HTA190 Discrete Event Simulation Model for Real-World Restricted Treatment Policies in End-Stage Heart Failure

Objectives: There is a shortage of transplantable donor hearts to treat end-stage
heart failure (ESHF). Further, high-cost left ventricular assist devices (LVADs) are
restricted to last resort therapy. Two discrete event simulation (DES) models were
developed; DES with queuing to represent restricted allocation of LVADs and the
waiting list for heart transplants (HTx); and DES no queuing to cross-validate with a
Markov model. The cost-effectiveness of various policies was assessed: ESHF policy
without LVADs (Policy A); the current ESHF policy with restricted LVADs (Policy B),
less restricted LVADs policy (Policy C) and less restricted HTx (Policy D).
Methods: The DES models were built using AnyLogic®. The DES model with queuing
included a ‘match block’ which pairs a stream of waitlisted patients and the
matching donor organ by blood type and weight. Time-to-event probabilities and
costs were obtained from an observational dataset at St. Vincent’s Hospital Sydney,
Australia. The incremental cost per quality-adjusted life-year was calculated over a
20-year time horizon. Results: Comparing the two DES models, there was a lower
proportion of patients receiving an LVAD under Policy B (39% DES with queuing vs.
85% DES no queuing). The predicted proportion of HTx differed between DES with
queuing (Policy D. Policy A . Policy B . Policy C), compared to DES no queuing
(Policy B=Policy C . Policy D . Policy A). Patients spent more time post-HTx in the
DES with queuing model than the other models. The DES models with queuing
produced more favourable ICERs compared to the Markov model. Conclusions: The
DES with queuing model captured competition for LVADs and HTx and the match
between the patient and the donor organ. The outputs (utilisation and waiting time)
from the DES with queuing model better reflected real-world data than the DES no
queuing or Markov model.