Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty.,Knee Surgery, Sports Traumatology, Arthroscopy

当前位置： X-MOL 学术 › Knee Surg. Sports Traumatol. Arthrosc. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty.
Knee Surgery, Sports Traumatology, Arthroscopy ( IF 3.8 ) Pub Date : 2019-12-12 , DOI: 10.1007/s00167-019-05822-7
Yong-Hao Pua ₁ , Hakmook Kang ₂ , Julian Thumboo ₃ , Ross Allan Clark ₄ , Eleanor Shu-Xian Chew ₁ , Cheryl Lian-Li Poon ₁ , Hwei-Chi Chong ₅ , Seng-Jin Yeo ₆

Affiliation

Purpose

Machine-learning methods are flexible prediction algorithms with potential advantages over conventional regression. This study aimed to use machine learning methods to predict post-total knee arthroplasty (TKA) walking limitation, and to compare their performance with that of logistic regression.

Methods

From the department’s clinical registry, a cohort of 4026 patients who underwent elective, primary TKA between July 2013 and July 2017 was identified. Candidate predictors included demographics and preoperative clinical, psychosocial, and outcome measures. The primary outcome was severe walking limitation at 6 months post-TKA, defined as a maximum walk time ≤ 15 min. Eight common regression (logistic, penalized logistic, and ordinal logistic with natural splines) and ensemble machine learning (random forest, extreme gradient boosting, and SuperLearner) methods were implemented to predict the probability of severe walking limitation. Models were compared on discrimination and calibration metrics.

Results

At 6 months post-TKA, 13% of patients had severe walking limitation. Machine learning and logistic regression models performed moderately [mean area under the ROC curves (AUC) 0.73–0.75]. Overall, the ordinal logistic regression model performed best while the SuperLearner performed best among machine learning methods, with negligible differences between them (Brier score difference, < 0.001; 95% CI [− 0.0025, 0.002]).

Conclusions

When predicting post-TKA physical function, several machine learning methods did not outperform logistic regression—in particular, ordinal logistic regression that does not assume linearity in its predictors.

Level of evidence

Prognostic level II

中文翻译：

在预测全膝关节置换术后严重的步行受限时，机器学习方法可与逻辑回归技术相媲美。

目的

机器学习方法是灵活的预测算法，具有优于传统回归的潜在优势。这项研究旨在使用机器学习方法来预测后膝关节置换术（TKA）的行走限制，并将其性能与逻辑回归进行比较。

方法

从该科室的临床登记处，确定了一组4026例患者，这些患者在2013年7月至2017年7月之间接受了选择性原发性TKA。候选预测指标包括人口统计学和术前临床，社会心理和结局指标。主要结局是在TKA后6个月出现严重的步行受限，即最长步行时间≤15分钟。实施了八种常见的回归（逻辑，惩罚性逻辑和带自然样条的序数逻辑）和整体机器学习（随机森林，极限梯度提升和SuperLearner）方法来预测严重步行受限的可能性。比较了模型的鉴别和校准指标。

结果

TKA后6个月，有13％的患者出现严重的步行受限。机器学习和逻辑回归模型的执行程度中等[ROC曲线下的平均面积（AUC）0.73–0.75]。总体而言，序数逻辑回归模型在机器学习方法中表现最好，而SuperLearner在机器学习方法中表现最好，两者之间的差异可以忽略不计（Brier得分差异，<0.001； 95％CI [-0.0025，0.002]）。