Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO.
Orthopedics Department, Kaiser Permanente Colorado, Denver, CO; Colorado Permanente Medical Group, Denver, CO.
J Arthroplasty. 2020 Jul;35(7):1840-1846.e2. doi: 10.1016/j.arth.2020.02.032. Epub 2020 Feb 21.
Demand for joint replacement is increasing, with many patients receiving postsurgical physical therapy (PT) in non-inpatient settings. Clinicians need a reliable tool to guide decisions about the appropriate PT setting for patients discharged home after surgery. We developed and validated a model to predict PT location for patients in our health system discharged home after total knee arthroplasty.
We analyzed data for patients who completed a preoperative total knee risk assessment in 2017 (model development cohort) or during the first 6 months of 2018 (model validation cohort). The initial total knee risk assessment, to guide rehabilitation disposition, included 28 variables in mobility, social, and environment domains, and on patient demographics and comorbidities. Multivariable logistic regression was used to identify factors that best predict discharge to home health service (HHS) vs home with outpatient PT. Model performance was assessed by standard criteria.
The development cohort included 259 patients (19%) discharged to HHS and 1129 patients (81%) discharged to home with outpatient PT. The validation cohort included 609 patients, with 91 (15%) discharged to HHS. The final model included age, gender, motivation for outpatient PT, and reliable transportation. Patients without motivation for outpatient PT had the highest probability of discharge to HHS, followed by those without reliable transportation. Model performance was excellent in the development and validation cohort, with c-statistics of 0.91 and 0.86, respectively.
We developed and validated a predictive model for total knee arthroplasty PT discharge location. This model includes 4 variables with accurate prediction to guide patient-clinician preoperative decision making.
对关节置换的需求不断增加,许多患者在非住院环境中接受术后物理治疗(PT)。临床医生需要一种可靠的工具来指导决策,以确定手术后出院回家的患者适合哪种 PT 环境。我们开发并验证了一种模型,以预测在我们的医疗系统中接受全膝关节置换手术后出院回家的患者的 PT 位置。
我们分析了 2017 年接受术前全膝关节风险评估(模型开发队列)或 2018 年前 6 个月接受评估的患者的数据。初始全膝关节风险评估旨在指导康复处置,包括移动性、社交和环境领域以及患者人口统计学和合并症的 28 个变量。多变量逻辑回归用于确定最佳预测出院至家庭健康服务(HHS)与家庭门诊 PT 的因素。通过标准标准评估模型性能。
开发队列包括 259 名(19%)出院至 HHS 和 1129 名(81%)出院至家庭门诊 PT 的患者。验证队列包括 609 名患者,其中 91 名(15%)出院至 HHS。最终模型包括年龄、性别、门诊 PT 的动机和可靠的交通工具。没有门诊 PT 动机的患者出院至 HHS 的可能性最高,其次是没有可靠交通工具的患者。该模型在开发和验证队列中的表现均出色,C 统计量分别为 0.91 和 0.86。
我们开发并验证了一种用于全膝关节置换术 PT 出院位置的预测模型。该模型包含 4 个变量,具有准确的预测能力,可指导患者-临床医生术前决策。