Fahimi F, Guo Y, Tong SC, et al. A Vital Signs Telemonitoring Programme Improves the Dynamic Prediction of Readmission Risk in Patients with Heart Failure. AMIA Annu Symp Proc. 2021;2020:432-441. Published 2021 Jan 25.
Abstract:
Heart failure (HF) is a leading cause of hospital readmissions. There is great interest in approaches to efficiently predict emerging HF-readmissions in the community setting. We investigate the possibility of leveraging streaming telemonitored vital signs data alongside readily accessible patient profile information for predicting evolving 30-day HF-related readmission risk. We acquired data within a non-randomized controlled study that enrolled 150 HF patients over a 1–year post-discharge telemonitoring and telesupport programme. Using the sequential data and associated ground truth readmission outcomes, we developed a recurrent neural network model for dynamic risk prediction. The model detects emerging readmissions with sensitivity > 71%, specificity > 75%, AUROC ~80%. We characterize model performance in relation to telesupport based nurse assessments, and demonstrate strong sensitivity improvements. Our approach enables early stratification of high-risk patients and could enable adaptive targeting of care resources for managing patients with the most urgent needs at any given time.
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Publisher Copyright
Funding Info:
This research / project is supported by the SERC, A*STAR - SERC Strategic Fund (SSF)
Grant Reference no. : SSF A1718g0044
This research / project is supported by the SERC, A*STAR - SERC Strategic Fund (SSF)
Grant Reference no. : SSF A1718g0045
This research / project is supported by the A*STAR - IAF-PP
Grant Reference no. : H19/01/a0/023
This research / project is supported by the Economic Development Board (Singapore Living Lab Fund) and Philips Electronics Hospital - Home Pilot Project
Grant Reference no. : S14-1035- RF-LLF H and W
Description:
The article is also archived in the following URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075426/