Periodic Reporting for period 1 - MACLEA-ENDO (Machine learning algorithm pipeline for endothelial damage detection and adverse outcome prediction.)
Reporting period: 2022-11-01 to 2024-10-31
The MACLEA-ENDO project focused on improving these techniques for assessing microvascular health in the critical care. It has investigated the relationships between various VOT-derived parameters and explored methods to stratify patients according to their microvascular reactivity aiding in the identification of patients who deviated from typical patterns. The project also aimed to refine the prediction of endothelial dysfunction and evaluate oxygen metabolism by utilizing only measurements at rest. This aspect could potentially eliminate the need for procedures like VOT, reducing patient discomfort and further protocol standardization. By doing so, it the project could potentially impact the development of individualized care, reduce reliance on complex protocols for patient monitoring in the intensive care unit (ICU).
For the first time, data from over 300 patients were utilized to characterize the microvascular and endothelial health in the diverse and heterogeneous population admitted to the ICU by means of optical data combined with VOT using a unique optical platform developed in the hosting group.
These data have been utilized to perform a comprehensive characterization of both healthy and general ICU population. The tissue oxygenation, perfusion as well as the local baseline metabolism have been characterized at rest and in the response to VOT. The patient displayed an impaired microvascular reactivity in all the parameters, in particular, to the ones related to endothelial function, with generally lower baseline metabolism and slower reoxygenation and reperfusion during the cuff deflation.
The initial analysis utilized basic statistical methods to explore the relationships between optical data and VOT-derived parameters, correlations with demographic variables and potential confounding factors.
Within this framework, the project focused on standardizing measurement protocols. Notably, measurements at rest were shown to provide valuable parameters, such as the oxygen metabolism, which correlate with VOT-derived metabolic data. Shortened VOTs and machine learning were also explored as a potential method for predicting VOT-derived endothelial parameters.
The project also explored various non-machine learning techniques to identify patterns and outliers in this heterogeneous ICU population, such as principal component analysis (PCA) using optics and VOT-derived parameters. A preliminary attempt was also made to analyse whole time-series data without extracting specific parameters, furthermore, providing quantitative analysis methods for standardization and to identify subjects whose behaviour deviated from the general ICU population.
This work represents a step forward in standardizing patient monitoring in the ICU, utilizing optics for more efficient and non-invasive assessments.
The MACLEA-ENDO project was conducted in collaboration with another Horizon 2020 initiative, VASCOVID, and significantly contributed to the advancement and improvement of devices capable of measuring parameters that could reduce or even replace the need for protocols involving VOT or similar manoeuvres. This collaboration has played a vital role in the development of more versatile devices, which are now being multiplied with the plan of being distributed across Europe for further research into these critical aspects.
These efforts are the foundation for continued exploration and refinement of the analysis and characterization of microcirculation in intensive care settings. Building on these advancements, I plan to further investigate and enhance these methods, ensuring their integration into clinical practice and contributing to improved patient outcomes in ICUs across Europe and beyond.