Langenberger Benedikt, Schrednitzki Daniel, Halder Andreas M, Busse Reinhard, Pross Christoph M
Health Care Management, Technische Universität Berlin, Berlin, Germany.
Orthopedics, Sana Kliniken Sommerfeld, Kremmen, Germany.
Bone Joint Res. 2023 Sep 1;12(9):512-521. doi: 10.1302/2046-3758.129.BJR-2023-0070.R2.
A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.
MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).
Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases.
MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases.
接受膝关节置换术（KA）或髋关节置换术（HA）的患者中有很大一部分未达到最小临床重要差异（MCID）那样高的改善程度，即未实现有意义的改善。使用三种患者报告结局指标（PROMs），我们的目的是：1）评估机器学习（ML）、术前简单PROM评分以及逻辑回归（LR）得出的指标在预测接受HA或KA的患者是否实现等于或高于计算出的MCID的改善方面的表现；2）测试ML在预测性能方面是否能够优于LR或术前PROM评分。
在1843例HA患者和1546例KA患者的样本中，使用变化差异法得出MCIDs。应用人工神经网络、梯度提升机、最小绝对收缩和选择算子（LASSO）回归、岭回归、弹性网络、随机森林、LR以及术前PROM评分来预测以下PROMs的MCID：欧洲五维健康量表五级问卷（EQ-5D-5L）、EQ视觉模拟量表（EQ-VAS）、髋关节功能障碍和骨关节炎结局评分-身体功能简表（HOOS-PS）以及膝关节损伤和骨关节炎结局评分-身体功能简表（KOOS-PS）。
每个结局的最佳模型的预测性能范围从HOOS-PS的0.71到EQ-VAS的0.84（HA样本）。在六种情况中的两种情况下，ML在统计学上显著优于LR和术前PROM评分。
可以以合理的性能预测MCIDs。ML能够优于传统方法，尽管仅在少数情况下。
