Greenstein Alexander S, Teitel Jack, Mitten David J, Ricciardi Benjamin F, Myers Thomas G
Department of Orthopaedics & Rehabilitation, University of Rochester Medical Center, Rochester, NY, USA.
University of Rochester Medical Center, University of Rochester Health Lab, Rochester, NY, USA.
Arthroplast Today. 2020 Oct 14;6(4):850-855. doi: 10.1016/j.artd.2020.08.007. eCollection 2020 Dec.
Determining discharge disposition after total joint arthroplasty (TJA) has been a challenge. Advances in machine learning (ML) have produced computer models that learn by example to generate predictions on future events. We hypothesized a trained ML algorithm's diagnostic accuracy will be better than that of current predictive tools to predict discharge disposition after primary TJA.
This study was a retrospective cohort study from a single, tertiary referral center for primary TJA. We trained and validated an artificial neural network (ANN) based on 4368 distinct surgical encounters between 1/1/2013 and 6/28/2016. The ANN's ability to identify discharge disposition was then tested on 1452 distinct surgical encounters between 1/3/17 and 11/30/17.
The area under the curve and accuracy achieved during model validation were 0.973 and 91.7%, respectively, with 25% of patients being discharged to skilled nursing facilities (SNFs). Within our testing data set, 6.7% of patients went to SNFs. The performance in the testing set included an area under the curve of 0.804, accuracy of 61.3%, sensitivity of 28.9%, and specificity of 93.8%.
This is the first prediction tool using an electronic medical record-integrated ANN to predict discharge disposition after TJA based on locally generated data. Dramatically reduced numbers of patients discharged to SNFs due to implementation of a bundled payment model lead to poor recall in the testing model. This model serves as a proof of concept for developing an ML prediction tool using a relatively small data set and subsequent integration into the electronic medical record.
确定全关节置换术（TJA）后的出院安排一直是一项挑战。机器学习（ML）的进展产生了通过示例学习以对未来事件进行预测的计算机模型。我们假设经过训练的ML算法在预测初次TJA后的出院安排方面，其诊断准确性将优于当前的预测工具。
本研究是一项来自单一三级转诊中心的初次TJA的回顾性队列研究。我们基于2013年1月1日至2016年6月28日期间的4368次不同手术病例训练并验证了一个人工神经网络（ANN）。然后在2017年1月3日至2017年11月30日期间的1452次不同手术病例上测试该ANN识别出院安排的能力。
模型验证期间获得的曲线下面积和准确率分别为0.973和91.7%，25%的患者出院后前往专业护理机构（SNFs）。在我们的测试数据集中，6.7%的患者前往SNFs。测试集的表现包括曲线下面积为0.804、准确率为61.3%、灵敏度为28.9%以及特异性为93.8%。
这是首个使用集成电子病历的ANN基于本地生成的数据预测TJA后出院安排的预测工具。由于实施捆绑支付模式，出院前往SNFs的患者数量大幅减少，导致测试模型中的召回率较低。该模型作为使用相对较小数据集开发ML预测工具并随后集成到电子病历中的概念验证。
