Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support.

ABSTRACT: Background: Existing prediction models for post-transplant mortality in patients bridged to heart transplantation with temporary mechanical circulatory support (tMCS) perform poorly. A more reliable model would allow clinicians to provide better pre-operative risk assessment and develop more targeted therapies for high-risk patients. Methods: We identified adult patients in the United Network for Organ Sharing database undergoing isolated heart transplantation between 01/2009 and 12/2017 who were supported with tMCS at the time of transplant. We constructed a machine learning model using extreme gradient boosting (XGBoost) with a 70:30 train:test split to predict 1-year post-operative mortality. All pre-transplant variables available in the UNOS database were included to trai

SUBMITTER: Shou BL 

PROVIDER: S-EPMC9500687 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature