Greenberg Elliot M, Watson Amanda, Helm Kimberly, Landrum Kevin, Lawrence J Todd R, Ganley Theodore J
Division of Orthopaedics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Orthop J Sports Med. 2025 Apr 11;13(4):23259671251329355. doi: 10.1177/23259671251329355. eCollection 2025 Apr.
After anterior cruciate ligament (ACL) reconstruction, adolescent athletes have a high risk of second ACL injuries, and revision ACL reconstruction is associated with increased medical costs, reduced activity levels, chronic knee pain, and higher rates of knee osteoarthritis, making the prevention of a reinjury a priority. While athlete clearance protocols and algorithms exist, the current methods of identifying the reinjury risk have limited predictive accuracy and are largely based on nonmodifiable risk factors, which limit their clinical application.
The goal of this study was to develop an ACL reinjury risk prediction (ACL-RRP) model capable of accurately classifying an individual patient's risk, identifying modifiable risk factors, and ranking these factors in the order of importance and ability to be modified.
Cohort study (Diagnosis); Level of evidence, 2.
A clinician-informed approach was utilized to develop the prediction model and an interpretable output system. The primary outcome variable was the likelihood of sustaining a repeat ACL injury. The data were split into training (80% [n = 628]) and holdout (20% [n = 158]) datasets to train and subsequently validate the model. The accuracy of classification was identified by the sensitivity, specificity, positive/negative predictive values, and odds ratio.
The final model included 33 predictor variables, 23 of which are modifiable. The model adjusted the weight of the risk classification and risk factors (predictor variables) on a case-by-case basis. The model demonstrated a sensitivity of 94% and a specificity of 76%. Patients classified as being high risk had 4.5 times the risk of repeat ACL injuries compared with those classified as being low risk.
This clinician-informed ACL-RRP model demonstrated a high degree of accuracy when classifying patients as having a high or low risk of repeat ACL injuries and generated patient-specific modifiable risk factors to guide ongoing rehabilitation or patient education to achieve the goals of reducing the ACL reinjury risk.
在前交叉韧带（ACL）重建术后，青少年运动员再次发生ACL损伤的风险很高，而翻修ACL重建与医疗费用增加、活动水平降低、慢性膝关节疼痛以及膝关节骨关节炎发病率升高相关，因此预防再次受伤成为当务之急。虽然存在运动员复出方案和算法，但目前识别再次受伤风险的方法预测准确性有限，且很大程度上基于不可改变的风险因素，这限制了它们的临床应用。
本研究的目的是开发一种ACL再次受伤风险预测（ACL-RRP）模型，该模型能够准确分类个体患者的风险，识别可改变的风险因素，并按重要性和可改变能力的顺序对这些因素进行排序。
队列研究（诊断）；证据等级，2级。
采用临床医生指导的方法开发预测模型和可解释的输出系统。主要结局变量为再次发生ACL损伤的可能性。数据被分为训练数据集（80% [n = 628]）和验证数据集（20% [n = 158]），用于训练并随后验证模型。通过敏感性、特异性、阳性/阴性预测值和比值比确定分类的准确性。
最终模型包括33个预测变量，其中23个是可改变的。该模型根据具体情况调整风险分类和风险因素（预测变量）的权重。该模型的敏感性为94%，特异性为76%。被分类为高风险的患者再次发生ACL损伤的风险是被分类为低风险患者的4.5倍。
这种临床医生指导的ACL-RRP模型在将患者分类为再次发生ACL损伤的高风险或低风险时显示出高度准确性，并生成了针对患者的可改变风险因素，以指导正在进行的康复或患者教育，实现降低ACL再次受伤风险的目标。
