R Kyle Martin1,2, Solvejg Wastvedt3, Ayoosh Pareek4, Andreas Persson5,6,7, Håvard Visnes6, Anne Marie Fenstad6, Gilbert Moatshe7,8, Julian Wolfson3, Lars Engebretsen7,8. 1. Department of Orthopedic Surgery, University of Minnesota, Minneapolis, Minnesota. 2. Department of Orthopedic Surgery, CentraCare, Saint Cloud, Minnesota. 3. Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota. 4. Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota. 5. Department of Orthopedic Surgery, Martina Hansens Hospital, Bærum, Norway. 6. Norwegian Knee Ligament Register, Haukeland University Hospital, Bergen, Norway. 7. Oslo Sport Trauma Research Center, Norwegian School of Sports Science, Oslo, Norway. 8. Orthopaedic Clinic, Oslo University Hospital Ullevål, Oslo, Norway.
Abstract
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.
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.
Authors: Thomas Tischer; Vincenzo Condello; Jacques Menetrey; David Dejour; Philippe Beaufils; Roland Becker Journal: Knee Surg Sports Traumatol Arthrosc Date: 2022-03-19 Impact factor: 4.342
Authors: Janina Kaarre; Bálint Zsidai; Eric Narup; Alexandra Horvath; Eleonor Svantesson; Eric Hamrin Senorski; Alberto Grassi; Volker Musahl; Kristian Samuelsson Journal: Curr Rev Musculoskelet Med Date: 2022-07-13
Authors: Kinjal Vasavada; Dhruv S Shankar; Andrew S Bi; Jay Moran; Massimo Petrera; Joseph Kahan; Erin F Alaia; Michael J Medvecky; Michael J Alaia Journal: Orthop J Sports Med Date: 2022-09-22