Speaker
Description
Heart transplantation is widely regarded as the gold standard for the treatment of end-stage heart failure. However, shortages of donor hearts necessitate the implementation of waiting lists and allocation algorithms. The German Transplantation Law stipulates the allocation of donor hearts based on urgency of and the benefit from a transplantation. This can be summarized into a single score, the Cardiac Allocation Score (CAS). The two components of the CAS are: firstly, urgency, measured by the life expectancy of a patient remaining on the waiting list; and secondly, benefit, the difference in life expectancy after a transplantation compared to remaining on the waiting list. Hence, in order to calculate an individual’s CAS, two counterfactual predictions are necessary, one for Restricted Mean Survival Time within 1-year (RMST1) if the individual would never receive a heart transplantation and one for RMST1 if the individual would receive the heart transplantation.
The development of these two prediction models gives rise to methodological challenges: 1) In predicting the survival on the waitlist, complete observation is obscured by transplantation. The prevailing allocation system in Germany and the Eurotransplant (ET) region primarily relies on urgency and waiting time and thus, is informative for censoring due to transplantation. 2) The positive outcomes associated with heart transplantation are masked during the initial postoperative period, which is largely attributable to the significant impact of the surgical intervention.
Additionally, varying data vintage and the requirement for instant predictions are further challenges. Data vintage means that urgency (life expectancy when remaining on waiting list) must be computed using the data of all persons on the waiting list that is available when a donor heart becomes available, but that data may have been updated at different time points in the past. Moreover, predictions have to be provided instantly, but RMST1 often require numerical integration.
Several approaches may be useful to consider when training the models, including inverse probability of censoring weighting, parametric accelerated failure time models with various distributions, direct estimation of RMST1 with pseudo-values, landmarking etc. In this talk, we will discuss advantages and disadvantages of possible analysis strategies resulting from combining these elements. Furthermore, we will report on simulations to compare their performances. Overall, our work aims to identify modelling strategies that can most reliably support CAS estimation—and thereby strengthen the fairness and effectiveness of heart allocation systems.
Schramm, R., Gummert, J.F. Herztransplantation. Chirurgie 95, 101–107 (2024).
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