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Computational models that incorporate human anatomy, tissue biomechanics, and experimental measurements from animals or cadavers to predict medical device performance have proven useful. Since implant choices made by clinicians and biological tissue properties can vary widely across patients, these models tend to suffer from a fundamental lack of information about such variations that impact the analysis. To demonstrate a new means of overcoming such paucity of input data, the authors focused on a tractable device concern (that of temporary continence care lead movement) and allowed input properties to vary within the bounds of experiment to generate many simulations that ultimately predicted device performance. The computational model results were then compared with experimental results to build confidence in the predictions. The results suggest that a new method considering intervals of poorly defined and highly variable biomechanical and structural modeling inputs can faithfully predict device mechanics as measured in a cadaver model. Moreover, both model and experiment suggest that a new basic evaluation lead can provide more reliable fixation compared to the predicate device.

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