Speeding heart transplant profile

Lund University in Sweden has used MATLAB, Neural Network Toolbox, Parallel Computing Toolbox and Distributed Computing Server to improve long-term survival rates for heart transplant recipients by identifying optimal recipient and donor matches.

Researchers at the university and Skåne University Hospital explored the complex relationships among multiple transplant variables, including the weight, gender, age and blood type of both donor and recipient, and the time during a transplant when there is no blood flow to the heart.

Analysing the six variables requires the simulation of 30,000 different combinations and simulating all these combinations for 50,000 patients took weeks using an open-source software package that proved to be unstable and inaccurate.

To address the speed and reliability challenges, the researchers used MATLAB and Neural Network Toolbox to develop predictive artificial neural network models. The models were built with donor and recipient data from two global databases - the International Society for Heart and Lung Transplantation registry and the Nordic Thoracic Transplantation Database.

Lund researchers used Parallel Computing Toolbox to program parallel applications and MATLAB Distributed Computing Server to scale those applications to a cluster to accelerate the simulation of more than 200,000 ANN configurations.

They then evaluated the results to find the best-performing configuration. The models showed that the prospective five-year survival rate for the ANN-selected patients was 5-10% higher than those matched with the criteria physicians use today.