PREDICTION OF SURVIVAL AND DECOMPENSATIONS OF CIRRHOSIS AMONG HIV/HCV-COINFECTED
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PREDICTION OF SURVIVAL AND DECOMPENSATIONS OF CIRRHOSIS AMONG HIV/HCV-COINFECTED PATIENTS: A COMPARISON OF LIVER STIFFNESS VERSUS LIVER BIOPSY.

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INTRODUCTION

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Introduction

PREDICTION OF SURVIVAL AND DECOMPENSATIONS OF CIRRHOSIS AMONG HIV/HCV-COINFECTED PATIENTS: A COMPARISON OF LIVER STIFFNESS VERSUS LIVER BIOPSY

Juan Macías1, Ángela Camacho2, Miguel A. von Wichmann3, Luis F. López-Cortés4, Enrique Ortega5, Cristina Tural6, MªJosé Ríos7, Dolores Merino8, Francisco Téllez9, Juan A. Pineda1.

1Hospital Universitario de Valme, Seville; 2Hospital Universitario Reina Sofía, Cordoba; 3Hospital de Donostia, San Sebastian; 4Hospital Universitario Virgen del Rocío, Seville; Hospital General Universitario de Valencia, Valencia; 6Hospital Universitario GermansTrias I Pujol, Barcelona; 7Hospital Universitario Virgen Macarena, Seville; 8Complejo Hospitalario de Huelva, Huelva; 9Hospital de La Línea de la Concepción, Cadiz. Spain


Introduction

INTRODUCTION

The survival of individuals with chronic hepatitis C depends on fibrosis stage.

Liver biopsy (LB):

Gold-standard to stage fibrosis.

Limitations: invasive, sampling and interobserver variability.

Transient hepatic elastography (TE):

Reliable non-invasive diagnosis of fibrosis.

Liver stiffness measurement (LSM) correlates with the portal venous pressure gradient.

TE could replace LB to assess the risk of death and liver events in HIV/HCV coinfection.


Objective

OBJECTIVE

To compare the prognostic performance of LB with that of LSM to predict survival and liver decompensations among HIV/HCV-coinfected patients.


Patients and methods

PATIENTS AND METHODS

  • Retrospective cohort study (2005-2011).

  • Inclusion criteria:

    • HIV infection.

    • HCV infection: Detectable plasma HCV-RNA at baseline.

    • LB and TE separated by ≤12 months.

  • Baseline: Half the period of time between LB and LSM.

  • Statistical analysis:

    • Primary end-points:

      • Death due to any cause.

      • First decompensation of cirrhosis.

    • Secondary end-point: Liver-related death.

    • Time to event:

      • Cox regression models: Overall mortality.

      • Competing risks regression models: Decompensations.

      • Comparison of models: Integrated discrimination improvement (IDI) test.


  • Results i baseline characteristics n 297

    RESULTS (I)Baselinecharacteristics (n=297)

    1: Median (IQR)


    Results ii baseline characteristics n 297

    RESULTS (II)Baseline characteristics (n=297)

    1: Median (IQR); 2: Not available in 7 patients; 3: SVR, sustained virological response, applicable to 178 patients who received therapy.


    Results iii

    RESULTS (III)

    Probability of all-cause death

    Median (IQR) follow-up: 5 (4.2-5.4) years. Lost to follow-up: 26 (8.8%) patients.

    • Deaths: 21 (7.1%, 95%CI: 4.1%-10%).

    • Liver-related deaths: 12 (57%).

    • Other causes of death: 9 (43%)

    According to fibrosis stage (LB)

    According to LSM category

    LSM ≤6 KPa

    F0

    LSM 6.1-8.9 KPa

    F1

    LSM 9-14.6 KPa

    F2

    LSM 14.6-21 KPa

    F3

    LSM ≥21 KPa

    F4

    Probability of survival

    Probability of survival

    p=0.005

    p<0.0001


    Results iv

    RESULTS (IV)

    Probability of decompensations of cirrhosis

    Median (IQR) follow-up: 5 (4.2-5.4) years. Lost to follow-up: 26 (8.8%) patients.

    • Decompensations: 21 (7.1%, 95%CI: 4.1%-10%).

    • Ascites: 12 (57%)

    • Portal hypertensive gastrointestinal bleeding: 4 (19%).

    • Hepatic encephalopathy: 2 (9.5%).

    According to fibrosis stage (LB)

    According to LSM category

    LSM ≤6 KPa

    F0

    LSM 6.1-8.9 KPa

    F1

    LSM 9-14.6 KPa

    F2

    LSM 14.6-21 KPa

    F3

    LSM ≥21 KPa

    F4

    Probability of remaining free of decompensation

    Probability of remaining free of decompensation

    p<0.0001

    p<0.0001


    Results v

    RESULTS (V)

    Probability of liver-related death

    Median (IQR) follow-up: 5 (4.2-5.4) years. Lost to follow-up: 26 (8.8%) patients.

    Liver-related deaths: 12 (4%).

    According to fibrosis stage (LB)

    According to LSM category

    LSM ≤6 KPa

    F0

    LSM 6.1-8.9 KPa

    F1

    LSM 9-14.6 KPa

    F2

    LSM 14.6-21 KPa

    F3

    LSM ≥21 KPa

    F4

    p=0.0004

    p<0.0001


    Results vi univariate cox regression analysis overall mortality

    RESULTS (VI)UnivariateCox regression analysis: Overall mortality

    1: Hazard Ratio; 2: 95% confidence interval.


    Results vii multivariate cox regression models overall mortality

    RESULTS (VII)Multivariate Cox regression models: Overall mortality

    Model based on LB

    Model based on TE

    Age

    (p=0.041)

    Age

    (p=0.038)

    1.07

    (1.003-1.14)

    1.07

    (1.004-1.14)

    SVR

    (p=0.151)

    SVR

    (p=0.071)

    0.15

    (0.02-1.17)

    0.23

    (0.03-1.72)

    CD4

    (p=0.159)

    CD4

    (p=0.066)

    0.93

    (0.84-1.03)

    0.91

    (0.83-1.01)

    Platelets

    (p=0.092)

    0.94

    (0.85-1.03)

    Platelets

    (p=0.453)

    0.97

    (0.90-1.05)

    Fibrosis stage

    (p=0.017)

    LSM

    (p=<0.001)

    1.52

    (1.08-2.15)

    1.28

    (1.12-1.46)

    0

    1

    1.25

    1.75

    2

    2.25

    0

    0.25

    0.5

    1

    1.5

    1.75

    2

    2.25

    0.25

    0.5

    1.5

    1.25

    0.75

    0.75

    The model based on TE performed 3.9% better than the model based on LB (p=0.072)

    Models adjusted by gender.


    Results viii univariate competing risks regression analysis decompensations of cirrhosis

    RESULTS (VIII)Univariate competing risks regression analysis: Decompensations of cirrhosis

    1: Subhazard ratio; 2: 95% confidence interval.


    Results ix m ultivariate competing risks regression models decompensations

    RESULTS (IX)Multivariate competing risks regression models:Decompensations

    Model based on LB

    Model based on TE

    Age

    (p=0.394)

    Age

    (p=0.460)

    1.03

    (0.95-1.12)

    1.04

    (0.96-1.12)

    SVR

    (p=0.063)

    0.15

    (0.02-1.11)

    SVR

    (p=0.150)

    0.22

    (0.03-1.73)

    CD4

    (p=0.486)

    0.97

    (0.88-1.06)

    0.94

    (0.86-1.04)

    CD4

    (p=0.274)

    Platelets

    (p=0.014)

    0.91

    (0.84-0.98)

    Platelets

    (p=0.439)

    0.97

    (0.89-1.05)

    Fibrosis stage

    (p=0.007)

    LSM

    (p=<0.001)

    1.37

    (1.21-1.54)

    1.67

    (1.15-2.43)

    0

    0.25

    1

    1.25

    1.5

    1.75

    2

    2.25

    0

    0.25

    0.75

    1.25

    1.5

    1.75

    2

    2.25

    0.5

    0.75

    0.5

    1

    The model based on TE performed 8.4% better than the model based on LB (p=0.045)

    Models adjusted by gender.


    Conclusions

    CONCLUSIONS

    • The performance of models based on TE to predict overall survival among HIV/HCV-coinfected patients was similar to that of LB-based models.

    • TE predicts decompensations better than LB-based models.

    • The non-invasive nature of TE should favor its use instead of LB when the only issue is predicting the clinical outcome of liver disease in HIV/HCV-coinfection.


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