Detection of occult hemorrhage using multivariate non-invasive technologies: a porcine study.

Abstract

Objective.Occult hemorrhage (OH) can emerge subtly post-trauma, especially when internal bleeding is not yet severe enough to result in noticeable hemodynamic changes or shock. Despite normal appearances of traditional vital signs like heart rate (HR) and blood pressure (BP), clinically significant OH may be present, posing a critical diagnostic challenge. Early detection of OH, before vital signs begin to deteriorate, is vital as delays in identifying such conditions are linked to poorer patient outcomes. We analyze the performance of poly-anatomic multivariate technologies-including electrical impedance tomography (EIT), near-infrared spectroscopy (NIRS), electrical impedance spectroscopy (EIS), plethysmography (Pleth), and ECG-in a porcine model of OH. The goal was to detect OH without the need to know the subject's pre-established normal baseline.Approach.Forty female swine were bled at slow rates to create an extended period of subclinical hemorrhage, during which the animals' HR and BP remained stable before hemodynamic deterioration. Continuous vital signs, Pleth, and continuous non-invasive data were recorded and analyzed with the objective of developing an improved means of detecting OH. This detection was set up as a supervised voting classification problem where the measurement of each technology (minimally transformed) was used to train a classifier. A soft majority voting classification technique was then used to detect the existence of OH.Main Results.When comparing the prediction performance of the most significant univariate technology (EIT) to that of a poly-anatomic multivariate approach, the latter achieved higher area-under-the-curve (AUC) values from receiver operating characteristic analyses in almost every observation interval duration. In particular, after 21 min of continuous observation, the best AUC of the multivariate approach was 0.98, while that of the univariate approach was 0.92. The best multivariate technologies, in descending order, appeared to be EIT on the thorax, NIRS on the abdomen, and EIS on the thorax.Significance.In this clinically relevant porcine model of clinically OH, multivariate non-invasive measurements may be superior to univariate ones in detecting OH. Advanced technologies such as EIT, NIRS, and EIS exhibit considerably greater potential to accurately predict OH than standard physiological measurements. From a practical standpoint, our approach would not require the medical device to have prior access to non-hemorrhage baseline data for each patient. Early detection of OH using these technologies could improve patient outcomes by allowing for timely intervention before vital signs begin to deteriorate.