Key result
Integrating Fitbit data with clinical variables improves 90-day sepsis readmission prediction to 0.85 AUC.
Why the study?
Patients with sepsis have high readmission risks, and physical activity levels associate with readmission risk, motivating whether combining wearable activity data with clinical measurements can predict rehospitalization.
Does integrating Fitbit data with clinical measurements predict 90-day unplanned rehospitalization in patients with sepsis?
Cohort (n=9,910)
Yes
Does integrating Fitbit data with clinical measurements predict 90-day unplanned rehospitalization in patients with sepsis?
Absolute Event Rate: 0.85% vs 0.74%
p-value: p=<1e-3
Integrating wearable activity data with clinical measurements may improve the prediction of 90-day readmission in sepsis patients.
May enhance sepsis readmission risk models; leaves open prospective validation and outcome impact.
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 patient's 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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Amrollahi et al. (2023) conducted a cohort in Sepsis (n=9,910). Integration of clinical and wearable (Fitbit) data vs. Clinical data alone was evaluated on Prediction of 90-day hospital readmission (AUCroc) (p=<1e-3). Integrating pre- and post-discharge wearable Fitbit data with clinical variables improved the prediction of 90-day hospital readmission in sepsis patients, achieving an AUCroc of 0.85.
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