Development and Validation of a Model to Predict Long‐Term Survival After Liver Transplantation

Abstract

Patients are prioritized for liver transplantation (LT) under an “urgency‐based” system using the Model for End‐Stage Liver Disease score. This system focuses solely on waitlist mortality, without considerations of posttransplant morbidity, mortality, and health care use. We sought to develop and internally validate a continuous posttransplant risk score during 5‐year and 10‐year time horizons. This retrospective cohort study used national registry data of adult deceased donor LT (DDLT) recipients with ≥90 days of pretransplant waiting time from February 27, 2002 to December 31, 2018. We fit Cox regression models at 5 and 10 years to estimate beta coefficients for a risk score using manual variable selection and calculated the absolute predicted survival time. Among 21,103 adult DDLT recipients, 11 variables were selected for the final model. The area under the curves at 5 and 10 years were 0.63 (95% confidence interval [CI], 0.60‐0.66) and 0.67 (95% CI, 0.64‐0.70), respectively. The group with the highest (“best”) scores had 5‐year and 10‐year survivals of 89.4% and 85.4%, respectively, compared with 45.9% and 22.2% for those with the lowest (“worst”) scores. Our score was significantly better at predicting long‐term survival compared with the existing scores. We developed and validated a risk score using nearly 17 years of data to prioritize patients with end‐stage liver disease based on projected posttransplant survival. This score can serve as the building block by which the transplant field can change the entire approach to prioritizing patients to an approach that is based on considerations of maximizing benefits (ie, survival benefit–based allocation) rather than simply waitlist mortality.

Accurate long‐term prediction of death for patients with cirrhosis

“…Renal function was modeled as (1) continuous estimated glomerular filtration rate (eGFR), using the Modification of Diet in Renal Disease-4 equation [31][32][33] ; (2) acute kidney injury (creatinine increase of ≥0.3 mg/dl in ≤48 h and/or increase of >50% in 7 days) [34][35][36] ; and (3) chronic kidney disease (Kidney Disease: Improving Global Outcomes classification of eGFR < 60 ml/min/1.73 m 2 at every time point over a 90-day period). [31][32][33] These methods allowed us to quantify the degree of renal impairment using a continuous value (eGFR) and, when abnormal, whether it was acute, chronic, or acute on chronic kidney injury. Complications of cirrhosis at diagnosis were ascertained using validated methods.…”

Building a Utility-based Liver Allocation Model in Preparation for Continuous Distribution

“…Many studies that modeled recipient posttransplant outcomes focused more on short-term posttransplant survival (eg, 1 y). [21][22][23] A more recent study 24 modeled long-term posttransplant survival at 5 and 10 y, which we agree is a more appropriate measure to incorporate into a continuous distribution model. To our knowledge, our study is the first to consider the interaction between donors and recipients.…”

“…There are multiple ways in which utility can be incorporated into a continuous distribution model along with other factors such as medical urgency, placement efficiency, candidate biology, and patient access. First, a calculation of a recipient’s long-term graft survival, such as that proposed by Goldberg et al 24 could be utilized. This would likely be the easiest way but would be a generalization of overall survival and would not incorporate the particular donor-recipient matching that we have shown is important.…”

Impact of Race‐Adjusted Glomerular Filtration Rate Estimation on Eligibility for Simultaneous Liver‐Kidney Transplantation

“…The latter criterion was imposed given the need for at least 90 days of data to reliably establish CKD in this cohort, per current OPTN policy, and is consistent with the methods in recent literature. ( 7 )…”