From the Warren Alpert Medical School of Brown University (Biron, Sinha, Dr. Kleiner, and Aluthge), Center for Biomedical Informatics (Biron, Sinha, Aluthge, and Dr. Sarkar), Brown University, and Department of Orthopaedic Surgery (Dr. Goodman, Dr. Cohen, and Dr. Daniels), The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, RI.
J Am Acad Orthop Surg. 2020 Jul 1;28(13):e580-e585. doi: 10.5435/JAAOS-D-19-00395.
Patient selection for outpatient total shoulder arthroplasty (TSA) is important to optimizing patient outcomes. This study aims to develop a machine learning tool that may aid in patient selection for outpatient total should arthroplasty based on medical comorbidities and demographic factors.
Patients undergoing elective TSA from 2011 to 2016 in the American College of Surgeons National Surgical Quality Improvement Program were queried. A random forest machine learning model was used to predict which patients had a length of stay of 1 day or less (short stay). A multivariable logistic regression was then used to identify which variables were significantly correlated with a short or long stay.
From 2011 to 2016, 4,500 patients were identified as having undergone elective TSA and having the necessary predictive features and outcomes recorded. The machine learning model was able to successfully identify short stay patients, producing an area under the receiver operator curve of 0.77. The multivariate logistic regression identified numerous variables associated with a short stay including age less than 70 years and male sex as well as variables associated with a longer stay including diabetes, chronic obstructive pulmonary disease, and American Society of Anesthesiologists class greater than 2.
Machine learning may be used to predict which patients are suitable candidates for short stay or outpatient TSA based on their medical comorbidities and demographic profile.
门诊全肩关节置换术（TSA）的患者选择对于优化患者预后非常重要。本研究旨在开发一种机器学习工具，该工具可能有助于根据医疗合并症和人口统计学因素选择门诊全肩置换术的患者。
在美国外科医师学会国家手术质量改进计划中，对 2011 年至 2016 年接受择期 TSA 的患者进行了查询。使用随机森林机器学习模型来预测哪些患者的住院时间为 1 天或更短（短时间住院）。然后，使用多变量逻辑回归来确定哪些变量与短时间或长时间住院显著相关。
2011 年至 2016 年，确定了 4500 名患者接受了择期 TSA 手术，且记录了必要的预测特征和结果。机器学习模型能够成功识别出短期住院患者，其受试者工作特征曲线下面积为 0.77。多变量逻辑回归确定了许多与短期住院相关的变量，包括年龄小于 70 岁和男性，以及与较长住院时间相关的变量，包括糖尿病、慢性阻塞性肺疾病和美国麻醉医师协会分类大于 2 级。
机器学习可用于根据患者的医疗合并症和人口统计学特征预测哪些患者适合短期住院或门诊 TSA。
