James Melissa K, Robitsek R Jonathan, Saghir Syed M, Gentile Patricia A, Ramos Marylin, Perez Frances
Department of Surgery, Jamaica Hospital Medical Center, Jamaica, New York, USA.
Department of Medicine, University of Nevada, Las Vegas, Nevada, USA.
Injury. 2018 May;49(5):975-982. doi: 10.1016/j.injury.2018.02.014. Epub 2018 Feb 14.
Falls can result in injuries that require rehabilitation and long-term care after hospital discharge. Identifying factors that contribute to prediction of discharge disposition is crucial for efficient resource utilization and reducing cost. Several factors may influence discharge location after hospitalization for a fall. The aim of this study was to examine clinical and non-clinical factors that may predict discharge disposition after a fall. We hypothesized that age, injury type, insurance type, and functional status would affect discharge location.
This two-year retrospective study was performed at an urban, adult level-1 trauma center. Fall patients who were discharged home or to a facility after hospital admission were included in the study. Data was obtained from the trauma registry and electronic medical records. Logistic regression modeling was used to assess independent predictors.
A total of 1,121 fallers were included in the study. 621 (55.4%) were discharged home and 500 (44.6%) to inpatient rehabilitation (IRF)/skilled nursing facility (SNF). The median age was 64 years (IQR: 49-79) and 48.4% (543) were male. The median length of hospital stay was 5 days (IQR: 2.5-8). Increasing age (p < 0.001), length of stay in the ICU (p < 0.001), injury severity (p < 0.001), number of comorbidities (p = 0.038), having Medicare insurance (p = 0.025), having a fracture at any body region (p < 0.001), and ambulation status (p = 0.025) significantly increased the odds of being discharged to IRF/SNF compared to home. The removal of injury severity score and ICU length of stay from the "late/regular discharge" model, to create an "early discharge" model, decreased the accuracy of the prediction rate from 78.5% to 74.9% (p < 0.001).
A combination of demographic, clinical, social, economic, and functional factors can together predict discharge disposition after a fall. The majority of these factors can be assessed early in the hospital stay, which may facilitate a timely discharge plan and shorter stays in the hospital.
跌倒可能导致受伤,出院后需要康复治疗和长期护理。识别有助于预测出院处置方式的因素对于有效利用资源和降低成本至关重要。住院跌倒后,有几个因素可能会影响出院地点。本研究的目的是探讨可能预测跌倒后出院处置方式的临床和非临床因素。我们假设年龄、损伤类型、保险类型和功能状态会影响出院地点。
本为期两年的回顾性研究在一家城市一级成人创伤中心进行。纳入研究的对象为入院后出院回家或前往机构的跌倒患者。数据来自创伤登记处和电子病历。采用逻辑回归模型评估独立预测因素。
共有1121名跌倒患者纳入研究。621名(55.4%)出院回家,500名(44.6%)入住 inpatient rehabilitation(IRF)/专业护理机构(SNF)。中位年龄为64岁(四分位间距:49 - 79岁),48.4%(543名)为男性。中位住院时间为5天(四分位间距:2.5 - 8天)。与出院回家相比,年龄增加(p < 0.001)、在重症监护病房的住院时间(p < 0.001)、损伤严重程度(p < 0.001)、合并症数量(p = 0.038)、拥有医疗保险(p = 0.025)、身体任何部位发生骨折(p < 0.001)以及行走状态(p = 0.025)均显著增加了出院至IRF/SNF的几率。从“延迟/常规出院”模型中去除损伤严重程度评分和重症监护病房住院时间,以创建“早期出院”模型,预测率的准确性从78.5%降至74.9%(p < 0.001)。
人口统计学、临床、社会、经济和功能因素相结合可共同预测跌倒后的出院处置方式。这些因素中的大多数可在住院早期进行评估,这可能有助于制定及时的出院计划并缩短住院时间。