Park Jaeyoung, Zhong Xiang, Miley Emilie N, Gray Chancellor F
Booth School of Business, University of Chicago, Chicago, IL, USA.
Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA.
Arthroplast Today. 2023 Jul 13;22:101166. doi: 10.1016/j.artd.2023.101166. eCollection 2023 Aug.
The aim of this study was to improve understanding of hospital length of stay (LOS) in patients undergoing total joint arthroplasty (TJA) in a high-efficiency, hospital-based pathway.
We retrospectively reviewed 1401 consecutive primary and revision TJA patients across 67 patient and preoperative care characteristics from 2016 to 2019 from the institutional electronic health records. A machine learning approach, testing multiple models, was used to assess predictors of LOS.
The median LOS was 1 day; outpatients accounted for 16.5%, 1-day inpatient stays for 38.0%, 2-day stays for 26.4%, and 3-days or more for 19.1%. Patients characteristically fell into 1 of 3 broad categories that contained relatively similar characteristics: outpatient (0-day LOS), short stay (1- to 2-day LOS), and prolonged stay (3 days or greater). The random forest models suggested that a lower Risk Assessment and Prediction Tool score, unplanned admission or hospital transfer, and a medical history of cardiovascular disease were associated with an increased LOS. Documented narcotic use for surgery preparation prior to hospitalization and preoperative corticosteroid use were factors independently associated with a decreased LOS.
After TJA, most patients have either an outpatient or short-stay hospital episode. Patients who stay 2 days do not differ substantially from patients who stay 1 day, while there is a distinct group that requires prolonged admission. Our machine learning models support a better understanding of the patient factors associated with different hospital LOS categories for TJA, demonstrating the potential for improved health policy decisions and risk stratification for centers caring for complex patients.
本研究旨在提高对高效、基于医院的全关节置换术（TJA）患者住院时间（LOS）的理解。
我们回顾性分析了2016年至2019年机构电子健康记录中1401例连续的初次和翻修TJA患者的67项患者和术前护理特征。采用机器学习方法，测试多种模型，以评估住院时间的预测因素。
中位住院时间为1天；门诊患者占16.5%，住院1天的患者占38.0%，住院2天的患者占26.4%，住院3天或更长时间的患者占19.1%。患者通常分为3大类中的1类，每类具有相对相似的特征：门诊患者（住院时间0天）、短期住院患者（住院时间1至2天）和长期住院患者（住院时间3天或更长）。随机森林模型表明，较低的风险评估和预测工具评分、非计划入院或医院转诊以及心血管疾病病史与住院时间延长有关。住院前记录的用于手术准备的麻醉药物使用和术前使用皮质类固醇是与住院时间缩短独立相关的因素。
TJA术后，大多数患者为门诊或短期住院。住院2天的患者与住院1天的患者没有实质性差异，而有一个明显的群体需要延长住院时间。我们的机器学习模型有助于更好地理解与TJA不同住院时间类别相关的患者因素，证明了改善卫生政策决策和为照顾复杂患者的中心进行风险分层的潜力。
