Facial Infrared Thermal Imaging for Continuous Contact-less Respiratory Distress Monitoring in Mechanically Ventilated Patients
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- Characterize facial expressions during a spontaneous breathing trial using the Facial Action Coding System
研究概览
简要总结
Critically ill patients are exposed to many sources of discomfort and traumatic experiences, especially if they require invasive mechanical ventilation (IMV). Dyspnea, or sensation of "not getting enough air - suffocation" is the most common and distressing symptom experienced by IMV patients, far more unpleasant than pain. But, contrarily to pain, dyspnea has received only little attention and is still markedly under-recognized in IMV patients. Moreover, given the deleterious short- and long-terms consequences of letting IMV patients with dyspnea, its assessment and treatment figures among the main next great cause in critical healthcare.
However, dyspnea assessment in IMV patients is a challenge since many of them cannot express their suffering (e.g. sedative drugs, mouth tubes). Dyspnea observation scales (DOS) are promising alternatives that allow to strongly suspecting dyspnea. These scales encompass the dyspnea multidimensionality assessing the respiratory drive (respiratory rate, excessive use neck muscle, nasal flaring), neurovegetative signs (heart rate) and emotions (fearful face). DOS allows calculation of scores strongly correlated with dyspnea in IMV communicative patients and responsive to dyspnea treatment even in noncommunicative ones.
However these scales (1) require human resources, (2) still elicit caregivers' subjectivity (fearful face), and (3) are discontinuous, whereas dyspnea is unpredictable, and thus may lead to false appreciation of clinical deterioration. Thus there is an urgent unmet need for technology-enhanced clinical surveillance tools that reliably detect dyspnea in IMV patients and tailor its relief.
Infrared thermal imaging (IRTI) offers a unique opportunity to automatically and continuously compute DOS. Indeed, it has been demonstrated as reliable to measure heart and respiratory rate in patients and detect facial expressions even during surgical intervention.
The study goal is to prove the concept that IRTI camera device is feasible and reliable to strongly suspect dyspnea, based on the calculation of DOS including heart and respiratory rate, facial expression of fear, and activation of Alae nasi muscle, in IMV patients experiencing an asphyxial threat during a spontaneous breathing trial.
The second study goal is to assess the performance of of this multidimensional video taped monitoring to predict the outcome of the spontaneous breathing trial.
This project deals with the perspective that artificial intelligence and the development of autonomous patient-machine interfaces will give access to patients' emotions by the analysis of behaviors including facial expressions, in order to improve comfort and reduce traumatic memories of the ICU stay.
详细描述
Scientific background and rational of the project Dyspnea, defined as "suffocation or not getting enough air" is the most common (50% of patients) and debilitating symptom experienced by patients receiving invasive mechanical ventilation (IMV). Dyspnea is a noxious sensation but is far more unpleasant than pain because it is constantly associated with anxiety (up to unquenchable fear of dying), and because patients cannot escape from their source of dyspnea, which also connect them to life (ventilator). The inability to verbally communicate with caregivers reinforces the sense of loss of control and fear.
Apart form generating immediate suffering, dyspnea is associated with negative ventilator weaning outcomes and plays a major role in the genesis of post traumatic stress disorders which concerns almost 20% of IMV patients. This should make it a major preoccupation for ICU physicians and nurses whose mission is to relieve symptoms.
However, very little attention is given to dyspnea in IMV patients, which remains markedly under-recognized. Besides this caregivers' unawareness it might be even more challenging to detect dyspnea in IMV patients, since at least one-third are unable to communicate with their caregivers (sedative drugs, delirium, endotracheal tube). However, the inability to communicate in no way negates the possibility that an individual is experiencing dyspnea. Noncommunicative IMV patients are exposed to the same risk factors of dyspnea as communicative patients and should rather be considered as a vulnerable population at high risk for misdiagnosis and under treatment of dyspnea.
In view of the listed issues, the use of a medical solution to robustly suspect dyspnea without eliciting patient cooperation is of utmost importance. It will warn caregiver to the possibility of dyspnea in a patient and will prompt patient/caregiver interactions aiming at relieve this discomfort, such as adjusting ventilator settings.
Dyspnea observation scales (DOS) do not require patient's self-report and are promising alternatives that overcome this "dyspnea invisibility". These DOS encompass the dyspnea multidimensionality and are based on physiological and emotional modification the most correlated to dyspnea including tachycardia (neurovegetative sign), facial expression of fear (emotional consequence) and observational signs of respiratory drive such as breathing frequency, excessive use extradiaphragmatic inspiratory muscle (neck or Alae nasi muscle). These DOS allows calculation of scores strongly correlated with dyspnea in communicative IMV patients and responsive to dyspnea treatment in noncommunicative ones.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 90 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Invasive mechanical ventilation> 48h
- •Deemed ready to perform a spontaneous breathing trial, according to current guidelines (readiness-to-wean criteria)
- •Decision to perform a spontaneous breathing trial
- •Patients or proxy who do not object to participation in the study
排除标准
- •Age < 18
- •Pregnancy
- •Richmond Analgesia And Sedation scale < 2 or > 2
- •Patient under legal protection
结局指标
主要结局
Characterize facial expressions during a spontaneous breathing trial using the Facial Action Coding System
时间窗: during the spontaneous breathing trial
Standard classification of facial expression
次要结局
- Test performances of machine learning algorithms to detect FACS action units(during the spontaneous breathing trial)
- Estimate the heart and breathing frequencies from the infrared thermal imaging of the face(during the spontaneous breathing trial)
- Test performances of machine learning algorithms to predict the outcome of the spontaneous breathing trial(during the spontaneous breathing trial)