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.

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