Spectral Reflectance as a Unique Tissue Identifier in Healthy Humans and Inhalation Injury Subjects.

Tracheal intubation is the preferred method of airway management, a common emergency trauma medicine problem. Currently, methods for confirming tracheal tube placement are lacking, and we propose a novel technology, spectral reflectance, which may be incorporated into the tracheal tube for verification of placement. Previous work demonstrated a unique spectral profile in the trachea, which allowed differentiation from esophageal tissue in ex vivo swine, in vivo swine, and human cadavers. The goal of this study is to determine if spectral reflectance can differentiate between trachea and other airway tissues in living humans and whether the unique tracheal spectral profile persists in the presence of an inhalation injury. Reflectance spectra were captured using a custom fiber-optic probe from the buccal mucosa, posterior oropharynx, and trachea of healthy humans intubated for third molar extraction and from the trachea of patients admitted to a burn intensive care unit with and without inhalation injury. Using ratio comparisons, we found that the tracheal spectral profile was significantly different from buccal mucosa or posterior oropharynx, but the area under the curve values are not high enough to be used clinically. In addition, inhalation injury did not significantly alter the spectral reflectance of the trachea. Further studies are needed to determine the utility of this technology in a clinical setting and to develop an algorithm for tissue differentiation.

An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability.

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient's status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

Review of Commercially Available Supraglottic Airway Devices for Prehospital Combat Casualty Care.

BACKGROUND: Airway obstruction is the second leading cause of potentially survivable death on the battlefield. The Committee on Tactical Combat Casualty Care lists airway optimization among the top 5 battlefield research and development priorities; however, studies show that combat medics lack access to the recommended supraglottic airway (SGA) devices. SGA devices are an alternative airway management technique to endotracheal tube intubation. Reports have shown SGA devices are easier to use and take fewer attempts to provide patent airflow to the patient when compared to endotracheal tube intubation. Military settings require a higher degree of skill to perform airway management on patients due to the environment, limited availability of equipment, and potential chaos of the battlefield. Finding the optimal SGA device for the military setting is an unmet need. The International Organization for Standardization describes basic functional requirements for SGA devices, as well as patient configurations and size limitations. Beyond that, no SGA device manufacturer states that their devices are intended for military settings. MATERIALS AND METHODS: We conducted a market review of 25 SGA devices that may meet inclusion into the medics' aid bag. The company's official "Instructions for Use" document, Google Scholar, and FDA reports were reviewed to obtain information for each SGA device. RESULTS: Twenty-five commercially available SGA devices are explored from manufacturer online sources. A commercially available device list is shown later in this paper, which provides the device's features, indications, and contraindications based on the manufacturer's product information documentation. CONCLUSIONS: There are a variety of devices that require further testing to determine whether they should be included in sets, kits, and outfits.