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Transient Elastography for Predicting Liver-Related Events in Cirrhotic HIV-Infected Patients | OMICS International
ISSN 2155-6113
Journal of AIDS & Clinical Research

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Transient Elastography for Predicting Liver-Related Events in Cirrhotic HIV-Infected Patients

Montes ML1*, Berenguer J2, Miró JM3, Quereda C4, Hernando A5, Sanz J6, Ortega E7, Tural C8, Wichmann MA9, Zamora FX1and Gónzalez-García JJ1

1HIV Unit, Service of Internal Medicine, Hospital Universitario La Paz, Universidad Autónoma de Madrid, IDIPAZ, Madrid, Spain

2Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain

3Service of Infectious Diseases, Hospital Clínic/IDIBAPS, University of Barcelona, Barcelona, Spain

4Service of Infectious Diseases, Hospital Ramón y Cajal, Madrid, Spain

5HIV Unit, Hospital 12 de Octubre and Universidad Europea de Madrid, Madrid, Spain

6Service of Internal Medicine, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Madrid, Spain

7Unit of Infectious Diseases, Hospital General Universitario de Valencia, Valencia, Spain

8HIV Clinical Unit, Internal Medicine Department and Fundació de la Lluita contra la SIDA, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Barcelona, Spain

9Unit of Infectious Diseases, Hospital Donostia, Donostia-San Sebastián, Spain

*Corresponding Author:
M. Luisa Montes
Hospital Universitario La Paz
Internal Medicine, HIV Unit
Madrid, Madrid 28002, Spain
Tel: +34686624821
E-mail: [email protected]

Received date: February 28, 2017; Accepted date: March 08, 2017; Published date: March 15, 2017

Citation: Montes ML, Berenguer J, Miró JM, Quereda C, Hernando A, et al. (2017) Transient Elastography for Predicting Liver-Related Events in Cirrhotic HIV-Infected Patients. J AIDS Clin Res 8:675. doi:10.4172/2155-6113.1000675

Copyright: © 2017 Montes ML, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Aim: We assessed liver stiffness measurement (LSM) for the prediction of mortality and decompensation in HIVinfected patients with compensated liver cirrhosis. Method: A prospective cohort study of HIV-infected patients with confirmed liver cirrhosis from 9 hospitals in Spain. LSM was undertaken for each patient; clinical events were collected prospectively after the baseline visit, and patients were followed until death or the censoring date. We used univariate/multivariate Cox proportional hazard models to evaluate the utility of LSM for predicting the first hepatic decompensation or overall mortality. The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+) and negative likelihood ratio (LR-) were calculated. The LSM cutoff was selected using ROC curves. Results: We included 102 patients with compensated liver cirrhosis; median [interquartile, (IQR)] follow-up was 36 (21-46) months, median (IQR) CD4+ cell count was 415 cells/μL (307-624) and 94% were receiving antiretroviral therapy. The median (IQR) LSM was 17 kPa (11.7-26). Nineteen events were recorded during follow-up. Multivariate analysis showed that time to hepatic decompensation was associated with CD4+ <200 cells/μL (HR, 26; 95% CI, 1.8- 377; p<0.02) and LSM ≥ 25 kPa (HR, 7.2; 95% CI, 1.1-47; p=0.04) and that time to overall mortality was associated with LSM ≥ 25 kPa (HR, 14.3; 95% CI, 1.5-138; p=0.02). The predictive values for decompensation (LSM ≥ 25 kPa) were as follows: SEN, 67%; SPE, 78%; NPV, 96%; PPV, 23%; LR+, 3; LR-, 0.4. The predictive values for overall mortality with this LSM cutoff were as follows: SEN, 86%; SPE, 79%; NPV, 99%; PPV, 23%; LR+, 4; LR-, 0.2. Conclusion: Our data suggest that LSM is an accurate method for the prediction of mortality and decompensation in HIV-infected patients with liver cirrhosis.


HIV-infected; Liver stiffness; Mortality; Prognosis; Endstage liver disease (ESLD)


End-stage liver disease (ESLD) is a major cause of morbidity and mortality in HIV-infected patients [1,2].The prognosis and management of chronic hepatitis depends on the progression of liver fibrosis and the development of cirrhosis, both of which are accelerated in HIV/HCV co-infected patients [3].

The natural history of cirrhosis is marked by the development of significant portal hypertension, which accurately predicts clinical events and the onset of decompensation [4,5]. Patients with decompensated disease have a much shorter median survival time, particularly HIVinfected patients with liver cirrhosis [6].

Novel non-invasive methodologies such as liver stiffness measurement (LSM) have been developed over the past decade and have replaced liver biopsy for evaluation of liver fibrosis and the progression of liver damage [7,8]. Various studies of ESLD conducted in different clinical settings have demonstrated that LSM values correlate with portal pressure (based on hepatic venous portal gradient) and predict clinical events. Consequently, LSM is beginning to be used to identify patients with cirrhosis who are at risk of disease progression [9-11].

Recent studies have shown that LSM can be used to predict overall mortality, decompensated cirrhosis, hepatocellular carcinoma (HCC), and liver-related mortality in patients with chronic hepatitis C, chronic hepatitis B and cholestatic liver diseases. Most observational studies have been pooled in a systematic review with meta-analyses by Singh et al. [12], who included data from HIVinfected and non–HIV-infected subjects. Findings suggest that LSM could be incorporated as a prognostic tool into the daily clinical care of patients with liver cirrhosis. Studies on HIV-infected patients are heterogeneous, and only that of Merchante et al. [13] included a large, prospective cohort of HIV/HCV co-infected patients with compensated liver cirrhosis managed under a uniform management protocol.

A study that was not included in the meta-analysis by Singh et al. [12] shows that LSM has a higher prognostic value for liver-related events—defined as decompensation or HCC in both HIV/HCV-coinfected patients and HCV-monoinfected patients—than measurement of hepatic venous pressure gradient. This study also showed for the first time that LSM has proven very useful for predicting the development of HCC [14].

The importance of the stage of compensated or decompensated liver disease and the dynamic nature of end-stage liver disease have revolutionized our understanding and care of patients with liver cirrhosis [4,5,15,16].The use of less invasive diagnostic methods and their strong correlation with classic, more invasive, and complicated procedures have made a significant contribution to our knowledge [8- 11,17-19]. These new data are from very different patient populations, whose liver disease had diverse etiologies and characteristics. HIV coinfection is one of the areas where new evidence is similar to that reported for non–HIV-infected persons [9,12-14,18].

Our objectives were to determine the predictive value of LSM for the first hepatic decompensation and overall mortality in HIV-infected patients with liver cirrhosis and to establish a cut-off value to identify those with a high risk of clinical events.


Study design and patient selection

Ours was a multicenter prospective cohort study of HIV-infected patients with a diagnosis of liver cirrhosis conducted in 9 hospitals in Spain. Enrollment of the cohort started in June 2004 and finished in June 2005. Patients are still under active follow-up. The population for this study comprised all patients with compensated liver cirrhosis who had undergone LSM. Subjects who were included in the global cohort with decompensated liver cirrhosis criteria or subjects who achieved sustained virological response (SVR) after receiving HCV therapy were excluded from this analysis.

The local ethics committees approved the study, and all patients gave their written informed consent to participate [6].

Patient evaluation and follow-up

Liver cirrhosis was diagnosed based on liver biopsy findings [20,21], previous diagnosis of liver decompensation, or a Bonacini score ≥ 8 [22]and was classified as decompensated or compensated based on the presence or absence of a history of hepatic decompensation (ascites, spontaneous bacterial peritonitis, hepatic encephalopathy, upper gastrointestinal bleeding due to portal hypertension or hepatorenal syndrome) [23,24].

Liver stiffness was measured at each center by a single experienced operator using a FibroScan device (EchoSens, Paris, France) with an M probe, as described elsewhere [25,26].

The baseline visit was the one in which the LSM measurement was performed. Clinical events were collected prospectively after the baseline visit and patients were followed until death or the censoring date. Vital status and cause of death were established from the database and clinical records. All patients attended follow-up visits in specialized HIV clinics every 6 months. At each visit, we reviewed clinical events, laboratory data, and Child-Pugh-Turcotte score (CPT). This score ranges from 5 to 15, with patients classified as follows: score of 5 or 6, CPT class A (well-compensated cirrhosis); score of 7 to 9, CPT class B (significant functional compromise); and score of 10 to 15, CPT class C (decompensated cirrhosis). We also recorded the MELD score (3.8*loge(serum bilirubin [mg/dL])+11.2*loge(INR)+9.6*loge(serum creatinine [mg/dL])+6.4), consumption of alcohol and illicit drugs, type of antiretroviral therapy (ART), type of treatment for hepatitis B and/or C, results of abdominal ultrasound and liver transplant criteria. Clinical care for cirrhosis and associated complications was standard at all the centers.

Study outcome

We considered first hepatic decompensation as the primary endpoint and death as the secondary endpoint. We excluded hepatocarcinoma as a decompensation event, since it is a biological phenomenon that is not exclusively associated with portal hypertension. Time to the primary endpoint was calculated as the number of months from the baseline visit until the episode of liver decompensation. Time to the secondary endpoint was calculated as months from the baseline visit to death (all causes). Liver-related death was also identified and analyzed.

Statistical analysis

Qualitative variables are described as frequencies and percentages. Quantitative variables are described as mean with standard deviation (SD) and median with interquartile range (IQR). The normality of the variables was tested using the Kolmogorov-Smirnov-Lilliefors test.

The cumulative incidence and the incidence rate were calculated as measures of frequency of the event for primary and secondary endpoints.

The ability of LSM to predict the first episode of hepatic decompensation or all-cause mortality was assessed using receiver operating characteristic (ROC) curves. We determined the optimal cut-off value of LSM for the first episode of hepatic decompensation or all-cause mortality based on the highest specificity with an acceptable sensitivity (>60%). The prognostic value for the first episode of hepatic decompensation or all-cause mortality of FIB-4 and LSM was compared by calculating the area under the ROC curves (AUROC) using the method proposed by Hanley and McNeil [27].

Survival was analyzed using Kaplan-Meier plots. Survival curves were compared using the log-rank test. We analyzed the impact of prognostic factors on the primary and secondary endpoints, and univariate analysis was performed using Cox regression. Variables with a p value ≤ 0.1 in the univariate analysis and clinically relevant variables were included in the multivariate Cox regression model. The continuous variables that could be converted into categorical variables (by defining a clinically relevant cutoff) and fulfilled the eligibility criteria for the analysis were thus included in the multivariate analysis. The variables included in the univariate analysis were age, gender, HCV genotype 2 or 3, HCV therapy, receiving HAART at baseline, CDC stage C, CD4+ cell count <200 cells/μL, HIV RNA below the limit of quantification at baseline, Child-Pugh-Turcotte classification stage B or C at baseline, platelet count (quantitative and <120,000 cells/mL), MELD score ≥ 14, FIB-4 index (quantitative and >3.5) and LSM ≥ 25 kPa. LSM was included as a categorical variable to maximize its clinical utility. We explored the interaction between LSM and all other covariates included in the multivariate analysis.

Contrast analysis was based on the exact method or normal approximation as required. IBM Statistics 19.0 (IBM Corp., Armonk, NY, USA) was used for the analysis. Statistical significance was set at p<0.05 (2-tailed) for all tests.

Baseline characteristics (at LSM visit) N=102
Median age, years (IQR) 45 (42-48)
Female, No. (%) 25 (24.5)
Diagnosis of cirrhosis based on
- Biopsy No. (%) 89 (87.3)
- Bonacini Score ≥ 8, No. (%) 13 (12.7)
Cause of cirrhosis, No. (%) *
- HCV+prior alcohol abuse
93 (91.2)
4 (3.9)
5 (4.9)
24 (23.5)
Child-Pugh-Turcotte class, No. (%)
-Not calculated
56 (55)
42 (42)
3 (3)
Median (IQR) duration of HIV infection (years) 18.1 (12-20.9)
Median (IQR) time since diagnosis of liver cirrhosis, (years) 5.6 (4.3-7.2)
Transmission route, IVDU, No. (%) 88 (86.3)
CDC stage C, No. (%) 30 (29.7)
Receiving HAART at baseline, No. (%) 96 (94.1)
HIV RNA load <50 copies/mL, No. (%) 86 (85.1)
Median (IQR) CD4 cell count, cells/µL
415 (307-624)
190 (73-318)
Previous therapy against HCV without SVR, No. (%) 79 (80.6)
Receiving therapy against HBV (TDF+3TC/FTC), No. (%) 9 (9)
Median (IQR) alanine aminotransferase, IU/L 59 (39-96)
Median (IQR) aspartate aminotransferase, IU/L 56 (41-94)
Baseline characteristics (at LSM visit) N=102
Median (IQR) total bilirubin, mg/dL 0.8 (0.5-1.5)
Median (IQR) platelet count, mm3 107 (76-169)
Median (IQR) MELD score 6 (6-9.3)
Median (IQR) liver stiffness, kPa 17 (11.7-26)
Liver stiffness, kPa, No. (%)
76 (74.5)
14 (13.7)
12 (11.8)
Median (IQR) FIB-4 index 2.8 (1.8-5.7)
FIB-4 index (cirrhosis stage), No. (%)  
->3.25 44 (44)

Table 1: Baseline characteristics.


Characteristics of the study population

We included 102 patients who fulfilled the inclusion criteria. The main characteristics of the study population are summarized in Table 1. The main cause of liver cirrhosis was chronic hepatitis C infection (91%); 10% of patients had chronic hepatitis B, and 23% reported previous alcohol abuse. Eighty-one percent of patients had received therapy against HCV infection.

With regard to HIV infection, 94% of patients were receiving ART and 85% had plasma HIV-RNA<50 copies/mL; median CD4 cell count was 415 cells/μL at the baseline visit.

The diagnosis of cirrhosis was based on liver biopsy in 89 patients, 55% of whom were CPT class A; 48% and 44% of patients had the highest values in the APRI and FIB-4 scores [28,29], respectively. Thirty-seven patients underwent esophagogastroduodenoscopy, which revealed esophageal varices and/or portal hypertensive gastropathy in 43%. The median value of LSM was 17 (11.7-26) kPa, and 25% of patients had an LSM ≥ 25 kPa.

Clinical outcomes

The median follow-up was 36.6 (IQR, 21-46) months, and during this time 9 patients developed hepatic decompensation, 3 developed HCC, 7 died (6 from liver-related causes, one from heart failure) and 1 underwent a liver transplant.

The incidence of hepatic decompensation was 3.1 cases (1.1- 5.1) per 100 person years. The most common types of first hepatic decompensation were ascites (6 cases [67%]) and encephalopathy (3 cases [33%], 1 of which was associated with portal hypertensive gastrointestinal bleeding).

The AUROC (95% confidence interval) of LSM was 0.74 (95% CI, 0.57-0.92; p=0.018) for the prediction of hepatic decompensation and 0.88 (95% CI, 0.79-0.97; p=0.001) for the prediction of all-cause mortality. Different LSM cut-off values for identifying cirrhotic HIVinfected patients with a low risk of developing clinical outcomes were analyzed. The cut-off ≥ 25 kPa was selected based on the highest diagnostic performance, with high sensitivity and specificity. The negative predictive value was over 95% for hepatic decompensation and mortality (Table 2).

  Diagnostic performance for decompensation Diagnostic performance for overall mortality
  LSM>21 Kpa LSM>25 Kpa LSM>40 Kpa LSM>21 Kpa LSM>25 Kpa LSM>40 Kpa
Sensitivity 66.7%
(95% CI, 30-100)
(95% CI, 30-100)
(95% CI, 0-70)
(95% CI, 93-100)
(95% CI, 53-100)
(95% CI, 13-100)
Specificity 63.4%
(95% CI, 53-74)
(95% CI, 70-87)
(95% CI, 84-97)
(95% CI, 55-75)
(95% CI, 70-88)
(95% CI, 85.5-98)
Positive predictive value 15%
(95% CI, 3-27)
(95% CI, 5-41)
(95% CI, 88-54)
(95% CI, 4.5-30.5)
(95% CI, 5-41)
(95% CI, 2.5-64)
Negative predictive value 95%
(95% CI, 89-100)
(95% CI, 91-100)
(95% CI, 88-99)
(95% CI, 99-100)
(95% CI, 95 -100)
(95% CI, 92-100)
Positive likelihood ratio 1.8
(95% CI, 1.1-3)
(95% CI, 1.7-5.7)
(95% CI 1.1-10.5)
(95% CI, 2.2-3.8)
(95% CI, 2.5-6.7)
(95% CI 2.7-17)
Negative likelihood ratio 0.5
(95% CI, 0.2-1.3)
(95% CI, 0.2-1.1)
(95% CI 0.5-1.2)
0 0.2
(95% CI, 0.03-1.1)
(95% CI 0.2-1.1)

Table 2: Liver stiffness diagnostic performance.

The probability of developing decompensation at 1 year, 2 years, and 3 years was 4%, 5% and 9%, respectively. Patients who had an LSM at baseline ≥ 25 kPa were significantly more likely to develop a first hepatic decompensation (Figure 1A).

The factors associated with a first hepatic decompensation were CD4+ cell count <200 cells/μL, receiving antiretroviral therapy at baseline, and LSM. Multivariate Cox regression analysis showed that an LSM ≥ 25 kPa was independently associated with development of a first hepatic decompensation. Confounding variables were included in the regression model and controlled for (Table 3).

The incidence rate for all-cause mortality was 2.3 (0.6-4) per 100 person-years, and the probability of death at 1 year, 2 years and 3 years was 1%, 4%, and 7%. Six patients died from liver-related causes with ESLD, and 1 patient died from heart failure. Patients with an LSM ≥ 25 kPa were significantly more likely to die during follow-up (Figure 1B).

LSM was associated with overall and liver-related death (data not shown), as were HCV/HBV therapy, CPT class B or C, CD4+ cell count <200 cells/μL, platelet count, and the highest FIB-4 score. Multivariate analysis showed that baseline LSM was independently associated with overall and liver-related mortality. Confounding variables were included in the regression model and controlled for (Table 4).

The multivariate Cox regression analysis did not demonstrate a significant interaction between LSM and any of the other covariates included in the multivariate model, both for the first hepatic decompensation and for overall mortality.


Figure 1A: Probability of remaining free from hepatic decompensation according to baseline liver stiffness.

N=102 Univariate analysis
HR (95% CI)
p Multivariate analysis
HR (95% CI)
Age (quantitative) 1.0 (0.9-1.1) 0.73    
Female 0.8 (0.2-4) 0.81    
HCV therapy 1.8 (0.2-14.6) 0.57 4.7 (0.5-44.4) 0.18
Receiving HAART at baseline 0.2 (0-1) 0.05 0.13 (0.02-0.8) 0.03
HIV RNA BLQ at baseline 0.5 (0.1-2.6) 0.45    
CD4<200 cells/µL at baseline 5.1 (1-24) 0.04 25.7 (1.8-377) 0.02
Child-Pugh-Turcotte stage B at baseline 0.5 (0.1-2.5) 0.6 0.2 (0.02-2.0) 0.17
CDC stage C 1.1 (0.2-14) 0.57    
Platelets (quantitative) Platelets<120,000 cells/mL 1 (1-1.1) 2.8 (0.6-13.5) 0.18 0.20    
MELD (quantitative)
0 (0-9286) 0.69 -  
FIB-4 (quantitative)
1.35 (1.1-1.5)
4 (0.8-20)
1.2 (0.2-7.9) 0.85
LSM ≥ 25 kPa 6 (1.5-24.2) 0.01 7.2 (1.1-46.9) 0.04
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