背景:关节成形术护理服务正面临日益增长的供需不匹配。为了满足未来关节置换术的需求,在骨科医师进行评估之前,系统需要确定潜在的手术候选者。
方法:2020年3月1日至7月31日,在两家学术医疗中心和三家社区医院进行了回顾性审查,以确定新的患者远程医疗遭遇(没有事先亲自评估),以考虑髋关节或膝关节置换术。主要结果是关节置换的手术指征。开发了五种机器学习算法来预测手术适应症的可能性,并通过区分进行评估。校准,整体性能,和决策曲线分析。
结果:总体而言,158名患者接受了新的患者远程医疗评估,以考虑THA,TKA,或UKA和65.2%(n=103)在亲自评估之前接受手术干预。中位年龄为65岁(四分位距59-70),女性占60.8%。发现与手术干预相关的变量是关节炎的影像学程度,关节内注射的先前试验,物理治疗的试验,阿片类药物的使用,烟草使用。在不用于算法开发的独立测试集(n=46)中,随机梯度提升算法取得了最佳性能,AUC0.83,校准截距0.13,校准斜率1.03,相对于零模型Brier得分为0.23的Brier得分为0.15,在决策曲线分析中,净收益高于默认替代方案.
结论:我们开发了一种机器学习算法,用于在没有亲自评估或体格检查的情况下,在骨关节炎的情况下识别关节置换术的潜在手术候选者。如果经过外部验证,该算法可以由各种利益相关者部署,包括患者,提供者,和卫生系统,指导骨关节炎患者采取适当的后续措施,并提高确定手术候选人的效率。
方法:III.
BACKGROUND: Arthroplasty care delivery is facing a growing supply-demand mismatch. To meet future demand for joint arthroplasty, systems will need to identify potential surgical candidates prior to evaluation by orthopaedic surgeons.
METHODS: Retrospective review was conducted at two academic medical centers and three community hospitals from March 1 to July 31, 2020 to identify new patient telemedicine encounters (without prior in-person evaluation) for consideration of hip or knee arthroplasty. The primary outcome was surgical indication for joint replacement. Five machine learning algorithms were developed to predict likelihood of surgical indication and assessed by discrimination, calibration, overall performance, and decision curve analysis.
RESULTS: Overall, 158 patients underwent new patient telemedicine evaluation for consideration of THA, TKA, or UKA and 65.2% (n = 103) were indicated for operative intervention prior to in-person evaluation. The median age was 65 (interquartile range 59-70) and 60.8% were women. Variables found to be associated with operative intervention were radiographic degree of arthritis, prior trial of intra-articular injection, trial of physical therapy, opioid use, and tobacco use. In the independent testing set (n = 46) not used for algorithm development, the stochastic gradient boosting algorithm achieved the best performance with AUC 0.83, calibration intercept 0.13, calibration slope 1.03, Brier score 0.15 relative to a null model Brier score of 0.23, and higher net benefit than the default alternatives on decision curve analysis.
CONCLUSIONS: We developed a machine learning algorithm to identify potential surgical candidates for joint arthroplasty in the setting of osteoarthritis without an in-person evaluation or physical examination. If externally validated, this algorithm could be deployed by various stakeholders, including patients, providers, and health systems, to direct appropriate next steps in patients with osteoarthritis and improve efficiency in identifying surgical candidates.
METHODS: III.