Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model

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...

Full description

Saved in:

Bibliographic Details
Published in:	Scientific reports Vol. 14; no. 1; p. 8719
Main Authors:	Murphy, Ethan K., Bertsch, Spencer R., Klein, Samuel B., Rashedi, Navid, Sun, Yifei, Joyner, Michael J., Curry, Timothy B., Johnson, Christopher P., Regimbal, Riley J., Wiggins, Chad C., Senefeld, Jonathon W., Shepherd, John R. A., Elliott, Jonathan Thomas, Halter, Ryan J., Vaze, Vikrant S., Paradis, Norman A.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 15-04-2024 Nature Publishing Group Nature Portfolio
Subjects:	631/443/1338/2729 639/166/985 692/308/575 692/53/2421 Biomarkers Blood pressure Cardiovascular System EKG Electrical impedance Electrocardiography Hemorrhage Humanities and Social Sciences Humans Hypovolemia - diagnosis Impedance Instability Learning algorithms Lower Body Negative Pressure Machine learning multidisciplinary Performance evaluation Science Science (multidisciplinary) Spectroscopy Vital Signs
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-024-59139-8