A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study.

BACKGROUND: Impaired microcirculation is a cornerstone of sepsis development and leads to reduced tissue oxygenation, influenced by fluid and catecholamine administration during treatment. Hyperspectral imaging (HSI) is a non-invasive bedside technology for visualizing physicochemical tissue characteristics. Machine learning (ML) for skin HSI might offer an automated approach for bedside microcirculation assessment, providing an individualized tissue fingerprint of critically ill patients in intensive care. The study aimed to determine if machine learning could be utilized to automatically identify regions of interest (ROIs) in the hand, thereby distinguishing between healthy individuals and critically ill patients with sepsis using HSI. METHODS: HSI raw data from 75 critically ill sepsis patients and from 30 healthy controls were recorded using TIVITA® Tissue System and analyzed using an automated ML approach. Additionally, patients were divided into two groups based on their SOFA scores for further subanalysis: less severely ill (SOFA ≤ 5) and severely ill (SOFA > 5). The analysis of the HSI raw data was fully-automated using MediaPipe for ROI detection (palm and fingertips) and feature extraction. HSI Features were statistically analyzed to highlight relevant wavelength combinations using Mann-Whitney-U test and Benjamini, Krieger, and Yekutieli (BKY) correction. In addition, Random Forest models were trained using bootstrapping, and feature importances were determined to gain insights regarding the wavelength importance for a model decision. RESULTS: An automated pipeline for generating ROIs and HSI feature extraction was successfully established. HSI raw data analysis accurately distinguished healthy controls from sepsis patients. Wavelengths at the fingertips differed in the ranges of 575-695 nm and 840-1000 nm. For the palm, significant differences were observed in the range of 925-1000 nm. Feature importance plots indicated relevant information in the same wavelength ranges. Combining palm and fingertip analysis provided the highest reliability, with an AUC of 0.92 to distinguish between sepsis patients and healthy controls. CONCLUSION: Based on this proof of concept, the integration of automated and standardized ROIs along with automated skin HSI analyzes, was able to differentiate between healthy individuals and patients with sepsis. This approach offers a reliable and objective assessment of skin microcirculation, facilitating the rapid identification of critically ill patients.

Bedside Hyperspectral Imaging and Organ Dysfunction Severity in Critically Ill COVID-19 Patients-A Prospective, Monocentric Observational Study.

Hyperspectral imaging (HSI) is a non-invasive technology that provides information on biochemical tissue properties, including skin oxygenation and perfusion quality. Microcirculatory alterations are associated with organ dysfunction in septic COVID-19 patients. This prospective observational study investigated associations between skin HSI and organ dysfunction severity in critically ill COVID-19 patients. During the first seven days in the ICU, palmar HSI measurements were carried out with the TIVITA® tissue system. We report data from 52 critically ill COVID-19 patients, of whom 40 required extracorporeal membrane oxygenation (ECMO). HSI parameters for superficial tissue oxygenation (StO2) and oxygenation and perfusion quality (NPI) were persistently decreased. Hemoglobin tissue content (THI) increased, and tissue water content (TWI) was persistently elevated. Regression analysis showed strong indications for an association of NPI and weaker indications for associations of StO2, THI, and TWI with sequential organ failure assessment (SOFA) scoring. StO2 and NPI demonstrated negative associations with vasopressor support and lactate levels as well as positive associations with arterial oxygen saturation. These results suggest that skin HSI provides clinically relevant information, opening new perspectives for microcirculatory monitoring in critical care.

Critically Ill COVID-19 Patients Show Reduced Point of Care-Measured Butyrylcholinesterase Activity-A Prospective, Monocentric Observational Study.

A biomarker for risk stratification and disease severity assessment in SARS-CoV-2 infections has not yet been established. Point of care testing (POCT) of butyrylcholinesterase (BChE) enables early detection of systemic inflammatory responses and correlates with disease severity in sepsis and burns. In acute care or resource-limited settings, POCT facilitates rapid clinical decision making, a particularly beneficial aspect in the management of pandemic situations. In this prospective observational study, POCT-measured BChE activity was assessed in 52 critically ill COVID-19 patients within 24 h of ICU admission and on the third and seventh day after ICU admission. Forty (77%) of these patients required venovenous extracorporeal membrane oxygenation (vvECMO). In critically ill COVID-19 patients, BChE activity is significantly decreased compared with healthy subjects, but also compared with other inflammatory conditions such as sepsis, burns, or trauma. POCT BChE activity reflects the severity of organ dysfunction and allows prediction of 28-day mortality in critically ill COVID-19 patients. Implementing early POCT BChE measurement could facilitate risk stratification and support admission and transfer decisions in resource-limited settings.