HEИндивидуална стипендия2022–2024

MACLEA-ENDO · Machine learning algorithm pipeline for endothelial damage detection and adverse outcome prediction.

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2022-11-01 → 2024-10-31
Финансиране от ЕС: 165 313 €
Участници: 1
Схема: HORIZON-TMA-MSCA-PF-EF

Линиите свързват координатора с партньорите.

Накратко на български

Здравето на микросъдовете се анализира чрез измерване на кислорода в тъканите и тестове с пневматична маншета за ръката. Това помага за по-точното разпознаване на пациенти с увреждания на съдовете и подобряването на индивидуалните стратегии за лечение в интензивната терапия.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Machine learning algorithm pipeline for endothelial damage detection and adverse outcome prediction.

Non-invasive assessment of the health of microvasculature in order to optimize treatment strategies and predict patient outcomes, remains a major challenge in intensive care settings. One non-invasive method that has been proposed combines microvascular tissue oxygenation (StO2) measurements with a vascular occlusion test (VOT) applied to the patient’s arm. This procedure interrogates the local microvascular reactivity by inflating a pneumatic cuff to locally stop blood flow. While the cuff is inflated, is possible to extract a surrogate biomarker of metabolism, while the cuff deflates it is possible to extract biomarkers related to endothelial function. While this approach provides valuable insights, the resulting parameters are limited, serving primarily as indirect surrogates of tissue metabolism and endothelial function, and depend heavily on performing a VOT. Furthermore, the absence of standardized protocols for parameter extraction has restricted the method's clinical adoption in the critical care. 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).

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Endothelial cells form the lining of the blood vessels of the entire vascular system, from the heart to the smallest capillary, regulating vascular tone, immune response and exchange of materials in and out the blood stream among others. Endothelial damage has been observed in the early stages of most cardiovascular diseases, atherosclerosis or in patients with iflammatory and infectious diseases (e.g. COVID-19, septic shock). Typically endothelial damage is measured by means of blood test analysis and provocative tests either invasive, performed by using pharmacological agents, or non-invasive, such as flow mediated dilation that on the other hand, suffer from high operator-independency and no-automatization. This proposal revolves around the design, development and validation of a supervised machine learning algorithm (ML) to evaluate endothelial damage and predict adverse outcome in critically ill patients in the ICU. The ML pipeline will use as input data the one from the Horizon 2020 project VASCOVID clinical validation. These data comprises of physiologically relevant variables that can be measured non-invasively with a completely automatized platform. This smart platform combines diffuse optics and an automatized tourniquet for performing a reactive test on peripheral muscle (thenar muscle). By means of this device it is possible to access in an accurate and robust way information about early impairment in perfusion, metabolic rate of oxygen consumption, and microvascular functionality and tissue capability of locally regulate the blood flow. These variables bring an important physiological insight concerning the interpretability of machine learning algorithm from the clinical community who does not fully trust this approaches.

Оригинален текст от CORDIS (на английски).

Участници

FUNDACIO INSTITUT DE CIENCIES FOTONIQUES · CastelldefelsКоординаторИспания