Abstract
Objectives:
As a surgical treatment option for cartilage defects in the knee, osteochondral allograft (OCA) transplantation reconstructs the damaged area with a fully formed articular hyaline cartilage/osseous unit. For patients undergoing this procedure, topography matching between the donor and recipient sites is essential to limit premature wear of the OCA. To help prevent graft failure, the surgeon must precisely reconstruct the original anatomic curvature of the articular surface. For this to be successful, the graft needs to be chosen and harvested from the correct location on the donor femoral condyle, and then placed correctly on the recipient condyle during surgery to minimize step-off relative to the surrounding cartilage. Currently, the process of donor and recipient graft matching as well as intra-operative techniques to match these surfaces are fairly rudimentary and require significant experience from the treating surgeon. The purpose of this study is to evaluate a novel topography matching technique for distal femoral condyle OCA transplantation using three-dimensional (3D) laser scanning to create 3D printed patient-specific instrumentation in a human cadaveric model.
Methods:
Twelve cadaveric knees were dissected to isolate the distal femoral condyles (DFC) with cartilage intact and then 3D laser scanned. An 18-mm circular osteochondral recipient defect was virtually created on the medial femoral condyle (MFC) and the position and orientation of the best topography matched osteochondral graft from a paired donor lateral femoral condyle (LFC) was determined using an in-silico analysis algorithm minimizing articular step-off (Figure 1). Donor (LFC) and recipient (MFC) 3D-printed patient-specific guides were created based on 3D reconstructions of the scanned condyles. Using the guides, OCAs were harvested from the previously defined best-match position on the LFC and transplanted to the reamed recipient defect site (MFC) (Figure 2). The post-OCA recipient condyles were laser scanned. The 360-degree articular step-off and cartilage mismatch topography were calculated by a point-to-surface distance calculation algorithm and compared to the in-silico result.
Results:
The in-silico cartilage step-off and graft perimeter mismatch for the virtual OCA transplant was 0.073 ± 0.029 mm (range: 0.005-0.113 mm) and 0.166 ± 0.039 mm (range: 0.120-0.243 mm), respectively. Comparatively, our cadaveric specimens post-implant had significantly larger step-off differences (0.173 ± 0.085 mm, range: 0.082-0.399 mm, P = 0.001) but equivalent surface topography matching (0.181 ± 0.080 mm, range: 0.087-0.396 mm, P = 0.678).
Conclusions:
This novel technique for OCA articular step-off and topography matching demonstrated the ability to optimize cartilage topography matching for LFC to MFC transplantation. Despite a larger mean step-off in the cadaveric specimens compared to the ideal in-silico model, our study demonstrated substantially lower mismatch values compared to previous orthopedic literature evaluating LFC to MFC transplantation that reported means of 0.48 mm for articular step-off (range: 0.335-0.738 mm) and 0.63 mm for surface area mismatch (range: 0.349-1.461 mm). It should be noted that clinically, surgeons performing OCA for the femoral condyles will preferentially select the ipsilateral condyle from the donor to take a plug for the recipient, as this will generally allow for optimal surface matching. Our results show better mismatch values than prior work using this contralateral technique, and even the maximum step-off distance in our cadaveric model fell well below the 0.5 cm –1.0 cm maximum step-off values assigned to be clinically significant by prior studies evaluating ipsilateral OCA transplantations. Using this novel technique in a model performing MFC to MFC transplantation would likely yield further enhanced results due to improved radii of curvature matching. Improving upon topography matched graft implantation for focal chondral defects of the knee in young patients will improve surface matching and associated long-term outcomes. Additionally, efficient selection of the allograft using patient-specific guides would also improve availability of the limited allograft sources and reduce surgical time.
