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

开发定量工具来预测插管困难的患者，以尽量减少治疗相关的并发症

• 批准号: 10374769
• 负责人: Muhammad Khalid Khan Niazi
• 金额: $ 19.38万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10374769
• 关键词: Accident and Emergency department; Anatomy; Anesthesia procedures; Anesthesiology; Anesthetics; Appearance; Bedside Testings; Breathing; Caring; Cessation of life; Clinical; Clinical Trials; Comprehension; Computer-Assisted Image Analysis; Consumption; Critical Illness; Data Set; Development; Diagnostic; Disease; Equipment; Etiology; Face; Failure; Floor; General Anesthesia; Health; Healthcare; Heart Arrest; Human; Human Resources; Image; Image Analysis; Imaging technology; Intensive Care Units; Interobserver Variability; Intubation; Medical; Methods; Mission; Modeling; Modernization; Morbidity - disease rate; Nose; Operative Surgical Procedures; Oral cavity; Patient Care; Patient imaging; Patients; Performance; Perioperative; Pre-hospital setting; Predictive Value; Probability; Procedures; Public Health; Reproducibility; Research; Respiratory Failure; Scientist; Software Tools; Specificity; Statistical Data Interpretation; System; Systems Analysis; Techniques; Test Result; Testing; Time; Trachea; Tracheostomy procedure; Training; Tube; United States National Institutes of Health; Visual; Work; autoencoder; automated image analysis; base; clinical care; clinical practice; deep learning; deep learning model; design; disorder risk; endotracheal; experience; generative adversarial network; improved; indexing; innovation; mortality; novel strategies; patient safety; recruit; respiratory; response; risk stratification; standard of care; tool

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

摘要 气管内 口/鼻 呼吸 需要 百分之十 这 到 执行 主观 差 不幸的是，这些气道检查系统 临床实践表现一般，敏感性为20- 62%，特异性为82- 97%， 阳性预测值，通常小于30%，除非使用非常宽松的困难定义。那里 可能有许多原因导致这种不良表现，包括困难插管的相对罕见， 多因素病因和插管困难的不同定义，检查结果的观察者间差异，失败 在患者中验证潜在的系统，而不依赖于用于推导测试的系统，以及 测试本身。 插管术（EI）是一种常见的医疗程序，其中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 在呼吸衰竭、心脏骤停或其他形式的危重疾病的情况下出现困难。全球 根据世界卫生组织对全世界手术需求的估计，EI可能至少为1.5亿。约 的EI尝试是困难的，并且大约1/2000被认为是不可能的。临床意义 “不能插管，不能插管”的情况是非常重要的：25%的麻醉相关死亡是由于 气道意外通常对患者进行解剖学特征评估，这些解剖学特征可能预测患者在手术中的困难。 在手术前。在实践中，麻醉师和其他气道专家可能会权衡其他 预测困难气道的因素，包括体质，面部外观，也许还有其他 了解hunches使用该检查预测插管困难被认为是 现代麻醉学实践中的护理。 当 人员 相反地， 不 学习 和 插管 识别 精度 （马兰帕蒂 麻醉医师 减少 动员 预期气道困难，可以采用更先进的技术， 可以招募协助，外科气道专业知识（即，气管切开术）可能处于待命状态 这些技术是昂贵的，耗时的，病人不舒服，所以他们应该 被过度使用。我们假设麻醉师的视觉评估可以通过深呼吸来模拟。 以高准确性识别插管困难的患者。通过创新地使用深度学习 复杂的图像分析，这项研究将确定面部特征，准确预测困难， 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， 困难插管患者。开发的模型将进行严格的统计分析， 和再现性。在一项临床试验中，该模型将与床边测试进行比较 + thryomental距离）。该项目将1）导致创新的软件工具，以促进 （二）大幅减少不必要的医疗开支。我们预计，这一模式将 意外困难插管的可能性，并允许麻醉师更好地准备， 替代技术、设备或操作员。 与 . 不