Surgical Simulations Based on Limited Quantitative Data: Understanding How Musculoskeletal Models Can Be Used to...

The utility of biomechanical models and simulations to examine clinical problems is currently limited by the need for extensive amounts of experimental data describing how a given procedure or disease affects the musculoskeletal system. Methods capable of predicting how individual biomechanical parameters are altered by surgery are necessary for the efficient development of surgical simulations. In this study, we evaluate to what extent models based on limited amounts of quantitative data can be used to predict how surgery influences muscle moment arms, a critical parameter that defines how muscle force is transformed into joint torque. We specifically examine proximal row carpectomy and scaphoid-excision four-corner fusion, two common surgeries to treat wrist osteoarthritis. Using models of these surgeries, which are based on limited data and many assumptions, we perform simulations to formulate a hypothesis regarding how these wrist surgeries influence muscle moment arms. Importantly, the hypothesis is based on analysis of only the primary wrist muscles. We then test the simulation-based hypothesis using a cadaveric experiment that measures moment arms of both the primary wrist and extrinsic thumb muscles. The measured moment arms of the primary wrist muscles are used to verify the hypothesis, while those of the extrinsic thumb muscles are used as cross-validation to test whether the hypothesis is generalizable. The moment arms estimated by the models and measured in the cadaveric experiment both indicate that a critical difference between the surgeries is how they alter radial-ulnar deviation versus flexion-extension moment arms at the wrist. Thus, our results demonstrate that models based on limited quantitative data can provide novel insights. This work also highlights that synergistically utilizing simulation and experimental methods can aid the design of experiments and make it possible to test the predictive limits of current computer simulation techniques.