Development of quantitative tools to predict patients with difficult intubation to minimize treatment related complications

摘要 气管内 口/鼻 呼吸 需要 10% 这 至 表演 主观性 差劲 标准不幸的是，这些呼吸道检查系统 临床实践表现平平，敏感度为20-62%，特异度为82-97%，且非常低 积极的预测值，通常低于30%，除非使用非常宽松的难度定义。那里 造成这种糟糕表现的原因可能有很多，包括相对罕见的困难插管， 多因素病因和困难插管的不同定义、检查结果的观察者间变异性、失败 以验证患者的潜在系统，独立于那些用于得出测试的系统，以及 测试本身。 插管(EI)是一种常见的医疗程序，通过导管插入塑料管 进入气管，在全身麻醉期间提供呼吸支持或改善 呼吸衰竭、心脏骤停或其他形式的危重疾病时的困难。《环球报》 根据世卫组织对全球外科需求的估计，EI可能至少有1.5亿人。大致 三分之一的EI尝试是困难的，大约1/2000被认为是不可能的。L的临床意义 “不能插管，不能呼吸”的情景是极其重要的：25%的麻醉相关死亡是由于 航空事故。患者通常会接受解剖学特征的评估，这些特征可能预示着 手术前的EI。在实践中，麻醉师和其他呼吸道专家可能会权衡其他 预计呼吸道困难的因素，包括习性、面部外观，也许还有其他 我能理解你的直觉。使用这种检查来预测困难的插管被认为是 在现代麻醉学实践中的护理价值。 什么时候 人事 相反， 不 学习 和 插管。 识别 精确度 (马兰帕蒂 麻醉师 减缩 动员 预计会出现呼吸道困难，可能会采用更先进的技术， 可以招募协助，外科呼吸道专家(例如，气管切开术)可能处于待命状态 这些技术昂贵、耗时，而且对患者来说不舒服，因此它们应该 被过度使用。我们假设麻醉师的视觉评估可以通过深部模拟 以高准确率识别插管困难的患者。通过创新地使用深度学习 精密的图像分析，这项研究将识别出难以准确预测的面部特征 这项研究将利用正面和侧面的面部照片来建立一个生成模型 困难的插管病人。开发的模型将经过严格的统计分析，以 和再现性。在临床试验中，建议的模型将与床边测试进行比较。 +拇指距离)。该项目将1)产生创新的软件工具，以促进 以及2)大幅减少不必要的医疗费用。我们预计这款车型将 意外困难插管的可能性，并允许麻醉医生更好地准备 指替代的技术、设备或操作员。 使用 。 T

ABSTRACT Endotracheal mouth/nose breathing need 10% this to performing subjective poorly standard Unfortunately, these airway examination systems in clinical practice perform only modestly, with sensitivities of 20-62%, specificities of 82-97%, and very low positive predictive values, generally less than 30%, unless very liberal definitions of difficulty are used. There are likely a number of reasons for this poor performance, including the relative rarity of difficult intubation, the multifactorial etiology and varying definition of difficult intubation, inter-observer variability in test results, failure to validate potential systems in patients independent of those used to derive the test, and the inadequacy of the tests themselves. intubation (EI) is a common medical procedure in which a plastic tube is introduced via the into the trachea, to provide respiratory support during general anesthesia or to ameliorate difficulty in cases of respiratory failure, cardiac arrest, or other forms of critical illness. The global for EI is likely at least 150 million based on the WHO estimate of surgical need worldwide. Approximately of EI attempts are difficult, and approximately 1/2000 are deemed impossible. The clinica l significance of “can't intubate, can't ventilate” scenario is extremely important: 25% of anesthetic related deaths are due airway mishaps. Patients are typically assessed for anatomic features that might predict difficulty in EI prior to the procedure. In practice, anesthesiologists and other airway experts likely weigh other factors in anticipating a difficult airway, including habitus, facial appearance, and perhaps other understood hunches. The use of this examination to predict difficult intubation is considered the of care in modern anesthesiology practice. When personnel Conversely, not learning and intubation. identify accuracy (Mallampati anesthesiologists reduce mobilization difficulty the airway is anticipated, more advanced techniques may be employed, additional may be recruited for assistance, surgical airway expertise (i.e., tracheostomy) may be on standby these techniques are expensive, time consuming, and uncomfortable to patients, so they should be overused. We hypothesize that anesthesiologists' visual assessment can be modeled through deep to identify patients with difficult intubation with high accuracy. Through innovative use of deep learning sophisticated image analysis, this research will identify facial features tha accurately predict difficult The research will utilize frontal as well as profile facial photographs to build a generative model to difficult intubation patients. The developed model will be subjected to rigorous statistical analysis for and reproducibility . In a clinical trial, the proposed model will be compared against the bedside tests + thryomental distance). The project will 1) result in innovative software tools to facilitate and 2) substantially reduce unnecessary healthcare expenses. We expect that this model will the probability of an unexpected difficult intubation and allow anesthesiologists to better prepare by of alternative techniques, equipment, or operators. with . t