Search

Background: Efficient and scalable solutions are needed to identify patients who qualify for germline cancer genetic testing. We evaluated the clinical validity of a brief, patient-administered hereditary cancer risk assessment digital tool programmed to assess if patients meet criteria for germline genetic testing, based on personal and family history, and in line with national guidelines., Methods: We applied the tool to cases seen in a nationwide telehealth genetic counseling practice. Validity of the tool was evaluated by comparing the tool's assessment to that of the genetic counselor who saw the patient. Patients' histories were extracted from genetic counselor-collected pedigrees and input into the tool by the research team to model how a patient would complete the tool. We also validated the tool's assessment of which specific aspects of the personal and family history met criteria for genetic testing. Descriptive statistics were used., Results: Of the 152 cases (80% female, mean age 52.3), 56% had a personal history of cancer and 66% met genetic testing criteria. The tool and genetic counselor agreed in 96% of cases. Most disagreements (4/6; 67%) occurred because the genetic counselor's assessment relied on details the tool was not programmed to collect since patients typically don't have access to the relevant information (pathology details, risk models). We also found complete agreement between the tool and research team on which specific aspects of the patient's history met criteria for genetic testing., Conclusion: We observed a high level of agreement with genetic counselor assessments, affirming the tool's clinical validity in identifying individuals for hereditary cancer predisposition testing and its potential for increasing access to hereditary cancer risk assessment., (© 2024. The Author(s).)

Computational wear prediction is an attractive concept for evaluating new total knee replacement designs prior to physical testing and implementation. An important hurdle to such technology is the lack of in vivo contact pressure predictions. To address this issue, this study evaluates a computationally efficient simulation approach that combines the advantages of rigid and deformable body modeling. The hybrid method uses rigid body dynamics to predict body positions and orientations and elastic foundation theory to predict contact pressures between general three-dimensional surfaces. To evaluate the method, we performed static pressure experiments with a commercial knee implant in neutral alignment using flexion angles of 0, 30, 60, and 90 degrees and loads of 750, 1500, 2250, and 3000N. Using manufacturer CAD geometry for the same implant, an elastic foundation model with linear or nonlinear polyethylene material properties was implemented within a commercial multibody dynamics software program. The model's ability to predict experimental peak and average contact pressures simultaneously was evaluated by performing dynamic simulations to find the static configuration. Both the linear and nonlinear material models predicted the average contact pressure data well, while only the linear material model could simultaneously predict the trends in the peak contact pressure data. This novel modeling approach is sufficiently fast and accurate to be used in design sensitivity and optimization studies of knee implant mechanics and ultimately wear.