Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Research Institute, Charlestown, MA, USA.
St David's Medical Center/St. David's Rehabilitation Hospital, Austin, TX, USA.
J Am Med Dir Assoc. 2021 Dec;22(12):2461-2467. doi: 10.1016/j.jamda.2021.03.033. Epub 2021 May 11.
To quantify the rate of readmission from inpatient rehabilitation facilities (IRFs) to acute care hospitals (ACHs) during the first 30 days of rehabilitation stay. To measure variation in 30-day readmission rate across IRFs, and the extent that patient and facility characteristics contribute to this variation.
Retrospective analysis of an administrative database.
Adult IRF discharges from 944 US IRFs captured in the Uniform Data System for Medical Rehabilitation database between October 1, 2015 and December 31, 2017.
Multilevel logistic regression was used to calculate adjusted rates of readmission within 30 days of IRF admission and examine variation in IRF readmission rates, using patient and facility-level variables as predictors.
There were a total of 104,303 ACH readmissions out of a total of 1,102,785 IRFs discharges. The range of 30-day readmission rates to ACHs was 0.0%‒28.9% (mean = 8.7%, standard deviation = 4.4%). The adjusted readmission rate variation narrowed to 2.8%‒17.5% (mean = 8.7%, standard deviation = 1.8%). Twelve patient-level and 3 facility-level factors were significantly associated with 30-day readmission from IRF to ACH. A total of 82.4% of the variance in 30-day readmission rate was attributable to the model predictors.
Fifteen patient and facility factors were significantly associated with 30-day readmission from IRF to ACH and explained the majority of readmission variance. Most of these factors are nonmodifiable from the IRF perspective. These findings highlight that adjusting for these factors is important when comparing readmission rates between IRFs.
量化康复住院患者(IRF)在康复住院的前 30 天内重新入住急性护理医院(ACH)的比例。测量 IRF 之间 30 天再入院率的差异,以及患者和设施特征对这种差异的影响程度。
对行政数据库进行回顾性分析。
2015 年 10 月 1 日至 2017 年 12 月 31 日期间,在医疗康复统一数据系统中捕获的来自美国 944 家 IRF 的成年 IRF 出院患者。
使用多水平逻辑回归计算 30 天内重新入院的调整后比率,并使用患者和设施水平变量作为预测因子,检查 IRF 再入院率的差异。
在总共 1102785 例 IRF 出院患者中,有 104303 例患者重新入住 ACH。30 天内重新入住 ACH 的再入院率范围为 0.0%至 28.9%(平均为 8.7%,标准差为 4.4%)。调整后的再入院率差异缩小至 2.8%至 17.5%(平均为 8.7%,标准差为 1.8%)。12 个患者水平和 3 个设施水平因素与从 IRF 重新入院到 ACH 显著相关。30 天再入院率的总方差中有 82.4%归因于模型预测因子。
15 个患者和设施因素与从 IRF 重新入院到 ACH 显著相关,并解释了大部分再入院的差异。这些因素中的大多数从 IRF 的角度来看是不可改变的。这些发现强调,在比较 IRF 之间的再入院率时,调整这些因素非常重要。