Analyzing Hospital Readmissions Using Creatinine Results for Patients with Many Visits

Analyzing who is likely to be readmitted and understanding some key factors contributing to preventable readmissions in hospitals is being widely researched, but few studies have examined the role of clinical disease markers in predicting frequent readmissions related to disease progression. In this study, we explore 7-day readmission risk prediction using data on patients' creatinine levels, a key laboratory marker of serious illness, as a potential predictor of future readmission for patients with a large number of repeat hospital visits. Using Electronic Health Record data on 5,103 patients, we explore prediction of readmission using a multivariate logistic regression model. Preliminary results suggest three significant components impacting the readmission occurrence: age, gender and creatinine levels. Further research will incorporate other disease markers using data mining methods, combined with time-invariant and time-varying covariates, to identify more insightful set of longitudinal factors for readmission risk reduction.