULTS

Liver stiffness mapping using SSIThe Young’s modulus corresponding to the stiffness

of the liver tissues are presented for 4 patients in Figure 4.The elasticity mapping is superimposed with the corre-sponding B-mode images on which the fat and muscleregion are well differentiated from the liver region andthe elasticity is mapped only in the liver region.Figure 4a, b, c and d show the elasticity mapping forpatients who have been classified as predicted fibrosislevels F1, F2, F3 and F4, respectively.

The median elasticity derived from these maps areequal to 4.786 0.83 kPa for the patient with F1, 10.646

Fig. 4. Bidimensional liver elasticity maps assessed using thesupersonic shear imaging (SSI) technique superimposed tothe corresponding B-scan. The Young’s modulus representingthe liver stiffness is represented in color levels. (a): patient59 - F1. E5 4.786 0.83 kPa (b): patient 51 - F2. E5 10.6461.10 kPa (c): patient 39 - F3. E5 14.526 2.20 kPa (d): patient

22 - F4. E 5 27.43 6 2.64 kPa.


  Principe

  Performances diagnostiques

  Comparaison avec les biomarqueurs

  Suivi de la progression de la fibrose

  Limites & perspectives

Plan

  Principe

Elastométrie (FibroScan)

= 100 x

Biopsie foie 2.5 cm

Volume exploré

4 cm

1 cm ∅

% -5

0

5

Dep

th (m

m)

Time (ms) 0 20 40 60

10 20 30 40 50 60

E = 3.0 kPa F0

Sandrin et al. UMB 2003; 12: 1705-13

VS = 1.0 m/s

E = 27.0 kPa F4

VS = 3.0 m/s

Principe “Plus le foie est dur, plus l’onde se propage vite”

Mesure de l’élasticité hépatique

75 kPa 3

15 65 5.5

Normale

Roulot et al. J Hepatol 2008; 48: 606-13

FibroScan en pratique

Indolore

Rapide (5 min)

Lit du malade/ consultation

Résultats immédiats

Formation courte (100 exam.)

Taux de succès > 60%

10 mesures valides

IQR < 30% médiane


Interprétation des résultats « recommandations du constructeur »

  Principe





Plan


F0 F1 F2 F3 F4

Indication du traitement antiviral

Dépistage du carcinome hépatocellulaire

Dépistage des varices oesophagiennes

Objectifs diagnostiques

PBH: un « gold » standard imparfait

Mehta et al. J Hepatol 2009; 50: 36-41. Bedossa & Carrat. J Hepatol 2009; 50: 1-3.

0.99

Fibrosis stage (Metavir) Fibrosis stage

1

10

100

F1 F2 F3 F4 Fibrosis stage (Metavir)

Elas

ticity

(kPa

)

Ziol et al. Hepatology 2005; 41: 48-54 Castera et al. Gastroenterology 2005; 128: 343-50.

N = 183 CHC patients N = 251 CHC patients

Hépatite C Performance Diagnostique

Quantité de fibrose vs. Stade de fibrose

Standish et al. Gut 2006; 55: 569-78.

Col

lage

n ar

ea (%

)

AUROC F2 : 0.83 F3 : 0.90 F4 : 0.95

0 0.2 0.4 0.6 0.8

1

0 0.5 1 1-Specificity

Sens

itivi

ty

Castera et al. Gastroenterology 2005; 128: 343-50.

AUROC F2 : 0.84 F3 : 0.90 F4 : 0.94

0

0.2

0.4 0.6

0.8

1

0 0.2 0.4 0.6 0.8 1 1-Specificity

Sens

itivi

ty

F2 F3 F4

AUROC F2 : 0.84 F3 : 0.90 F4 0.94

Ziol et al. Hepatology 2005; 41: 48-54

Hépatite C Performance Diagnostique

75 KPa 3


7.1 / 8.7

F2

PPV: 88-95% NPV: 48-56%

9.5

F3

12.5 / 14.5

F4

PPV: 71-87% NPV: 81-93%

PPV: 77-78% NPV: 95-97%

Hépatite C: seuils

7.1

Fibrose significative (n=1307 patients atteints d’hépatites virales, 746 F≥2)

3 75 F ≥ 2 75%

50%

F < 2 61%

50%

AUROC=0.76 Bien classés 68 %

Degos et al. J Hepatol 2010; 53: 1013-21

Hépatite B performance diagnostique

N= 161 HBV patients; F2-F4: 77%; F4: 25%

Chan et al. J Viral Hepat 2009 ; 16: 36-44

AUROCs

F ≥ 1 0.80

F ≥ 3 0.87

F = 4 0.93

AUROCs

F ≥ 2 0.81

F ≥ 3 0.93

F = 4 0.93

N= 173 HBV patients; F2-F4: 50%; F4: 8%

Marcellin et al. Liver Int 2009; 29: 242-7

Friedrich-Rust et al. Gastroenterology 2008; 134: 960-74

AUROC: 0.84 (0.82-0.86)

Seuil optimal: 7.6 kPa

Performances diagnostiques pour F≥2 Meta-analyse

Performances diagnostiques pour F≥2 Meta-analyse

Talwalkar et al. Clin Gastroenterol Hepatol 2007; 5: 1214-20

Sensibilité: 70% (67-73) Spécificité: 84% (80-88)

Friedrich-Rust et al. Gastroenterology 2008; 134: 960-74

AUROC: 0.94 (0.93-0.95)

Optimal cut-off: 13.0 kPa

Performances diagnostiques pour F4 Meta-analyse

Talwalkar et al. Clin Gastroenterol Hepatol 2007; 5: 1214-20

Sensibilité: 87% (84-90) Specificité: 91% (89-92)

Performances diagnostiques pour F4 Meta-analyse

14.6

Performance diagnostique pour cirrhose (n=1007 patients avec CLD, 165 cirrhotiques)

3 75

Bien classés 92%

Ganne-Carrié et al. Hepatology 2006; 44: 1511-7

F = 4 74%

4.5% Mal classés

17%

3.5 % Mal classés

F < 4 96%

83%

12.9 3 75 F = 4 53%

19%

F < 4 95%

81%

AUROC=0.90 patients bien classés 87 %


Performance diagnostique pour cirrhose (n=1307 patients avec hépatites virales, 180 cirrhotiques)

  Principe





Plan


Comparaison des approches fibrose significative

P=NS


P=NS

N= 298 CHC patients; F4: 25%

Comparaison des approches cirrhose

1,0 0,8 0,6 0,4 0,2 0,0 1 - Specificity

1,0

0,8

0,6

0,4

0,2

0,0

Sens

itivi

ty

.

F0123 vs F4

APRI

Lok

FT

FS

Platelet

PI

AAR

0.96

0.84

0.82

0.82

0.80

0.76

0.67

P<0.001

Castera et al. J Hepatol 2009; 50: 59-68.

N= 1307 patients; F4: 25% .

P<0.0001



N= 436 patients; F4: 14% .


Zarski et al. J Hepatol 2012; 56: 55-62

ZARSKI

superior to the best blood tests or Fibroscan™ alone in the ‘‘per-protocol’’ analysis (382 patients). However, when we consideredthe population of 436 patients (‘‘intention to diagnose popula-tion’’) the combination of Fibroscan™ plus a blood test markedlyimproved the percentage of well classified patients for both sig-nificant fibrosis and cirrhosis.

Other analyses

Wealso calculated thenumberof ‘‘theoretically avoided liver biop-sies’’ for the diagnosis of significant fibrosis using negative andpositive predictive values of 90% (Supplementary Table S7). No dif-ference was found between Fibrometer! (36.6%), Fibrotest!

(35.6%), Hepascore! (30.5%), and interpretable Fibroscan™(45.8%).

Discussion

Blood tests and Fibroscan™ have been recently developed asalternatives to liver biopsy [24]. Retrospective studies[14,25,26] have compared several of these markers to liver biopsybut to our knowledge this is one of the first independent prospec-tive validation of all relevant blood tests, and Fibroscan™ com-pared to liver biopsy in untreated patients with CHC. The trueindicator of liver disease status would be the histological analysisof the entire liver, but impossible to obtain in routine practiceand thus liver biopsy is considered at best as an ‘‘imperfect goldstandard’’ [27]. Reduced sensitivity for the detection of significantfibrosis has been demonstrated with biopsies of less than 30 mm,fragmented specimens and steatosis. Concerning errors consecu-tive to the biopsy itself, Metha et al. [28] have demonstrated thatthe AUROC for a perfect marker would not exceed 0.90 or 0.83according to 40% or 50% prevalence of significant disease in esti-mations where liver biopsy accuracy is highest (sensitivity andspecificity of 90%). However, our study especially takes into con-sideration the methodological aspects so as to optimize the inter-pretation of the stage of fibrosis. Firstly, the liver specimens hadto answer to quality criteria [29] to prevent a high risk of discor-dance for fibrosis staging [3,4,30]. Until now no study hasincluded patients with such a high mean length of biopsy withoutfragmentation, cirrhosis excepted. By using the METAVIR scoringsystem, 65% of liver biopsies with a length of 15 mm are usually

classified. This percentage increases to 75% for a length of 25 mm[3]. Also, a 25 mm biopsy is considered the optimal length foraccurate liver evaluation. Considering this, in our study a sam-pling error for liver biopsy remains since only 50% of patientshad a liver biopsy length greater than 25 mm. In addition, twosenior liver pathologists independently reviewed biopsies [4]which were re-examined to reach a consensus in cases of dis-agreement. The agreement between the two expert pathologistswas better than those previously published [4]. In order toexclude inter-laboratory variability the biochemical analyseswere centralized with standardized methods and enzymatic cal-ibration [31]. All serum samples were stored at !80 "C since thestability of different parameters could be affected by storage [32]such as marked transaminase activity loss at !20 "C [33].

The AUROCs of each test were comparable to those reported inthe original publications [6–15,18,20] when expressed usingobserved-AUROCs according to the prevalence of stages definingadvanced and non-advanced fibrosis. We observed similar AUR-OCs to those reported in meta-analyses [34–36] for the most val-idated biomarkers, Fibrotest!, Fibrometer!, and Apri and withoutmajor differences with interpretable Fibroscan™, Hepascore!, andELFG. In diagnosing cirrhosis, the ‘‘Fibrostic’’ study [37] showed asignificantly better performance of Fibroscan™ compared toserum markers while in contrast, our study shows that all thetests performed equivalently. This difference between these tworecent multicentre studies might be due to differences in design.Indeed in the ‘‘Fibrostic’’ study, Fibroscan™ was used in firstintent and analysed apart from blood tests, while in our studywe tried to compare in first intent all tests in ‘‘intention to diag-nose’’. The methodology used for Fibroscan™ was equivalent inthe two studies but the blood tests were performed in each centrein the Fibrostic study, using assay methods that might possiblyhave not always been homogeneous, while they were centralizedin the Fibrostar study, except when impossible, and rigorouslystandardized analytical conditions were respected.

For differentiating between adjacent stages, F1 vs. F2, onlyHyaluronate was inferior to Fibrometer!. For this adjacent com-parison, AUROCs could appear low, but the performances weresimilar relative to liver biopsy. Indeed comparison between abiopsy of 25 mm (mean length in our study) and the true goldstandard consisting of a large surgical sample showed 25% offalse negative/positives and an AUROC evaluated at 85% for F2vs. F1 [3].

Table 3. Performance of blood tests and Fibroscan™ for the diagnosis of cirrhosis (F4).

n = 436* n = 382‡

AUROC 95% CI p Sidak AUROC 95% CI p Sidak

FIBROMETER® 0.89 [0.86;0.93] 0.90 [0.86;0.93]

FIBROTEST® 0.86 [0.83;0.90] 0.325 0.87 [0.82;0.91] 0.321

APRI 0.86 [0.81;0.91] 0.141 0.87 [0.82;0.91] 0.410

ELFG 0.88 [0.83;0.92] 0.883 0.87 [0.83;0.92] 0.860

HEPASCORE® 0.89 [0.86;0.93] 1.000 0.89 [0.85;0.92] 0.998

FIB4 0.83 [0.76;0.89] 0.018 0.84 [0.77;0.90] 0.069

FIBROSCAN™ (interpretable results)

- - - 0.93 [0.89;0.96] 0.559

⁄CHC patients having all blood tests; !CHC patients with all the tests and interpretable Fibroscan™.

JOURNAL OF HEPATOLOGY

Journal of Hepatology 2012 vol. 56 j 55–62 59


Elastométrie Marqueurs sériques

+ Bien

classés F≥2: 75%

La combinaison augmente les performances diagnostiques

Poynard et al. Plos One 2008

Concordance in world without gold standard: a new way to increase diagnostic accuracy

Boursier et al. Am J Gastroenterol 2011; 106: 1255-63 N= 729 patients with CHC

La combinaison augmente les performances diagnostiques

APRI

Sebastiani et al. J Hepatol 2006; 44: 686-93.

F2-F3-F4 (>95% accuracy)

F0-F1 (20-30% false -)

F0-F1 (20-30% false -)

F2-F3-F4 (>95% accuracy)

Unclassified

FIBROTEST

LIVER BIOPSY Liver biopsy not needed

Combinaison des marqueurs sériques Sequential Algorithm for Fibrosis Evaluation

Comparaison des algorithmes fibrose significative


PBH évitées: 48%

Padoue

?

Bordeaux

Castéra et al. J Hepatol 2010; 52: 191-8.

PBH évités: 72% < P<0.001

N=302 HCV patients

Comparaison entre algorithmes Cirrhose


PBH évitées: 75%

Padova

?

Bordeaux

Castéra et al. J Hepatol 2010; 52: 191-8.

PBH évitées: 79% =

N=302 HCV patients

  Principe





Plan


La cirrhose: une entité hétérogène ?

F0 F1 F2 F3 F4

HVPG>10

Risque significatif de RVO

Complications cliniques

HVPG>12

Garcia-Tsao, Friedman, Iredale & Pinzani. Hepatology 2010; 51: 1444-49

Garcia-Tsao et al. Hepatology 2010; 51: 1445-9.

varices (stage 2). However, staging of compensated cir-rhosis could be further refined as (1) no portal hyperten-sion (HVPG !6 mmHg); (2) portal hypertension that isnot clinically significant (HVPG between 6 and 10mmHg); and (3) clinically significant portal hypertension(HVPG " 10 mmHg or presence of collaterals). Substag-ing of decompensated cirrhosis is not as well-defined butwould likely be classified according to both the degree ofportal hypertension and the degree of liver/circulatorydysfunction (with recurrent variceal hemorrhage, refrac-tory ascites, and hepatorenal syndrome representing moresevere stages) (Fig. 1). It remains possible that additionaltechnologies apart from HVPG will emerge that can fur-ther discriminate the pathological and functional state ofthe liver. Such information could be vital to optimize thetiming and nature of antifibrotic therapies, or the need forliver transplantation. Thus far, liver stiffness measure-ment (LSM) obtained by transient elastography is themost promising noninvasive approach for monitoring fi-brosis progression associated with worsening portal hy-pertension. LSM has an excellent correlation with HVPGvalues below a threshold of 10-12 mmHg.29,30 Althoughthese findings need to be further substantiated in largerindependent studies, they suggest that LSM may be usefulin the detection of clinically significant portal hyperten-sion and, thereby, in further subclassifying compensated

cirrhosis. On the other hand, LSM may not be accurate indecompensated cirrhosis where, in addition to intrahe-patic vascular resistance, there are complex hemodynamicchanges.31 Nonetheless, it will be important to evaluate,in longitudinal studies, whether single LSM values or dy-namic changes over time are predictive of initial or furtherdecompensation, or the response to pharmacologicaltherapy.32,33

We encourage the practicing community, patholo-gists, and investigators to move beyond the simple char-acterization of cirrhosis as a single stage and instead beginthinking of cirrhosis as a series of critical steps that, if leftunchecked, culminate in hepatic decompensation. A newframework for classifying cirrhosis will require integrationof both current and emerging knowledge about liverstructure and function. From one stage, there shouldemerge many.

References1. Pinzani M, Rombouts K, Colagrande S. Fibrosis in chronic liver diseases:

diagnosis and management. J Hepatol 2005;42(Suppl. 1):S22-S36.2. Anthony PP, Ishak KG, Nayak NC, Poulsen HE, Scheuer PJ, Sobin LH.

The morphology of cirrhosis. Recommendations on definition, nomencla-ture, and classification by a working group sponsored by the World HealthOrganization. J Clin Pathol 1978;31:395-414.

3. Poynard T, Bedossa P, Opolon P, for the OBSVIRC MCADg. Naturalhistory of liver fibrosis progression in patients with chronic hepatitis C.Lancet 1997;349:825-832.

Fig. 1. Classification of chronic liver disease based on histological, clinical, hemodynamic, and biological parameters. In the noncirrhotic stage(METAVIR F1-F3), there is no clinical evidence of cirrhosis, the HVPG is below 6 mmHg, and at this stage there is fibrogenesis and neovascularization.The cirrhotic stage (METAVIR F4) is broadly classified into two stages: compensated and decompensated, with clinical decompensation being definedby the development of ascites, variceal hemorrhage (VH), encephalopathy, and jaundice. Within the compensated stage, patients can be subclassifiedinto those without varices (stage 1) and those with varices (stage 2). Those without varices can be further subclassified into those with an hepaticvenous pressure gradient (HVPG) ! or " 10 mmHg, which is the threshold pressure that predicts development of varices and decompensation.HVPG " 10 mmHg (clinically significant portal hypertension) correlates with thick scar and small nodules. In the decompensated stage, the scar isresistant to degradation. Complex circulatory (splanchnic and systemic) abnormalities that occur at this decompensated stage contribute to furtherdecompensation, i.e., recurrent variceal hemorrhage, refractory ascites, and hepatorenal syndrome, and although HVPG retains prognostic value, otherparameters that take into account liver insufficiency and a deranged circulatory state, such as the MELD score, are more predictive of death (notdepicted in figure). Figure is modified from Friedman.23

1448 GARCIA-TSAO ET AL. HEPATOLOGY, April 2010

Now There Are Many (Stages) Where Before ThereWas One: In Search of a Pathophysiological

Classification of CirrhosisGuadalupe Garcia-Tsao,1 Scott Friedman,2 John Iredale,3 and Massimo Pinzani4

For more than a century and a half, the descriptionof a liver as “cirrhotic” was sufficient to connoteboth a pathological and clinical status, and to as-

sign the prognosis of a patient with liver disease. How-ever, as our interventions to treat advanced liver diseasehave progressed (e.g., antiviral therapies), the inadequacyof a simple one-stage description for advanced fibroticliver disease has become increasingly evident. Until re-cently, refining the diagnosis of cirrhosis into more thanone stage hardly seemed necessary when there were nointerventions available to arrest its progression. Now,however, understanding the range of potential outcomesbased on the severity of cirrhosis is essential in order topredict outcomes and individualize therapy. This positionpaper, rather than providing clinical guidelines, attemptsto catalyze a reformulation of the concept of cirrhosisfrom a static to a dynamic one, creating a template forfurther refinement of this concept in the future.

We already make the clinical distinction between com-pensated and decompensated cirrhosis, and are incremen-tally linking these clinical entities to quantitative variablessuch as portal pressure measurements and emerging non-invasive diagnostics. Moreover, mounting evidence sug-gests that cirrhosis encompasses a pathological spectrumwhich is neither static nor relentlessly progressive, butrather dynamic and bidirectional, at least in some pa-tients. Thus, there is a pressing need to redefine cirrhosisin a manner that better recognizes its underlying relation-ship to portal hypertension and related circulatory

changes, and more faithfully reflects its progression, re-versibility and prognosis, ultimately linking these param-eters to clinically relevant outcomes and therapeuticstrategies. The Child-Pugh and Model for End-StageLiver Disease (MELD) scores are currently deployed todefine prognosis by modeling hepatic dysfunction, but donot provide direct evidence of the stage or dynamic stateof cirrhosis. The need for more refined cirrhosis staging isespecially germane given the increasing use of effectiveantiviral treatments in patients with hepatitis B virus(HBV) and hepatitis C virus (HCV) cirrhosis and theemergence of effective antifibrotic agents, wherein wemust define favorable or unfavorable endpoints that cor-relate with a discrete clinical outcome in patients withcirrhosis.

The normal liver has only a small amount of fibroustissue in relation to its size. As a result of continued liverinjury, however, there is progressive accumulation of ex-tracellular matrix, or scar. Although different chronic liverdiseases are characterized by distinct patterns of fibrosisdeposition,1 the development of cirrhosis represents acommon outcome leading to similar clinical conse-quences that impose an increasing burden in clinical prac-tice.

Anatomical-Pathological ContextCirrhosis is defined histologically as a diffuse process in

which the normal anatomical lobules are replaced by ar-chitecturally abnormal nodules separated by fibrous tis-sue.2 Progressive histological stages have been defined inthe process leading to the development of cirrhosis.Among the more common staging systems, the META-VIR scale is distinguished by four stages, with stage F0representing lack of fibrosis; stage F1, portal fibrosis; stageF2, periportal fibrosis; stage F3, bridging fibrosis; and,finally, stage F4 representing cirrhosis.3 Similarly, theIshak4 and Scheuer scoring systems5,6 attempt to semi-quantitatively define progressive fibrosis based on the pat-tern and relative amounts of scar within a liver biopsyspecimen. In this context, once fibrosis reaches the finalstages, the diagnosis of cirrhosis is established and the

Abbreviations: HVPG, hepatic venous pressure gradient; LSM, liver stiffnessmeasurement.

From the 1Section of Digestive Diseases, Yale University School of Medicine, VAConnecticut Healthcare System, New Haven, CT; 2Division of Liver Diseases,Mount Sinai School of Medicine, New York, NY; 3Medical Research CouncilCentre for Inflammation Research, University of Edinburgh, Edinburgh, UK; and4Department of Internal Medicine, Center for Research, High Education andTransfer “DENOThe”, Universita degli Studi di Firenze, Firenze, Italy.

Received September 1, 2009; accepted October 29, 2009.Address reprint requests to: Scott L. Friedman, M.D., Box 1123, Mount Sinai

School of Medicine, 1425 Madison Avenue, Room 11-70C, New York, NY 10029-6574. E-mail: [email protected]; fax: 212-849-2574.

Copyright © 2009 by the American Association for the Study of Liver Diseases.Published online in Wiley InterScience (www.interscience.wiley.com).DOI 10.1002/hep.23478Potential conflict of interest: Nothing to report.

1445

notably activated hepatic stellate cells and portal myofi-broblasts, as well as key cytokines such as platelet-derivedgrowth factor and transforming growth factor beta.23 Theroles of bone marrow–derived cells and those arising fromepithelial-mesenchymal transition are still under evalua-tion, but it is unlikely that these sources of fibrogenic cellsprovide a major contribution to hepatic extracellular ma-trix in chronic human liver disease. Cellular sources ofproteases that degrade scar and the pathways that regulatethem are better understood. Moreover, a more nuancedunderstanding of distinctive pathogenic features of fibro-sis at different stages and from different etiologies meansthat fibrosis may be customized according to its durationand underlying cause.

Cirrhosis in experimental models and human diseasemay be reversible.24 Following withdrawal of an injuriousstimulus, a dense micronodular cirrhosis can undergo re-modeling to a more attenuated, macronodular pattern.However, some septa will persist, likely representing thoselaid down early in the injury and are therefore the most“mature” (i.e., cross-linked).

Moreover, in experimental models, such mature scarsmay be the site of neoangiogenesis. Such angiogenesis isalready present in chronic inflammatory liver diseases25

concurrent with the fibrogenic process and may also playa role in the pathogenesis of portal hypertension.26 Theeffectiveness of therapeutic angiogenic inhibitors in notonly improving fibrosis, but also in reducing portal pres-sure, is suggested by data from animal models but has notbeen established in humans.27 Although there are no datalinking septal remodeling to portal pressure changes, re-cent work correlating increased portal hypertension withsmaller nodule size and septal thickening suggests thatreversal of these events might lower portal pressure.20

These rodent models and human studies throw intorelief the inadequacy of a simple one stage classification,because although the micronodular and remodeled atten-uated macronodular cirrhosis are very different, they areboth defined by the same original pathologic description:“cirrhosis”. These same experimental models have alsofacilitated the comparative study of reversible and irre-versible septa. Specific septal changes that are associatedwith irreversibility include: matrix modification withcross-linking, elastin-rich scars, and septal neovasculariza-tion. Additionally, the loss of cells that drive matrix turn-over from the septa combined with vascular extinctionmay both limit reversibility. Lastly, of course, the persis-tence and intensity of the initiating injury will affect theprogression of cirrhosis via recurrent cycles of inflamma-tion and repair, regardless of the capacity of the liver torestore a more normal architecture.

Should antifibrotic therapies emerge, the challenges oftherapeutically resorbing fibrosis in a cirrhotic liver will bequite different from those of a noncirrhotic liver for sev-eral reasons. First, whereas evidence clearly indicates re-versibility of fibrosis in precirrhotic disease, thedeterminants of fibrosis regression in cirrhosis are notsufficiently clear, and the point at which cirrhosis is trulyirreversible is not established, either in morphologic orfunctional terms. Second, there is a heightened sense ofurgency in attempting to regress fibrosis in cirrhosis, be-cause continued progression might lead to imminent de-compensation, whereas noncirrhotic disease could bedecades away from clinical consequences. Thus, the speedof regression in cirrhosis may need to be greater, yet, thecirrhotic liver with its thicker, more cross-linked septa anddistorted vasculature may be less amenable to treatment.On the other hand, since fibrosis is part of a chronicwound healing reaction to encapsulate tissue damage,preventing the formation of scar tissue without removingthe cause of damage might be detrimental by amplifyingthe injury. Ideally, therefore, administration of an antifi-brotic agent would be most useful when coupled with aneffective treatment for the underlying liver disease (e.g.,antiviral drugs in patients with HBV or HCV). In con-trast, in cirrhotic liver, where the ultimate goal is thereduction of portal pressure, the use of antifibrotic agentscoupled with effective treatments to reduce portal pres-sure and its hemodynamic consequences might be morerational.

What Is Cirrhosis?Currently, the diagnosis of cirrhosis in diffuse disease

(viral hepatitis, alcohol) relies primarily on histopatholog-ical evidence of late-stage fibrosis (e.g., stage 4 fibrosisusing the METAVIR system, or stages 5 or 6 in the Ishakscoring system). In this context, and particularly inchronic hepatitis C, sampling errors may lead to underdi-agnosis28 or overdiagnosis of cirrhosis.19 Regardless, whenusing these and related staging systems, “cirrhosis” is astatic diagnosis reflecting the end stage of the wound heal-ing process, without adequately signifying the complexityof its pathogenesis, or its functional, hemodynamic andprognostic correlates. Because these collective changes arefundamental to provoking the transition from compen-sated to decompensated cirrhosis, we need a far morerefined pathophysiological classification of compensatedcirrhosis based on morphological, functional, and clinicaldata (Fig. 1).

At the least, a revised staging of cirrhosis should startwith its main classification of compensated and decom-pensated cirrhosis. Compensated cirrhosis in turn wouldcomprise two substages: without varices (stage 1) or with

HEPATOLOGY, Vol. 51, No. 4, 2010 GARCIA-TSAO ET AL. 1447

75 KPa 3

12.5 / 14.6

F4

Signification clinique dans la cirrhose?

?


OV grade II / III

27

Ascites

49

HCC

54

Bleeding

63 kPa 12 75

Foucher et al. Gut 2006; 55: 403-8.

Complications de la cirrhose

711 patients with liver diseases F3F4 144

N= 124 patients avec récidive VHC post TH Carrion et al. Liver Transpl 2006; 12: 1791-8.

80726456484032241680

30

24

18

12

6

0

HVP

G (m

m H

g)

Liver stiffness (kPa)

Pearson’s coefficient = 0.84

p < 0.001

Corrélation élasticité hépatique et HVPG

previous or confirmed diagnosis of cirrhosis, 28 (59.57%)were classified as Child-Pugh class A, 14 (29.79%) asChild-Pugh class B, and 5 (10.64%) as Child-Pugh classC. Considering the whole population, six patients(9.83%) had no portal hypertension (i.e., HVPG !5 mmHg), 8 patients (13.11%) had a pre-clinical portal hyper-tension (i.e., HVPG more than 5 but less than 10 mmHg), 47 patients (77.05%) had a clinically significant por-tal hypertension (i.e., HVPG "10 mm Hg), of whom 35(57.38%) had severe portal hypertension (i.e., HVPG"12 mm Hg).13-17 Twelve patients were listed in theHVPG range "10 !12 mm Hg (19.67%). Esophagealvarices were present in 30 of 47 patients with cirrhosis,gastroesophageal varices in 4, isolated gastric varices in 1,and moderate to severe portal hypertensive gastropathy in26. At the time of the study no patient with cirrhosisshowed clinical features of clinical decompensation (en-cephalopathy, recent gastrointestinal bleeding, ascites, or

peripheral edema), or ultrasonographic evidence of HCCand portal vein thrombosis. Patients with cirrhosisshowed significantly higher HVPG and liver stiffnesswhen compared with patients with advanced liver fibrosis(P ! 0.0001 and P ! 0.0001, respectively). The successrate of liver stiffness measurements was 90.22% "14.72%, and the mean interquartile range 3.45 " 3.

Relationship Between HVPG and LSM. Consider-ing the whole patient population, a statistically signifi-cant, positive correlation between HVPG and LSM wasfound (r # 0.81, P ! 0.0001). Figure 1 illustrates linearregression analysis between HVPG and LSM performedin the complete study population (r2 # 0.61, P !0.0001). In patients with HVPG !10 mm Hg or !12mm Hg there was a statistically significant correlationwith LSM (r # 0.81, P # 0.0003 and r # 0.91, P !0.0001, respectively). Liver stiffness was significantlyhigher in patients with an HVPG "10 and "12 mm Hgthan in patients with an HVPG !10 (P ! 0.0001) and!12 mm Hg (P ! 0.0001), respectively. Moreover, apositive correlation also existed in patients with HVPG"10 mm Hg and, although less relevant, in patients withHVPG "12 mm Hg (r # 0.59, P ! 0.0001 and r # 0.37,P # 0.03, respectively). Figure 2 illustrates linear regres-sion analysis in the different subgroups of HVPG. Note-worthy, when patients with an HVPG value "12 mm Hgwere considered, the correlation with LSM hardly reachedstatistical significance, with very poor r values.

Non-invasive Prediction of Clinically SignificantPortal Hypertension (i.e., HVPG >10 mm Hg) andSevere Portal Hypertension (i.e., HVPG >12 mmHg). Figure 3 shows the ROC curve of LSM for theprediction of clinically significant portal hypertension

Fig. 1. Linear regression analysis between HVPG and LSM in wholepatient population. Abbreviations: HVPG, hepatic vein pressure gradient;kPa, kilopascal.

Fig. 2. Linear regression analysis betweenLSM (kPa) and different degrees of HVPG (mmHg). (A) Regression values for HVPG !10 mmHg and panel B for HVPG "10 mm Hg (r2 #0.72, P ! 0.0001 and r2 # 0.35, P !0.0001, respectively). (C) Regression analysisfor HVPG !12 mm Hg and (D) for HVPG "12mm Hg (r2 # 0.67, P ! 0.0001 and r2 #0.17, P # 0.02, respectively). Between 10 and12 mm Hg some scattering of values from thetheoretical regression line (C) was observed.This became clearly evident for HVPG values"12 mm Hg (panel D). Abbreviations: HVPG,hepatic vein pressure gradient; kPa, kilopascal.

HEPATOLOGY, Vol. 45, No. 5, 2007 VIZZUTTI ET AL. 1293

Vizzutti et al. Hepatology 2007; 45: 1290-7

R²= 0.61 P<0.0001

61 patients VHC F3-F4 (47); VO grade II-III : 38 %

Correlation élasticité hépatique et HVPG oui… mais

R²= 0.67 P<0.0001

R²= 0.17 P=0.02

previous or confirmed diagnosis of cirrhosis, 28 (59.57%)were classified as Child-Pugh class A, 14 (29.79%) asChild-Pugh class B, and 5 (10.64%) as Child-Pugh classC. Considering the whole population, six patients(9.83%) had no portal hypertension (i.e., HVPG !5 mmHg), 8 patients (13.11%) had a pre-clinical portal hyper-tension (i.e., HVPG more than 5 but less than 10 mmHg), 47 patients (77.05%) had a clinically significant por-tal hypertension (i.e., HVPG "10 mm Hg), of whom 35(57.38%) had severe portal hypertension (i.e., HVPG"12 mm Hg).13-17 Twelve patients were listed in theHVPG range "10 !12 mm Hg (19.67%). Esophagealvarices were present in 30 of 47 patients with cirrhosis,gastroesophageal varices in 4, isolated gastric varices in 1,and moderate to severe portal hypertensive gastropathy in26. At the time of the study no patient with cirrhosisshowed clinical features of clinical decompensation (en-cephalopathy, recent gastrointestinal bleeding, ascites, or

peripheral edema), or ultrasonographic evidence of HCCand portal vein thrombosis. Patients with cirrhosisshowed significantly higher HVPG and liver stiffnesswhen compared with patients with advanced liver fibrosis(P ! 0.0001 and P ! 0.0001, respectively). The successrate of liver stiffness measurements was 90.22% "14.72%, and the mean interquartile range 3.45 " 3.

Relationship Between HVPG and LSM. Consider-ing the whole patient population, a statistically signifi-cant, positive correlation between HVPG and LSM wasfound (r # 0.81, P ! 0.0001). Figure 1 illustrates linearregression analysis between HVPG and LSM performedin the complete study population (r2 # 0.61, P !0.0001). In patients with HVPG !10 mm Hg or !12mm Hg there was a statistically significant correlationwith LSM (r # 0.81, P # 0.0003 and r # 0.91, P !0.0001, respectively). Liver stiffness was significantlyhigher in patients with an HVPG "10 and "12 mm Hgthan in patients with an HVPG !10 (P ! 0.0001) and!12 mm Hg (P ! 0.0001), respectively. Moreover, apositive correlation also existed in patients with HVPG"10 mm Hg and, although less relevant, in patients withHVPG "12 mm Hg (r # 0.59, P ! 0.0001 and r # 0.37,P # 0.03, respectively). Figure 2 illustrates linear regres-sion analysis in the different subgroups of HVPG. Note-worthy, when patients with an HVPG value "12 mm Hgwere considered, the correlation with LSM hardly reachedstatistical significance, with very poor r values.

Non-invasive Prediction of Clinically SignificantPortal Hypertension (i.e., HVPG >10 mm Hg) andSevere Portal Hypertension (i.e., HVPG >12 mmHg). Figure 3 shows the ROC curve of LSM for theprediction of clinically significant portal hypertension

Fig. 1. Linear regression analysis between HVPG and LSM in wholepatient population. Abbreviations: HVPG, hepatic vein pressure gradient;kPa, kilopascal.

Fig. 2. Linear regression analysis betweenLSM (kPa) and different degrees of HVPG (mmHg). (A) Regression values for HVPG !10 mmHg and panel B for HVPG "10 mm Hg (r2 #0.72, P ! 0.0001 and r2 # 0.35, P !0.0001, respectively). (C) Regression analysisfor HVPG !12 mm Hg and (D) for HVPG "12mm Hg (r2 # 0.67, P ! 0.0001 and r2 #0.17, P # 0.02, respectively). Between 10 and12 mm Hg some scattering of values from thetheoretical regression line (C) was observed.This became clearly evident for HVPG values"12 mm Hg (panel D). Abbreviations: HVPG,hepatic vein pressure gradient; kPa, kilopascal.

HEPATOLOGY, Vol. 45, No. 5, 2007 VIZZUTTI ET AL. 1293

Vizzutti et al. Hepatology 2007; 45: 1290-7

R²= 0.61 P<0.0001

61 patients VHC F3-F4 (47); VO grade II-III : 38 %

Correlation élasticité hépatique et HVPG oui… mais

R²= 0.67 P<0.0001

R²= 0.17 P=0.02

Au delà d’un gradient >10-12 mmHg

la pression portale devient largement

indépendante de l’élasticité

Kazemi et al. J Hepatol 2006; 45: 230-5

165 patients cirrhotiques; VO grade≥ II: 28 %

Nonen=91

grade II n=41

grade III n=6

grade I n=27

Corrélation avec les Varices Oesophagiennes

P<0.0001

19

54%

VO ≥ II 48%

47 patients Mal classés

3 75

4 patients Mal classés

VO < II 95%

46%

Prédiction des VO grade II-III

Kazemi et al. J Hepatol 2006; 45: 230-5

AUROC = 0.83 Fibroscopie évitée 69 %

Performance pour la prédiction des VO FibroScan

Castera, Pinzani & Bosch. J Hepatol 2012; in press

Authors,[Ref.]

Patients(n)

Etiologies Studydesign

Child-PughA (%)

End point

PrevalenceOV (%)

Cut-offs(kPa)

AUC Se(%)

Sp(%)

PPV(%)

NPV(%)

+LR -LR Savedendoscopy(%)

Kazemiet al., [45]

165 CLD Retro.mono.

n.a. OVLOV

4528

13.919.0

0.830.84

9591

4360

5748

9195

1.72.3

0.130.14

6669

Vizzuttiet al., [36]

47 HCV Pro.mono.

60 OV 66 17.6 0.76 90 43 77 66 1.6 0.23 74

Pritchettet al., [48]

211 CLD Retro.mono.

n.a. OVLOV

n.a.37

19.519.8

0.740.76

7691

6656

5691

8255

2.22.1

0.360.16

n.a.69

Bureauet al., [37]

89 CLD Pro.mono.

34 OVLOV

7248

21.129.3

0.850.76

8481

7161

2.92.1

0.220.31

8171

Castera et al., [46]

70 HCV Retro.mono.

100 OVLOV

3619

21.530.5

0.840.87

7677

7885

6856

8494

3.55.1

0.310.27

7379

Pineda,et al., [47]

102 HIV-HCV Pro.multi.

76 CROV* 13 21.0 0.71 100 32 25 100 1.5 0.0 44

Nguyen et al. [49]

18358103

CLDHCV/HBVAlcohol

Retro.mono.

63 LOV 221725

48.019.847.2

0.760.730.77

738985

735564

442744

909793

2.72.02.4

0.370.200.23

736069

Maliket al., [50]

124 CLD Retro.mono.

n.a. OV 51 20.0 0.85 n.a. n.a. 80 75 n.a. n.a. n.a.

Authors,[Ref.]

Patients(n)


Child-PughA (%)

End point

PrevalenceOV (%)

Cut-offs(kPa)

AUC Se(%)

Sp(%)

PPV(%)

NPV(%)


Kazemiet al., [45]

165 CLD Retro.mono.

n.a. OVLOV

4528

13.919.0

0.830.84

9591

4360

5748

9195

1.72.3

0.130.14

6669


47 HCV Pro.mono.

60 OV 66 17.6 0.76 90 43 77 66 1.6 0.23 74


211 CLD Retro.mono.

n.a. OVLOV

n.a.37

19.519.8

0.740.76

7691

6656

5691

8255

2.22.1

0.360.16

n.a.69

Bureauet al., [37]

89 CLD Pro.mono.

34 OVLOV

7248

21.129.3

0.850.76

8481

7161

2.92.1

0.220.31

8171


70 HCV Retro.mono.

100 OVLOV

3619

21.530.5

0.840.87

7677

7885

6856

8494

3.55.1

0.310.27

7379

Pineda,et al., [47]


76 CROV* 13 21.0 0.71 100 32 25 100 1.5 0.0 44

Nguyen et al. [49]

18358103

CLDHCV/HBVAlcohol

Retro.mono.

63 LOV 221725

48.019.847.2

0.760.730.77

738985

735564

442744

909793

2.72.02.4

0.370.200.23

736069

Maliket al., [50]

124 CLD Retro.mono.

n.a. OV 51 20.0 0.85 n.a. n.a. 80 75 n.a. n.a. n.a.

Authors,[Ref.]

Patients(n)


Child-PughA (%)

End point

PrevalenceOV (%)

Cut-offs(kPa)

AUC Se(%)

Sp(%)

PPV(%)

NPV(%)


Kazemiet al., [45]

165 CLD Retro.mono.

n.a. OVLOV

4528

13.919.0

0.830.84

9591

4360

5748

9195

1.72.3

0.130.14

6669


47 HCV Pro.mono.

60 OV 66 17.6 0.76 90 43 77 66 1.6 0.23 74


211 CLD Retro.mono.

n.a. OVLOV

n.a.37

19.519.8

0.740.76

7691

6656

5691

8255

2.22.1

0.360.16

n.a.69

Bureauet al., [37]

89 CLD Pro.mono.

34 OVLOV

7248

21.129.3

0.850.76

8481

7161

2.92.1

0.220.31

8171


70 HCV Retro.mono.

100 OVLOV

3619

21.530.5

0.840.87

7677

7885

6856

8494

3.55.1

0.310.27

7379

Pineda,et al., [47]


76 CROV* 13 21.0 0.71 100 32 25 100 1.5 0.0 44

Nguyen et al. [49]

18358103

CLDHCV/HBVAlcohol

Retro.mono.

63 LOV 221725

48.019.847.2

0.760.730.77

738985

735564

442744

909793

2.72.02.4

0.370.200.23

736069

Maliket al., [50]

124 CLD Retro.mono.

n.a. OV 51 20.0 0.85 n.a. n.a. 80 75 n.a. n.a. n.a.

Thabut, Moreau & Lebrec. Hepatology 2011; 53: 683-94

Castera et al. J Hepatol 2009; 50: 59-68.

VO II-III

76% 77% 79% 64% 79% 76% 63%

Ratio ASAT/ALAT Index de Lok FibroScan Fibrotest Taux de Prothrombine Taux de plaquettes APRI

VO

81% 77% 73% 70% 70% 69% 66%

Endoscopies évitées

Performance pour la prédiction des VO Biomarqueurs vs. FibroScan

N=70 patients cirrhose C

Combinaison élasticité hépatique taille de la rate + plaquettes = LSPS

LSM (kPa) x Spleen diameter (cm)

Platelet (109/L) LSPS =

N = 401 patients VHB cirrhotiques (evaluation 280; validation 121) VO « haut risque » (Baveno V): 32%

Kim et al. Am J Gastroenterol 2010; 105:1382-90

Liver stiffness Spleen diameter to Platelet ratio Score

3.5 5.5 VOHR +

93%

24.8%

Absence de VOHR 95%

62.8%

AUROC 0.95 Fibroscopie évitée 83%

LSPS Performance détection VO à «haut risque »


LSPS Risque de rupture de VO


The American Journal of GASTROENTEROLOGY VOLUME 106 | SEPTEMBER 2011 www.amjgastro.com

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higher albumin and lower prothrombin times were observed in subgroup 1 with marginal di! erence ( Table 4 ).

" erea# er, we re-divided subgroup 2 into subgroup 2-A (6.5 ! LSPS < 9.5) and 2-B (LSPS " 9.5) for further quantitative risk strati$ cation by LSPS value. " e cumulative EVB risk at 1, 2, 3, and 4 years was 7.43, 19.22, 22.58, and 32.0 % , respectively, in subgroup 2-A; 13.09, 31.78, 42.12, and 42.12 % , respectively, in subgroup 2-B. " erefore, among three strati$ ed subgroups (1, 2-A, and 2-B), the bleeding risk progressively increased in association with the elevated LSPS value (log-rank test, P = 0.001) with an HR of 5.408 (95 % CI, 1.484 – 19.704; P = 0.011) in subgroup 2-A and an HR of 7.722 (95 % CI, 2.080 – 28.668; P = 0.002) in subgroup 2-B, with ref-erence to subgroup 1 ( Figure 4 ).

DISCUSSION Since the current endoscopic screening involving all cirrhotic patients to detect HEVs will lead to a number of unnecessary procedures, various noninvasive screening tools have been devel-oped, allowing endoscopy to be selectively restricted to high-risk patients. Recently, the LSM-based prediction model from a cross-sectional study, LSPS, has shown improved diagnostic accuracy vs. LSM alone, by combining other parameters re% ecting portal

Among 150 subjects who had HEVs at enrollment (364 per-son-years), 25 experienced their $ rst EVB episodes (6.9 % per 1 person-year). To di! erentiate EVB risks among those with HEVs, we divided them into subgroup 1 (LSPS < 6.5) and subgroup 2 (LSPS " 6.5) according to LSPS threshold 6.5, which was a point with a maximum sum of sensitivity and speci$ city from time-dependent ROC curve using Youden ’ s method ( 25 ). " e incidences at 1, 2, 3, and 4 years were 1.82, 3.95, 6.78, and 6.78 % , respec-tively, in subgroup 1; 8.68, 25.09, 31.42, and 36.8 % , respectively, in subgroup 2 ( Figure 3 ). " e bleeding risk from EVs was signi-$ cantly higher in subgroup 2 than in subgroup 1 (log-rank test, P < 0.001) with a HR of 6.522 (95 % CI, 1.951 – 21.809) ( Figure 3 ; Table 3 ). From subsequent multivariate analysis, higher LSPS ( P = 0.003) remained a signi$ cant predictor for EVB, showing that subgroup 2 retained an independently higher risk of EVB, com-pared with subgroup 1. Besides, the large variceal size ( P = 0.004) and Child-Pugh classi$ cation B or C ( P = 0.001) were independ-ently associated with the higher bleeding risk ( Table 3 ). In con-trast to subgroup 2, the EVB risk of subgroup 1 was converged at a similarly low level to that of patients with low-risk EVs (log-rank test, P = 0.507; Figure 3 ). " en, we examined the clinical character-istics between subgroup 1 (LSPS < 6.5) and subgroup 2 (LSPS " 6.5; Table 4 ). " ere was no statistical di! erence in variables except LSM, spleen diameter, and platelet counts. However, trends toward

1.0

Entire population (n= 577)

Patients with LSPS 3.5–5.5

Patients with LSPS < 3.5

Patients withLSPS 3.5 – 5.5

Patients withLSPS < 3.5

Patients with LSPS " 5.5

Cum

ulat

ive

EV

ble

edin

g ris

k 0.8

0.6

0.4

0.2

0.0

0

107 76 51 33 18

11

140

20

208

35

273

43

354

52

418

No. at risk

Patients withLSPS " 5.5

1 2 3 4

Years

Figure 2 . Cumulative incidence rates of EV bleeding based on previously suggested LSPS values in the entire population ( n = 577). Its incidence increased signifi cantly in association with higher LSPS value (log-rank test, P < 0.001). EV, esophageal varice; LSPS, LSM – spleen diameter to platelet ratio score.

1.0

Cum

ulat

ive

EV

ble

edin

g ris

k

Patients with EVs (n= 214)

Subgroup 2

Subgroup 1

Low-risk EVs0.8

0.6

0.4

0.2

0.0

0

No. at risk

Subgroup 2

Subgroup 1

Low-risk EVs

88 61

53

49

41

41

44

28 16

19

20

28

35

62

64

1 2 3 4

Years

Figure 3 . To differentiate EV bleeding risks among patients with high-risk EVs ( n = 150), those were divided into subgroup 1 (high-risk EVs and LSPS < 6.5) and subgroup 2 (high-risk EVs and LSPS " 6.5). Cumulative incidence rates of EV bleeding was signifi cantly higher in subgroup 2 than in subgroup 1 (log-rank test, P < 0.001). In contrast to subgroup 2, EV bleeding risk of subgroup 1 was as low as that of low-risk EVs (log-rank test, P = 0.507). EV, esophageal varice; LSPS, LSM – spleen diameter to platelet ratio score.


1658 LI

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1.0






Patients with LSPS " 5.5C

umul

ativ

e E

V b

leed

ing

risk 0.8

0.6

0.4

0.2

0.0

0

107 76 51 33 18

11

140

20

208

35

273

43

354

52

418

No. at risk


1 2 3 4

Years


1.0

Cum

ulat

ive

EV

ble

edin

g ris

k


Subgroup 2

Subgroup 1

Low-risk EVs0.8

0.6

0.4

0.2

0.0

0

No. at risk

Subgroup 2

Subgroup 1

Low-risk EVs

88 61

53

49

41

41

44

28 16

19

20

28

35

62

64

1 2 3 4

Years



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1.0






Patients with LSPS " 5.5

Cum

ulat

ive

EV

ble

edin

g ris

k 0.8

0.6

0.4

0.2

0.0

0

107 76 51 33 18

11

140

20

208

35

273

43

354

52

418

No. at risk


1 2 3 4

Years


1.0

Cum

ulat

ive

EV

ble

edin

g ris

k


Subgroup 2

Subgroup 1

Low-risk EVs0.8

0.6

0.4

0.2

0.0

0

No. at risk

Subgroup 2

Subgroup 1

Low-risk EVs

88 61

53

49

41

41

44

28 16

19

20

28

35

62

64

1 2 3 4

Years


N=577 patients VHB

Résumé

  L’élasticité hépatique est bien corrélée avec le

gradient portal et la présence (taille?) des VO.

  Les performances de l’élastométrie sont

cependant insuffisantes pour remplacer la

fibroscopie pour la recherche de VO.

Elasticité hépatique & cancer du foie Liaisons dangereuses ?

Masuzaki et al. Hepatology 2009; 49: 1954 N= 866 HCV patients

Stepwise variable selection with AIC was used to findthe best model in multivariate analysis (Table 5). Patientswith higher LSM were revealed to be at a significantlyhigher risk, with an HR of 16.7 (95% confidential inter-val [CI], 3.71-75.2; P ! 0.001) with LSM 10.1-15 kPa,20.9 (95% CI, 4.43-98.8; P ! 0.001) with 15.1-20 kPa,25.6 (95%CI, 5.21-126.1; P ! 0.001) with 20.1-25 kPa,and 45.5 (95% CI, 9.75-212.3; P ! 0.001) with "25kPa, as compared to LSM !10 kPa. The presence ofclinical cirrhosis is also found to be a significant risk factorfor HCC development. LSM is thought to represent thedegree of live fibrosis, whereas clinical cirrhosis is basednot directly on fibrosis but on the degree of liver dysfunc-tion and portal hypertension. Thus, these two factors aremutually related but not identical, and may be comple-mentary in evaluating the risk of HCC. The other riskfactors considered significant are older age, male gender,and serum albumin level.

The effects of LSM on the risk of HCC developmentwere also evaluated in subgroup analyses to check whetherhigher LSM was a significant risk factor over strata (Fig.2). Indeed, higher LSM was found to be a significant riskfactor for HCC development in almost every sub-group. Interestingly, the HR attributed to higher LSM("15 kPa) was greater in the subgroups unlikely todevelop HCC, such as those with higher platelet count,absence of clinical cirrhosis, or lower AFP, than in thealternative subgroups. Higher LSM was a significantrisk factor in both IFN-treated and IFN-untreated pa-tients. Higher LSM may indicate a risk of HCC alsoamong IFN-treated patients who achieved SVR, al-though statistical significance was not reached becauseof the small number of events among the subgroup(n # 2).

DiscussionLiver fibrosis is the strongest prognostic indicator of

chronic hepatitis, which is currently best evaluated byliver biopsy.6,17,36,37 However, liver biopsy has several dis-advantages, including poor patient compliance, samplingerrors, limited usefulness for dynamic follow-up, and arisk of complications. LSM has been confirmed to be wellcorrelated with histological fibrosis stage in the litera-ture.18-22 We have previously shown the relationship be-tween LSM and hepatocarcinogenesis in a cross-sectionalstudy.27 However, the results remained to be confirmedprospectively.

Various risk factors have been reported for HCC de-velopment among patients with HCV: older age,6 malesex,6 heavy alcohol intake,38 high BMI,39 cirrhosis,6,16

lower platelet count, high serum AFP level,40 low serumalbumin level,38 and high serum ALT level.40 Our resultswere consistent with these findings. In the present cohort

Table 2. Baseline Characteristics of Patients According to HCC DevelopmentVariables HCC Development (!), n " 77 HCC Development (#), n " 789 P Value

Age (years) * 68.2 $ 8.0 (50–89) 61.6 $ 11.5 (17–88) !0.001Male, n (%) 41 (53.2) 357 (45.2) 0.19Alcohol consumption " 80 g/day, n (%) 7 (9.1) 26 (3.2) 0.02BMI (kg/m2)* 23.3 $ 3.1 (16.8–29.7) 22.4 $ 3.1 (14.4–36.9) 0.02Serum albumin (g/dL)* 3.6 $ 0.4 (2.7–4.5) 4.0 $ 0.4 (2.5–5.0) !0.001Total bilirubin (mg/dL)* 1.1 $ 0.6 (0.4–3.3) 0.9 $ 0.4 (0.3–4.6) !0.001AST (IU/L)* 70 $ 33 (29–217) 49 $ 34 (9–286) !0.001ALT (IU/L)* 66 $ 42 (19–231) 53 $ 47 (2–503) 0.019Prothrombin time activity (%)* 73.6 $ 10.9 (50–100) 86.1 $ 14.8 (38.9–100) !0.001Platelet count (109/L)* 104 $ 44 (36–246) 166 $ 66 (21–436) !0.001AFP (ng/mL)* 53.4 $ 111 (2.0–591.8) 11.1 $ 28.8 (0.8–339.4) !0.001Liver stiffness (kPa)* 26.0 $ 13.8 (8.9–69.1) 10.5 $ 8.0 (2.5–75) !0.001Clinical cirrhosis, n (%) 57 (74.0) 139 (17.6) !0.001

*Expressed as mean $ SD (range).Abbreviations: AFP, alpha fetoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index.

Fig. 1. Cumulative incidence of HCC development stratified based onLSM (N # 866). LSM, liver stiffness measurement.

HEPATOLOGY, Vol. 49, No. 6, 2009 MASUZAKI ET AL. 1957

Stepwise variable selection with AIC was used to findthe best model in multivariate analysis (Table 5). Patientswith higher LSM were revealed to be at a significantlyhigher risk, with an HR of 16.7 (95% confidential inter-val [CI], 3.71-75.2; P ! 0.001) with LSM 10.1-15 kPa,20.9 (95% CI, 4.43-98.8; P ! 0.001) with 15.1-20 kPa,25.6 (95%CI, 5.21-126.1; P ! 0.001) with 20.1-25 kPa,and 45.5 (95% CI, 9.75-212.3; P ! 0.001) with "25kPa, as compared to LSM !10 kPa. The presence ofclinical cirrhosis is also found to be a significant risk factorfor HCC development. LSM is thought to represent thedegree of live fibrosis, whereas clinical cirrhosis is basednot directly on fibrosis but on the degree of liver dysfunc-tion and portal hypertension. Thus, these two factors aremutually related but not identical, and may be comple-mentary in evaluating the risk of HCC. The other riskfactors considered significant are older age, male gender,and serum albumin level.

The effects of LSM on the risk of HCC developmentwere also evaluated in subgroup analyses to check whetherhigher LSM was a significant risk factor over strata (Fig.2). Indeed, higher LSM was found to be a significant riskfactor for HCC development in almost every sub-group. Interestingly, the HR attributed to higher LSM("15 kPa) was greater in the subgroups unlikely todevelop HCC, such as those with higher platelet count,absence of clinical cirrhosis, or lower AFP, than in thealternative subgroups. Higher LSM was a significantrisk factor in both IFN-treated and IFN-untreated pa-tients. Higher LSM may indicate a risk of HCC alsoamong IFN-treated patients who achieved SVR, al-though statistical significance was not reached becauseof the small number of events among the subgroup(n # 2).

DiscussionLiver fibrosis is the strongest prognostic indicator of

chronic hepatitis, which is currently best evaluated byliver biopsy.6,17,36,37 However, liver biopsy has several dis-advantages, including poor patient compliance, samplingerrors, limited usefulness for dynamic follow-up, and arisk of complications. LSM has been confirmed to be wellcorrelated with histological fibrosis stage in the litera-ture.18-22 We have previously shown the relationship be-tween LSM and hepatocarcinogenesis in a cross-sectionalstudy.27 However, the results remained to be confirmedprospectively.

Various risk factors have been reported for HCC de-velopment among patients with HCV: older age,6 malesex,6 heavy alcohol intake,38 high BMI,39 cirrhosis,6,16

lower platelet count, high serum AFP level,40 low serumalbumin level,38 and high serum ALT level.40 Our resultswere consistent with these findings. In the present cohort

Table 2. Baseline Characteristics of Patients According to HCC DevelopmentVariables HCC Development (!), n " 77 HCC Development (#), n " 789 P Value

Age (years) * 68.2 $ 8.0 (50–89) 61.6 $ 11.5 (17–88) !0.001Male, n (%) 41 (53.2) 357 (45.2) 0.19Alcohol consumption " 80 g/day, n (%) 7 (9.1) 26 (3.2) 0.02BMI (kg/m2)* 23.3 $ 3.1 (16.8–29.7) 22.4 $ 3.1 (14.4–36.9) 0.02Serum albumin (g/dL)* 3.6 $ 0.4 (2.7–4.5) 4.0 $ 0.4 (2.5–5.0) !0.001Total bilirubin (mg/dL)* 1.1 $ 0.6 (0.4–3.3) 0.9 $ 0.4 (0.3–4.6) !0.001AST (IU/L)* 70 $ 33 (29–217) 49 $ 34 (9–286) !0.001ALT (IU/L)* 66 $ 42 (19–231) 53 $ 47 (2–503) 0.019Prothrombin time activity (%)* 73.6 $ 10.9 (50–100) 86.1 $ 14.8 (38.9–100) !0.001Platelet count (109/L)* 104 $ 44 (36–246) 166 $ 66 (21–436) !0.001AFP (ng/mL)* 53.4 $ 111 (2.0–591.8) 11.1 $ 28.8 (0.8–339.4) !0.001Liver stiffness (kPa)* 26.0 $ 13.8 (8.9–69.1) 10.5 $ 8.0 (2.5–75) !0.001Clinical cirrhosis, n (%) 57 (74.0) 139 (17.6) !0.001

*Expressed as mean $ SD (range).Abbreviations: AFP, alpha fetoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index.

Fig. 1. Cumulative incidence of HCC development stratified based onLSM (N # 866). LSM, liver stiffness measurement.

HEPATOLOGY, Vol. 49, No. 6, 2009 MASUZAKI ET AL. 1957

p<0.001

Elasticité hépatique & cancer du foie Hépatite B

Risk Analysis of HCC Development According toLSM Value. The proportion of patients with cLC atenrollment and HCC development were significantlygreater in the groups with higher LSM value (Mantel-Haenszel tests, P < 0.001) (Fig. 2). In the univariateanalysis and subsequent multivariate analysis, togetherwith older age, male sex, heavy alcohol consumption(>80 g/day), lower serum albumin level, and HBeAgpositivity, higher LSM values (>8 kPa) were associatedwith a significantly greater risk of HCC development,with the following hazard ratios: 3.07 (95% CI, 1.01-9.31; P ! 0.047) for LSM 8.1-13 kPa; 4.68 (95% CI,1.40-15.64; P ! 0.012) for LSM 13.1-18 kPa; 5.55(95% CI, 1.53-20.04; P ! 0.009) for LSM 18.1-23kPa; and 6.60 (95% CI, 1.83-23.84; P ! 0.004) forLSM >23 kPa (Table 3).The cumulative incidence rates of HCC increased

significantly in association with elevated LSM amongthe five stratified groups (log-rank test, P < 0.001)(Fig. 3). The cumulative incidence rates at 1, 2, and 3years were 0.17%, 1.12%, and 1.58% in patients withLSM value "8 kPa (0.54% per 1 person-year); 1.05%,2.51%, and 6.28% in patients with 8 kPa< LSMvalue "13 kPa (1.75% per 1 person-year); 2.33%,5.63%, and 8.77% in patients with 13kPa< LSMvalue "18 kPa (2.94 % per 1 person-year); 0%,7.86%, and 19.07% in patients with 18 kPa< LSMvalue "23 kPa (7.04% per 1 person-year); 4.48%,16.8%, and 24.76% in patients with 23 kPa> LSMvalue (9.80% per 1 person-year).

Discordance in the Diagnosis of Cirrhosis UsingLSM and Clinical Criteria. We investigated the dis-cordance that could occur when diagnosing cirrhosis

using LSM and clinical criteria, and evaluated any dif-ferences in the risk of HCC development. For this suba-nalysis, we assessed 1,110 patients without baseline liverhistology at enrollment (Fig. 4).22 Overall, 874 (78.7%)patients had LSM "13 kPa and 236 (21.3%) had LSM>13 kPa. In patients with LSM "13 kPa, the incidenceof HCC estimated using person-years was not signifi-cantly different between patients with cLC (n ! 45,5.1%) and patients without cLC (n ! 829, 94.9%)(0.87% versus 0.89% per 1 person-year; P ! 0.518). Bycontrast, among patients with LSM >13 kPa, HCCdeveloped more frequently when liver cirrhosis wasdiagnosed according to clinical criteria (n ! 132,55.9%) than when it was not (n ! 104, 44.1%) (5.84%versus 3.26% per 1 person-year; P < 0.001).One hundred forty-nine (13.4%) patients showed

discordance in the diagnosis of cirrhosis when compar-ing LSM and clinical criteria. The incidence of HCCwas higher in 104 patients who showed LSM >13 kPaand no cLC than 45 who showed LSM "13 kPa withcLC (3.26% versus 0.87% per 1 person-year) (Fig. 4).

Risk Analysis of HCC Development According toLSM Change. After excluding two patients whounderwent follow-up LSM after HCC development,822 patients underwent a second LSM after a medianof 18.2 months (range, 11.9-23.0 months), and HCCdeveloped in 26 (3.2%) patients. To estimate the inci-dence of HCC according to the LSM change, westratified the patients into four groups as follows: bothinitial and follow-up LSM "13 kPa (group 1), initial

Fig. 3. Cumulative incidence rates of HCC based on stratified LSM(Kaplan-Meier plot). The cumulative incidence rates increased signifi-cantly in association with higher LSM (log-rank test, P < 0.001).

Fig. 4. The incidence rates of HCC in patients assessed for liver cir-rhosis based on LSM and clinical criteria at enrollment (n ! 1,110).In patients with LSM "13.0 kPa, the incidence of HCC was not signifi-cantly different between those with and without liver cirrhosis by clini-cal criteria (0.89% versus 0.87% per 1 person-year; P ! 0.518). Inpatients with LSM >13.0 kPa, HCC developed more frequently whenliver cirrhosis was diagnosed by clinical criteria (5.84% versus 3.26%per 1 person-year; P < 0.001).

890 JUNG, KIM, ET AL. HEPATOLOGY, March 2011

Jung et al. Hepatology 2011; 53: 885-94 N= 1130 patients VHB

Ogawa et al. Antiviral Res 2009; 83: 127-34. Vergniol et al. JVH 2009; 16: 132-40.

Suivi de la fibrose Traitement antiviral

  Principe





Plan


Reproductibilité ?

Fraquelli et al. Gut 2007; 56: 968-73.

200 patients with CLD (800 measurements)

Inter-observer variability (ICC= 0.98) Intra-observer variability (ICC= 0.98)

First measure Se

cond

mea

sure

First observer

Seco

nd o

bser

ver

Reproductibilité ?

Fraquelli et al. Gut 2007; 56: 968-73.

u  Moindre reproductibilité: ICC

―  Mild fibrosis (F0-F1) 0.60

―  Steatosis (>25% hepatocytes) 0.90

―  Increased BMI (>25) 0.94

Influence de la stéatose ?

Wong et al. Hepatology 2010; 51: 454-62. Gaia et al. J Hepatology 2011; 54: 64-71.

Taux de succès > 60%

10 mesures valides

IQR < 30% médiane


Elastométrie « recommandations du constructeur »

Limites : echec n=13369

Echec: 3.1 %

- Experience operateur

- BMI > 30

Castéra et al. Hepatology 2010; 51: 828-35

Limites : echec n=13369

BMI (kg/m²)

LSM

failu

re ra

tes

0%

20%

40%

60%

80%

12.4% 16.9%

24.9%

41.7%

8.1% 1.0%

< 25

(n=4172) ≥ 25

(n=3089) ≥ 28

(N=1568) ≥ 30

(n=967) ≥ 35

(n=225) ≥ 40

(n=48)


15.8%

IQR/LSM > 30% 9.2%

SR < 60% 8.1%

VS < 10 3.1%

Limites: résultats non fiables (n=12 949)

> 500 exams

15.6%

< 500 exams

30.5% 7.2%

Man Age < 52 BMI < 25

No Diabetes No hypertension

60.4%

Woman Age > 52 BMI > 30 Diabetes

Hypertension


Applicabilité de l’élastométrie

Non fiable 15.8%

IQR/LSM > 30% 9.2%

SR < 60% 8.1%

VS < 10 3.1%

Echec 3.1%

Valid shot = 0


FibroScan non applicable

dans 20% des cas

N=13669 examinations

Sonde XL : la réponse aux limites du FibroScan?

Myers et al. Hepatology 2012; 55:199-208.

N= 276 patients with BMI > 28 kg/m2

Page 33 of 41

Hepatology

Hepatology

Echec sonde XL vs. M : 1% vs. 16%

Page 35 of 41

Hepatology

Hepatology

N= 276 patients with BMI > 28 kg/m2

Résultats non fiables sonde XL vs. M : 27% vs. 50%

Myers et al. Hepatology 2012; 55:199-208.


Page 39 of 41

Hepatology

Hepatology

Median: 6.8 vs. 7.8 kPa (p<0.0001)

AUC F2 0.83 vs 0.86 (NS) AUC F4 0.94 vs 0.91 (NS)

N= 276 patients with BMI > 28 kg/m2 Myers et al. Hepatology 2012; 55:199-208.


Discordances avec la sonde XL 11%

Myers et al. J Hepatol 2012; 20: 2390-6. N= 210 patients BMI > 28 kg/m2

met. Future studies in larger cohorts are necessary to define theinfluence of these parameters on the performance of the XLprobe.

In addition to these TE quality criteria, we assessed the impactof biopsy quality on the accuracy of LSM using the XL probe. Nei-ther length nor the number of portal tracts was significantly asso-ciated with discordance. These findings support data fromLucidarme [14] and Myers [15]; however, Wong reported a 2.7-fold odds of discordance in HBV patients with biopsies <2.0 cm[23]. Indeed, numerous studies have described the importanceof biopsy length when assessing the performance of surrogatemarkers [35,36], since the risk of false negatives with histologyis higher with shorter biopsies [3]. Our findings may be attribut-able to our exclusion of inadequate biopsies plus centralized stag-ing by experts.

Our study has several strengths including centralized fibrosisassessment, the exclusion of small biopsies, and inclusion ofexperienced FibroScan operators, which likely reduced

discordances due to poor quality biopsies and TE. However, ourstudy has several limitations. First, fibrosis was staged accordingto different classification systems for patients with NAFLD [26]and viral hepatitis [27]. Although the NAS and METAVIR systemsare not directly interchangeable (e.g. regarding perisinusoidalfibrosis), sensitivity analyses restricted to NAFLD patients – inwhom the XL probe will likely have the greatest role – revealedsimilar findings. Similarly, due to the small number of patientswith viral hepatitis studied using the XL probe, we could notincorporate different liver stiffness cut-offs for patients withHBV and HCV. Although exclusion of patients with HBV yieldeda similar prevalence of discordance (11%), future studies shouldconsider this issue when stiffness cut-offs are available for allconditions with the XL probe. Third, we assessed only the diag-nostic accuracy of TE although additional properties includingagreement and precision deserve mention. Moreover, becauseour study was cross-sectional, we could not determine the asso-ciation between liver stiffness measured using the XL probe andclinical outcomes, the ultimate measure of its accuracy.

In conclusion, discordance between liver biopsy and TE occursin approximately one in 8 obese patients when the FibroScan XLprobe is used for LSM. Discordance in these patients is predomi-nantly related to overestimation of fibrosis by TE. TE results mustbe interpreted cautiously in the severely obese, particularly whenhigher liver stiffness values are obtained. Moreover, a high IQR/Mmay indicate a risk of discordance. To avoid erroneous clinicaldecisions based on TE, it is vital to recognize these risk factorsand consider alternative means of assessing fibrosis in patientsat risk for discordance.

Financial support

This study was funded by Echosens (Paris, France). Dr. Myers issupported by a New Investigator Award from the Canadian Insti-tutes of Health Research and Clinical Investigator Award from theAlberta Heritage Foundation for Medical Research (now AlbertaInnovates – Health Solutions).

Conflict of interest

Dr. Myers has received lecture fees from KNS Canada, the distrib-utors of the FibroScan in Canada, and research support fromEchosens.

Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.jhep.2011.10.007.

References

[1] Rockey DC, Caldwell SH, Goodman ZD, et al. Liver biopsy. Hepatology2009;49:1017–1044.

Table 4. Logistic regression analysis of factors associated with discordance.

Variable Univariate analysis Multivariate analysis

Odds ratio (95% CI)

p value Odds ratio (95% CI)

p value

BMI (per kg/m2)

1.13 (1.06-1.21)

<0.0005 1.09 (1.01-1.18)

0.04

Skin-capsular distance !35 mm

10.0 (2.30-43.3)

0.002 3.33 (0.59-18.9)

0.17

Liver stiffness (log

10-

transformed)

1.98 (1.18-3.31)

0.009 1.73 (0.95-3.18)

0.08

Unreliable LSM#

3.33 (1.39-7.94)

0.007 2.09 (0.75-5.82)

0.16

#<10 valid shots, SR <60%, or IQR/M >30%.

0

10

20

30

40

Pre

vale

nce

of

dis

cord

ance

(%

)

10

15

11

19

40

3.20 0

<30

30-3

4.9

35-3

9.9

!40

<30

30-3

4.9

35-3

9.9

!40

52617431636 0202 = n

Stiffness <7.0 kPa Stiffness !7.0 kPa

p = 0.35 p = 0.03

Body mass index (kg/m2)

Fig. 1. Percentage of patients with discordance of at least two stages between

TE using the XL probe and biopsy according to liver stiffness and BMI.

Research Article

Please cite this article in press as: Myers RP et al. Discordance in fibrosis staging between liver biopsy and transient elastography using the FibroScan XL

probe. J Hepatol (2011), doi:10.1016/j.jhep.2011.10.007

6 Journal of Hepatology 2011 vol. xxx j xxx–xxx

met. Future studies in larger cohorts are necessary to define theinfluence of these parameters on the performance of the XLprobe.

In addition to these TE quality criteria, we assessed the impactof biopsy quality on the accuracy of LSM using the XL probe. Nei-ther length nor the number of portal tracts was significantly asso-ciated with discordance. These findings support data fromLucidarme [14] and Myers [15]; however, Wong reported a 2.7-fold odds of discordance in HBV patients with biopsies <2.0 cm[23]. Indeed, numerous studies have described the importanceof biopsy length when assessing the performance of surrogatemarkers [35,36], since the risk of false negatives with histologyis higher with shorter biopsies [3]. Our findings may be attribut-able to our exclusion of inadequate biopsies plus centralized stag-ing by experts.

Our study has several strengths including centralized fibrosisassessment, the exclusion of small biopsies, and inclusion ofexperienced FibroScan operators, which likely reduced

discordances due to poor quality biopsies and TE. However, ourstudy has several limitations. First, fibrosis was staged accordingto different classification systems for patients with NAFLD [26]and viral hepatitis [27]. Although the NAS and METAVIR systemsare not directly interchangeable (e.g. regarding perisinusoidalfibrosis), sensitivity analyses restricted to NAFLD patients – inwhom the XL probe will likely have the greatest role – revealedsimilar findings. Similarly, due to the small number of patientswith viral hepatitis studied using the XL probe, we could notincorporate different liver stiffness cut-offs for patients withHBV and HCV. Although exclusion of patients with HBV yieldeda similar prevalence of discordance (11%), future studies shouldconsider this issue when stiffness cut-offs are available for allconditions with the XL probe. Third, we assessed only the diag-nostic accuracy of TE although additional properties includingagreement and precision deserve mention. Moreover, becauseour study was cross-sectional, we could not determine the asso-ciation between liver stiffness measured using the XL probe andclinical outcomes, the ultimate measure of its accuracy.

In conclusion, discordance between liver biopsy and TE occursin approximately one in 8 obese patients when the FibroScan XLprobe is used for LSM. Discordance in these patients is predomi-nantly related to overestimation of fibrosis by TE. TE results mustbe interpreted cautiously in the severely obese, particularly whenhigher liver stiffness values are obtained. Moreover, a high IQR/Mmay indicate a risk of discordance. To avoid erroneous clinicaldecisions based on TE, it is vital to recognize these risk factorsand consider alternative means of assessing fibrosis in patientsat risk for discordance.

Financial support

This study was funded by Echosens (Paris, France). Dr. Myers issupported by a New Investigator Award from the Canadian Insti-tutes of Health Research and Clinical Investigator Award from theAlberta Heritage Foundation for Medical Research (now AlbertaInnovates – Health Solutions).

Conflict of interest

Dr. Myers has received lecture fees from KNS Canada, the distrib-utors of the FibroScan in Canada, and research support fromEchosens.

Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.jhep.2011.10.007.

References

[1] Rockey DC, Caldwell SH, Goodman ZD, et al. Liver biopsy. Hepatology2009;49:1017–1044.

Table 4. Logistic regression analysis of factors associated with discordance.

Variable Univariate analysis Multivariate analysis

Odds ratio (95% CI)

p value Odds ratio (95% CI)

p value

BMI (per kg/m2)

1.13 (1.06-1.21)

<0.0005 1.09 (1.01-1.18)

0.04

Skin-capsular distance !35 mm

10.0 (2.30-43.3)

0.002 3.33 (0.59-18.9)

0.17

Liver stiffness (log

10-

transformed)

1.98 (1.18-3.31)

0.009 1.73 (0.95-3.18)

0.08

Unreliable LSM#

3.33 (1.39-7.94)

0.007 2.09 (0.75-5.82)

0.16

#<10 valid shots, SR <60%, or IQR/M >30%.

0

10

20

30

40

Pre

vale

nce

of

disc

orda

nce

(%)

10

15

11

19

40

3.20 0

<30

30-34.9

35-39.9

!40

<30

30-34.9

35-39.9

!40

52617431636 0202 = n

Stiffness <7.0 kPa Stiffness !7.0 kPa

p = 0.35 p = 0.03

Body mass index (kg/m2)

Fig. 1. Percentage of patients with discordance of at least two stages between

TE using the XL probe and biopsy according to liver stiffness and BMI.

Research Article

Please cite this article in press as: Myers RP et al. Discordance in fibrosis staging between liver biopsy and transient elastography using the FibroScan XL

probe. J Hepatol (2011), doi:10.1016/j.jhep.2011.10.007

6 Journal of Hepatology 2011 vol. xxx j xxx–xxx

Facteurs confondants

Inflammation aigue Coco et al. J Viral Hepat 2007 Arena et al. Hepatology 2008 Sagir et al. Hepatology 2008

Cholestase extra-hepatique Millonig et al. Hepatology 2008

Congestion Millonig et al. J Hepatol 2010

75 KPa 3

FibroScan : quels seuils?

Ziol et al. Hepatology 2005; 41: 48-54

12.5

HCV

10.3

HBV

14.5

HCV

Marcellin et al. Liver Int 2008 Castera et al. Gastroenterology 2005; 128: 343-50.

F4:

17.1

PBC/PSC

Corpechot et al. Hepatology 2006; 43: 1118-24.

8% 25% 19% 19%

Poynard et al. Clin Chem 2007; 53: 1615-22.

DANA = 1

DANA = 4

AUC = 0.67

AUC = 0.98

AUROC standardisation according to fibrosis prevalence

75 KPa

FibroScan : un nouvel outil pour un nouveau concept

3

fibrose significative

9.5

fibrose Absente minime

7.0 12.5

fibrose Severe

Cirrhose


Biomarqueurs vs. FibroScan Avantages & inconvénients

Castera L. Gastroenterology 2012; in press

Meilleures performances

Détection cirrhose Bonnes

performances

Critères

Biomarqueurs FibroScan

Applicabilité

95%

81%

Rapidité résultats

1-3 jours

10 min

Perspectives

Elasticité hépatique survie sans complications

Robic et al. J Hepatol 2011; 55: 1017-24 N=100 patients CLD

liver related complication were 32.2% and 84.1%, respectively(Log Rank p <0.001) (Fig. 2A).

The actuarial rates of remaining free of any complicationrelated to the liver disease were 29.5% and 85.4%, respectively,if LS was P21.1 kPa or <21.1 kPa (Log Rank p <0.001) (Fig. 2B).

Performance of LS and HVPG in predicting the risk of PHT relatedcomplications

The AUROC of HVPG and LS values in predicting PHT related com-plications were 0.830 [0.751–0.910] and 0.845 [0.767–0.923](Fig. 3A). When considering only cirrhotic patients, HVPGand LS had similar performances, AUROC values being 0.725[0.602–0.849] and 0.734 [0.609–0.859], respectively. (Fig. 3B).

According to the presence or the absence of significant PHT(HVPG P10 mmHg), the actuarial rates of remaining free ofPHT complications were 51.3% and 100%, respectively (Log Rankp <0.001) (Fig. 4A). In fact, none of the patients with aHVPG <10 mmHg developed PHT related complications. In thesubgroup of cirrhotic patients, the HVPG with a 10 mmHg thresh-old had a sensitivity of 100%, a specificity of 36.1%, a PPV of 37.5%,and a NPV of 100%. In this subgroup, the actuarial rates ofremaining free of PHT complications with a HVPG P10 mmHgor <10 mmHg were 47.8% and 100%, respectively.

When LS value was P21.1 kPa or <21.1 kPa, the actuarial ratesof remaining free of PHT related complications were 47.5% and100%, respectively (Log Rank p <0.001) (Fig. 4B). No patient witha LS value <21.1 kPa experienced PHT related complications. Forthe subgroup of patients with cirrhosis, LS with a 21.1 kPa cutoffhad a 100% sensitivity, 41% specificity, 41% PPV, and 100% NPV.The actuarial rates of remaining free of PHT complications withLS P21.1 kPa or <21.1 kPa were 47% and 100%, respectively.

Discussion

The present study shows a strong relationship between LS andthe risk of decompensation in patients with chronic liver disease,regardless of the presence of cirrhosis.

To the best of our knowledge this is the first study that pro-spectively assessed the performance of LS for predicting clinicalcomplications and that compared it with HVPG measurement.HVPG is a well established index in different clinical settingsfor patients with cirrhosis [2,3]. However, the only availablemethod to assess HVPG is a direct measurement of pressure ina hepatic vein. This method is not available in all medical centersand is considered invasive, cost-ineffective, and hardly reproduc-ible by most authors [18]. Until now, most non-invasiveapproaches for assessing short-term prognosis in patients withchronic liver disease have proved inaccurate. The need for pro-spective longitudinal studies to assess new non-invasive proce-dures was recommended at the Baveno IV ConsensusConference [19].

Foucher et al. have previously found a correlation between LSand severity of cirrhosis [20]. However, in this study, PHT compli-cations were retrospectively assessed as a history of ascites or

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Fig. 1. Performance of liver stiffness and HVPG for the prediction of liverrelated complications.

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Fig. 2. Risk of liver related complications according to HVPG or liver stiffness.(A) Probability of remaining free of liver related complications according to the10 mmHg-threshold for HVPG. (B) Probability of remaining free of liver relatedcomplications according to the 21.1 kPa-threshold for liver stiffness.

JOURNAL OF HEPATOLOGY

Journal of Hepatology 2011 vol. 55 j 1017–1024 1021

Elasticité hépatique & survie

Vergniol et al. Gastroenterology 2011;

4.00*FibroTest ! 1.12* LSM in 3 classes (0: LSM ! 7, 1: 7" LSM ! 12.5, 2: LSM # 12.5) $1.68*treatment in 3classes (0: no treatment, 1: nonresponder, 2: sustained viro-logical response). In the core group the corresponding prog-nostic AUROC for survival was 0.907 (95% CI: 0.825–0.952;P " .0001). In the whole group, the corresponding prog-nostic AUROC for survival was 0.888 (95% CI: 0.843–0.921;P " .0001).

As obesity has been recognized as an independent predic-tor of prognosis in chronic liver disease, in a sensitivityanalysis we added body mass index as another possibleprognostic factor in model 5; there was not significantprognostic value (hazard ratio % 0.98; 95% CI: 0.92–1.04).

As HIV infection has been recognized as an independentpredictor of prognosis in chronic liver disease, in a sensitiv-ity analysis we added HIV infection as another possible

Figure 2. Overall survival probability according to liver stiffness, biomarkers, and liver biopsy. (A) Overall survival according to published cut-offs forthe diagnosis of severe fibrosis or cirrhosis. (B) Overall survival according to different cut-offs of liver stiffness (kPa). Overall survival (95% confidenceinterval) using liver stiffness: !9.5 kPa: 96% (94%–98%); #9.5 kPa: 77% (72%–82%); #20 kPa: 66% (61%–71%); #30 kPa: 57% (50%–64%); #40kPa: 47% (37%–57%); #50 kPa: 42% (29%–55%). (C) Overall survival according to different cut-offs of FibroTest. Overall survival (95% confidenceinterval) using FibroTest: !0.75: 97% (96%–98%); #0.75 and !0.80: 87% (78%–95%); #0.80 and !0.85: 84% (75%–94%); #0.85 and !0.90:75% (61%–90%); #0.90 and !0.95: 69% (54%–84%); #0.95 to 1: 0% (0%–22%).

CLIN

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,PA

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1976 VERGNIOL ET AL GASTROENTEROLOGY Vol. 140, No. 7

4.00*FibroTest ! 1.12* LSM in 3 classes (0: LSM ! 7, 1: 7" LSM ! 12.5, 2: LSM # 12.5) $1.68*treatment in 3classes (0: no treatment, 1: nonresponder, 2: sustained viro-logical response). In the core group the corresponding prog-nostic AUROC for survival was 0.907 (95% CI: 0.825–0.952;P " .0001). In the whole group, the corresponding prog-nostic AUROC for survival was 0.888 (95% CI: 0.843–0.921;P " .0001).

As obesity has been recognized as an independent predic-tor of prognosis in chronic liver disease, in a sensitivityanalysis we added body mass index as another possibleprognostic factor in model 5; there was not significantprognostic value (hazard ratio % 0.98; 95% CI: 0.92–1.04).

As HIV infection has been recognized as an independentpredictor of prognosis in chronic liver disease, in a sensitiv-ity analysis we added HIV infection as another possible

Figure 2. Overall survival probability according to liver stiffness, biomarkers, and liver biopsy. (A) Overall survival according to published cut-offs forthe diagnosis of severe fibrosis or cirrhosis. (B) Overall survival according to different cut-offs of liver stiffness (kPa). Overall survival (95% confidenceinterval) using liver stiffness: !9.5 kPa: 96% (94%–98%); #9.5 kPa: 77% (72%–82%); #20 kPa: 66% (61%–71%); #30 kPa: 57% (50%–64%); #40kPa: 47% (37%–57%); #50 kPa: 42% (29%–55%). (C) Overall survival according to different cut-offs of FibroTest. Overall survival (95% confidenceinterval) using FibroTest: !0.75: 97% (96%–98%); #0.75 and !0.80: 87% (78%–95%); #0.80 and !0.85: 84% (75%–94%); #0.85 and !0.90:75% (61%–90%); #0.90 and !0.95: 69% (54%–84%); #0.95 to 1: 0% (0%–22%).

CLIN

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1976 VERGNIOL ET AL GASTROENTEROLOGY Vol. 140, No. 7

N=1457 patients VHC

Nouvelles techniques ARFI

Acoustic Radiation Force Impulse Imaging

Lupsor et al. J Gastrointestin Llver Dis 2009; 18: 303-10 N=112 HCV patients

Velocity: meter/sec

Friedrich-Rust et al. J Viral Hepat 2012 ; in press

ARFI performances diagnostiques Meta-analyse

N=518 patients

Tests were two-sided if not indicated otherwise, and

P-values below 5% were considered significant.

RESULTS

Diagnostic accuracy of Acoustic Radiation Force Impulse

The overall diagnostic accuracy of ARFI was analysed in 518

patients from eight studies. The diagnostic accuracies of ARFI

for the different fibrosis stages are shown in Table 3, Figs 1 &

2. When assessing potential publication bias, there is no

indication that studies with high standard errors, for example

caused by small sample sizes, had a trend to higher AUROCs.

The optimal cut-off for the diagnosis of significant fibrosis

was 1.34 m/s, for the diagnosis of severe fibrosis 1.55 m/s

and for the diagnosis of liver cirrhosis 1.80 m/s, respectively.

Sensitivity, specificity, positive predictive value (PPV), neg-

ative predictive value (NPV) and likelihood ratios for these

cut-offs from a random effect analysis are shown in Table 3.

For the diagnosis of significant fibrosis, besides ARFI levels as

strongest predictor (P < 0.0001) also age (P < 0.0001), sex

(P = 0.038) and diagnosis (P = 0.048) are all significant

independent predictors. Similarly, ARFI levels are the

strongest predictor for the diagnosis of liver cirrhosis

(P < 0.0001) and also age is an additional significant pre-

dictor (P = 0.0073).

Diagnostic accuracy of Acoustic Radiation Force Impulsefor different underlying liver diseases

The overall diagnostic accuracy of ARFI analysed separately

for 380 patients from six studies infected with HCV [17–

21,23], 51 patients from four studies infected with HBV

[17,19,21,23] and 77 patients from four studies with

NAFLD [19,22–24] is shown in Table 4 and Fig. S1.

Comparison of Acoustic Radiation Force Impulseand FibroScan

Both, ARFI and transient elastography median values were

available for 312 patients from four studies [17,18,22,23].

The diagnostic accuracy of ARFI was inferior to transient

elastography for the diagnosis of significant fibrosis [mean

difference of AUROC 0.05, P = 0.037 (s. Fig. 3)] and for the

diagnosis of liver cirrhosis (mean difference of AUROC 0.04,

P = 0.048). No significant difference but a significant het-

erogeneity was found for the diagnosis of severe fibrosis

(mean difference of AUROC 0.04, P = 0.092).

Measurement failure and measurement site

Acoustic Radiation Force Impulse failure was reported for 16

patients from three studies [18,21,24], while ARFI mea-

Table 3 Diagnostic accuracy and optimal cut-offs of ARFI for the diagnosis of liver fibrosis in all 518 patients from a random

effect analysis

ARFI AUROC

Cut-off

(m/s)

Sensitivity

(%)

Specificity

(%)

PPV

(%)

NPV

(%) +LR

F ‡ 2 0.87 1.34 79 85 91 66 5.07

F ‡ 3 0.91 1.55 86 86 82 89 5.94

F = 4 0.93 1.80 92 86 71 97 6.72

ARFI, Acoustic Radiation Force Impulse; F, fibrosis stage; AUROC, area under the ROC curve; CI, confidence interval; PPV,

positive predictive value; NPV, negative predictive value; LR, likelihood ratio.

Fig. 1 10Forrest plot of the AUROC (seAUROC denotes the standard error of AUROC) for all patients in the eight studies for the

diagnosis of significant fibrosis (F ‡ 2). The random effect model estimates for AUROC and the 95% confidence interval are

described by the dotted line and the lower diamond, respectively. As comparison, also the fixed effect estimates are shown.

AUROC, area under the ROC curve.

! 2011 Blackwell Publishing Ltd

4 M. Friedrich-Rust et al.

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ARFI vs. TE

Friedrich-Rust et al. Radiology 2009 ; 252: 595-604.

Significant fibrosis Cirrhosis

AUROC

ARFI: 0.82 TE: 0.84

AUROC

ARFI: 0.91 TE: 0.91

N=81 patients with viral hepatitis

Friedrich-Rust et al. Radiology 2009 ; 252: 595-604.

Quels seuils en pratique?

N=81 patients with viral hepatitis

Huwart et al. Gastroenterology 2008; 135: 32-40.

Nouvelles techniques Elasto-IRM vs. TE

AUROC F≥2 0.99 F=4 0.99

AUROC F≥2 0.84 F=4 0.93

N= 96 patients with various chronic liver diseases: F≥2 54%; F4 19%

MR elastography Transient elastography

Bavu et al. UMB 2011;37: 1361-73

and FS elasticity values for each predicted liver fibrosislevel. Although the predicted fibrosis level is not exclu-sively derived from the gold standard method (LB exam-ination), this preliminary study allows the comparison ofboth techniques with a unique reference: the predictedfibrosis level, which is derived from the blood markersvalues and liver biopsy, when available. The correspond-ing one-way analysis of variance (ANOVA) gives both a pindex of,1025. In this analysis, the liver elasticity distri-butions are normalized by log transformation to ensurethe validity of the analysis of variance. SSI allows evalu-ating the patients liver fibrosis with a smaller variancethan FS for all liver predicted fibrosis levels. To analysethe fibrosis evaluation performances, FS and SSI arecompared by using receiver operating characteristic(ROC) curves in the following.

Figure 6 shows ROC curves comparison and thesignificance level p for AUROC comparison using De-long method (DeLong et al. 1988) for different degreesof predicted liver fibrosis levels. The correspondingcomparison between AUROCs and confidence levelsare shown in Table 1 and the most discriminant cutoffvalues are shown in Table 2.

As shown in Table 1, the FS examination gives worseAUROCs for each predicted fibrosis level than SSI. TheAUROCs values for SSI and FS are, respectively, 0.948and 0.846 for the diagnosis of significant fibrosis(F$ 2), 0.962 and 0.857 for the diagnosis of severefibrosis (F$ 3); for the diagnosis of cirrhosis (F5 4),the AUROC values are 0.968 and 0.940, respectively.

Furthermore, other indicators of the performances ofdiagnosis tests are derived from ROC curves for SSI andFS measurements. For all predicted liver fibrosis levels,misclassification rates and Youden’s index confirm thefact that the diagnosis accuracy is better using SSI thanFS when comparing predicted liver fibrosis levels usingnoninvasive markers (and biopsy when available). Forall stages of liver fibrosis, the specificity at 95% of sensi-tivity is higher for SSI than FS, as well as the sensitivity at95% of specificity is higher for SSI than FS (see Table 2).

Liver stiffness evaluation: comparison between FS andSSI

Liver elasticity values assessed using SSI rangedfrom 4.50 kPa to 33.96 kPa (median 9.14 kPa, standarddeviation 6.27 kPa). Liver elasticity values assessed usingFS ranged from 2.60 kPa to 46.50 kPa (median 6.10 kPa,standard deviation 6.41 kPa). The liver stiffness distribu-tions assessed by FS and SSI are presented on Figure 7using a scatter plot (with liver stiffness distributionnormalized by log transformation) and a Bland-Altmanrepresentation. This multivariate analysis shows a goodcorrelation (r50:8296, p, 1025) between the elasticityvalues assessed by FS and SSI apparatus. Moreover, theBland-Altman analysis shows a good agreement betweenthe two methods, with a mean offset between SSI and FSof 2.40 kPa (standard deviation of the difference betweenSSI and FS: 3.61 kPa).

Figures 7 and 8 are represented to explain twoimportant points. On one hand, FS and SSI values arenot strictly identical (as shown in Fig. 7). On the otherhand, SSI values contain the information provided bythe FS (as shown in Fig. 8). Figure 8 is obtained by usingthe SWS processing on SSI data: a linear fit of the shearwave velocity dispersion curve was performed for each

Table 1. AUROC and 95% confidence interval for SSI and FS according to METAVIR fibrosis stages

Method F $ 2 F $ 3 F 5 4

SSI 0.95 [0.91;0.99] 0.96 [0.92;1] 0.97 [0.90;1]FS 0.85 [0.77;0.92] 0.86 [0.77;0.93] 0.94 [0.85;1]FS (Cast!era et al. 2005) 0.83 [0.76;0.88] 0.90 [0.85;0.94] 0.95 [0.91;0.98]D 0.102 6 0.0367 0.105 6 0.0407 0.027 6 0.0193P 0.005 0.001 0.154

SSI 5 supersonic shear imaging; FS 5 FibroScan; AUROC 5 area under the receiver operating characteristic curve.The results from a previous study (Cast!era et al. 2005) on fibrosis staging using FS are shown for reference.D, the difference between AUROC for SSI

and FS are also presented. The significance level P of the comparison between ROC curves is also given.

Table 2. Liver stiffness cutoff values in this study andperformance indicators of diagnosis accuracy(sensitivity, specificity, Youden’s index and

misclassification rate) for SSI and FS elasticitymeasurement methods

Value Method F$ 2 F$ 3 F5 4

Optimal cutoff (kPa) SSI 9.12 10.08 13.30FS 5.80 7.20 10.30

Specificity at 95% SSI 0.81 0.75 0.80of sensitivity FS 0.64 0.49 0.47Sensitivity at 95% SSI 0.72 0.78 0.87of specificity FS 0.20 0.21 0.76Youden’s index SSI 0.78 0.82 0.83

FS 0.61 0.69 0.79Misclassification rate SSI 0.11 0.10 0.10

FS 0.19 0.16 0.13

SSI 5 supersonic shear imaging; FS 5 FibroScan.These cutoff values are shown as a preliminary result, as cutoff elas-

ticity values will have to be optimized in further clinical studies relyingon more patients.


n=113 Patients VHC Reference = combinaison de marqueurs seriques

Nouvelles techniques Supersonic shear Imaging vs. TE

Castera L & Pinzani M. Lancet 2010; 375: 419-20.

Perspectives: Dépistage de la fibrose dans la population générale ?

Roulot et al. Gut 2011

1190 healthy subjects

Fibrosis (≥2) 7.5 %

Cirrhosis 0.7%

FibroScan

7463 healthy subjects

Fibrosis (≥2) 2.8 %

Cirrhosis 0.3%

Poynard et al. BMC Gastroenterol 2010

FibroTest

Dépistage population générale ?

Take Home messages (1)

  L’élastométrie a été principalement validée dans les

hépatites virales mais nécessitent d’étre validée dans

d’autres etiologies (NAFLD, etc..).

  La combinaison de l’élastométrie et des marqueurs

sanguins est la stratégie de choix pour la détection de la

fibrose en 1ère intention dans l’hépatite C (HAS).

  La principale limite de l’élastométrie est son

applicablité limitée (80%) en cas d’obésité.

  L’élastométrie est actuellement la méthode la plus

performante pour le diagnostic de cirrhose mais du fait

de sa moins bonne applicabilité ses performances sont

comparables aux biomarqueurs.


  Les performances de l’élastométrie sont insuffisantes

pour remplacer la fibroscopie pour le dépistage des

VO.

  La mesure de l’élasticité hépatique a une valeur

pronostique au cours de la cirrhose.

  Malgré ses limites, l’élastométrie est une technique

prometteuse pour le suivi des maladies du foie mais

nécessite d’être mieux évaluée de façon longitudinale.


Health & Medicine

Castera du pitie 17 janvier 2012 selection