Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care.

Affiliation

Hernandez L(1), Kim R(1), Tokcan N(2), Derksen H(3), Biesterveld BE(4), Croteau A(5), Williams AM(4), Mathis M(6), Najarian K(7), Gryak J(8).
Author information:
(1)Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States.
(2)The Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States.
(3)Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, United States.
(4)Department of Surgery, University of Michigan, Ann Arbor, MI 48109, United States.
(5)Hartford HealthCare Medical Group, Hartford, CT 06106, United States.
(6)Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48109, United States.
(7)Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States; Michigan Center for Integrative Research in Critical Care
(MCIRCC), University of Michigan, Ann Arbor, MI 48109, United States; Michigan Institute for Data Science
(MIDAS), University of Michigan, Ann Arbor, MI 48109, United States.
(8)Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States; Michigan Institute for Data Science
(MIDAS), University of Michigan, Ann Arbor, MI 48109, United States. Electronic address: [Email]

Abstract

Patients recovering from cardiovascular surgeries may develop life-threatening complications such as hemodynamic decompensation, making the monitoring of patients for such complications an essential component of postoperative care. However, this need has given rise to an inexorable increase in the number and modalities of data points collected, making it challenging to effectively analyze in real time. While many algorithms exist to assist in monitoring these patients, they often lack accuracy and specificity, leading to alarm fatigue among healthcare practitioners. In this study we propose a multimodal approach that incorporates salient physiological signals and EHR data to predict the onset of hemodynamic decompensation. A retrospective dataset of patients recovering from cardiac surgery was created and used to train predictive models. Advanced signal processing techniques were employed to extract complex features from physiological waveforms, while a novel tensor-based dimensionality reduction method was used to reduce the size of the feature space. These methods were evaluated for predicting the onset of decompensation at varying time intervals, ranging from a half-hour to 12 h prior to a decompensation event. The best performing models achieved AUCs of 0.87 and 0.80 for the half-hour and 12-h intervals respectively. These analyses evince that a multimodal approach can be used to develop clinical decision support systems that predict adverse events several hours in advance.