Machine Learning Predictions of Subjective Function, Symptoms, and Psychological Readiness at 12 Months After ACL Reconstruction Based on Physical Performance in the Early Rehabilitation Stage: Retrospective Cohort Study

被引:0
作者
Hwang, Ui-jae [1 ,3 ]
Kim, Jin-seong [1 ,3 ]
Chung, Kyu Sung [2 ,3 ]
机构
[1] Yonsei Univ, Coll Hlth Sci, Dept Phys Therapy, Lab KEMA AI Res KAIR, Wonju, South Korea
[2] Hanyang Univ Hosp Guri, Dept Orthopaed Surg, 153 Gyeongchun ro, Guri Si 04551, Gyeonggi Do, South Korea
[3] Inje Univ, Seoul Paik Hosp, Seoul, South Korea
关键词
ACL; machine learning; Patient Acceptable Symptom State; physical therapy/rehabilitation; CRUCIATE LIGAMENT RECONSTRUCTION; QUADRICEPS STRENGTH ASYMMETRY; MUSCLE STRENGTH; GRAFT RUPTURE; BALANCE TEST; RETURN; SPORT; INJURY; RISK; MECHANICS;
D O I
10.1177/23259671251319512
中图分类号
R826.8 [整形外科学]; R782.2 [口腔颌面部整形外科学]; R726.2 [小儿整形外科学]; R62 [整形外科学(修复外科学)];
学科分类号
摘要
Background: Anterior cruciate ligament (ACL) reconstruction (ACLR) aims to restore knee stability and function; however, recovery outcomes vary widely, highlighting the need for predictive tools to guide rehabilitation and patient readiness. Purpose: To identify the most effective machine learning models for predicting the successful recovery of Patient Acceptable Symptom State (PASS) in terms of subjective function, symptoms, and psychological readiness 12 months after ACLR using physical performance measures obtained 3 months after ACLR. Study Design: Cohort study; Level of evidence, 3. Methods: The authors retrospectively analyzed the data of 113 patients who underwent single-bundle anatomic ACLR. Physical performance measures at 3 months after ACLR included the Y-balance and isokinetic muscle strength tests. The successful recovery of PASS outcomes at 12 months were assessed using the International Knee Documentation Committee (IKDC) and the ACL-Return to Sport after Injury (ACL-RSI) scale. Five machine learning algorithms were assessed: logistic regression, decision tree, random forest, gradient boosting, and support vector machines. Results: The gradient boosting model demonstrated the highest area under the curve (AUC) scores for predicting SRPAS of the IKDC (AUC, 0.844; F1, 0.889), and the random forest model demonstrated the highest AUC scores for predicting the successful recovery of PASS of the ACL-RSI (AUC, 0.835; F1, 0.732) during test models. Key predictors of the successful recovery of PASS outcomes included young age and low deficits in the 60 deg/s flexor and extensor peak torque for the IKDC, low 180 deg/s extensor and flexor mean power deficit, and low 60 deg/s flexor peak torque deficits for the ACL-RSI. Conclusion: Machine learning showed that younger age and greater 3-month isokinetic strength at 60 deg/s predicted attainment of the successful recovery of PASS of the IKDC at 1 year after ACL. Greater 3-month isokinetic strength at 180 deg/s was most predictive of attaining the successful recovery of PASS of the ACL-RSI at 12 months.
引用
收藏
页数:13
相关论文
共 49 条
  • [1] 2016 Consensus statement on return to sport from the First World Congress in Sports Physical Therapy, Bern
    Ardern, Clare L.
    Glasgow, Philip
    Schneiders, Anthony
    Witvrouw, Erik
    Clarsen, Benjamin
    Cools, Ann
    Gojanovic, Boris
    Griffin, Steffan
    Khan, Karim M.
    Moksnes, Havard
    Mutch, Stephen A.
    Phillips, Nicola
    Reurink, Gustaaf
    Sadler, Robin
    Silbernagel, Karin Gravare
    Thorborg, Kristian
    Wangensteen, Arnlaug
    Wilk, Kevin E.
    Bizzini, Mario
    [J]. BRITISH JOURNAL OF SPORTS MEDICINE, 2016, 50 (14) : 853 - 864
  • [2] Return to sport following anterior cruciate ligament reconstruction surgery: a systematic review and meta-analysis of the state of play
    Ardern, Clare L.
    Webster, Kate E.
    Taylor, Nicholas F.
    Feller, Julian A.
    [J]. BRITISH JOURNAL OF SPORTS MEDICINE, 2011, 45 (07) : 596 - 606
  • [3] Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting
    Ayaru, Lakshmana
    Ypsilantis, Petros-Pavlos
    Nanapragasam, Abigail
    Choi, Ryan Chang-Ho
    Thillanathan, Anish
    Min-Ho, Lee
    Montana, Giovanni
    [J]. PLOS ONE, 2015, 10 (07):
  • [4] Factors Used to Determine Return to Unrestricted Sports Activities After Anterior Cruciate Ligament Reconstruction
    Barber-Westin, Sue D.
    Noyes, Frank R.
    [J]. ARTHROSCOPY-THE JOURNAL OF ARTHROSCOPIC AND RELATED SURGERY, 2011, 27 (12) : 1697 - 1705
  • [5] Relationships of Muscle Function and Subjective Knee Function in Patients After ACL Reconstruction
    Bodkin, Stephan
    Goetschius, John
    Hertel, Jay
    Hart, Joe
    [J]. ORTHOPAEDIC JOURNAL OF SPORTS MEDICINE, 2017, 5 (07):
  • [6] Survival of the Anterior Cruciate Ligament Graft and the Contralateral ACL at a Minimum of 15 Years
    Bourke, Henry E.
    Salmon, Lucy J.
    Waller, Alison
    Patterson, Victoria
    Pinczewski, Leo A.
    [J]. AMERICAN JOURNAL OF SPORTS MEDICINE, 2012, 40 (09) : 1985 - 1992
  • [7] What Are Our Patients Really Telling Us? Psychological Constructs Associated With Patient-Reported Outcomes After Anterior Cruciate Ligament Reconstruction
    Burland, Julie P.
    Howard, Jennifer S.
    Lepley, Adam S.
    DiStefano, Lindsay J.
    Frechette, Laura
    Lepley, Lindsey K.
    [J]. JOURNAL OF ATHLETIC TRAINING, 2020, 55 (07) : 707 - 716
  • [8] Clinical Outcome Measures and Return-to-Sport Timing in Adolescent Athletes After Anterior Cruciate Ligament Reconstruction
    Burland, Julie P.
    Kostyun, Regina O.
    Kostyun, Kyle J.
    Solomito, Matthew
    Nissen, Carl
    Milewski, Matthew D.
    [J]. JOURNAL OF ATHLETIC TRAINING, 2018, 53 (05) : 442 - 451
  • [9] Predicting risk for adverse health events using random forest
    Cafri, Guy
    Li, Luo
    Paxton, Elizabeth W.
    Fan, Juanjuan
    [J]. JOURNAL OF APPLIED STATISTICS, 2018, 45 (12) : 2279 - 2294
  • [10] The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review
    Chan, Victor C. H.
    Ross, Gwyneth B.
    Clouthier, Allison L.
    Fischer, Steven L.
    Graham, Ryan B.
    [J]. APPLIED ERGONOMICS, 2022, 98