El Morr Christo, Ginsburg Liane, Nam Seungree, Woollard Susan
Faculty of Health, School of Health Policy and Management, York University, Toronto, ON, Canada.
North York General Hospital, Medicine, North York General Hospital, Toronto, ON, Canada.
Interact J Med Res. 2017 Mar 8;6(1):e2. doi: 10.2196/ijmr.7183.
The LACE index was designed to predict early death or unplanned readmission after discharge from hospital to the community. However, implementing the LACE tool in real time in a teaching hospital required practical unavoidable modifications.
The purpose of this study was to validate the implementation of a modified LACE index (LACE-rt) and test its ability to predict readmission risk using data in a hospital setting.
Data from the Canadian Institute for Health Information's Discharge Abstract Database (DAD), the National Ambulatory Care Reporting System (NACRS), and the hospital electronic medical record for one large community hospital in Toronto, Canada, were used in this study. A total of 3855 admissions from September 2013 to July 2014 were analyzed (N=3855) using descriptive statistics, regression analysis, and receiver operating characteristic analysis. Prospectively collected data from DAD and NACRS were linked to inpatient data.
The LACE-rt index was a fair test to predict readmission risk (C statistic=.632). A LACE-rt score of 10 is a good threshold to differentiate between patients with low and high readmission risk; the high-risk patients are 2.648 times more likely to be readmitted than those at low risk. The introduction of LACE-rt had no significant impact on readmission reduction.
The LACE-rt is a fair tool for identifying those at risk of readmission. A collaborative cross-sectoral effort that includes those in charge of providing community-based care is needed to reduce readmission rates. An eHealth solution could play a major role in streamlining this collaboration.
LACE指数旨在预测从医院出院回到社区后的早期死亡或计划外再入院情况。然而，在教学医院实时应用LACE工具需要进行一些实际且不可避免的调整。
本研究旨在验证改良版LACE指数（LACE-rt）的实施情况，并利用医院环境中的数据测试其预测再入院风险的能力。
本研究使用了来自加拿大卫生信息研究所的出院摘要数据库（DAD）、国家门诊护理报告系统（NACRS）以及加拿大多伦多一家大型社区医院的医院电子病历数据。采用描述性统计、回归分析和受试者工作特征分析，对2013年9月至2014年7月期间的3855例入院病例（N = 3855）进行了分析。前瞻性收集的DAD和NACRS数据与住院患者数据相关联。
LACE-rt指数是预测再入院风险的一项尚可的测试（C统计量 = 0.632）。LACE-rt评分为10是区分低再入院风险和高再入院风险患者的良好阈值；高风险患者再入院的可能性是低风险患者的2.648倍。引入LACE-rt对降低再入院率没有显著影响。
LACE-rt是识别有再入院风险患者的一项尚可的工具。需要包括负责提供社区护理的人员在内的跨部门协作努力，以降低再入院率。电子健康解决方案在简化这种协作方面可以发挥重要作用。
