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.
The rising costs of clinical trials for medical devices in recent years has led to an increased interest in so-called in silico clinical trials, where simulation results are used to supplement or to replace those obtained from human patients. Here we present a framework for executing such a trial. This framework relies heavily on ideas already developed for model verification, validation, and uncertainty quantification. The framework uses results from an initial cohort of human patients as model validation data, recognizing that the best model credibility evidence usually comes from real patients. The validation exercise leads to an assessment of the model’s suitability based on pre-defined acceptance criteria. If the model meets these criteria, then no additional human patients are required and the study endpoints that can be addressed using the model are met using the simulation results. Conversely, if the model is found to be inadequate, it is abandoned, and the clinical study continues using only human patients in a second cohort. Compared to other frameworks described in the literature based on Bayesian methods, this approach follows a strict model build-validate-predict structure. It can handle epistemic uncertainties in the model inputs, which is a common trait of models of biomedical systems. Another idea discussed here is that the outputs of engineering models rarely coincide with measures that are the basis for clinical endpoints. This manuscript discusses how the link between the model and clinical measure can be established during the trial.
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