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Reliability-Based Optimal Design of Orthopedic Implants, 18-9927

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Principal Investigator
Daniel P. Nicolella

Inclusive Dates: 10/01/95 - Current

Background - The long-term reliability of today's orthopedic implants is a major concern. As a result, much of today's research is aimed at improving the overall reliability (or some specific aspect of reliability) of orthopedic implants. During current analyses, the actual probability of failure is not computed per se, rather a relative measure of performance is inferred. Therefore, the goal of this program is to improve the long-term reliability of orthopedic implants by using a design optimization approach that integrates probabilistic analysis methods and systematically accounts for uncertainty and randomness to calculate a measure of implant reliability.

Approach - A numerical shape optimization of the femoral component of a total hip replacement was performed using engineering design optimization techniques that incorporate a measure of implant reliability. The implant reliability was calculated using advanced probabilistic methods developed at the Institute (NESSUS™). Cemented femoral components were addressed specifically in this work, but the techniques developed can be modified to investigate non-cemented femoral components as well. A previously developed probabilistic finite element model (ABAQUS™) was modified to model the cement mantle and the implant-cement and cement-bone interfaces and then integrate the model with a numerical optimal implant design framework. The femur-implant finite element model input variables included trabecular and cortical bone properties, joint loading, cement properties, interface descriptions, implant material properties, and initial implant geometry. Based on existing literature and expert opinion, reasonable assumptions were made to develop input probability distributions. The NESSUS™-ABAQUS™ interface was used with the ABAQUSmodel of the femur-implant system. Reliability was calculated based on performance functions involving the cement, implant-cement interface, and bone-cement interface strength characteristics. From this analysis, sensitivities of the optimal implant design reliability to uncertainties in the system were quantified.

Accomplishments - Using a general computational framework that couples the NESSUS probabilistic analysis code with a general-purpose design optimization program, the reliability of a cemented femoral orthopedic implant was investigated. This framework allows the existing engineering optimal design models to be incorporated into a reliability-based optimal design methodology. In cemented orthopedic implants, failures are typically initiated by deterioration of the cement mantle or the implant-cement interface or both. Using failure criteria to monitor the stress in the bone-cement mantle and the implant-cement interface, the probability of failure of an implant was calculated. The probability that stress caused by forces generated at the hip during walking would reach a level that initiates failure of the cement mantle was calculated to be 0.42 or 42 percent. The shape of the implant was then optimized to minimize the probability of failure of the implant. Although the implant design optimization analysis resulted in a 15-percent decrease in stress in the cement mantle, the reduction in stress resulted in only a 2-percent decrease in the probability of failure to 40 percent. Additionally, the resulting probabilistic sensitivities indicate the importance of understanding joint loading during normal, everyday activities, one of the least understood biomechanical parameters required for implant design. The implication of this analysis is that optimal design methods may result in implant designs that satisfy specific deterministic design constraints and performance goals, but the reliability of the implant is not necessarily improved. Advanced design and analysis methods are required to quantify the effects of uncertainties or lack of knowledge of crucial information (such as applied joint loads) and variability due to differences within and between patient populations and to incorporate this uncertainty into the design process.

Materials Research and Structural Mechanics Program
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