Implications of the uncertainty of postoperative functional parameters for the preoperative planning of total hip arthroplasty

The functional parameters pelvic tilt (PT) and hip joint force (HJF) are required to calculate patient-specific target zones based on the range of motion (ROM) and implant loading for preoperative planning of total hip arthroplasty (THA). Both functional parameters may change after THA. The preoperative prediction of the postoperative PT and HJF is associated with a specific amount of uncertainty. The prediction uncertainty has to be considered in the preoperative planning process to avoid a suboptimal implantation. So far, very little attention has been paid to the necessary reduction of patient-specific target zones by the prediction uncertainties of postoperative functional parameters. Prediction models for the postoperative PT in standing position and for the HJF during one-leg stance as a surrogate for the peak force phase during level walking were used to quantify the reduction of the ROM- and load-based target zones of 196 Japanese THA patients. The prediction uncertainty was about 14 degrees for the postoperative standing PT and ranged from 17% body weight to 37% body weight for the components of the HJF. On average, the prosthetic ROM-based target zone had to be significantly reduced by 43% and the load-based target zone by 39%. This led to a median reduction of the combined prosthetic ROM- and load-based target zone of 96%. The study sharpens the awareness for the substantial reduction of ROM- and load-based target zones by prediction uncertainties of the postoperative PT and HJF and highlights the importance of further research to improve prediction models for both functional parameters.

Automated summary

The prediction uncertainties of postoperative pelvic tilt and hip joint force have a significant impact on reducing the patient-specific target zones in preoperative planning of total hip arthroplasty. This study highlights the substantial reduction of ROM- and load-based target zones and emphasizes the importance of further research to improve prediction models for both functional parameters.