Hemodynamic Monitoring Using Switching Autoregressive Dynamics of Multivariate Vital Sign Time Series

• Type of Material: Conference Paper
• Authors: Li-Wei H. Lehman, MIT; Shamim Nemati, Emory University; Roger G. Mark, MIT
• Language: English
• Date: 2016-02-18
• Publisher: Emory University Libraries
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2015, IEEE
• Final Published Version (URL): http://dx.doi.org/10.1109/CIC.2015.7411098
• Title of Journal or Parent Work: Computing in Cardiology Conference (CinC), 2015
• Conference or Event Name: 2015 Comp in Cardiol Conference (CinC)
• Volume: 42
• Start Page: 1065
• End Page: 1068
• Grant/Funding Information: This work was supported by the National Institutes of Health (NIH) grants R01-EB017205 and R01-GM104987.
• Keywords: Science & Technology; Engineering, Biomedical; Computer Science, Interdisciplinary Applications; Technology; Computer Science; Engineering, Multidisciplinary; Engineering; MANAGEMENT
• Research Categories: Health Sciences, Medicine and Surgery; Health Sciences, General; Health Sciences, Health Care Management

Abstract

In a critical care setting, shock and resuscitation endpoints are often defined based on arterial blood pressure values. Patient-specific fluctuations and interactions between heart rate (HR) and blood pressure (BP), however, may provide additional prognostic value to stratify individual patients' risks for adverse outcomes at different blood pressure targets. In this work, we use the switching autoregressive (SVAR) dynamics inferred from the multivariate vital sign time series to stratify mortality risks of intensive care units (ICUs) patients receiving vasopressor treatment. We model vital sign observations as generated from latent states from an autoregressive Hidden Markov Model (AR-HMM) process, and use the proportion of time patients stayed in different latent states to predict outcome. We evaluate the performance of our approach using minute-by-minute HR and mean arterial BP (MAP) of an ICU patient cohort while on vasopressor treatment. Our results indicate that the bivariate HR/MAP dynamics (AUC 0.74 [0.64, 0.84]) contain additional prognostic information beyond the MAP values (AUC 0.53 [0.42, 0.63]) in mortality prediction. Further, HR/MAP dynamics achieved better performance among a subgroup of patients in a low MAP range (median MAP < 65 mmHg) while on pressors. A realtime implementation of our approach may provide clinicians a tool to quantify the effectiveness of interventions and to inform treatment decisions.