Anthropometric and Skeletal Parameters Predict 2-Strand Semitendinosus Tendon Size in Double-Bundle Anterior Cruciate Ligament Reconstruction

BACKGROUND Few studies have examined whether skeletal parameters predict hamstring graft size during anterior cruciate ligament reconstruction (ACLR). PURPOSE/HYPOTHESIS The purpose of this study was to examine whether preoperative anthropometric and radiographic skeletal parameters could predict hamstring graft size during ACLR. We hypothesized that both anthropometric and skeletal parameters can be used to predict graft size in our double-bundle procedure and that the use of skeletal parameters will improve the accuracy of graft size prediction. STUDY DESIGN Cross-sectional study; Level of evidence, 3. METHODS A total of 200 patients were recruited and underwent double-bundle ACLR using a semitendinosus (ST) graft. The harvested tendon was measured to determine graft length (GL) and then split at its midpoint. The graft diameters of the anteromedial (GDAM) and posterolateral bundles (GDPL) were measured at the femoral aspect of the 2-stranded graft. The mean diameters of both bundles were included in the analysis. On the coronal radiograph, femorotibial angle (FTA), femoral interepicondylar distance (IED), and tibial plateau width (coronal tibial width [CTW]) were measured. Blumensaat line length (BLL) and the lateral tibial width (LTW) were measured on the lateral radiograph. A linear regression analysis was conducted using graft size as the dependent variable and age, sex, height, weight, Tegner activity score, and skeletal parameters as the independent variables. RESULTS Mean GL was 258.9 ± 21.9 mm, GDAM was 5.9 ± 0.5 mm, and GDPL was 5.7 ± 0.6 mm. Single regression analysis showed that GL was significantly predicted by sex, height, weight, Tegner activity score, IED, CTW, BLL, and LTW (R2 range, 0.033-0.342). GD was predicted by sex, height, weight, IED, CTW, BLL, and LTW (R2 range, 0.094-0.207). Stepwise multiple linear regression analysis significantly confirmed sex, height, and age as the variables to comprehensively predict GL (R2 = 0.384). With regard to GD, stepwise multiple regression confirmed height and IED as significant variables (R2 = 0.224). CONCLUSION Both preoperative anthropometric and radiographic parameters on plain radiographs were able to predict harvested GL and 2-strand GD. Multivariate regression slightly improved the prediction of graft dimensions compared with univariate regression.