A Validated Risk Model for 30-Day Readmission for Heart Failure Mahajan, S. M., Burman, P., Newton, A., Heidenreich, P. A., Gundlapalli, A. V., Jaulent, M. C., Zhao, D. IOS PRESS. 2017: 506–10


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.

View details for DOI 10.3233/978-1-61499-830-3-506

View details for Web of Science ID 000449471200105

View details for PubMedID 29295146