Development and validation of primary graft dysfunction predictive algorithm for lung transplant candidates.

ABSTRACT:

Background

Primary graft dysfunction (PGD) is the leading cause of early morbidity and mortality after lung transplantation. Accurate prediction of PGD risk could inform donor approaches and perioperative care planning. We sought to develop a clinically useful, generalizable PGD prediction model to aid in transplant decision-making.

Methods

We derived a predictive model in a prospective cohort study of subjects from 2012 to 2018, followed by a single-center external validation. We used regularized (lasso) logistic regression to evaluate the predictive ability of clinically available PGD predictors and developed a user interface for clinical application. Using decision curve analysis, we quantified the net benefit of the model across a range of PGD risk thresholds and assesse

SUBMITTER: Diamond JM 

PROVIDER: S-EPMC10947904 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature