Cui Yang, Metge Colleen, Ye Xibiao, Moffatt Michael, Oppenheimer Luis, Forget Evelyn L
The George and Fay Yee Centre for Healthcare Innovation, Winnipeg, Manitoba, Canada Winnipeg Regional Health Authority, Winnipeg, Manitoba, Canada Department of Community Health Sciences, Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
The George and Fay Yee Centre for Healthcare Innovation, Winnipeg, Manitoba, Canada Winnipeg Regional Health Authority, Winnipeg, Manitoba, Canada Department of Community Health Sciences, Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada.
J Health Serv Res Policy. 2015 Apr;20(2):83-91. doi: 10.1177/1355819614565498. Epub 2015 Jan 8.
A number of predictive models have been developed to identify patients at risk of hospital readmission. Most of these have focused on readmission within 30 days of discharge. We used population-based health administrative data to develop a predictive model for hospital readmission within 12 months of discharge in Winnipeg, Canada.
This was a retrospective cohort study with derivation and validation data sets. Multivariable logistic regression analyses were performed and factors significantly associated with readmission were selected to construct a risk scoring tool.
Several variables were identified that predicted readmission (i.e. older age, male, at least one hospital admission in the previous two years, an emergent (index) hospital admission, Charlson comorbidity score >0 and length of stay). Discrimination power was acceptable (C statistic =0.701). At a median risk score threshold, the sensitivity, specificity, positive and negative predictive values were 45.5%, 79%, 68.8% and 58.6%.
This predictive model demonstrated that hospital readmission within 12 months of discharge can be reasonably well predicted based on administrative data. It will help health care providers target interventions to prevent unnecessary hospital readmissions.
已开发出多种预测模型来识别有再次入院风险的患者。其中大多数模型聚焦于出院后30天内的再次入院情况。我们利用基于人群的卫生行政数据，开发了一种针对加拿大温尼伯市出院后12个月内再次入院情况的预测模型。
这是一项采用推导数据集和验证数据集的回顾性队列研究。进行了多变量逻辑回归分析，并选择与再次入院显著相关的因素来构建风险评分工具。
确定了几个可预测再次入院的变量（即年龄较大、男性、过去两年内至少有一次住院、急诊（索引）住院、查尔森合并症评分>0以及住院时间）。判别能力尚可（C统计量=0.701）。在中位风险评分阈值下，敏感性、特异性、阳性预测值和阴性预测值分别为45.5%、79%、68.8%和58.6%。
该预测模型表明，基于行政数据能够对出院后12个月内的再次入院情况进行较为合理的预测。这将有助于医疗服务提供者针对干预措施，以防止不必要的再次入院。
