Abstract

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95–1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0–15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38–0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

Details

Title
Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model
Author
Murphy, Ethan K. 1 ; Bertsch, Spencer R. 1 ; Klein, Samuel B. 2 ; Rashedi, Navid 1 ; Sun, Yifei 1 ; Joyner, Michael J. 3 ; Curry, Timothy B. 3 ; Johnson, Christopher P. 3 ; Regimbal, Riley J. 3 ; Wiggins, Chad C. 3 ; Senefeld, Jonathon W. 3 ; Shepherd, John R. A. 3 ; Elliott, Jonathan Thomas 4 ; Halter, Ryan J. 5 ; Vaze, Vikrant S. 1 ; Paradis, Norman A. 2 

 Dartmouth College, Thayer School of Engineering, Hanover, USA (GRID:grid.254880.3) (ISNI:0000 0001 2179 2404) 
 Dartmouth College, Geisel School of Medicine, Hanover, USA (GRID:grid.254880.3) (ISNI:0000 0001 2179 2404); Dartmouth-Hitchcock Medical Center, Lebanon, USA (GRID:grid.413480.a) (ISNI:0000 0004 0440 749X) 
 Mayo Clinic, Department of Anesthesiology and Perioperative Medicine, Rochester, USA (GRID:grid.66875.3a) (ISNI:0000 0004 0459 167X) 
 Dartmouth College, Thayer School of Engineering, Hanover, USA (GRID:grid.254880.3) (ISNI:0000 0001 2179 2404); Dartmouth College, Geisel School of Medicine, Hanover, USA (GRID:grid.254880.3) (ISNI:0000 0001 2179 2404); Dartmouth-Hitchcock Medical Center, Lebanon, USA (GRID:grid.413480.a) (ISNI:0000 0004 0440 749X) 
 Dartmouth College, Thayer School of Engineering, Hanover, USA (GRID:grid.254880.3) (ISNI:0000 0001 2179 2404); Dartmouth College, Geisel School of Medicine, Hanover, USA (GRID:grid.254880.3) (ISNI:0000 0001 2179 2404) 
Pages
8719
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3038973994
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.