Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, Boston, MA, 02114, USA.
Knee Surg Sports Traumatol Arthrosc. 2022 Aug;30(8):2582-2590. doi: 10.1007/s00167-021-06794-3. Epub 2021 Nov 11.
This study aimed to develop and validate machine-learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection.
A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine-learning models which were developed to predict recurrent periprosthetic joint. The machine-learning models were assessed by discrimination, calibration and decision curve analysis.
The factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with/without modular component exchange (p < 0.001), > 4 prior open surgeries (p < 0.001), metastatic disease (p < 0.001), drug abuse (p < 0.001), HIV/AIDS (p < 0.01), presence of Enterococcus species (p < 0.01) and obesity (p < 0.01). The machine-learning models all achieved excellent performance across discrimination (AUC range 0.81-0.84).
This study developed three machine-learning models for the prediction of recurrent infections in patients following revision total knee arthroplasty for periprosthetic joint infection. The strongest predictors were previous irrigation and debridement with or without modular component exchange and prior open surgeries. The study findings show excellent model performance, highlighting the potential of these computational tools in quantifying increased risks of recurrent PJI to optimize patient outcomes.
IV.
本研究旨在开发和验证机器学习模型，以预测假体周围关节感染行翻修全膝关节置换术后患者的复发性感染。
共有 618 例连续患者因假体周围关节感染而行翻修全膝关节置换术。该患者队列包括 165 例确诊的复发性假体周围关节感染（PJI）患者。潜在的危险因素包括患者人口统计学和手术特征，作为输入输入到三个机器学习模型中，这些模型用于预测假体周围关节的复发性感染。通过判别、校准和决策曲线分析评估机器学习模型。
与假体周围关节感染行翻修全膝关节置换术后患者复发性 PJI 最显著相关的因素包括灌洗清创术伴/不伴模块化组件置换（p<0.001）、>4 次开放性手术（p<0.001）、转移性疾病（p<0.001）、药物滥用（p<0.001）、HIV/AIDS（p<0.01）、肠球菌属（p<0.01）和肥胖（p<0.01）。所有机器学习模型在判别（AUC 范围 0.81-0.84）方面均表现出优异的性能。
本研究开发了三种用于预测假体周围关节感染行翻修全膝关节置换术后患者复发性感染的机器学习模型。最强的预测因素是先前的灌洗清创术伴或不伴模块化组件置换和先前的开放性手术。研究结果表明模型性能优异，突出了这些计算工具在量化复发性 PJI 风险增加以优化患者结局方面的潜力。
IV。
