Deep Learning model to maximize lifespan after liver transpl
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Researchers from University Health Network have developed and validated an innovative deep learning model to predict a patient's long-term outcome after receiving a liver transplant. The study, published in Lancet Digital Health, shows it can significantly improve long-term survival and quality of life for liver transplant recipients.

Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. In this retrospective analysis, researchers aimed to assess the ability of deep learning algorithms of longitudinal data from two prospective cohorts to predict complications resulting in death after liver transplantation over multiple timeframes, compared with logistic regression models.

In this machine learning analysis, model development was done on a set of 42146 liver transplant recipients from the Scientific Registry of Transplant Recipients (SRTR) in the USA. Transferability of the model was further evaluated by fine-tuning on a dataset from the University Health Network (UHN) in Canada. The primary outcome was cause of death, as recorded in the databases, due to cardiovascular causes, infection, graft failure, or cancer, within 1 year and 5 years of each follow-up examination after transplantation. They compared the performance of four deep learning models against logistic regression, assessing performance using the area under the receiver operating characteristic curve (AUROC).

--In both datasets, deep learning models outperformed logistic regression, with the Transformer model achieving the highest AUROCs in both datasets.

--The AUROC for the Transformer model across all outcomes in the SRTR dataset was 0·804 for 1-year predictions and 0·733 for 5-year predictions.

--In the UHN dataset, the AUROC for the top-performing deep learning model was 0·807 for 1-year predictions and 0·722 for 5-year predictions.

--AUROCs ranged from 0·695 for prediction of death from infection within 5 years to 0·859 for prediction of death by graft failure within 1 year.

Deep learning algorithms can incorporate longitudinal information to continuously predict long-term outcomes after liver transplantation, outperforming logistic regression models. Physicians could use these algorithms at routine follow-up visits to identify liver transplant recipients at risk for adverse outcomes and prevent these complications by modifying management based on ranked features.

Lancet Digital Health