A Vital Signs Telemonitoring Programme Improves the Dynamic Prediction of Readmission Risk in Patients with Heart Failure

• Published in: AMIA ... Annual Symposium proceedings Vol. 2020; pp. 432 - 441
• Main Authors: Fahimi, Fatemeh, Guo, Yang, Tong, Shao Chuen, Ng, Angela, Bing, Sharon Ong Yu, Choo, Bryan, Weiliang, Huang, Lee, Sheldon, Ramasamy, Savitha, Chow, Wai Leng, Choon, Oh Hong, Krishnaswamy, Pavitra
• Format: Journal Article
• Language: English
• Published: United States American Medical Informatics Association 2020

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.