Fisher Steve R, Graham James E, Krishnan Shilpa, Ottenbacher Kenneth J
S.R. Fisher, PT, PhD, Department of Physical Therapy, The University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555-0460 (USA).
J.E. Graham, DC, PhD, Division of Rehabilitation Sciences, The University of Texas Medical Branch.
Phys Ther. 2016 Jan;96(1):62-70. doi: 10.2522/ptj.20150034. Epub 2015 Sep 10.
The proposed Centers for Medicare & Medicaid Services (CMS) 30-day readmission risk standardization models for inpatient rehabilitation facilities establish readmission risk for patients at admission based on a limited set of core variables. Considering functional recovery during the rehabilitation stay may help clinicians further stratify patient groups at high risk for hospital readmission.
The purpose of this study was to identify variables in the full administrative medical record, particularly in regard to physical function, that could help clinicians further discriminate between patients who are and are not likely to be readmitted to an acute care hospital within 30 days of rehabilitation discharge.
This study used an observational cohort with a 30-day follow-up of Medicare patients who were deconditioned and had medically complex diagnoses and who were receiving postacute inpatient rehabilitation in 2010 to 2011.
Patients in the highest risk quartile for readmission (N=25,908) were selected based on the CMS risk prediction model. Hierarchical generalized linear models were built to compare the relative effectiveness of motor functional status ratings in predicting 30-day readmission. Classification and regression tree analysis was used to create a hierarchical order among predictors based on variable importance in classifying patients based on readmission status.
Approximately 34% of patients in the high-risk quartile were readmitted within 30 days. Functional outcomes and rehabilitation length of stay were the best predictors of 30-day rehospitalization. A 3-variable algorithm classified 4 clinical subgroups with readmission probabilities ranging from 28% to 75%.
Although planned readmissions were accounted for in the outcome, potentially preventable readmissions were not distinguished from unpreventable readmissions.
For older patients who are deconditioned and have medically complex diagnoses admitted to postacute inpatient rehabilitation, information on functional status measures that are easily monitored by health care providers may improve plans for care transition and reduce the risk of hospital readmission.
美国医疗保险和医疗补助服务中心（CMS）提议的针对住院康复机构的30天再入院风险标准化模型，基于一组有限的核心变量在患者入院时确定其再入院风险。考虑康复住院期间的功能恢复情况可能有助于临床医生进一步对有高再入院风险的患者群体进行分层。
本研究的目的是在完整的行政病历中识别变量，尤其是与身体功能相关的变量，这些变量可帮助临床医生进一步区分康复出院后30天内可能再次入住急症医院和不太可能再次入住的患者。
本研究采用观察性队列研究，对2010年至2011年接受急性后期住院康复治疗、身体状况不佳且患有复杂医疗诊断的医疗保险患者进行了30天的随访。
根据CMS风险预测模型选择再入院风险最高四分位数的患者（N = 25,908）。构建分层广义线性模型以比较运动功能状态评分在预测30天再入院方面的相对有效性。使用分类与回归树分析，根据变量在基于再入院状态对患者进行分类中的重要性，在预测变量之间创建分层顺序。
高风险四分位数中的患者约34%在30天内再次入院。功能结果和康复住院时间是30天再住院的最佳预测指标。一种三变量算法将4个临床亚组分类，再入院概率范围为28%至75%。
尽管在结果中考虑了计划内再入院，但潜在可预防的再入院与不可预防的再入院未作区分。
对于身体状况不佳且患有复杂医疗诊断并入住急性后期住院康复机构的老年患者，医疗保健提供者易于监测的功能状态测量信息可能会改善护理过渡计划并降低医院再入院风险。
