Bayesian modeling of pretransplant variables accurately predicts kidney graft survival.

Introduction: Machine learning can enable the development of predictive models that incorporate multiple variables for a systems approach to organ allocation. We explored the principle of Bayesian Belief Network (BBN) to determine whether a predictive model of graft survival can be derived using pretransplant variables. Our hypothesis was that pretransplant donor and recipient variables, when considered together as a network, add incremental value to the classification of graft survival.

Sixty-four slice multidetector computed tomographic angiography in the evaluation of vascular trauma.

Background: Artifacts produced by metallic fragments and orthopedic hardware limit the usefulness of conventional computed tomography in many military trauma patients. Contemporary literature suggests that multidetector computed tomographic angiography (MDCTA) by resolving these limitations may provide a useful noninvasive alternative to invasive arteriography. The objective of this study is to review the utility of MDCTA in the evaluation of recent combat casualties with vascular injuries.