A Validated Risk Model for 30- Day Readmission for Heart Failure

摘要：

　　One of the goals of the Precision Medicine Initiative launched in the United States in 2016 is to use innovative tools and sources in data science. We realized this goal by implementing a use case that identified patients with heart failure at Veterans Health Administration using data from the Electronic Health Records from multiple health domains between 2005 and 2013. We applied a regularized logistic regression model and predicted 30-day readmission risk for 1210 unique patients. Our validation cohort resulted in a C-statistic of 0.84. Our top predictors of readmission were prior diagnosis of heart failure, vascular and renal diseases, and malnutrition as comorbidities, compliance with outpatient follow-up, and low socioeconomic status. This validated risk prediction scheme delivered better performance than the published models so far (C-Statistics: 0.69). It can be used to stratify patients for readmission and to aid clinicians in delivering precise health interventions.

关键词： Patient Readmission Models,Theoretical Heart Failure

作者: Satish M.Mahajan Prabir Burman Ana Newton Paul A.Heidenreich

作者单位: Veterans Health Administration,Palo Alto,California,USA;School of Nursing,University of California D Department of Statistics,University of California Davis,Davis,California,USA College of Public Health,University of San Francisco,San Francisco,California,USA Veterans Health Administration,Palo Alto,California,USA;Department of Cardiology,Stanford University

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 506-510

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）