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.

Pedicled ALT-flap for soft tissue reconstruction in the trochanteric area.

BACKGROUND: Coverage of soft tissue defects following surgery at the trochanteric area is challenging. Revision surgery in case of compromised wound healing may lead to soft tissue defects requiring reconstruction with pedicled or free flaps. Previous access to the hip joint may jeopardize neurovascular structures relevant to the flap. OBJECTIVE: In this study, we evaluated if the use of a pedicled anterior lateral thigh (ALT) flap is a valuable option for soft tissue reconstruction. METHODS: In this retrospective study, seven patients were included. Defect etiology was tumor resection in one case, screw osteosynthesis in another case and total hip arthroplasty in three cases. All patients underwent reconstruction by proximal pedicled anterior lateral thigh (ALT) flap. RESULTS: Pedicled ALT flap was a safe procedure in all cases. One patient showed delayed wound healing with need for additional surgery. No further complications were observed. CONCLUSIONS: Pedicled ALT flap transfer represents a reliable option for soft tissue coverage in the trochanteric area after primary surgery such as hip arthroplasty, osteosynthesis or tumor resection.