Prediction of Polyethylene Wear Rates from Gait Biomechanics and Implant Positioning in Total Hip Replacement

Abstract

Background: Patient-specific gait and surgical variables are known to play an important role in wear of total hip replacements (THR). However a rigorous model, capable of predicting wear rate based on a comprehensive set of subject-specific gait and component-positioning variables, has to our knowledge, not been reported.

Questions/purpose: (1) Are there any differences between patients with high, moderate, and low wear rate in terms of gait and/or positioning variables? (2) Can we design a model to predict the wear rate based on gait and positioning variables? (3) Which group of wear factors (gait or positioning) contributes more to the wear rate?

Patients and methods: Data on patients undergoing primary unilateral THR who performed a postoperative gait test were screened for inclusion. We included patients with a 28-mm metal head and a hip cup made of noncrosslinked polyethylene (GUR 415 and 1050) from a single manufacturer (Zimmer, Inc). To calculate wear rates from radiographs, inclusion called for patients with a series of standing radiographs taken more than 1 year after surgery. Further, exclusion criteria were established to obtain reasonably reliable and homogeneous wear readings. Seventy-three (83% of included) patients met all criteria, and the final dataset consisted of 43 males and 30 females, 69 ± 10 years old, with a BMI of 27.3 ± 4.7 kg/m². Wear rates of these patients were determined based on the relative displacement of the femoral head with regard to the cup using a validated computer-assisted X-ray wear-analysis suite. Three groups with low (< 0.1 mm/year), moderate (0.1 to 0.2 mm/year), and high (> 0.2 mm/year) wear were established. Wear prediction followed a two-step process: (1) linear discriminant analysis to estimate the level of wear (low, moderate, or high), and (2) multiple linear and nonlinear regression modeling to predict the exact wear rate from gait and implant-positioning variables for each level of wear.

Results: There were no group differences for positioning and gait suggesting that wear differences are caused by a combination of wear factors rather than single variables. The linear discriminant analysis model correctly predicted the level of wear in 80% of patients with low wear, 87% of subjects with moderate wear, and 73% of subjects with high wear based on a combination of gait and positioning variables. For every wear level, multiple linear and nonlinear regression showed strong associations between gait biomechanics, implant positioning, and wear rate, with the nonlinear model having a higher prediction accuracy. Flexion-extension ROM and hip moments in the sagittal and transverse planes explained 42% to 60% of wear rate while positioning factors, (such as cup medialization and cup inclination angle) explained only 10% to 33%.

Conclusion: Patient-specific wear rates are associated with patients' gait patterns. Gait pattern has a greater influence on wear than component positioning for traditional metal-on-polyethylene bearings.

Clinical relevance: The consideration of individual gait bears potential to further reduce implant wear in THR. In the future, a predictive wear model may identify individual, modifiable wear factors for modern materials.

Fig. 1A–D

Component positioning factors consisted of the (A) abductor lever arm (1), horizontal offset (2), and cup medialization (3), (B) vertical offset (4), (C) cup anteversion (5), and (D) cup inclination (6).

Fig. 2

The proposed three-layer artificial neural network had 14 input nodes including hip range of motion (HROM), hip flexion moment (HMXFLEX), hip extension moment (HMXEXT), hip abduction moment (HMYABD), hip adduction moment (HMYADD), hip hip external rotation (HMZEXT), internal rotation (HMZINT), cup medialization (C-MED), cup inclination (C-INCL), cup anteversion (C-ANTE), horizontal offset (H-SET), vertical offset (V-SET), abductor lever arm (ABD-L), and subject age (Age).

Fig. 3

The association between time in situ and wear rate represented a weak correlation (adjusted R² = 0.09).

Fig. 4A–C

Class-specific linear regression predictions of linear wear rate compared with radiographic wear assessment for patients with (A) low wear, (B) moderate wear, and (C) high wear are shown. MLR = multiple linear regression.

Fig. 5A–C

Class-specific artificial neural network (ANN) predictions of linear wear rate compared with radiographic wear assessment for patients with (A) low wear, (B) moderate wear, and (C) high wear are shown.