Clinical Factors That Predict a Second ACL Injury After ACL Reconstruction and Return to Sport: Preliminary Development of a Clinical Decision Algorithm.
Clinical Factors That Predict a Second ACL Injury After ACL Reconstruction and Return to Sport: Preliminary Development of a Clinical Decision Algorithm.
复制标题
DOI：
10.1177/2325967117745279
复制
发表时间：
2017-12
影响因子：
2.6
通讯作者：
Schmitt LC
中科院分区：
文献类型：
--
作者：
Paterno MV;Huang B;Thomas S;Hewett TE;Schmitt LC
前交叉韧带（ACL）重建（ACLR）和恢复运动（RTS）后第二次前交叉韧带（ACL）损伤的生物力学预测因子已经确定;然而，这些措施在标准临床环境中可能不可行。本研究的目的是评估标准临床指标是否可预测第二次ACL损伤的风险。检验的假设是，RTS时的力量、功能和患者报告的测量值的组合将以高灵敏度和特异性预测第二次ACL损伤的风险。病例对照研究;证据等级，3和队列研究（预后）;证据等级，1。共对163名接受初次ACLR并能够进行RTS的参与者（平均年龄，16.7 ± 3.0岁）进行了评价。所有参与者都完成了等速肌力、跳跃测试、平衡和膝关节损伤和骨关节炎结局评分（KOOS）的评估。对参与者进行了至少24个月的跟踪，以确定第二次ACL损伤的发生。最初招募的120名参与者用于开发利用分类和回归树（CART）分析的临床预测模型，其余43名参与者用作验证数据集。使用Kaplan-Meier分析和考克斯比例风险模型对所有163名参与者进行了额外的分析。该队列的初始亚组中约20%（23/114）发生了第二次ACL损伤。CART分析确定了年龄、性别、膝关节相关的信心和RTS时三跳距离的表现作为第二次ACL损伤的主要预测因素。使用这些变量，生成了一个模型，从中确定了高风险组（n = 53）和低风险组（n = 61）。高风险组中共有22名参与者和低风险组中的1名参与者遭受了第二次ACL损伤。高风险参与者符合以下2种情况之一：（1）年龄<19岁，三跳距离在1.34和1.90倍身高之间，三跳距离肢体对称指数（LSI）<98.5%（n = 43）或（2）年龄<19岁，三跳距离> 1.34倍身高，三跳距离LSI> 98.5%，女性，和高膝关节相关置信度（n = 10）。验证步骤确定高风险组发生第二次ACL损伤的可能性高5倍（比值比，5.14 [95% CI，1.00 - 26.46]），灵敏度为66.7%，特异性为72.0%。这些发现认可了准确识别RTS后24个月内第二次ACL损伤风险高的年轻患者的措施。开发临床决策算法以识别高风险患者，包括临床可行的变量，如年龄、性别、信心和三跳距离的表现，可以作为重新评价RTS适当出院标准的基础。
Biomechanical predictors of a second anterior cruciate ligament (ACL) injury after ACL reconstruction (ACLR) and return to sport (RTS) have been identified; however, these measures may not be feasible in a standard clinical environment. The purpose of this study was to evaluate whether standard clinical measures predicted the risk of second ACL injuries. The hypothesis tested was that a combination of strength, function, and patient-reported measures at the time of RTS would predict the risk of second ACL injuries with high sensitivity and specificity. Case-control study; Level of evidence, 3 and Cohort study (prognosis); Level of evidence, 1. A total of 163 participants (mean age, 16.7 ± 3.0 years) who underwent primary ACLR and were able to RTS were evaluated. All participants completed an assessment of isokinetic strength, hop testing, balance, and the Knee Injury and Osteoarthritis Outcome Score (KOOS). Participants were tracked for a minimum of 24 months to identify occurrences of a second ACL injury. The initial 120 participants enrolled were used to develop a clinical prediction model that utilized classification and regression tree (CART) analysis, and the remaining 43 participants enrolled were used as a validation dataset. Additional analyses were performed in all 163 participants using Kaplan-Meier analysis and Cox proportional hazards modeling. Approximately 20% (23/114) of the initial subset of the cohort suffered a second ACL injury. CART analysis identified age, sex, knee-related confidence, and performance on the triple hop for distance at the time of RTS as the primary predictors of a second ACL injury. Using these variables, a model was generated from which high-risk (n = 53) and low-risk groups (n = 61) were identified. A total of 22 participants in the high-risk group and 1 participant in the low-risk group suffered a second ACL injury. High-risk participants fit 1 of 2 profiles: (1) age <19 years, triple hop for distance between 1.34 and 1.90 times body height, and triple hop for distance limb symmetry index (LSI) <98.5% (n = 43) or (2) age <19 years, triple hop for distance >1.34 times body height, triple hop for distance LSI >98.5%, female sex, and high knee-related confidence (n = 10). The validation step identified the high-risk group as being 5 times (odds ratio, 5.14 [95% CI, 1.00-26.46]) more likely to suffer a second ACL injury, with a sensitivity of 66.7% and specificity of 72.0%. These findings recognize measures that accurately identify young patients at high risk of sustaining a second ACL injury within 24 months after RTS. The development of a clinical decision algorithm to identify high-risk patients, inclusive of clinically feasible variables such as age, sex, confidence, and performance on the triple hop for distance, can serve as a foundation to re-evaluate appropriate discharge criteria for RTS.
登录
查看更多内容
影响因子：
1.7
作者：
Brent, Jensen L.;Myer, Gregory D.;Hewett, Timothy E.
通讯作者：
Hewett, Timothy E.
影响因子：
3.3
作者：
Hamilton, R. Tyler;Shultz, Sandra J.;Perrin, David H.
通讯作者：
Perrin, David H.
DOI：
10.2519/jospt.2012.4194
发表时间：
2012-09
期刊：
The Journal of orthopaedic and sports physical therapy
影响因子：
--
作者：
Schmitt LC;Paterno MV;Hewett TE
通讯作者：
Hewett TE
DOI：
10.2519/jospt.2001.31.10.588
发表时间：
2001-10-01
影响因子：
6.1
作者：
Fitzgerald, GK;Lephart, SA;Wainner, MRS
通讯作者：
Wainner, MRS
影响因子：
18.4
作者：
Grindem H;Snyder-Mackler L;Moksnes H;Engebretsen L;Risberg MA
通讯作者：
Risberg MA