Comparison of transient elastography (FibroScan), FibroTest, APRI and two algorithms combining these non-invasive tests for liver fibrosis staging in HIV/HCV coinfected patients: ANRS CO13 HEPAVIH and FIBROSTIC collaboration

Authors


Abstract

Objectives

Combining noninvasive tests increases diagnostic accuracy for staging liver fibrosis in hepatitis C virus (HCV)-infected patients, but this strategy remains to be validated in HIV/HCV coinfection. We compared the performances of transient elastography (TE), Fibrotest (FT), the aspartate aminotransferase-to-platelet ratio index (APRI) and two algorithms combining TE and FT (Castera) or APRI and FT (SAFE) in HIV/HCV coinfection.

Methods

One hundred and sixteen HIV/HCV-coinfected patients (64% male; median age 44 years) enrolled in two French multicentre studies (the HEPAVIH cohort and FIBROSTIC) for whom TE, FT and APRI data were available were included in the study. Diagnostic accuracies for significant fibrosis (METAVIR F ≥ 2) and cirrhosis (F4) were evaluated by measuring the area under the receiver-operating characteristic curve (AUROC) and calculating percentages of correctly classified (CC) patients, taking liver biopsy as a reference.

Results

For F ≥ 2, both TE and FT (AUROC = 0.87 and 0.85, respectively) had a better diagnostic performance than APRI (AUROC = 0.71; P < 0.005). Although the percentage of CC patients was significantly higher with Castera's algorithm than with SAFE (61.2% vs. 31.9%, respectively; P < 0.0001), this percentage was lower than that for TE (80.2%; P < 0.0001) or FT (73.3%; P < 0.0001) taken separately. For F4, TE (AUROC = 0.92) had a better performance than FT (AUROC = 0.78; P = 0.005) or APRI (AUROC = 0.73; P = 0.025). Although the percentage of CC patients was significantly higher with the SAFE algorithm than with Castera's (76.7% vs. 68.1%, respectively; P < 0.050), it was still lower than that for TE (85.3%; P < 0.033).

Conclusions

In HIV/HCV-coinfected patients, TE and FT have a similar diagnostic accuracy for significant fibrosis, whereas for cirrhosis TE has the best accuracy. The use of the SAFE and Castera algorithms does not seem to improve diagnostic performance.

Introduction

Among the 35 million people currently living with HIV world-wide, around one-fourth are coinfected with hepatitis C virus (HCV) [1]. HIV-positive individuals with chronic hepatitis C show a faster progression of liver fibrosis [2, 3]. On average, nearly half of patients have developed liver cirrhosis after 25 years of HCV infection. Assessment of liver fibrosis is thus of critical importance in HIV/HCV-coinfected patients not only for prognosis but also for antiviral therapy indications. Indeed, two endpoints are clinically relevant: (i) the presence of significant fibrosis, which is the hallmark of progressive disease and an indication for antiviral treatment; (ii) the presence of cirrhosis, which is an indication for specific monitoring of complications related to portal hypertension and to the increased risk of developing hepatocellular carcinoma [4].

Liver biopsy (LB) is classically considered the gold standard for staging fibrosis [5], although it has several limitations: it is an invasive and painful procedure [6-8], with rare but potentially life-threatening complications [9], and prone to sampling errors [10-12]. Thus, many patients are reluctant to undergo LB, especially HIV/HCV-coinfected patients who may be discouraged from initiating anti-HCV treatment for this reason.

These limitations have stimulated the search for noninvasive approaches [13-15]. A variety of methods have been proposed for the noninvasive assessment of fibrosis in patients with HCV monoinfection. These include the use of serum markers, ranging from those employed in simple routine laboratory tests, such as the aspartate aminotransferase-to-platelet ratio index (APRI) [16], to more complex scores, such as the Fibrotest (FT) [17], and more recently measurement of liver stiffness by transient elastography (TE) [18, 19].

Algorithms combining different noninvasive tests, such as TE and FT (Castera) [18] or FT and APRI (SAFE) [20, 21], have been proposed and suggested to improve diagnostic accuracy in HCV-monoinfected patients [22]. However, no information is available regarding the performance of such algorithms in HIV/HCV-coinfected patients.

The aim of this study was to compare, taking LB as a reference, the diagnostic performances for significant fibrosis and cirrhosis of TE, FT and APRI, as well as two algorithms combining these noninvasive tests, in HIV/HCV-coinfected patients.

Patients and methods

Patients

The study population consisted of 116 patients with HIV/HCV coinfection, selected from two French multicentre studies (the Agence Nationale de Recherche sur le SIDA (ANRS) CO13 HEPAVIH cohort and FIBROSTIC; see Appendix), who underwent noninvasive tests for fibrosis evaluation (TE, FT and APRI) within a maximum of 1 year from LB. ANRS CO13 HEPAVIH is a nationwide French cohort, supported by the National Agency for AIDS and Viral Hepatitis, which recruited 1175 HIV/HCV-coinfected individuals from 17 centres between 2006 and 2008 [23]. The FIBROSTIC study is a multicentre prospective cross-sectional diagnostic accuracy study conducted in 23 French centres, which included 1839 patients with chronic viral hepatitis who underwent TE, tests for biomarkers and LB, of whom 110 had HIV/HCV coinfection [24].

A total of 245 patients met the inclusion criteria. Of these, 129 patients could not be included in analyses for the following reasons: missing data (n = 30), antiviral HCV treatment initiated between noninvasive tests and LB (n = 38), coinfection with hepatitis B virus (n = 15), decompensated cirrhosis (n = 4), liver transplantation (n = 35), hepatocellular carcinoma (n = 1) or LB unavailable for central reading (n = 6). Finally, data for 116 patients were analysed (84 patients from the ANRS CO13 HEPAVIH cohort and 32 from the FIBROSTIC study).

Liver stiffness measurement

Liver stiffness measurements were performed using TE (FibroScan®; Echosens, Paris, France). Details of the technical background and examination procedure have been given previously [25]. Ten successful measurements were performed on each patient. The success rate was calculated as the number of validated measurements divided by the total number of measurements. The results were expressed in kilopascals (kPa). The median value of successful measurements was considered representative of the liver stiffness in a given patient, according to the manufacturer's recommendations [26].

Serum fibrosis scores

Biological parameters (aspartate aminotransferase, alanine aminotransferase, γ-glutamyl-transpeptidase, total bilirubin, α2-macroglobulin, apolipoprotein A1, haptoglobin and platelet count) used to calculate FT and APRI were determined. The FT score was purchased from the Biopredictive website (http://www.biopredictive.com). The APRI was calculated as follows: aspartate transaminase (× upper limit of normal) × 100/platelet count (109/L). Cut-offs for APRI and FT were taken from the original publications [16, 17].

Liver histology and staging of liver fibrosis

LBs were performed by senior operators at each site. Biopsy specimens were fixed in formalin and embedded in paraffin. They were then centralized and re-analysed by the same trained pathologist (VP) blinded to the results of noninvasive tests. Liver fibrosis was staged on a F0-to-F4 scale according to the METAVIR scoring system [27], as follows: F0 = no fibrosis; F1= portal fibrosis without septa; F2 = portal fibrosis with rare septa; F3 = numerous septa without cirrhosis; F4 = cirrhosis. Specimens with pathology other than HCV-associated fibrosis were excluded from the analysis.

Algorithms

The SAFE algorithm

Two distinct SAFE algorithms [20] for detection of significant fibrosis and cirrhosis, respectively, based on sequential use of APRI, FT and LB were applied to the data for the 116 patients and the results were compared with the histological diagnosis based on LB, which was taken as the gold standard. Both algorithms use APRI as the initial screening test, followed by FT as a second step, and limits the use of LB to those patients for whom noninvasive markers were inaccurate. The only differences between these algorithms are the thresholds set for each endpoint. Figures 1a and 2a describe the SAFE algorithms (respectively for significant fibrosis and cirrhosis), including cut-off values for APRI and FT and the related decisional tree.

Figure 1.

SAFE (a) or Castera (b) algorithms for significant fibrosis (≥F2 by METAVIR score). Each figure reports the cut-offs used for each noninvasive test, and the number of patients for each branch when the algorithm was applied to the 116 HIV HCV coinfected patients. APRI, aspartate aminotransferase-to-platelet ratio index; FS, FibroScan; FT, Fibrotest; HCV, hepatitis C virus.

Figure 2.

SAFE (a) or Castera (b) algorithms for cirrhosis (F4 by METAVIR score). Each figure reports the cut-offs used for each noninvasive test, and the number of patients for each branch when the algorithm was applied to the 116 HIV HCV coinfected patients. APRI, aspartate aminotransferase-to-platelet ratio index; FS, FibroScan; FT, Fibrotest; HCV, hepatitis C virus.

The Castera algorithm

The proposed algorithm uses the combination of TE and FT as first-line assessment of fibrosis and is based on the agreement or disagreement of the results of these tests: when TE and FT agree, no LB is performed, whereas when TE and FT disagree, an LB is needed (Figs 1b,2b). Cut-offs used are those proposed in the original publication [18].

Statistical analysis

Patients' characteristics are given as median values [with interquartile range (IQR): Q1−Q3] or percentages. Cochran's Q test or McNemar's χ2 test was used as appropriate for comparison of qualitative data. Tests were two-tailed and P-values < 0.05 were considered as significant. The diagnostic accuracy of the different tests was evaluated by constructing receiver-operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC) and its corresponding 95% confidence intervals (CIs). Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios (LRs) and the percentage of correctly classified patients were calculated using previously described cut-offs for noninvasive tests [16-18]. AUROCs were adjusted according to the prevalence of fibrosis stages using DANA (difference between advanced and nonadvanced fibrosis = [(prevalence F2 × 2 + prevalence F3 × 3 + prevalence F4 × 4)/(prevalence F2 + prevalence F3 + prevalence F4)] − [prevalence F1/(prevalence F0 + prevalence F1)]) as proposed by Poynard et al. [28]. The adjusted AUROCs were calculated as follows: adjAUROC = obAUROC + (0.1056) × (2.5 − DANA). Comparisons of AUROCs were performed using the method described by Delong et al. [29]. Performance of noninvasive tests was further assessed by calculating the percentage of correctly classified patients (true positive and true negative) taking LB as a reference in patients for whom LB was unnecessary. Overall performance of algorithms was assessed using both the percentage of ‘saved LB’ (no need for LB according to the algorithm) as well as the percentage of correctly classified patients.

Results

Patients

The characteristics of the 116 patients are shown in Table 1. There were 74 men and 42 women, the median age being 44 (IQR: 41−48) years. The median LB length was 19.5 mm and the median number of portal tracts was 14. LB length was ≥15 mm in 87 patients (75%). Significant fibrosis (F≥2) was present in 41% of patients and cirrhosis (F4) in 11%. The DANA was 1.79.

Table 1. Characteristics of patients from the HEPAVIH cohort and FIBROSTIC study
 Total (n = 116)
  1. Results are expressed as median (interquartile range: Q1−Q3) unless otherwise stated.
  2. ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; HCV, hepatitis C virus; LB, liver biopsy; NIT, noninvasive test.
  3. *Available for 84 patients.
Gender (male) [n (%)]74 (64)
Age (years)44 (41–48)
BMI (kg/m2)22 (20–25)
AST (IU/L)50 (38–70)
ALT (IU/L)54 (33–91)
Platelets (109/L)202 (157–256)
CD4 count (109/L)*416 (297–598)
HIV RNA < 50 copies/mL (%)*65
HCV RNA (log IU/mL)*6.3 (5.7-6.8)
LB 
Length (mm)19.5
Number of portal tracts14
Delay between LB and NIT (months)1 (0–5.7)
Fibrosis stage according to METAVIR [n (%)] 
F03 (3)
F165 (56)
F225 (21)
F310 (9)
F413 (11)

Comparison of TE, FT and APRI

Diagnosis of significant fibrosis

The performances of TE, FT and APRI for the diagnosis of significant fibrosis are described in Table 2. TE and FT had a better diagnostic performance than APRI (AUROC 0.87, 0.85 and 0.71, respectively; P < 0.004). The percentage of correctly classified patients according to LB was significantly higher for TE and FT than for APRI (80.2, 73.3 and 38.8%, respectively; P < 0.0001).

Table 2. Performance of transient elastography (TE), Fibrotest (FT) and the aspartate aminotransferase-to-platelet ratio index (APRI) for the diagnosis of significant fibrosis and cirrhosis (in patients from the HEPAVIH cohort and FIBROSTIC study; n = 116)
 Significant fibrosisCirrhosis
TE (FibroScan)FibrotestAPRITE (FibroScan)FibrotestAPRI
  1. Significant fibrosis: global comparison between AUROCs: P = 0.004; TE vs. FT: P = 0.611; TE vs. APRI: P = 0.001; FT vs. APRI: P = 0.005; global comparison between correctly classified patients: P < 0.0001; TE vs. FT: P = 0.182; TE vs. APRI: P < 0.0001; FT vs. APRI: P < 0.0001.
  2. Cirrhosis: global comparison between AUROCs: P = 0.007; TE vs. FT: P = 0.005; TE vs. APRI: P = 0.025; FT vs. APRI: P = 0.593; global comparison between correctly classified patients: P = 0.001; TE vs. FT: P = 0.003; TE vs. APRI: P < 0.001; FT vs. APRI: P = 0.398.
  3. AUROC, area under the receiver-operating characteristic curve; CI, confidence interval.
AUROC (95% CI)0.87 (0.81–0.94)0.85 (0.78–0.92)0.71 (0.61–0.81)0.92 (0.86–0.98)0.78 (0.66–0.89)0.73 (0.58–0.88)
Cut-offs≥ 7.1 kPa> 0.48≤ 0.5> 1.5≥ 12.5 kPa≥ 0.75≤ 1.0> 2.0
Sensitivity (%)85.489.681.335.476.961.576.930.8
Specificity (%)76.561.841.291.286.473.872.888.3
Positive predictive value (%)71.962.349.473.941.722.926.325.0
Negative predictive value (%)88.189.475.766.796.793.896.291.0
Positive likelihood ratio3.62.31.44.05.72.32.82.6
Negative likelihood ratio0.20.20.50.70.30.50.30.8
Correctly classified (%)80.273.338.885.372.468.1

Diagnosis of cirrhosis

The performances of TE, FT and APRI for the diagnosis of cirrhosis are described in Table 2. TE had a better performance for cirrhosis than FT (AUROC 0.92 and 0.78, respectively; P = 0.005) or APRI (AUROC 0.92 and 0.73, respectively; P = 0.025). The percentage of correctly classified patients according to LB was significantly higher for TE than for FT or APRI (85.3, 72.4 and 68.1%, respectively; P < 0.003).

Comparison of algorithms

Diagnosis of significant fibrosis

As shown in Figure 1a, with the SAFE algorithm, an LB was deemed unnecessary for diagnosing the presence of significant fibrosis in 57 patients (49.1%). Among these 57 patients, the presence of significant fibrosis was confirmed by LB in 37 patients (65%). Overall, the percentage of correctly classified patients was 31.9% (37 of 116) (Table 3), which is much lower than that for TE (80.2%; P < 0.0001) or FT (73.3%; P < 0.0001) taken separately.

Table 3. Performance of the two algorithms for the diagnosis of significant fibrosis and cirrhosis (for patients from the HEPAVIH cohort and FIBROSTIC study; n = 116)
 PPV (%)NPV (%)Saved biopsies (%)Correctly classified patients (%)
  1. Comparison for correctly classified patients: F ≥ 2, P < 0.0001; F4, P = 0.050. Comparison for saved biopsies: F ≥ 2, P = 0.003; F4, P = 0.011.
  2. PPV, positive predictive value; NPV, negative predictive value.
  3. *Not applicable.
Significant fibrosis    
SAFE algorithm64.9*49.131.9
Castera algorithm84.494.369.061.2
Cirrhosis    
SAFE algorithm36.895.390.576.7
Castera algorithm41.297.378.468.1

Using Castera's algorithm, an LB was deemed unnecessary for diagnosing the presence or the absence of significant fibrosis in 80 patients (69.0%) (Fig. 1b). When there was agreement between TE and FT results for the absence (35 patients) or the presence (45 patients) of significant fibrosis, the result was also confirmed by LB for 33 (94%) and 38 (84%) patients, respectively. Overall, the percentage of correctly classified patients was 61.2% (71 of 116), which is significantly higher than that obtained for the SAFE algorithm (31.9%; 37 of 116; P < 0.0001) but lower than that obtained for TE (80.2%; P < 0.0001) or FT (73.3%; P = 0.0002) taken separately. However, the negative predictive value of the Castera algorithm for excluding significant fibrosis was very good (94.3%) and higher than those of TE, FT and APRI taken separately (88.1, 89.4 and 75.7%, respectively).

Diagnosis of cirrhosis

Using the SAFE biopsy algorithm, an LB was deemed unnecessary for diagnosing the absence or the presence of cirrhosis in 105 patients (90.5%) (Fig. 2a). Among the 86 patients in whom the SAFE algorithm suggested the absence of cirrhosis, this was confirmed by LB in 82 patients (95%), whereas in the 19 patients in whom SAFE suggested the presence of cirrhosis, this was confirmed by LB in seven patients (37%). Overall, the percentage of correctly classified patients was 76.7% (89 of 116), which was significantly lower than that for TE (85.3%; P = 0.003).

Using the Castera algorithm, an LB was deemed unnecessary for diagnosing the absence or the presence of cirrhosis in 91 patients (78.4%) (Fig. 2b). Among the 74 patients in whom TE and FT agreed for the absence of cirrhosis, this was confirmed by LB in 72 patients (97%), whereas in the 17 patients in whom TE and FT agreed for the presence of cirrhosis, this was confirmed by LB in seven patients (41%). Overall, the percentage of correctly classified patients was 68.1% (79 of 116), which is significantly lower than that for the SAFE algorithm (76.7%; 89 of 116; P = 0.05) as well as that for TE (85.3%; P < 0.0001) or FT (72.4%; P = 0.025) taken separately.

Diagnostic performances of noninvasive tests and algorithms according to fibrosis stage distribution and liver biopsy length

Given the uneven distribution of fibrosis stages in our population, we adjusted the AUROCs according to the DANA. After adjustment, TE and FT still had a better performance for diagnosing significant fibrosis than APRI (adjAUROC 0.95, 0.93 and 0.78, respectively). Regarding cirrhosis, TE still had the best performance (adjAUROC 0.99 vs. 0.85 and 0.80, respectively).

Diagnostic performances of noninvasive tests were also analysed in the subgroup of 87 patients for whom LB was ≥15mm. For the diagnosis of significant fibrosis, TE and FT had a better diagnostic performance than APRI (AUROC 0.86, 0.87 and 0.71, respectively; P = 0.015); also, percentages of correctly classified patients for TE and FT were significantly higher than that for APRI (79.3 and 75.9 vs. 39.1%, respectively; P < 0.0001). TE had a better performance for cirrhosis than FT and APRI (AUROC 0.86, 0.69 and 0.70, respectively; P = 0.045) as well as a higher percentage of correctly classified patients (83.9, 71.3 and 66.7%, respectively; P = 0.004).

Regarding the algorithms for the diagnosis of significant fibrosis, Castera's allowed the correct classification of a higher number of patients than the SAFE algorithm (62.1 vs. 33.3%, respectively; P < 0.0001). Conversely, for the diagnosis of cirrhosis, although there was a trend towards a higher percentage of patients correctly classified by SAFE (75.9%) compared with Castera's algorithm (66.7%), the difference did not reach statistical significance (P = 0.073).

Discussion

Noninvasive diagnosis of liver fibrosis is one of the fields that have evolved most rapidly in recent years. Noninvasive methods are now increasingly used in clinical practice in HCV-monoinfected patients, resulting in a significant decrease in the number of LBs performed in France [30]. In addition, the use of either TE or biomarkers for first-line assessment of liver fibrosis in HCV-infected patients without comorbidities has been approved, after an independent systematic review, by the French Health Authorities [31], and recently endorsed by the European Association for Study of Liver Clinical Practice guidelines [32]. As regards HIV/HCV-coinfected patients, noninvasive methods still remain to be validated and the performance of serum biomarkers may be not as good as in HCV-monoinfected patients [33, 34].

In the present study, we assessed the performance of algorithms combining different noninvasive tests in HIV/HCV-coinfected patients. Overall, our results suggest that the use of algorithms does not improve the diagnostic performance in this population.

Among noninvasive methods available, we chose to evaluate two different and complementary approaches: (i) a physical approach based on the measurement of liver stiffness using TE, and (ii) a biological approach based on serum biomarkers including a patented algorithm (FT) and a free nonpatented index (APRI) [35]. These three noninvasive methods are by far the most widely used and validated in HCV monoinfection [36-38] and the study was conducted independently from the promoters of the tests.

The population of HIV/HCV-coinfected patients we studied was selected from two large French cohorts of HCV-infected patients with or without HIV infection. Our methods, consisting of selecting high-quality LBs (median length = 19.5 mm) as a standard reference, performed within a median of 1 month (IQR 0−5.7) of noninvasive tests, and analysis of LBs by a single pathologist blinded to the results of noninvasive methods, are in accordance with recent recommendations for accurate fibrosis staging [39].

Also, detection of significant fibrosis and detection of cirrhosis are two clinically relevant endpoints. The presence of significant fibrosis in HIV/HCV-coinfected patients is considered a hallmark of a progressive liver disease and an indication for antiviral treatment. Although antiviral therapy has been shown to increase life expectancy, to improve quality of life and to be cost-effective, it can be associated with severe side effects. In addition, more than half of HIV/HCV-coinfected patients are still resistant to the current treatment regimen based on the combination of peginterferon and ribavirin, given for 48 weeks [1], and fibrosis stage is also an independent predictor of sustained HCV eradication [40]. Finally, early diagnosis of cirrhosis is important in HIV/HCV-coinfected patients not only because it triggers screening for complications such as oesophageal varices and hepatocellular carcinoma, but also because these patients have the most urgent need for antiviral therapy.

In the first part of the study, we showed that, when taken separately, TE and FT had similar diagnostic accuracies for significant fibrosis, whereas TE had the best accuracy for cirrhosis. These results are consistent with previously reported findings in HCV-monoinfected patients [18, 24, 41]. Diagnostic performances of TE for significant fibrosis and cirrhosis (AUROC 0.87 and 0.92, respectively) were consistent with those reported in meta-analyses [36, 42] as well as in HIV/HCV coinfection [43-45]. Similarly, diagnostic performances of FT in our population were consistent with those previously reported [46, 47]. The fact that FT outperformed APRI in diagnosing significant fibrosis is consistent with the findings of Cacoub et al. [46]. Finally, the better diagnostic performance of TE for cirrhosis compared with APRI has already been reported by Sanchez-Conde et al. [48].

In the second part of the study, we evaluated algorithms combining TE and FT (Castera) or APRI and FT (SAFE) to assess whether their use might increase diagnostic accuracy. For the diagnosis of significant fibrosis, the number of saved LBs using the Castera algorithm was significantly higher than that for SAFE (69 vs. 49.1%, respectively; P = 0.003), a finding consistent with those reported in a recent comparative study in HCV monoinfection [22]. The percentages of LBs saved by both algorithms were similar to those reported in the original studies [18, 21], as well as in a large-scale validation study [20]. However, although the percentage of correctly classified patients was significantly higher using the Castera algorithm than using SAFE (61.2 vs. 31.9%, respectively; P < 0.0001), it was lower than those obtained when using TE or FT separately (80.2 and 73.3%, respectively). Nevertheless, the important advantage of using the Castera algorithm is its high negative predictive value, allowing significant fibrosis to be ruled out more safely than when using single tests.

For the diagnosis of cirrhosis, the percentages of correctly classified patients using Castera's algorithm or SAFE (68.1 and 76.7%, respectively) were also lower than that obtained using TE (85.3%). Thus, it seems that there is no advantage to using algorithms combining either TE and FT or APRI and FT in HIV/HCV-coinfected patients for the diagnosis of either significant fibrosis or significant cirrhosis. These results are in contradiction with those reported in HCV-monoinfected patients [18, 20, 21]. Although there is no clearcut explanation for this finding, differences in studied populations and the prevalence of fibrosis stages may account for this discrepancy. However, when the AUROCs were adjusted according to the DANA as proposed by Poynard et al. [28], although a global increase in the AUROCs of TE, FT and APRI was observed, our findings were unchanged, showing similar diagnostic accuracies of TE and FT for significant fibrosis and the best accuracy for TE for cirrhosis. Increasing LB length did not significantly change the results either, although the percentage of correctly classified patients for cirrhosis did not differ between the two algorithms. Furthermore, the performance of APRI and FT could be affected in HIV/HCV-coinfected patients by factors such as HIV-induced thrombocytopenia [49] and drug-related toxicity (e.g. bilirubin elevation caused by atazanavir or gamma-glutamyl transpeptidase abnormalities caused by nonnucleoside reverse transcriptase inhibitors) [1, 50, 51]. Finally, our results may not be applicable to all HIV/HCV-coinfected patients, given the retrospective nature of the present study and the potential selection bias of our population.

In conclusion, in HIV/HCV-coinfected patients, TE and FT had similar diagnostic accuracies for significant fibrosis, whereas TE had the best accuracy for cirrhosis. The use of algorithms combining these tests does not seem to improve diagnostic performance.

Acknowledgement

Conflicts of interest

The authors have no conflicts of interest to declare.

Appendix: Appendix

ANRS CO13 HEPAVIH cohort

Scientific committee

D. Salmon, F. Dabis, M. Winnock, M. A. Loko, P. Sogni, Y. Benhamou, P. Trimoulet, J. Izopet, V. Paradis, B. Spire, P. Carrieri, C. Katlama, G. Pialoux, I. Poizot-Martin, B. Marchou, E. Rosenthal, A. Bicart See, M. Bentata, A. Gervais, C. Lascoux-Combe, C. Goujard, K. Lacombe, C. Duvivier, D. Vittecoq, D. Neau, P. Morlat, F. BaniSadr, L. Meyer, F. Boufassa, S. Dominguez, B. Autran, A.M. Roque, C. Solas, H. Fontaine, L. Serfaty, G. Chêne, D. Costagliola and S. Couffin-Cadiergues.

Clinical centres (ward/participating physicians)

CHU Cochin (Médecine Interne et Maladies Infectieuses/D. Salmon; Hépato-gastro-entérologie/P. Sogni; Anatomo-pathologie/B. Terris, Z. Makhlouf, G. Dubost, F. Tessier, L. Gibault, F. Beuvon, E. Chambon and T. Lazure; Virologie/A. Krivine); CHU Pitié-Salpétrière (Maladies Infectieuses et Tropicales/C. Katlama, M. A. Valantin and S. Dominguez; Hépato-gastro-entérologie/Y. Benhamou; Anatomo-pathologie/F. Charlotte; Virologie/S. Fourati); CHU Sainte-Marguerite, Marseille (Hématologie et VIH/I. Poizot-Martin and O. Zaegel; Virologie/C. Tamalet); CHU Tenon (Maladies Infectieuses et Tropicales/G. Pialoux, P. Bonnard and F. Bani-Sadr; Anatomo-pathologie/P. Callard and F. Bendjaballah; Virologie/H. Assami); CHU Purpan Toulouse (Maladies Infectieuses et Tropicales/B. Marchou; Hépato-gastro-entérologie/L. Alric, K. Barange and S. Metivier; Anatomo-pathologie/J. Selves; Virologie/F. Nicot); CHU Archet, Nice (Médecine Interne/E. Rosenthal; Infectiologie/C. Pradier; Anatomo-pathologie/J. Haudebourg and M. C. Saint-Paul); CHU Avicenne, Paris (Médecine Interne – Unité VIH/A. Krivitzky and M. Bentata; Anatomo-pathologie/M. Ziol; Virologie/Y. Baazia); Hôpital Joseph-Ducuing, Toulouse (Médecine Interne/M. Uzan, A. Bicart-See and D. Garipuy; Anatomo-pathologie/J. Selves; Virologie/F. Nicot); CHU Bichat–Claude-Bernard, Paris (Maladies Infectieuses/P. Yéni and A. Gervais; Anatomo-pathologie/H. Adle-Biassette); CHU Saint-Louis (Médecine Interne/D. Séréni and C. Lascoux Combe; Anatomo-pathologie/P. Bertheau and J. Duclos; Virologie/P. Palmer); CHU Saint Antoine (Maladies Infectieuses et Tropicales/P. M. Girard, K. Lacombe and P. Campa; Anatomo-pathologie/D. Wendum, P. Cervera and J. Adam; Virologie/N. Harchi); CHU Bicêtre (Médecine Interne/J. F. Delfraissy, C. Goujard and Y. Quertainmont; Virologie/C. Pallier); CHU Paul-Brousse (Maladies Infectieuses/D. Vittecoq); CHU Necker (Maladies Infectieuses et Tropicales/O. Lortholary, C. Duvivier and S. Boucly), ANRS CO 3 Aquitaine cohort (D. Neau, P. Morlat, I. Raymond and I. Louis; Anatomo-pathologie/P. Bioulac-Sage; Virologie/P. Trimoulet and P. Pinson).

Pathologists

A. Martin, M. Ziol, A. Janin, J. Duclos, P. Bertheau, C. Guettier, T. Lazure, D. Henin, L. Choudat, H. Adle-Biassette, F. Capron, F. Charlotte, J. F. Flejou, D. Wendum, M. C. Vacher-Lavenu, F. Beuvon, L. Gibault, E. Chambon, B. Terris, R. Belkhir, P. Brousset, J. Selves, P. Callard, A. Martin, B. Guigui, A. Chollat-Namy, D. Basbous, F. Daussy Bonamour, J. C. Poluzzi, J. Haudebourg, M. C. Saint-Paul, P. Denis, P. Roux and M. Pizzi-Anselme.

Data collection, management and statistical analyses

D. Beniken, A. Ivanova, A. Fooladi, M. Azar, P Honoré, S. Breau, L. Serini, M. Mole, M. Malet, C. Bolliot, O. Eldbouni, A. Maignan, S. Mellul, G. Alexandre, A. Ganon, S. Thirrée, S. Gillet, J. Delaune, L. Dequae Merchadou, E. Pambrun, A. Frosch, J. Cohen, V. Villes, P. Kurkdji, M. A. Loko and M. Winnock.

FIBROSTIC study

List of coinvestigators

APHP: Hôpital Beaujon: Professor P. Bedossa, Dr F. Degos, Professor P. Marcellin and Professor M. Vidaud; Hôpital Cochin-Necker: Professor S. Pol, Professor Ph. Sogni and Professor D. Salmon; Hôpital Jean Verdier: Dr A. Mahmoudi; Hôpital Paul Brousse: Dr B. Roche; Hôpital Saint Antoine: Professor R. Poupon and Dr L. Serfaty; Hôpital Pitié-Salpétrière: Professor C. Katlama, Dr M. Munteanu and Dr P. Lebray; CHU Amiens: Dr E. Nguyen Khac; CHU Angers: Professor P. Cales; CHU Besançon: Professor V. Di Martino; CHU Bordeaux Pessac: Professor V. de Ledinghen; CHU Brest: Professor J. B. Nousbaum; CHU Clermont Ferrand: Dr A. Abergel; CHU Grenoble: Professor J. P. Zarski; CHU Lille: Professor Ph. Mathurin; CHU Limoges: Dr D. Loustau-Ratti; CHU Hôtel Dieu Lyon: Professor C. Trépo; Hôpital Saint Joseph Marseille: Dr M. Bourlière; CHU Montpellier: Professor D. Larrey; CHU Nice: Professor A. Tran; CHU Nancy: Professor J. P. Bronowicki; CHU Nantes: Dr J. Gournay; Hôpital d'Orleans: Dr X. Causse; CHU Rennes: Professor D. Guyader; CHU Tours: Professor Y. Bacq; CHU Strasbourg: Professor M. Doffoel.

Data collection, management and statistical analyses

J. Asselineau, I. Perrot and P. Perez.

Ancillary