Anatomic Factors Associated With Osteochondral Allograft Matching for Trochlear Cartilage Defects: A Computer-Simulation Study. Issue 13 (November 2022)

• Record Type: Journal Article
• Authors: Dandu, Navya; Horner, Nolan S.; Trasolini, Nicholas A.; Hevesi, Mario; Cregar, William M.; Inoue, Nozomu; Yanke, Adam B.
• Is Part Of: American journal of sports medicine. Volume 50:Issue 13(2022)
• Page Start: 3571
• Page End: 3578
• Publication Date: 2022-11
• Subjects: cartilage -- knee -- osteochondral allograft -- patellofemoral -- trochlea; Sports medicine -- Periodicals; Sports injuries -- Periodicals; Orthopedic surgery -- Periodicals; 617.102705
• DOI: 10.1177/03635465221121586
• Languages: English
• ISSNs: 0363-5465

Abstract

Background: Articular step-off between the donor and recipient in osteochondral allograft transplant has been shown to alter contact pressures. Currently, commercial allograft donor selection is primarily based on simple anatomic parameters such as trochlear length, trochlear width, and tibial width. Purpose: To identify anatomic factors associated with optimal graft matching by using a 3-dimensional simulation model. Study Design: Descriptive laboratory study. Methods: Computed tomography images of 10 cadaveric trochlear specimens were obtained to generate 3-dimensional models. Circular defects were created virtually in the recipient trochleae at both superolateral (18.0 mm and 22.5 mm) and central (18.0 mm, 22.5 mm, 30.0 mm) locations. The donor models were virtually projected onto the defect models, and the most optimal graft from any location of the donor specimen was selected. Cartilage incongruity, subchondral bone incongruity, and peripheral articular step-off were calculated for each graft-defect combination. Linear regression models were generated to identify predictors of incongruity, step-off, and the effect of sulcus and sagittal angle mismatch. Akaike information criterion–driven stepwise regression models were generated to identify multivariate predictors. Results: Ideal matches were found for 100% of superolateral defects but for only 15% to 53% of central defects, depending on the defect size. Multivariate stepwise regression identified laterality (odds ratio [OR], 0.54; P = .081), sulcus angle (OR, 0.79; P < .001), sagittal angle (OR, 0.83; P = .001), lateral radius of curvature (OR, 0.81; P < .001), and medial facet width (OR, 0.86; P = .155) as predictors of ideal graft matching. In central defects with proud grafts, increasing sagittal angle and sulcus angle resulted in significantly ( P < .001) increased articular step-off, which became sequentially larger with defect size. Conclusion: Sagittal angle, sulcus angle, and lateral radius of curvature mismatch should be used to determine optimal donor allografts, especially in the setting of large (30-mm) central defects. Increasing sulcus angle and sagittal angle mismatch correlated with increasing step-off in proud grafts, whereas sulcus angle and sagittal angle inconsistently correlated with step-off in recessed grafts. Clinical Relevance: Additional descriptive trochlear measurements should be incorporated into the algorithm for donor selection. These findings can be used to identify acceptable mismatch parameters.