Mannani Mehran, Motififard Mehdi, Farajzadegan Ziba, Nemati Amin
Medical Student, Student Research Committee, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Professor of Orthopedic Surgery, Orthopedics Department, Kashani Hospital, Isfahan University of Medical Sciences, Isfahan, Iran.
J Orthop. 2022 Jun 2;32:121-124. doi: 10.1016/j.jor.2022.05.018. eCollection 2022 Jul-Aug.
Minimizing costs associated with the care of patients undergoing total knee arthroplasty (TKA) can reduce the burden on health systems that regularly struggle with limited resources. Predicting and reducing TKA associated length of stay (LoS) can therefore be invaluable. This study aimed to determine the factors that impact LoS in patients undergoing TKA and propose a model design to predict LoS.
A retrospective study was performed on patients undergoing TKA in a tertiary teaching hospital. Patients who underwent TKA from March 2007 to March 2021 were included in the study. Data were extracted from available electronic and paper records. Variables evaluated included: patients' demographic data, general admission data, laboratory data, transfusion, operation data, and preoperative comorbidities and medical history. Independent T-test, one-way ANOVA, and Pearson correlation were used for univariate data analysis. For multivariate analysis and model designing, multiple regression stepwise methods were used.
878 patients were included in this study. Mean LoS was 6.09 (SD = 1.83) with a median of 6 days. Factors found to have a significant effect on length of stay were age, revision surgery, Anesthesia type, intensive care unit admission, insurance, transfusion, preoperative hemoglobin level, and pre-operative platelet (Plt) count. Applying a multiple regression stepwise model to these variables showed that the following pre-operative factors can be predictive for LoS: revision surgery, sex, medical insurance, hemoglobin level, and Plt count.
It was deduced that sex, revision, pre-operative hemoglobin and Plt level and health insurance were the best predictors for LoS in patients undergoing TKA.
将全膝关节置换术（TKA）患者的护理成本降至最低，可以减轻经常面临资源有限问题的卫生系统的负担。因此，预测并缩短与TKA相关的住院时间可能非常重要。本研究旨在确定影响TKA患者住院时间的因素，并提出一个预测住院时间的模型设计。
对一家三级教学医院接受TKA的患者进行了一项回顾性研究。纳入了2007年3月至2021年3月期间接受TKA的患者。数据从可用的电子和纸质记录中提取。评估的变量包括：患者的人口统计学数据、一般入院数据、实验室数据、输血情况、手术数据以及术前合并症和病史。采用独立样本t检验、单因素方差分析和Pearson相关性分析进行单变量数据分析。对于多变量分析和模型设计，使用了多元逐步回归方法。
本研究共纳入878例患者。平均住院时间为6.09天（标准差=1.83），中位数为6天。发现对住院时间有显著影响的因素包括年龄、翻修手术、麻醉类型、重症监护病房入住情况、保险、输血、术前血红蛋白水平和术前血小板计数。将多元逐步回归模型应用于这些变量显示，以下术前因素可预测住院时间：翻修手术、性别、医疗保险、血红蛋白水平和血小板计数。
推断出性别、翻修手术、术前血红蛋白和血小板水平以及医疗保险是TKA患者住院时间的最佳预测因素。
