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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.

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INTRODUCTION

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  1. 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

  2. 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.

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

  4. 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.

  5. RESULTS (I)Baselinecharacteristics (n=297) 1: Median (IQR)

  6. 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.

  7. 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

  8. 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

  9. 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

  10. RESULTS (VI)UnivariateCox regression analysis: Overall mortality 1: Hazard Ratio; 2: 95% confidence interval.

  11. 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.

  12. RESULTS (VIII)Univariate competing risks regression analysis: Decompensations of cirrhosis 1: Subhazard ratio; 2: 95% confidence interval.

  13. 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.

  14. 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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