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UTILIZATION OF HEPATITIS C + LIVERS FOR TRANSPLANTATION: LONG TERM COMPREHENSIVE OUTCOMES FROM A QUATERNARY TRANSPLANT CENTER
Date
May 6, 2023
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Society: AASLD
Background: Organ allocation based on the principles of urgency vs utility presents an ethical conundrum given the scarcity of liver grafts. The current Model for End-Stage Liver Disease (MELD) score prioritizes the sickest patients but fails to consider potential additional life-years gained from liver transplant (LT). We aimed to develop a new algorithm for equitable prioritization of liver grafts.
Methods: Using OPTN/UNOS data up to December 2021, we developed a robust model to estimate LT survival benefit defined as extra years of life that a candidate can achieve with LT versus remaining on the waitlist (life-years from transplant [LYFTs]). Weibull regression model was fitted using recipient-only characteristics at waitlist registration. The model was manually built based on a-priori clinical knowledge and flexible splines and interaction terms were generated for 86 estimated parameters. The model predicted the marginal median life expectancies of a patient under the counterfactual circumstances of receiving LT vs remaining waitlisted, and the LYFT score for the patient was calculated as the difference between life expectancy after LT vs waitlist. We proposed a novel score (MoNaLISA: Maximization Of Net Liver Survival benefit and medical Acuity) as the geometric average of the LYFT and MELD score which conceptually balances principles of utility (LYFT) and urgency (MELD). Monte Carlo simulations with bootstrap resampling were conducted to assess the impact of (i) MELD, MELD-Na, and MELD 3.0 score, (ii) MaxLYFT which prioritizes recipients by LYFT scores to maximize LT survival benefit at a population level, (iii) MoNaLISA score.
Results: 219,384 adult LT candidates were included in complete-case analyses with 1.64 million person-years follow-up. The model exhibited excellent goodness-of-fit and Harrell’s C-index of 0.784 (Figure 1). The model also affirmed numerous “clinically-expected” statistical interactions. For example, age was an important effect modifier, as the gain in life expectancy after LT was generally lesser for older recipients (predicted LYFT of 23.1 years for a recipient aged 30 years vs LYFT of 13.8 years for a recipient aged 55 years). Through resampling-based simulations, MELD, MELD-Na, MELD 3.0, MoNaLISA and MaxLYFT respectively yielded 9.5 vs 12.8 vs 12.9 vs 14.2 and 14.5 additional years-of-life per liver graft. Interestingly, MoNaLISA had a negligible adverse impact on 6-month waitlist mortality (22.5%) vs MELD schemes (21.9%-22.6%). MaxLYFT, unsurprisingly, had the highest 6-month waitlist mortality (27.8%) (Figure 2).
Conclusion: We present a novel liver allocation score that maximizes LT survival benefit while minimizing 6-month waitlist mortality. Assuming 13,000 waitlist registrations and 7,000 LTs occur annually in the USA, MoNaLISA adds 9,800 life-years with a negligible increase in waitlist deaths.
Methods: Using OPTN/UNOS data up to December 2021, we developed a robust model to estimate LT survival benefit defined as extra years of life that a candidate can achieve with LT versus remaining on the waitlist (life-years from transplant [LYFTs]). Weibull regression model was fitted using recipient-only characteristics at waitlist registration. The model was manually built based on a-priori clinical knowledge and flexible splines and interaction terms were generated for 86 estimated parameters. The model predicted the marginal median life expectancies of a patient under the counterfactual circumstances of receiving LT vs remaining waitlisted, and the LYFT score for the patient was calculated as the difference between life expectancy after LT vs waitlist. We proposed a novel score (MoNaLISA: Maximization Of Net Liver Survival benefit and medical Acuity) as the geometric average of the LYFT and MELD score which conceptually balances principles of utility (LYFT) and urgency (MELD). Monte Carlo simulations with bootstrap resampling were conducted to assess the impact of (i) MELD, MELD-Na, and MELD 3.0 score, (ii) MaxLYFT which prioritizes recipients by LYFT scores to maximize LT survival benefit at a population level, (iii) MoNaLISA score.
Results: 219,384 adult LT candidates were included in complete-case analyses with 1.64 million person-years follow-up. The model exhibited excellent goodness-of-fit and Harrell’s C-index of 0.784 (Figure 1). The model also affirmed numerous “clinically-expected” statistical interactions. For example, age was an important effect modifier, as the gain in life expectancy after LT was generally lesser for older recipients (predicted LYFT of 23.1 years for a recipient aged 30 years vs LYFT of 13.8 years for a recipient aged 55 years). Through resampling-based simulations, MELD, MELD-Na, MELD 3.0, MoNaLISA and MaxLYFT respectively yielded 9.5 vs 12.8 vs 12.9 vs 14.2 and 14.5 additional years-of-life per liver graft. Interestingly, MoNaLISA had a negligible adverse impact on 6-month waitlist mortality (22.5%) vs MELD schemes (21.9%-22.6%). MaxLYFT, unsurprisingly, had the highest 6-month waitlist mortality (27.8%) (Figure 2).
Conclusion: We present a novel liver allocation score that maximizes LT survival benefit while minimizing 6-month waitlist mortality. Assuming 13,000 waitlist registrations and 7,000 LTs occur annually in the USA, MoNaLISA adds 9,800 life-years with a negligible increase in waitlist deaths.
Figure 1: Goodness-of-fit of the LYFT survival model across various clinical subgroups, showing predicted survivorship versus observed Kaplan-Meier survival curves
Figure 2: Comparison between MELD versus proposed MaxLYFT and MONALISA allocation schemes
Objective: To compare waitlist mortality in older ( ≥ 65 years) and younger patients (18-64 years) with acute on chronic liver failure (ACLF) and identify independent risk factors. In addition, we also wanted to compare 90-day and 1-year patient survival (PS) following liver transplant (LT) in older and younger patients.
Methods: All older and younger adults who underwent LT for ACLF (using the European Association for the Study of Liver-Chronic Liver Failure (EASL-CLIF) Criteria) between 2005-2021 were identified using the UNOS database.We excluded those listed with status 1, 1A, or 1B, multi-organ transplant and living donor transplant. Unadjusted KM survival curves were used to evaluate patient survival (PS). The Cox proportional hazards (CPH) regression model was used to evaluate the risk factors for survival. We started with univariate analysis, followed by multivariate analysis.
Results: 4313 older patients were listed and 2142 were transplanted with ACLF. During the same period, 26,628 younger patients were listed and 16,931 were transplanted with ACLF. Older patients were more likely to die or be removed from the waitlist at 30-days compared to younger patients (20.4% vs 16.7%, P < 0.0001) (Figure 1). Older patients had significantly higher wait-list mortality across all grades of ACLF but were more pronounced in ACLF-2 (23.7% vs 14.8%, P <0.0001 and ACLF-3 (43.3% vs 29.9 %, P < 0.0001). On multivariable competing risk analysis age (HR 1.04, 95% CI 1.017-1.07), female sex ( HR 1.22, 95% CI 1.06-1.42), MELD-Na score (OR 1.09, 95% CI 1.08-1.1), hepatic encephalopathy grades 1-2 ( HR 1.36, 95% CI 1.10-1.68), grades 3/4 ( HR 1.51, 95% CI 1.19-1.92) and respiratory failure (OR 2.64, 95% CI 2.15-3.23) were risk factors for increased waitlist mortality in older patients, while higher albumin (HR 0.82, 95% CI 0.74-0.90) and black race ( HR 0.65, 95% CI 0.50-0.93 compared to whites) were associated with lower mortality (Table 1).
Post LT- survival: 90- day (P=0.0003, figure 2) and 1-year (P<0.0001, Figure 3) PS in older patients were lower than younger patients. Older patients with ACLF-1 had similar 90-day PS compared to younger patients with ACLF-1 (P = 0.46), but PS were significantly lower in ACLF grades 2 and 3. Similarly, 1-year PS was lower in older patients across ACLF grades (Figure 3).
Conclusion
Older patients (> 65 years) with ACLF have significantly higher waitlist mortality and lower post-LT survival than younger patients.
Methods: All older and younger adults who underwent LT for ACLF (using the European Association for the Study of Liver-Chronic Liver Failure (EASL-CLIF) Criteria) between 2005-2021 were identified using the UNOS database.We excluded those listed with status 1, 1A, or 1B, multi-organ transplant and living donor transplant. Unadjusted KM survival curves were used to evaluate patient survival (PS). The Cox proportional hazards (CPH) regression model was used to evaluate the risk factors for survival. We started with univariate analysis, followed by multivariate analysis.
Results: 4313 older patients were listed and 2142 were transplanted with ACLF. During the same period, 26,628 younger patients were listed and 16,931 were transplanted with ACLF. Older patients were more likely to die or be removed from the waitlist at 30-days compared to younger patients (20.4% vs 16.7%, P < 0.0001) (Figure 1). Older patients had significantly higher wait-list mortality across all grades of ACLF but were more pronounced in ACLF-2 (23.7% vs 14.8%, P <0.0001 and ACLF-3 (43.3% vs 29.9 %, P < 0.0001). On multivariable competing risk analysis age (HR 1.04, 95% CI 1.017-1.07), female sex ( HR 1.22, 95% CI 1.06-1.42), MELD-Na score (OR 1.09, 95% CI 1.08-1.1), hepatic encephalopathy grades 1-2 ( HR 1.36, 95% CI 1.10-1.68), grades 3/4 ( HR 1.51, 95% CI 1.19-1.92) and respiratory failure (OR 2.64, 95% CI 2.15-3.23) were risk factors for increased waitlist mortality in older patients, while higher albumin (HR 0.82, 95% CI 0.74-0.90) and black race ( HR 0.65, 95% CI 0.50-0.93 compared to whites) were associated with lower mortality (Table 1).
Post LT- survival: 90- day (P=0.0003, figure 2) and 1-year (P<0.0001, Figure 3) PS in older patients were lower than younger patients. Older patients with ACLF-1 had similar 90-day PS compared to younger patients with ACLF-1 (P = 0.46), but PS were significantly lower in ACLF grades 2 and 3. Similarly, 1-year PS was lower in older patients across ACLF grades (Figure 3).
Conclusion
Older patients (> 65 years) with ACLF have significantly higher waitlist mortality and lower post-LT survival than younger patients.
Introduction:
Introduction of direct acting antivirals for hepatitis C (HCV) has enabled transplant centers to combat organ shortages with an enlarged pool of donor organs. We and others have shown transplantation of HCV + organs (D+) into aviremic recipients (R-) leads to excellent short-term outcomes and rates of sustained viral response. Little data exists, however, in characterizing long term (beyond 12 months) outcomes in liver recipients, rates of rejection and manifestations of recipient hepatitis C.
We compared 2-year survival, rejection and manifestations of HCV between recipients of D+ and D- organs.
Methods:
The UNOS database was queried for all patients who underwent liver transplant at our center from January 2018-September 2022. Baseline pre-transplant and mortality data was extracted; clinical data on post-transplant outcomes was manually extracted to compare recipients of D+ and D- grafts.
Results:
Recipients of 88 D+ and 739 D- donors were included in analysis. Mortality for D+/R- and D-/R- was similar (13.41% vs 8.95%, respectively, p=0.267). Two-year prediction models reveal donor HCV status was not associated with death (HR 1.20, p=0.6) or graft failure (HR 0.38, p=0.20). Subgroup analysis based on recipient and donor HCV RNA status revealed similar patient and graft 2-year survival in all groups (figure 1). Rates of acute cellular rejection, chronic rejection and antibody-mediated rejection for D+ recipients were 12.64%, 1.15% and 2.29% respectively. One D+/R- patient had biopsy-proven fibrosing cholestatic hepatitis (FCH), with resolution with glecaprevir-pibrentasvir therapy. No extra-hepatic manifestations of HCV were noted in D+ recipients, including nephropathy, neuropathy, cryoglobulinemia or dermatitis. Overall, 98.85% of D+/R- patients had HCV viremia post-transplant. One had concomitant acquisition of HBV from a high-risk donor, and requires chronic tenofovir. Only 1 (1.36%) failed to achieve sustained viral response at 12 weeks on glecaprevir-pibrentasvir, and was started on 24 weeks of sofosbuvir-velpatasvir-voxilaprevir, on which they remain currently. Genotype analysis revealed a statistical difference in mortality between patients with genotype 1a and 2 (p=0.005), though comparison is limited by sample size, as only 6 patients had genotype 2 infection.
Conclusion:
Our results show that over two years, graft failure and mortality are uninfluenced by donor HCV status. Incidence of acute cellular rejection in patients receiving HCV- organs has been reported in literature to be 11.5-26.9%, comparable to our cohort of HCV D+ liver recipients. Reported rates of chronic and antibody-mediated rejection are also comparable to our cohort‘s rates. With timely management of viremia and vigilance for fibrosing cholestatic hepatitis, outcomes of recipients of HCV+ liver grafts are non-inferior to HCV- grafts over two years.
Introduction of direct acting antivirals for hepatitis C (HCV) has enabled transplant centers to combat organ shortages with an enlarged pool of donor organs. We and others have shown transplantation of HCV + organs (D+) into aviremic recipients (R-) leads to excellent short-term outcomes and rates of sustained viral response. Little data exists, however, in characterizing long term (beyond 12 months) outcomes in liver recipients, rates of rejection and manifestations of recipient hepatitis C.
We compared 2-year survival, rejection and manifestations of HCV between recipients of D+ and D- organs.
Methods:
The UNOS database was queried for all patients who underwent liver transplant at our center from January 2018-September 2022. Baseline pre-transplant and mortality data was extracted; clinical data on post-transplant outcomes was manually extracted to compare recipients of D+ and D- grafts.
Results:
Recipients of 88 D+ and 739 D- donors were included in analysis. Mortality for D+/R- and D-/R- was similar (13.41% vs 8.95%, respectively, p=0.267). Two-year prediction models reveal donor HCV status was not associated with death (HR 1.20, p=0.6) or graft failure (HR 0.38, p=0.20). Subgroup analysis based on recipient and donor HCV RNA status revealed similar patient and graft 2-year survival in all groups (figure 1). Rates of acute cellular rejection, chronic rejection and antibody-mediated rejection for D+ recipients were 12.64%, 1.15% and 2.29% respectively. One D+/R- patient had biopsy-proven fibrosing cholestatic hepatitis (FCH), with resolution with glecaprevir-pibrentasvir therapy. No extra-hepatic manifestations of HCV were noted in D+ recipients, including nephropathy, neuropathy, cryoglobulinemia or dermatitis. Overall, 98.85% of D+/R- patients had HCV viremia post-transplant. One had concomitant acquisition of HBV from a high-risk donor, and requires chronic tenofovir. Only 1 (1.36%) failed to achieve sustained viral response at 12 weeks on glecaprevir-pibrentasvir, and was started on 24 weeks of sofosbuvir-velpatasvir-voxilaprevir, on which they remain currently. Genotype analysis revealed a statistical difference in mortality between patients with genotype 1a and 2 (p=0.005), though comparison is limited by sample size, as only 6 patients had genotype 2 infection.
Conclusion:
Our results show that over two years, graft failure and mortality are uninfluenced by donor HCV status. Incidence of acute cellular rejection in patients receiving HCV- organs has been reported in literature to be 11.5-26.9%, comparable to our cohort of HCV D+ liver recipients. Reported rates of chronic and antibody-mediated rejection are also comparable to our cohort‘s rates. With timely management of viremia and vigilance for fibrosing cholestatic hepatitis, outcomes of recipients of HCV+ liver grafts are non-inferior to HCV- grafts over two years.
Table 1. Cox proportional hazard models for 2-year patient mortality and graft failure. These demonstrate no association between either recipient or graft HCV NAT status on either outcome.
Figure 1. Kaplan Meier curves comparing 2-year survival and graft failure post-transplant on the bases of donor and recipient HCV NAT status.
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