Department of Occupational Therapy, University of Texas Medical Branch, Galveston, Texas, United States of America.
Division of Rehabilitation Sciences, University of Texas Medical Branch, Galveston, Texas, United States of America.
PLoS One. 2020 May 7;15(5):e0232017. doi: 10.1371/journal.pone.0232017. eCollection 2020.
Methods used to categorize functional status to predict health outcomes across post-acute care settings vary significantly.
We compared three methods that categorize functional status to predict 30-day and 90-day hospital readmission across inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF) and home health agencies (HHA).
Retrospective analysis of 2013-2014 Medicare claims data (N = 740,530). Data were randomly split into two subsets using a 1:1 ratio. We used half of the cohort (development subset) to develop functional status categories for three methods, and then used the rest (testing subset) to compare outcome prediction. Three methods to generate functional categories were labeled as: Method I, percentile based on proportional distribution; Method II, percentile based on change score distribution; and Method III, functional staging categories based on Rasch person strata. We used six differentiation and classification statistics to determine the optimal method of generating functional categories.
IRF, SNF and HHA.
We included 130,670 (17.7%) Medicare beneficiaries with stroke, 498,576 (67.3%) with lower extremity joint replacement and 111,284 (15.0%) with hip and femur fracture.
Unplanned 30-day and 90-day hospital readmission.
For all impairment conditions, Method III best predicted 30-day and 90-day hospital readmission. However, we observed overlapping confidence intervals among some comparisons of three methods. The bootstrapping of 30-day and 90-day hospital readmission predictive models showed the area under curve for Method III was statistically significantly higher than both Method I and Method II (all paired-comparisons, p<.001), using the testing sample.
Overall, functional staging was the optimal method to generate functional status categories to predict 30-day and 90-day hospital readmission. To facilitate clinical and scientific use, we suggest the most appropriate method to categorize functional status should be based on the strengths and weaknesses of each method.
用于对功能状态进行分类以预测不同康复治疗机构（包括住院康复机构、熟练护理机构和家庭保健机构）健康结局的方法差异显著。
我们比较了三种用于对功能状态进行分类以预测住院康复患者 30 天和 90 天内再入院的方法。
这是一项对 2013 年至 2014 年医疗保险索赔数据（N=740530）的回顾性分析。使用 1:1 的比例将数据随机分为两组。我们使用队列的一半（开发子集）来为三种方法生成功能状态类别，然后使用其余部分（测试子集）来比较结果预测。用于生成功能类别的三种方法分别标记为：方法 I，基于比例分布的百分位数；方法 II，基于变化得分分布的百分位数；方法 III，基于 Rasch 个体层的功能分期类别。我们使用六个区分和分类统计数据来确定生成功能类别最佳的方法。
住院康复机构、熟练护理机构和家庭保健机构。
我们纳入了 130670 名（17.7%）患有脑卒中的、498576 名（67.3%）下肢关节置换术患者和 111284 名（15.0%）髋部和股骨骨折患者。
无计划的 30 天和 90 天内再次住院。
对于所有功能障碍情况，方法 III 最能预测 30 天和 90 天内的医院再入院。然而，我们观察到三种方法之间的一些比较存在重叠的置信区间。使用测试样本对 30 天和 90 天内医院再入院预测模型进行的自举分析显示，方法 III 的曲线下面积在统计学上显著高于方法 I 和方法 II（所有配对比较，p<.001）。
总体而言，功能分期是生成功能状态类别以预测 30 天和 90 天内医院再入院的最佳方法。为便于临床和科学应用，我们建议应根据每种方法的优缺点选择最合适的方法对功能状态进行分类。
