Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine-learning Algorithm.
Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine-learning Algorithm.
复制标题
DOI：
10.1097/corr.0000000000002283
复制
发表时间：
2022-12-01
影响因子：
4.2
通讯作者：
Hendrickx, Laurent A. M.
中科院分区：
文献类型：
--
作者：
van de Kuit, Anouk;Oosterhoff, Jacobien H. F.;Dijkstra, Hidde;Sprague, Sheila;Bzovsky, Sofia;Bhandari, Mohit;Swiontkowski, Marc;Schemitsch, Emil H. H.;IJpma, Frank F. A.;Poolman, Rudolf W. W.;Doornberg, Job N. N.;Hendrickx, Laurent A. M.
股骨颈骨折是常见的骨折，通常采用内固定治疗。内固定的一个主要缺点是由于骨不连、畸形愈合、缺血性坏死或植入物失效而大量转为关节成形术。临床预测模型识别转换为关节成形术的高风险患者可能有助于临床医生选择最初可能从关节成形术中受益的患者。机器学习（ML）算法预测股骨颈骨折患者内固定术后24个月内转为关节置换术的预测性能如何？我们纳入了875名来自使用替代植入物固定治疗髋关节骨折（FAITH）试验的患者。FAITH试验由低能量股骨颈骨折患者组成，这些患者被随机分配接受滑动髋螺钉或松质骨螺钉内固定。在这些患者中，18%（155/875）在前24个月内转为THA或半髋关节置换术。所有患者被随机分为训练集（80%）和测试集（20%）。首先，基于生物力学原理和既往研究，我们确定了27个可能与我们的主要结局相关的潜在患者和骨折特征。然后，随机森林算法（一种ML学习，基于决策树的算法，选择变量）确定了10个转换的预测因素：BMI，心脏病，Garden分类，心脏药物的使用，肺部药物的使用，年龄，肺部疾病，骨关节炎，性别和骨折线的水平。基于这些变量，我们训练了五种不同的ML算法来识别与转化相关的模式。基于以下性能指标，在训练集和测试集上评估这些训练ML算法的预测性能：（1）区分度（模型区分转换患者与未转换患者的能力;用接受者工作特征曲线下面积[AUC]表示），（2）校准（绘制的估计概率与观察到的概率;用校准曲线截距和斜率表示），以及（3）总体模型性能（Brier评分：区分度和校准的综合）。在训练集和测试集中，5种ML算法在预测转为关节成形术方面均表现不佳;训练集中算法的AUC范围为0.57 - 0.64，校准曲线斜率范围为0.53 - 0.82，校准截距范围为-0.04至0.05，Brier评分范围为0.14 - 0.15。在测试集中进一步评价了算法; AUC范围为0.49至0.73，校准斜率范围为0.17至1.29，校准截距范围为-1.28至0.34，Brier评分范围为0.13至0.15。训练算法的预测性能很差，尽管使用了世界上最好的数据集之一。如果当前数据集包含不同的变量或更多的患者，性能可能会更好。此外，在本研究中汇总了转换为关节成形术的各种原因，但单独预测潜在病理（如缺血性坏死或骨不连）可能更准确。最后，仅根据术前变量预测转关节置换术本身可能是困难的。因此，未来的研究应旨在包括更多的变量，并区分关节置换术的各种原因。III级，预后研究。
Femoral neck fractures are common and are frequently treated with internal fixation. A major disadvantage of internal fixation is the substantially high number of conversions to arthroplasty because of nonunion, malunion, avascular necrosis, or implant failure. A clinical prediction model identifying patients at high risk of conversion to arthroplasty may help clinicians in selecting patients who could have benefited from arthroplasty initially. What is the predictive performance of a machine‐learning (ML) algorithm to predict conversion to arthroplasty within 24 months after internal fixation in patients with femoral neck fractures? We included 875 patients from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial. The FAITH trial consisted of patients with low-energy femoral neck fractures who were randomly assigned to receive a sliding hip screw or cancellous screws for internal fixation. Of these patients, 18% (155 of 875) underwent conversion to THA or hemiarthroplasty within the first 24 months. All patients were randomly divided into a training set (80%) and test set (20%). First, we identified 27 potential patient and fracture characteristics that may have been associated with our primary outcome, based on biomechanical rationale and previous studies. Then, random forest algorithms (an ML learning, decision tree–based algorithm that selects variables) identified 10 predictors of conversion: BMI, cardiac disease, Garden classification, use of cardiac medication, use of pulmonary medication, age, lung disease, osteoarthritis, sex, and the level of the fracture line. Based on these variables, five different ML algorithms were trained to identify patterns related to conversion. The predictive performance of these trained ML algorithms was assessed on the training and test sets based on the following performance measures: (1) discrimination (the model’s ability to distinguish patients who had conversion from those who did not; expressed with the area under the receiver operating characteristic curve [AUC]), (2) calibration (the plotted estimated versus the observed probabilities; expressed with the calibration curve intercept and slope), and (3) the overall model performance (Brier score: a composite of discrimination and calibration). None of the five ML algorithms performed well in predicting conversion to arthroplasty in the training set and the test set; AUCs of the algorithms in the training set ranged from 0.57 to 0.64, slopes of calibration plots ranged from 0.53 to 0.82, calibration intercepts ranged from -0.04 to 0.05, and Brier scores ranged from 0.14 to 0.15. The algorithms were further evaluated in the test set; AUCs ranged from 0.49 to 0.73, calibration slopes ranged from 0.17 to 1.29, calibration intercepts ranged from -1.28 to 0.34, and Brier scores ranged from 0.13 to 0.15. The predictive performance of the trained algorithms was poor, despite the use of one of the best datasets available worldwide on this subject. If the current dataset consisted of different variables or more patients, the performance may have been better. Also, various reasons for conversion to arthroplasty were pooled in this study, but the separate prediction of underlying pathology (such as, avascular necrosis or nonunion) may be more precise. Finally, it may be possible that it is inherently difficult to predict conversion to arthroplasty based on preoperative variables alone. Therefore, future studies should aim to include more variables and to differentiate between the various reasons for arthroplasty. Level III, prognostic study.