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• W2896531953 abstract "Historically, exception points for hepatocellular carcinoma (HCC) led to higher transplant rates and lower waitlist mortality for HCC candidates compared to non-HCC candidates. As of October 2015, HCC candidates must wait 6 months after initial application to obtain exception points; the impact of this policy remains unstudied. Using 2013-2017 SRTR data, we identified 39 350 adult, first-time, active waitlist candidates and compared deceased donor liver transplant (DDLT) rates and waitlist mortality/dropout for HCC versus non-HCC candidates before (October 8, 2013-October 7, 2015, prepolicy) and after (October 8, 2015-October 7, 2017, postpolicy) the policy change using Cox and competing risks regression, respectively. Compared to non-HCC candidates with the same calculated MELD, HCC candidates had a 3.6-fold higher rate of DDLT prepolicy (aHR = 3.49 3.69 3.89) and a 2.2-fold higher rate of DDLT postpolicy (aHR = 2.09 2.21 2.34). Compared to non-HCC candidates with the same allocation priority, HCC candidates had a 37% lower risk of waitlist mortality/dropout prepolicy (asHR = 0.54 0.63 0.73) and a comparable risk of mortality/dropout postpolicy (asHR = 0.81 0.95 1.11). Following the policy change, the DDLT advantage for HCC candidates remained, albeit dramatically attenuated, without any substantial increase in waitlist mortality/dropout. In the context of sickest-first liver allocation, the revised policy seems to have established allocation equity for HCC and non-HCC candidates. Historically, exception points for hepatocellular carcinoma (HCC) led to higher transplant rates and lower waitlist mortality for HCC candidates compared to non-HCC candidates. As of October 2015, HCC candidates must wait 6 months after initial application to obtain exception points; the impact of this policy remains unstudied. Using 2013-2017 SRTR data, we identified 39 350 adult, first-time, active waitlist candidates and compared deceased donor liver transplant (DDLT) rates and waitlist mortality/dropout for HCC versus non-HCC candidates before (October 8, 2013-October 7, 2015, prepolicy) and after (October 8, 2015-October 7, 2017, postpolicy) the policy change using Cox and competing risks regression, respectively. Compared to non-HCC candidates with the same calculated MELD, HCC candidates had a 3.6-fold higher rate of DDLT prepolicy (aHR = 3.49 3.69 3.89) and a 2.2-fold higher rate of DDLT postpolicy (aHR = 2.09 2.21 2.34). Compared to non-HCC candidates with the same allocation priority, HCC candidates had a 37% lower risk of waitlist mortality/dropout prepolicy (asHR = 0.54 0.63 0.73) and a comparable risk of mortality/dropout postpolicy (asHR = 0.81 0.95 1.11). Following the policy change, the DDLT advantage for HCC candidates remained, albeit dramatically attenuated, without any substantial increase in waitlist mortality/dropout. In the context of sickest-first liver allocation, the revised policy seems to have established allocation equity for HCC and non-HCC candidates. The Organ Procurement and Transplantation Network (OPTN) implemented a revised policy in October 2015 to modify the timing and maximum value of exception points for hepatocellular carcinoma (HCC) candidates on the deceased donor liver transplant (DDLT) waitlist.1U.S. Organ Procurement and Transplantation Network Policies: Policy 9: Allocation of Livers and Liver-Intestines. Department of Health and Human Services, Health Resources and Services Administration, Healthcare Systems Bureau, Division of Transplantation, Rockville, MD2014: 115-116Google Scholar,2US Organ Procurement and Transplantation Network: Revised liver policy regarding HCC exception scores. October 6, 2015. https://optn.transplant.hrsa.gov/news/revised-liver-policy-regarding-hcc-exception-scores/. Accessed March 21, 2017.Google Scholar Before the policy change, HCC candidates received exception points of 22 for the first 3 months after initial application, followed by exception points of 25 for the first 3-month extension, 28 for the second 3-month extension, and 29 for the third 3-month extension.2US Organ Procurement and Transplantation Network: Revised liver policy regarding HCC exception scores. October 6, 2015. https://optn.transplant.hrsa.gov/news/revised-liver-policy-regarding-hcc-exception-scores/. Accessed March 21, 2017.Google Scholar Since the October 2015 policy change, HCC candidates are listed at their calculated MELD scores for the first 3 months after initial application and for the first 3-month extension.2US Organ Procurement and Transplantation Network: Revised liver policy regarding HCC exception scores. October 6, 2015. https://optn.transplant.hrsa.gov/news/revised-liver-policy-regarding-hcc-exception-scores/. Accessed March 21, 2017.Google Scholar Subsequently, they receive exception points of 28 for the second 3-month extension at 6 months and 29 for the third 3-month extension at 9 months.1U.S. Organ Procurement and Transplantation Network Policies: Policy 9: Allocation of Livers and Liver-Intestines. Department of Health and Human Services, Health Resources and Services Administration, Healthcare Systems Bureau, Division of Transplantation, Rockville, MD2014: 115-116Google Scholar, 2US Organ Procurement and Transplantation Network: Revised liver policy regarding HCC exception scores. October 6, 2015. https://optn.transplant.hrsa.gov/news/revised-liver-policy-regarding-hcc-exception-scores/. Accessed March 21, 2017.Google Scholar, 3Schilsky ML Moini M Advances in liver transplantation allocation systems.World J Gastroenterol. 2016; 22: 2922-2930Crossref PubMed Scopus (28) Google Scholar The revised policy also reduces the maximum exception points for HCC candidates from 40 to 34.1U.S. Organ Procurement and Transplantation Network Policies: Policy 9: Allocation of Livers and Liver-Intestines. Department of Health and Human Services, Health Resources and Services Administration, Healthcare Systems Bureau, Division of Transplantation, Rockville, MD2014: 115-116Google Scholar, 2US Organ Procurement and Transplantation Network: Revised liver policy regarding HCC exception scores. October 6, 2015. https://optn.transplant.hrsa.gov/news/revised-liver-policy-regarding-hcc-exception-scores/. Accessed March 21, 2017.Google Scholar, 3Schilsky ML Moini M Advances in liver transplantation allocation systems.World J Gastroenterol. 2016; 22: 2922-2930Crossref PubMed Scopus (28) Google Scholar Historically, HCC candidates have experienced a substantial advantage in deceased donor liver allocation with lower waitlist mortality/dropout within 1 year of listing compared to non-HCC candidates (11.5% vs 17.7%).4Washburn K Edwards E Harper A Freeman R Hepatocellular carcinoma patients are advantaged in the current liver transplant allocation system.Am J Transplant. 2010; 10: 1643-1648Crossref PubMed Scopus (41) Google Scholar Our group previously showed that HCC candidates had 1.6-fold higher odds of transplant and 53% lower odds of 90-day waitlist mortality/dropout.5Massie AB Caffo B Gentry S et al.MELD exceptions and rates of waiting list outcomes.Am J Transplant. 2011; 11: 2362-2371Crossref PubMed Scopus (157) Google Scholar Additionally, waitlist mortality/dropout for HCC candidates was found not to increase with higher exception points (4.2% vs 4.6% vs 3.0% for exception points of 22, 25, and 28 respectively), as compared to non-HCC candidates for whom 90-day mortality/dropout increased with higher model end-stage liver disease (MELD) scores (11.0% vs 17.3% vs 23.6% for MELD scores of 21-23, 24-26, and 27-29 respectively).6Goldberg D French B Abt P Feng S Cameron AM Increasing disparity in waitlist mortality rates with increased model for end-stage liver disease scores for candidates with hepatocellular carcinoma versus candidates without hepatocellular carcinoma.Liver Transpl. 2012; 18: 434-443Crossref PubMed Scopus (0) Google Scholar Moreover, there was an increase in the proportion of waitlist candidates who obtained HCC exception points from 2005 to 2012 (15.7% to 21.6%).7Northup PG Intagliata NM Shah NL Pelletier SJ Berg CL Argo CK Excess mortality on the liver transplant waiting list: unintended policy consequences and Model for End-Stage Liver Disease (MELD) inflation.Hepatology (Baltimore, MD). 2015; 61: 285-291Crossref PubMed Scopus (0) Google Scholar Prior to implementation of the revised policy, a simulation study conducted by Heimbach et al predicted that a 6-month delay in exception point allocation would equalize the transplant and mortality/dropout rates for those with and without HCC exceptions.8Heimbach JK Hirose R Stock PG et al.Delayed hepatocellular carcinoma MELD exception score improves disparity in access to liver transplant in the US.Hepatology (Baltimore, MD). 2015; 61: 1643-1650Crossref PubMed Google Scholar However, this delay might create a window in which candidates with rapidly progressive HCC, who may have poor posttransplant outcomes, are removed from the waitlist.9Proposal to Delay HCC Exception Score Assignment. 2014. https://optn.transplant.hrsa.gov/media/1446/pubcommentpropsub_331.pdf. Accessed December 22, 2017.Google Scholar Therefore, the revised policy has the potential to increase the waitlist mortality or removal for HCC candidates, potentially overcorrecting the prior advantage and introducing a disadvantage for HCC candidates. To better understand the effectiveness of the revised allocation policy and to address the OPTN public comments proposal requesting early postimplementation analysis,9Proposal to Delay HCC Exception Score Assignment. 2014. https://optn.transplant.hrsa.gov/media/1446/pubcommentpropsub_331.pdf. Accessed December 22, 2017.Google Scholar we conducted an analysis of prospectively maintained national registry data to estimate the association between HCC and DDLT, waitlist mortality/dropout before and after the policy change. In addition, we compared posttransplant outcomes for HCC DDLT recipients before and after implementation of the revised policy. This study used data from the Scientific Registry of Transplant Recipients (SRTR). The SRTR data system includes data on all donors, waitlisted candidates, and transplant recipients in the United States, submitted by the members of the OPTN, and has been described elsewhere.10Leppke S Leighton T Zaun D et al.Scientific registry of transplant recipients: collecting, analyzing, and reporting data on transplantation in the United States.Transplant Rev (Orlando, FL). 2013; 27: 50-56Crossref PubMed Scopus (0) Google Scholar,11Massie AB Kucirka LM Segev DL Big data in organ transplantation: registries and administrative claims.Am J Transplant. 2014; 14: 1723-1730Crossref PubMed Scopus (215) Google Scholar The Health Resources and Services Administration (HRSA), U.S. Department of Health and Human Services, provides oversight to the activities of the OPTN and SRTR contractors. We identified 39 350 first-time, active, adult waitlist candidates listed for DDLT between October 8, 2013 and October 7, 2017. We excluded candidates who were prevalent on the waitlist on October 8, 2013 to prevent any possible effects of previous DDLT allocation policies. In addition, we excluded candidates diagnosed with HCC but not approved for HCC exception points, non-HCC candidates with exception points, and candidates ever listed as Status 1 (Figure 1). The primary exposure was HCC; thus, we identified all candidates approved for HCC exception points (HCC candidates) and compared waitlist outcomes to candidates without any exception points who were never diagnosed with HCC (non-HCC candidates). Finally, to identify changes in waitlist and posttransplant outcomes among HCC candidates under the new policy, we defined two eras using the recent policy implementation date: prepolicy was defined as October 8, 2013-October 7, 2015 and postpolicy was defined as October 8, 2015-October 7, 2017. Candidates entered the study at the time of approval of their exception (HCC candidates) or first active date on the waitlist (non-HCC candidates). Candidates were followed until DDLT, waitlist removal (for mortality/dropout or other reasons), or date of administrative censorship. Two years of follow-up were available for postpolicy candidates; therefore, we restricted our prepolicy follow-up period to 2 years as well. In other words, candidates studied during the pre- and postpolicy were administratively censored on October 7, 2015 and October 7, 2017, respectively. We used a Cox proportional hazards model to compare time to DDLT among HCC and non-HCC candidates in the pre- and postpolicy eras, adjusting for age, sex, race, and time varying calculated MELD score (cMELD). This model treated waitlist mortality/dropout as a censored observation and did not assume independence between DDLT and waitlist mortality/dropout. Thus, the reported hazard ratio (HR) indicates the association between HCC and DDLT and should not be interpreted directly as a cumulative incidence function (CIF) of DDLT.12Lau B Cole SR Gange SJ Competing risk regression models for epidemiologic data.Am J Epidemiol. 2009; 170: 244-256Crossref PubMed Scopus (780) Google Scholar Under the policy, chance of DDLT was time varying for HCC candidates, so we estimated the hazard of DDLT within the first 6 months of HCC exception point approval and the hazard of DDLT from 6-24 months after HCC exception point approval. The overall hazard of DDLT within each policy era is also presented. We tested the interaction between HCC and policy era to determine whether the advantage of HCC exception points in access to DDLT changed after the October 2015 policy. Results are reported stratified by policy era. To illustrate time to DDLT graphically, we estimated the cumulative incidence of DDLT accounting for waitlist mortality/dropout as a competing risk, as described by Coviello and Boggess.13Coviello V Boggess M Cumulative incidence estimation in the presence of competing risks.STATA J. 2004; 4: 103-112Crossref Google Scholar We used competing risk regression analysis to determine whether waitlist mortality/dropout associated with HCC changed under the October 2015 policy. Waitlist mortality/dropout was defined as removal from the waitlist because of death, deteriorating condition, or medical unsuitability (too sick for transplant); candidates removed for other reasons (eg, living donor liver transplant, transplanted at another center) were censored. Patient mortality was ascertained using SRTR data supplemented with linkage to the Social Security Death Master File. Using the Fine and Gray method,14Fine JP Gray RJ A proportional hazards model for the subdistribution of a competing risk.J Am Stat Assoc. 1999; 94: 496-509Crossref Google Scholar we estimated the association between waitlist mortality/dropout and HCC while accounting for the competing risk of transplantation. This method does not censor the transplanted candidates and therefore allows direct modeling of the subdistribution CIF of waitlist mortality/dropout.12Lau B Cole SR Gange SJ Competing risk regression models for epidemiologic data.Am J Epidemiol. 2009; 170: 244-256Crossref PubMed Scopus (780) Google Scholar Similar to the Cox regression model, we estimated the time varying hazard of waitlist mortality/dropout, reporting the subhazard ratio (sHR) of mortality/dropout in the first 6 months after HCC exception point approval and between 6 and 24 months after HCC exception point approval. The overall sHR of waitlist mortality/dropout within each policy era is also reported. The final model was adjusted for age, sex, race, and allocation MELD (aMELD) as determined by the OPTN based on cMELD or exception points. We tested the interaction between HCC and policy era to determine whether the risk of waitlist mortality/dropout for HCC patients changed under the new policy; results are reported stratified by policy era. To illustrate time to waitlist mortality/dropout graphically, we also estimated the cumulative incidence of waitlist mortality/dropout accounting for DDLT as a competing risk, as described by Coviello and Boggess.13Coviello V Boggess M Cumulative incidence estimation in the presence of competing risks.STATA J. 2004; 4: 103-112Crossref Google Scholar Waitlist dropout was defined as removal from the waitlist because of deteriorating condition or medical unsuitability. We estimated the association between waitlist dropout and HCC while accounting for the competing risk of transplantation and waitlist mortality using Fine and Gray method14Fine JP Gray RJ A proportional hazards model for the subdistribution of a competing risk.J Am Stat Assoc. 1999; 94: 496-509Crossref Google Scholar with adjustment for age, sex, race, and aMELD. As described previously, we report the subhazard ratio (sHR) of waitlist dropout for 0-6 months, 6-24 months, and 0-24 months. Similar to previous models, we tested the interaction between HCC and policy era to determine whether the risk of waitlist dropout for HCC patients changed under the new policy. We also estimated the cumulative incidence of waitlist dropout accounting for DDLT and waitlist mortality as a competing risk.13Coviello V Boggess M Cumulative incidence estimation in the presence of competing risks.STATA J. 2004; 4: 103-112Crossref Google Scholar We repeated this analysis to estimate the association between waitlist mortality and HCC while accounting for the competing risk of transplantation and waitlist dropout before and after the policy change using Fine and Gray method14Fine JP Gray RJ A proportional hazards model for the subdistribution of a competing risk.J Am Stat Assoc. 1999; 94: 496-509Crossref Google Scholar and adjusting for age, sex, race, and aMELD. To illustrate the changes in DDLT rate and waitlist mortality/dropout incidence pre- and postpolicy by United Network for Organ Sharing (UNOS) regions, we repeated the analysis, stratified by region, and present the results in Figure 4. To determine whether posttransplant outcomes were affected by the delay in exception point allocation among HCC DDLT recipients under the new policy, we compared all-cause graft failure among HCC DDLT recipients pre- vs postpolicy change. Because the DDLT rate for HCC candidates within the first 6 months of their exception point approval decreased substantially following the policy change, median post-DDLT follow-up time in the postpolicy era was 8.8 months. As such, in our study of all-cause graft failure we censored all participants at 1 year posttransplant. We estimated the cumulative incidence of all-cause graft failure (defined as retransplant or death) within 1 year of transplantation for HCC DDLT recipients pre- and postpolicy using Kaplan-Meier methods. We estimated the association between all-cause graft failure and the policy change among HCC recipients using Cox proportional hazards regression, adjusting for recipient age, sex, race, and donor risk index (DRI). All statistical analyses were performed using Stata 14.2/SE for Linux (Stata Corp., College Station, TX). HCC and non-HCC candidates were compared using chi-square tests for categorical variables and Wilcoxon rank-sum tests for continuous variables. Comparisons between HCC and non-HCC candidates were made separately for both eras. All tests were two sided, and a P value of ≤ .05 was considered statistically significant. Confidence intervals were reported as per the method of Louis and Zeger.15Louis TA Zeger SL Effective communication of standard errors and confidence intervals.Biostatistics. 2009; 10: 1-2Crossref PubMed Scopus (164) Google Scholar Characteristics of HCC and non-HCC candidates at study entry were compared to each other for the pre- and postpolicy eras separately (Table 1). At study entry, HCC candidates were older than non-HCC candidates in both eras (prepolicy: median [IQR] age 61 [56-65] years vs 56 [49-62] years, P < .001; postpolicy: median [IQR] age 62 [58-66] years vs 56 [48-62] years, P < .001) and less likely to be female (prepolicy: 23.3% vs 39.2%, P < .001; postpolicy: 22.4% vs 40.4%, P < .001). In both eras, median cMELD at study entry was 10 (IQR: 8-13) for HCC candidates and 18 (IQR: 14-26) (P < .001) for non-HCC candidates. In the prepolicy era, HCC candidates had higher aMELD at study entry compared to non-HCC candidates (median [IQR] aMELD 22 [22-22] vs 18 [14-26], P < .001). Conversely, in the postpolicy era, HCC candidates had significantly lower aMELD at study entry compared to non-HCC candidates (median [IQR] aMELD 10 [8-15] vs 21 [15-28], P < .001) (Table 1).TABLE 1Baseline characteristics of the study population comparing HCC vs non-HCC candidates in prepolicy and postpolicy eraPrepolicy (N = 19 057)Postpolicy (N = 20 293)HCCNon-HCCP valueHCCNon-HCCP valueN501914 038482215 471Age, median (IQR)61 (56-65)56 (49-62)< .00162 (58-66)56 (48-62)< .001Female, %23.339.2< .00122.440.4< .001Race, %White64.172.2< .00164.972.8< .001Black10.68.69.97.2Hispanic/Latino16.114.717.215.5Asian7.83.06.63.0Other1.41.61.41.5cMELD, median (IQR)10 (8-13)18 (14-26)< .00110 (8-13)18 (14-26)< .001aMELD, median (IQR)22 (22-22)18 (14-26)< .00110 (8-15)21 (15-28)< .001aMELD, allocation MELD; cMELD, calculated MELD; HCC, hepatocellular carcinoma; IQR, Interquartile range. Open table in a new tab aMELD, allocation MELD; cMELD, calculated MELD; HCC, hepatocellular carcinoma; IQR, Interquartile range. Prepolicy, 38.7% of HCC candidates received DDLT within 6 months of study entry, compared to 34.4% of non-HCC candidates. Postpolicy, only 14.1% of HCC candidates received DDLT within 6 months of study entry, compared to 44.1% of non-HCC candidates (Figure 2). In both eras, more HCC candidates received DDLT within 24 months of study entry as compared to non-HCC candidates (prepolicy: 88.4% vs 46.3%; postpolicy: 90.6% vs 57.2%) (Figure 2). Prepolicy, HCC candidates had 2.83-fold higher DDLT rate compared to non-HCC candidates with the same cMELD within the first 6-months after study entry (aHR = 2.66 2.83 3.02, P < .001), which increased to 9.02-fold higher between 6 and 24 months after study entry (aHR = 8.14 9.02 10.00, P < .001). Over the 24 months as a whole, DDLT rate was 3.69-fold higher for HCC candidates compared to non-HCC candidates (aHR = 3.49 3.69 3.89, P < .001) (Table 2).TABLE 2DDLT for HCC vs non-HCC candidates, prepolicy and postpolicy, regardless of waitlist mortality (Cox). In the prepolicy era, HCC candidates had substantially higher DDLT rate than non-HCC candidates with comparable cMELD. Postpolicy, HCC candidates had slightly lower DDLT rate than non-HCC candidates in the first 6 months of study entry (aHR = 0.76), but a substantially higher DDLT rate between 6 and 24 months poststudy entry (aHR = 11.97). Within the first 24 months overall, HCC candidates had a 2.2-fold higher DDLT rate compared to non-HCC candidates (aHR = 2.21)DDLT rate: HCC vs non-HCC candidates (Cox aHR)PrepolicyPostpolicyInteraction P0-6 m2.66 2.83 3.020.69 0.76 0.83< .0016-24 m8.14 9.02 10.0010.99 11.97 13.03< .0010-24 m3.49 3.69 3.892.09 2.21 2.34< .001aHR, adjusted hazard ratio; Cox, Cox regression; DDLT, deceased donor liver transplant; HCC, hepatocellular carcinoma.Models adjusted for age (spline at 55), sex, race, and cMELD (spline at 12 and 35).Bold values denote P<0.05. Open table in a new tab aHR, adjusted hazard ratio; Cox, Cox regression; DDLT, deceased donor liver transplant; HCC, hepatocellular carcinoma. Models adjusted for age (spline at 55), sex, race, and cMELD (spline at 12 and 35). Bold values denote P<0.05. Postpolicy, HCC candidates had 24% lower DDLT rate compared to non-HCC candidates with the same cMELD within the first 6 months of study entry (aHR = 0.69 0.76 0.83, P = .001), which increased to 11.97-fold higher between 6 and 24 months poststudy entry (aHR = 10.99 11.97 13.03, P < .001). Over the 24 months as a whole, HCC candidates had 2.21-fold greater DDLT rate compared to non-HCC candidates (aHR = 2.09 2.21 2.34, P < .001) (Table 2). In both eras, waitlist mortality/dropout for HCC candidates was lower compared to non-HCC candidates within 6 months of study entry (prepolicy: 4.8% vs 7.6%; postpolicy: 4.9% vs 5.9%) and also within 24 months of study entry (prepolicy: 8.7% vs 14.1%; postpolicy: 9.3% vs 9.6%) (Figure 3A). Prepolicy, the risk of waitlist mortality/dropout for HCC candidates was 41% lower within the first 6 months of study entry compared to non-HCC candidates with the same aMELD (asHR = 0.50 0.59 0.70, P < .001). HCC and non-HCC candidates had a similar risk of waitlist mortality/dropout between 6 and 24 months poststudy entry (asHR = 0.68 0.94 1.29, P = .7). Over the 24 months as a whole, HCC candidates had 37% lower risk of waitlist mortality/dropout compared to non-HCC candidates with the same aMELD (asHR = 0.54 0.63 0.73, P < .001) (Table 3A).TABLE 3(A) Waitlist mortality/dropout, (B) waitlist dropout, and (C) waitlist mortality for HCC vs non-HCC, prepolicy and postpolicy, accounting for competing risks (CR). Prepolicy, HCC candidates had substantially lower incidence of waitlist mortality/dropout (asHR = 0.59), dropout (asHR = 0.73) and mortality (asHR = 0.49) compared to non-HCC candidates at the same aMELD during the first 6 months after study entry, but comparable incidence of waitlist mortality/dropout, dropout, mortality at 6-24 months after study entry; Within the first 24 months overall, their mortality/dropout risk was substantially lower compared to non-HCC candidates (sHR = 0.63). Postpolicy, HCC and non-HCC candidates had similar incidence of waitlist mortality/dropout (sHR = 0.95). However, risk of dropout was higher for HCC candidates compared to non-HCC candidates in postpolicy era (sHR = 1.93)A Waitlist mortality/dropout incidence accounting for competing risks of DDLT (asHR)PrepolicyPostpolicyInteraction P0-6 m0.50 0.59 0.700.71 0.86 1.03.0046-24 m0.68 0.94 1.290.96 1.25 1.63.10-24 m0.54 0.63 0.730.81 0.95 1.11<.001B Waitlist dropout incidence accounting for competing risks of DDLT and waitlist mortality (asHR)PrepolicyPostpolicyInteraction P0-6 m0.57 0.73 0.931.39 1.79 2.31<.0016-24 m0.91 1.44 2.261.65 2.39 3.47.050-24 m0.64 0.80 1.011.54 1.93 2.42<.001C Waitlist mortality incidence accounting for competing risks DDLT and waitlist dropout (asHR)PrepolicyPostpolicyInteraction P0-6 m0.40 0.49 0.620.34 0.46 0.62.76-24 m0.48 0.74 1.170.52 0.78 1.17.90-24 m0.43 0.52 0.640.41 0.53 0.68.9asHR, adjusted subhazard ratio.Models adjusted for age (spline at 55), sex, race, and aMELD (spline at 12 and 35).Bold values denote P<0.05. Open table in a new tab asHR, adjusted subhazard ratio. Models adjusted for age (spline at 55), sex, race, and aMELD (spline at 12 and 35). Bold values denote P<0.05. Postpolicy, HCC and non-HCC candidates with the same aMELD experienced a comparable risk of waitlist mortality/dropout within the first 6 months poststudy entry (asHR = 0.71 0.86 1.03, P = .1), between 6 and 24 months poststudy entry (asHR = 0.96 1.25 1.63, P = .1), and over the 24 months as a whole (asHR = 0.81 0.95 1.11, P = .5) (Table 3A). Prepolicy, 2.2% of HCC candidates dropped out within 6 months of study entry, compared to 2.5% of non-HCC candidates. Postpolicy, 3.3% of HCC candidates dropped out within 6 months of study entry, compared to 1.8% of non-HCC candidates (Figure 3B). In both eras, more HCC candidates dropped out within 24 months of study entry compared to non-HCC candidates (prepolicy: 4.8% vs 4.6%; postpolicy: 5.8% vs 3.0%) (Figure 3B). Prepolicy, the risk of waitlist dropout for HCC candidates was 27% lower within the first 6-months of study entry compared to non-HCC candidates with the same aMELD (asHR = 0.57 0.73 0.93, P = .01). HCC and non-HCC candidates had a similar risk of waitlist dropout between 6 and 24 months poststudy entry (asHR = 0.91 1.44 2.26, P = .1). Over the 24 months as a whole, HCC candidates had comparable risk of waitlist dropout compared to non-HCC candidates with the same aMELD (asHR = 0.64 0.80 1.01, P = .05) (Table 3B). Postpolicy, the risk of waitlist dropout for HCC candidates was 1.79-fold higher within the first 6-months of study entry compared to non-HCC candidates with the same aMELD (asHR = 1.39 1.79 2.31, P < .001), which increased to 2.39-fold higher between 6 and 24 months after study entry (asHR = 1.65 2.39 3.47, P < .001). Over the 24 months as a whole, risk of dropout was 1.93-fold higher for HCC candidates compared to non-HCC candidates with the same aMELD (asHR = 1.54 1.93 2.42, P < .001) (Table 3B). In both eras, waitlist mortality for HCC candidates was lower compared to non-HCC candidates within 6 months of study entry (prepolicy: 2.6% vs 5.1%; postpolicy: 1.6% vs 4.1%) and also within 24 months of study entry (prepolicy: 3.9% vs 9.5%; postpolicy: 3.5% vs 6.6%) (Figure 3C). In both eras, HCC candidates and non-HCC candidates had a similar mortality risk. Prepolicy, the risk of waitlist mortality for HCC candidates was 51% lower within the first 6 months of study entry compared to non-HCC candidates with the same aMELD (asHR = 0.40 0.49 0.62, P < .001). HCC and non-HCC candidates had a similar risk of waitlist mortality between 6 and 24 months poststudy entry (asHR = 0.48 0.74 1.17, P = .2). Over the 24 months as a whole, HCC candidates had 48% lower risk of waitlist mortality compared to non-HCC candidates with the same aMELD (asHR = 0.43 0.52 0.64, P < .001) (Table 3C). Postpolicy, the risk of waitlist mortality for HCC candidates was 54% lower within the first 6 months of study entry compared to non-HCC candidates with the same aMELD (asHR = 0.34 0.46 0.62, P < .001). HCC and non-HCC candidates had a similar risk of waitlist mortality between 6 and 24 months poststudy entry (asHR = 0.52 0.78 1.17, P = .2). Over the 24 months as a whole, HCC candidates had 47% lower risk of waitlist mortality compared to non-HCC candidates with same aMELD (asHR = 0.41 0.53 0.68, P < .001) (Table 3C). Prepolicy, DDLT rate for HCC candidates was much higher compared to non-HCC candidates in all regions. Postpolicy, DDLT rate for HCC candidates was still higher compared to non-HCC candidates in all regions. However, the difference in DDLT rate between HCC and non-HCC candidates was attenuated in the postpolicy era compared to the prepolicy era. The reduction in DDLT rate from pre- to postpolicy era was statistically significant for all regions except region 1 and region 9 (Figure 4A). Prepolicy, HCC candidates had substantially lower incidence of mortality/dropout compared to non-HCC candidates in regions 1, 2, 3, 5, 7, and 9. Postpolicy, HCC and non-HCC candidates had comparable chance of waitlist mortality/dropout in all regions (Figure 4B). Among HCC DDLT recipients, 1-year graft failure was 7.5% prepolicy and 5.9% postpolicy (log-rank P = .1). After adjusting for recipient age (splined at 40, 55, 75), race, sex, DRI (splined at 2.5), there was no evidence of association between the policy change and 1-year graft failure (aHR = 0.60 0.79 1.03; P = .1; Figure 5). In this national study of waitlist and posttransplant outcomes in liver waitlist registrants, we found that the substantial allocation advantage to HCC candidates in the prepolicy era (aHR = 3.7 compared to non-HCC candidates) remained, albeit attenuated, in the postpolicy era (aHR = 2.2). Furthermore, although HCC candidates had a 37% lower risk of waitlist mortality/dropout in the prepolicy era (asHR = 0.63), they experienced a comparable risk of mortality/dropout in the postpolicy era (asHR = 0.95) compared to non-HCC candidates with similar allocation priority. Both before and after the policy change, HCC candidates had substantially lower risk of waitlist mortality compared to non-HCC candidates (asHR = 0.5). However, although the risk of dropout for HCC candidates was comparable to the risk for non-HCC candidates before the policy change (asHR = 0.8), it was substantially higher than the risk for non-HCC candidates after the policy change (asHR = 1.9). This study explored the equity in organ allocation for waitlist candidates in the 2 years preceding the October 2015 policy change. Our findings that HCC candidates had a 37% lower risk of mortality/dropout because of the 3.7-fold greater DDLT rate compared to non-HCC candidates in the prepolicy era are consistent with previous findings of lower waitlist mortality/dropout and higher DDLT rate for HCC candidates.4Washburn K Edwards E Harper A Freeman R Hepatocellular carcinoma patients are advantaged in the current liver transplant allocation system.Am J Transplant. 2010; 10: 1643-1648Crossref PubMed Scopus (41) Google Scholar, 5Massie AB Caffo B Gentry S et al.MELD exceptions and rates of waiting list outcomes.Am J Transplant. 2011; 11: 2362-2371Crossref PubMed Scopus (157) Google Scholar, 6Goldberg D French B Abt P Feng S Cameron AM Increasing disparity in waitlist mortality rates with increased model for end-stage liver disease scores for candidates with hepatocellular carcinoma versus candidates without hepatocellular carcinoma.Liver Transpl. 2012; 18: 434-443Crossref PubMed Scopus (0) Google Scholar, 7Northup PG Intagliata NM Shah NL Pelletier SJ Berg CL Argo CK Excess mortality on the liver transplant waiting list: unintended policy consequences and Model for End-Stage Liver Disease (MELD) inflation.Hepatology (Baltimore, MD). 2015; 61: 285-291Crossref PubMed Scopus (0) Google Scholar The present study extended previous work by comparing DDLT rate for HCC and non-HCC candidates with the same cMELD in the pre- and postpolicy eras. In the postpolicy era, for the first time ever, HCC candidates had lower rates of transplant compared to non-HCC candidates with the same cMELD score in the first 6 months after approval of their exception. However, after this 6-month period, HCC candidates had 11-fold higher rates of transplant compared to non-HCC candidates with the same cMELD, such that, overall, HCC candidates continued to experience higher transplant rates. Our findings reinforce the results of simulation studies conducted prior to the 2015 policy change. Using a liver simulation allocation model (LSAM) for waitlisted candidates in 2010, Heimbach and colleagues compared the effect of delaying exception point allocation on transplant rate and waitlist mortality/dropout for HCC and non-HCC candidates.8Heimbach JK Hirose R Stock PG et al.Delayed hepatocellular carcinoma MELD exception score improves disparity in access to liver transplant in the US.Hepatology (Baltimore, MD). 2015; 61: 1643-1650Crossref PubMed Google Scholar Their prediction model found that a 6-month delay in exception point allocation would result in a closer alignment between DDLT rates for HCC and non-HCC candidates by 1-year postlisting without substantial changes in the rate of waitlist mortality/dropout.8Heimbach JK Hirose R Stock PG et al.Delayed hepatocellular carcinoma MELD exception score improves disparity in access to liver transplant in the US.Hepatology (Baltimore, MD). 2015; 61: 1643-1650Crossref PubMed Google Scholar Additionally, in another simulation study, Alver et al compared the projected DDLT and waitlist mortality/dropout rate under an allocation system with 6-month delay (postpolicy) and with MELDEQ (a novel HCC-specific scoring system based on cMELD, alpha-fetoprotein, and tumor characteristics), using a nonparametric multistate model on 2009-2014 UNOS data (18). Their prediction model found that a 6-month delay would result in reduced DDLT rate at 6 months, equivalent access at 12 months, and increased access at 18 months postlisting for HCC candidates compared to non-HCC candidates, with a consistently lower risk of mortality/dropout and similar posttransplant survival in each interval.16Alver SK Lorenz DJ Marvin MR Brock GN Projected outcomes of 6-month delay in exception points versus an equivalent Model for End-Stage Liver Disease score for hepatocellular carcinoma liver transplant candidates.Liver Transpl. 2016; 22: 1343-1355Crossref PubMed Scopus (0) Google Scholar In studying the real-world effects of this 6-month delay in exception point allocation after implementation of the revised OPTN/UNOS policy, we have confirmed that the delay did, in fact, reduce the advantage HCC candidates previously experienced in access to DDLT, while maintaining a comparable risk of waitlist mortality/dropout for HCC candidates compared to their non-HCC counterparts. Our study must be understood in the context of several limitations. The hazard ratios for 6-24 months could be affected by survival bias, as they were estimated using only candidates who were not removed in first 6 months. We were able to study posttransplant outcomes only for the first 12 months following the implementation of the October 2015 revised liver policy regarding HCC exception points. Thus, it remains uncertain whether the patterns we identified in posttransplant outcomes will persist after 1 year. We also recognize the potential limitations of using registry-based data. OPTN data are gathered across hundreds of centers, potentially with varying degrees of quality control and different policies for checking and updating MELD scores. Additionally, we adjusted for a limited number of covariates based on which factors were available in this registry; for instance, we were unable to adjust for factors such as pretransplant HCC treatment, as such data were not available. However, despite these limitations, national registries constitute the only comprehensive data source for studies of changes in organ allocation at the national level. Despite the aforementioned limitations, our study also has several key strengths. To our knowledge, this is the first study of changes in DDLT, risk of waitlist mortality, and posttransplant outcomes in light of recent modifications to the exception point allocation policy for HCC candidates. The sample size of our study was large enough to provide sufficient power in the stratified analysis. Other strengths include accounting for the dynamic nature of MELD and the use of competing risks methods to elucidate the relationship between allocation priority and waitlist mortality. In conclusion, our findings suggest that allocation of DDLT remained higher for HCC candidates than non-HCC candidates with the same calculated MELD 2 years after the implementation of the 2015 revised liver allocation policy; although the magnitude of the difference in DDLT rate was attenuated in the postpolicy era. Despite a significant reduction of the allocation advantage for HCC candidates following the 2015 policy change, waitlist mortality/dropout remained comparable for both HCC and non-HCC candidates. The revised HCC exception policy seems to have achieved its goal of establishing equity in waitlist mortality/dropout for HCC and non-HCC candidates. However, it will be important to carefully monitor waitlist and posttransplant outcomes as the length of follow-up time since policy implementation increases. Funding for this study was provided by the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK): grant numbers K24DK101828 (PI: Segev), K01DK101677 (PI: Massie) and K01DK114388 (PI: Henderson); and the National Institute on Aging: F32AG053025 (PI: Haugen). The analyses described here are the responsibility of the authors alone and do not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the U.S. government. The data reported here have been supplied by the Minneapolis Medical Research Foundation (MMRF) as the contractor for the Scientific Registry of Transplant Recipients (SRTR). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the SRTR or the U.S. government. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation." @default.
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