Abstract [from journal]
Background & aims: Liver transplant priority in the US and Europe follows the 'sickest-first 'principle. However, for patients with hepatocellular carcinoma (HCC), priority is based on binary tumor criteria (e.g., Milan) to expedite transplant for patients with 'acceptable' post-transplant outcomes. Newer risk scores developed to overcome limitations of these binary criteria (e.g., Metroticket, HALT-HCC) are insufficient to be used for waitlist priority as they focus solely on HCC-related pre-transplant variables. We sought to develop a risk score to predict post-transplant survival for HCC patients using HCC- and non-HCC related variables.
Methods: Retrospective cohort study using national registry data of adult deceased-donor liver transplant (DDLT) recipients with HCC from 2/27/02-12/31/18. We fit Cox regression models focused on 5- and 10-year survival to estimate beta coefficients for a risk score using manual variable selection and calculated absolute predicted survival time and compared it to available risk scores.
Results: Among 6,502 adult HCC LT recipients, 11 variables were selected in the final model. The AUC's at 5- and 10-years were: 0.62, 95% CI: 0.57-0.67 and 0.65, 95% CI: 0.58-0.72, which was not statistically significantly different than the Metroticket and HALT-HCC scores. The LiTES-HCC score was able to discriminate patients based on post-transplant survival among those meeting Milan and UCSF.
Conclusion: We developed and validated a risk score to predict post-transplant survival for patients HCC. By including HCC- and non-HCC related variables (e.g., age, chronic kidney disease), this score could allow transplant professionals to prioritize patients with HCC in terms of predicted survival. In the future, this score could be integrated into survival benefit-based models to lead to meaningful improvements in life-years at the population-level.
Lay summary: We created a risk score to predict how long patients with liver cancer will live if they get a liver transplant. In the future, this could be used to decide which waitlisted patients should get the next transplant.