Predicting Hospital Readmission among Patients with Sepsis using Clinical and Wearable Data
Amrollahi, F.; Shashikumar, S. P.; Yhdego, H.; Nayebnazar, A.; Yung, N.; Wardi, G.; Nemati, S.
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Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patients physical activity levels and readmission risk. In this study, we show that distribution of activity level prior and post-discharge among patients with sepsis are predictive of unplanned rehospitalization in 90 days (P-value<1e-3). Our preliminary results indicate that integrating Fitbit data with clinical measurements may improve model performance on predicting 90 days readmission.
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