Moatshe, Gilbert

Multiligament knee injuries (MLKIs) represent a broad spectrum of pathology with potentially devastating consequences. Currently, disagreement in the terminology, diagnosis and treatment of these injuries limits clinical care and research. This study aimed to develop consensus on the nomenclature, diagnosis, treatment and rehabilitation strategies for patients with MLKI, while identifying important research priorities for further study. An international consensus process was conducted using validated Delphi methodology in line with British Journal of Sports Medicine guidelines. A multidisciplinary panel of 39 members from 14 countries, completed 3 rounds of online surveys exploring aspects of nomenclature, diagnosis, treatment, rehabilitation and future research priorities. Levels of agreement (LoA) with each statement were rated anonymously on a 5-point Likert scale, with experts encouraged to suggest modifications or additional statements. LoA for consensus in the final round were defined ‘a priori’ if >75% of respondents agreed and fewer than 10% disagreed, and dissenting viewpoints were recorded and discussed. After three Delphi rounds, 50 items (92.6%) reached consensus. Key statements that reached consensus within nomenclature included a clear definition for MLKI (LoA 97.4%) and the need for an updated MLKI classification system that classifies injury mechanism, extent of non-ligamentous structures injured and the presence or absence of dislocation. Within diagnosis, consensus was reached that there should be a low threshold for assessment with CT angiography for MLKI within a high-energy context and for certain injury patterns including bicruciate and PLC injuries (LoA 89.7%). The value of stress radiography or intraoperative fluoroscopy also reached consensus (LoA 89.7%). Within treatment, it was generally agreed that existing literature generally favours operative management of MLKI, particularly for young patients (LoA 100%), and that single-stage surgery should be performed whenever possible (LoA 92.3%). This consensus statement will facilitate clinical communication in MLKI, the care of these patients and future research within MLKI.

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.

Background: Several factors are associated with an increased risk of anterior cruciate ligament (ACL) reconstruction revision. However, the ability to accurately translate these factors into a quantifiable risk of revision at a patient-specific level has remained elusive. We sought to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can identify the most important risk factors associated with subsequent revision of primary ACL reconstruction and develop a clinically meaningful calculator for predicting revision of primary ACL reconstruction. Methods: Machine learning analysis was performed on the NKLR data set. The primary outcome was the probability of revision ACL reconstruction within 1, 2, and/or 5 years. Data were split randomly into training sets (75%) and test sets (25%). Four machine learning models were tested: Cox Lasso, survival random forest, generalized additive model, and gradient boosted regression. Concordance and calibration were calculated for all 4 models. Results: The data set included 24,935 patients, and 4.9% underwent a revision surgical procedure during a mean follow-up (and standard deviation) of 8.1 ± 4.1 years. All 4 models were well-calibrated, with moderate concordance (0.67 to 0.69). The Cox Lasso model required only 5 variables for outcome prediction. The other models either used more variables without an appreciable improvement in accuracy or had slightly lower accuracy overall. An in-clinic calculator was developed that can estimate the risk of ACL revision (Revision Risk Calculator). This calculator can quantify risk at a patient-specific level, with a plausible range from near 0% for low-risk patients to 20% for high-risk patients at 5 years. Conclusions: Machine learning analysis of a national knee ligament registry can predict the risk of ACL reconstruction revision with moderate accuracy. This algorithm supports the creation of an in-clinic calculator for point-of-care risk stratification based on the input of only 5 variables. Similar analysis using a larger or more comprehensive data set may improve the accuracy of risk prediction, and future studies incorporating patients who have experienced failure of ACL reconstruction but have not undergone subsequent revision may better predict the true risk of ACL reconstruction failure. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.