Fracture Prediction of Cardiac Lead Medical Devices Using Bayesian Networks

Abstract

A novel Bayesian Network methodology has been developed to enable the prediction of fatigue fracture of cardiac lead medical devices. The methodology integrates in-vivo measurements of device loading, patient demographics, patient activity level, in-vitro measurements of fatigue strength, and cumulative damage modeling techniques. Many plausible combinations of these variables can be simulated within a Bayesian Network framework to generate a family of fatigue fracture survival curves, enabling sensitivity analyses and the construction of confidence bounds on survival.