Nieboer, Micah J

Purpose To determine the relationship between cam morphology of the hip and ipsilateral sacroiliac motion compared to the native hip in a cadaveric model. Methods A simulated cam state was created using a 3‐dimensional printed cam secured to the head‐neck junction of 5 cadaveric hips. Hips were studied using a computed tomography–based optic metrology system and a 6 degree‐of‐freedom robot to exert an internal rotation torque at 3 different torque levels (6 N‐m, 12 N‐m, 18 N‐m). Outcomes included translational and rotational movement about 3 axes and composite (total) translational motion at the ipsilateral sacroiliac (SI) joint. Statistical analysis included a linear mixed model regression with repeated measures. Results The presence of a simulated cam was associated with medial motion in the coronal plane ( P = .03) and posterior motion in the sagittal plane ( P < .01) but not composite motion ( P = .37). Motion in the axial plane was in an inferior direction ( P = .08). Cam morphology significantly changed rotation in the sagittal plane ( P < .01) but not in the coronal ( P = .63) or axial plane ( P = .18). Composite motion was related to the amount of torque applied to the hip ( P < .01). The amount of torque applied to the hip was related to rotation in the coronal plane ( P < .01), axial plane ( P < .01), and sagittal plane ( P < .01) with increased effects as torque increased. Torque was not associated with translation movement in any of the anatomic planes. Conclusions The presence of simulated cam morphology is associated with motion in a more medial, inferior, and posterior direction at the ipsilateral SI joint relative to a native state. Increasing torque affects the magnitude of translation, but not its direction, which in this study is primarily influenced by cam morphology. Clinical Relevance This biomechanical connection between cam‐type femoroacetabular impingement syndrome and the ipsilateral SI joint provides insight into SI joint dysfunction in patients with femoroacetabular impingement syndrome.

Purpose To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings. Methods Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push‐off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation. Results Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging‐related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c‐statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Conclusions In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model. Level of Evidence Level III, diagnostic case‐control study.