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首页> 外文期刊>Orthopaedic Journal of Sports Medicine >MACHINE-LEARNING IDENTIFIES BEST MEASURES TO PREDICT ACL RECONSTRUCTION OUTCOME
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MACHINE-LEARNING IDENTIFIES BEST MEASURES TO PREDICT ACL RECONSTRUCTION OUTCOME

机译：机器学习可识别预测ACL重建结果的最佳方法

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Introduction: Knee injury and Osteoarthritis Outcome Score (KOOS) is a widely used patient-reported outcome measurement to track recovery after ACL surgery. This study focuses on the function of daily living subscale (KOOS ADL), which is calculated based on 17 questions. By employing machine learning to predict KOOS ADL scores, we sought to better understand the relative importance of the survey questions and thereby identify its most critical components as well as questions that do not adequately predict outcomes. Methods: Pre- and post-operative patient reported KOOS ADL survey responses and outcomes scores following ACL surgery were obtained from the Surgical Outcome System data registry(SOS), an international patient-reported outcomes database sponsored and maintained by Arthrex. Patients with missing KOOS ADL survey responses were excluded from the study. Machine learning (ML) algorithms such as Random Forest and Gradient Boosting were used to identify the most critical survey questions that predict KOOS ADL scores with high accuracy. These decision tree-based algorithms predict patient outcomes using several decision rules and thereby determining the relative value of individual questions at predicting patient deficits (e.g., if patients have “Severe” difficulty in ascending stairs, they are more likely to have globally worse scores than those with difficulty with other tasks). Results: 4996 patients were initially identified. Based on compliance with the survey, 2407, 2407, 1817 and 1193 patients records for pre-surgery, 3 month, 6 month and 1 year post-surgery responses respectively underwent further analysis. The dataset consisted of 53.9% males and 46.1% females. Mean age was 29 (range 11 to 70 years). Results from the ML models indicated that by 6 key questions, over 80% of the variance in KOOS ADL scores could be explained instead of standard 17 survey questions (Table 1). Interestingly, the analysis provided similar accuracy at both 6 months and 1 year. Discussion and Conclusion: Most patients have similar functional deficits that can be captured using a simplified version of the KOOS ADL survey. The abbreviated survey would result in a better patient reporting experience while still obtaining quality data. Additional work on predicting post-surgery scores using ML from pre-surgery responses and other patient information would provide valuable insights; however, predicting outcome scores with high accuracy remains challenging. We advocate for novel methods to identify and measure meaningful data to assist with understanding patient outcomes and thereby proving the true value of orthopaedic interventions on functional status. Table 1. –Questions with high predictive value A1 - Descending stairs A2 - Ascending stairs A3 - Rising from sitting A7 - Putting on socks/stockings A9 - Taking off socks/stockings A16 - Heavy domestic duties
机译：简介：膝关节损伤和骨关节炎结局评分（KOOS）是一项广泛使用的患者报告结局指标，用于追踪ACL手术后的恢复情况。这项研究的重点是基于17个问题计算的日常生活分量表（KOOS ADL）的功能。通过使用机器学习来预测KOOS ADL分数，我们试图更好地理解调查问题的相对重要性，从而确定其最关键的组成部分以及不能充分预测结果的问题。方法：手术前和术后患者报告的AOS手术后KOOS ADL调查反应和结局评分来自手术结果系统数据注册表（SOS），这是由Arthrex赞助和维护的国际患者报告结果数据库。缺少KOOS ADL调查回答的患者被排除在研究之外。诸如随机森林和梯度提升之类的机器学习（ML）算法用于识别最关键的调查问题，这些问题可以高精度地预测KOOS ADL分数。这些基于决策树的算法使用多个决策规则来预测患者结果，从而确定各个问题在预测患者缺陷时的相对价值（例如，如果患者在上楼梯时遇到“严重”困难，则他们的总体得分可能会比那些在其他任务上有困难的人）。结果：最初确定了4996例患者。根据调查的依从性，分别对2407、2407、1817和1193例患者的手术前，手术后3个月，6个月和1年的反应记录进行了进一步分析。该数据集由53.9％的男性和46.1％的女性组成。平均年龄为29岁（11至70岁）。机器学习模型的结果表明，通过6个关键问题，可以解释KOOS ADL得分差异的80％以上，而不是标准的17个调查问题（表1）。有趣的是，该分析在6个月和1年时提供了相似的准确性。讨论与结论：大多数患者具有相似的功能缺陷，可以使用简化版本的KOOS ADL调查来捕获。简短的调查将在提供高质量数据的同时，为患者提供更好的报告体验。使用来自手术前反应和其他患者信息的ML预测手术后得分的其他工作将提供有价值的见解;然而，以高准确度预测结果分数仍然具有挑战性。我们主张采用新颖的方法来识别和测量有意义的数据，以帮助了解患者的预后，从而证明矫形外科干预对功能状态的真正价值。表1. –具有较高预测值的问题A1-下降楼梯A2-上升楼梯A3-从坐姿​​上升A7-穿袜子/长袜A9-脱下袜子/长袜A16-繁重的家务

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