Osteochondral defects in the human knee: influence of defect size on cartilage rim stress and load redistribution to surrounding cartilage.

PURPOSE To determine the influence of osteochondral defect size on defect rim stress concentration, peak rim stress, and load redistribution to adjacent cartilage over the weightbearing area of the medial and lateral femoral condyles in the human knee. METHODS Eight fresh-frozen cadaveric knees were mounted at 30 degrees of flexion in a materials testing machine. Digital electronic pressure sensors were placed in the medial and lateral compartments of the knee. Each intact knee was first loaded to 700 N and held for 5 seconds. Dynamic pressure readings were recorded throughout the loading and holding phases. Loading was repeated over circular osteochondral defects (5, 8, 10, 12, 14, 16, 18, and 20 mm) in the 30 degrees weightbearing area of the medial and lateral femoral condyles. RESULTS Stress concentration around the rims of defects 8 mm and smaller was not demonstrated, and pressure distribution in this size range was dominated by the menisci. For defects 10 mm and greater, distribution of peak pressures followed the rim of the defect with a mean distance from the rim of 2.2 mm on the medial condyle and 3.2 mm on the lateral condyle. An analysis of variance with Bonferroni correction revealed a statistically significant trend of increasing radius of peak pressure as defect size increased for defects from 10 to 20 mm (P = .0011). Peak rim pressure values did not increase significantly as defects were enlarged from 10 to 20 mm. Load redistribution during the holding phase was also observed. CONCLUSIONS Rim stress concentration was demonstrated for osteochondral defects 10 mm and greater in size. This altered load distribution has important implications relating to the long-term integrity of cartilage adjacent to osteochondral defects in the human knee. Although the decision to treat osteochondral lesions is certainly multifactorial, a size threshold of 10 mm, based on biomechanical data, may be a useful adjunct to guide clinical decision making.