Deep Learning Assisted Scoring of Point of Care Lung Ultrasound for Acute Decompensated Heart Failure in the Emergency Department

深度学习辅助急诊室急性失代偿性心力衰竭护理点肺部超声评分

• 批准号: 10741596
• 负责人: Andrew Goldsmith
• 金额: $ 35.8万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10741596
• 关键词: Accident and Emergency department; Acute; Address; Admission activity; Algorithms; Attention; Automation; Beds; Biological Markers; Blood Tests; COVID-19; COVID-19 pandemic; Caring; Clinical; Clinical Markers; Clinical Research; Clinical assessments; Clip; Computing Methodologies; Congestive Heart Failure; Correlation Studies; Critical Illness; Data; Decision Making; Dependence; Detection; Devices; Diagnosis; Emergency Care; Emergency Department Physician; Etiology; Evaluation; Functional disorder; Funding; Goals; Grant; Heart failure; Hospitalization; Hospitals; Hour; Human; Image; Inpatients; Laboratories; Length; Length of Stay; Lung; Manuals; Measures; Methods; Modeling; Multi-Institutional Clinical Trial; Nursing Staff; Observational Study; Outcome; Patient Admission; Patient Care; Patient-Focused Outcomes; Patients; Performance; Physical Examination; Physicians; Provider; Public Health; Recording of previous events; Role; Sensitivity and Specificity; Severities; Severity of illness; Specialist; Standardization; Therapeutic Intervention; Thoracic Radiography; Time; Training; Ultrasonography; United States National Institutes of Health; Variant; care outcomes; clinical care; clinical examination; cognitive load; cohort; computerized tools; deep learning; experience; improved; inpatient service; mortality; new technology; novel; novel therapeutics; pandemic disease; participant enrollment; point of care; prevent; prognostic value; prospective; research study; skills; tool; ultrasound; ward

Since the onset of the COVID-19 pandemic, the practice of “boarding” patients admitted to the hospital in the Emergency Department (ED) has reached unprecedented levels. For critically ill patients including those with acute decompensated heart failure (ADHF), ED boarding worsens outcomes as patients spend hours in the ED waiting to be transferred to the appropriate inpatient ward for specialized care. Given the unabated increase in ED boarding, length of ED stay, and subsequent time to specialist evaluation and management, developing new technologies to enable rapid reassessment of ADHF patients during these protracted ED stays is critical for improved care and patient outcomes. In a typical workflow in the Emergency Department, physicians perform bedside lung ultrasound once, at time of initial patient presentation, and use the presence or absence of ‘B- Lines’ in the images as a biomarker for pulmonary congestion. Often assessed by ED physicians in a binary manner, the presence of B-lines is used in conjunction with a clinical exam and blood tests to rule in acute ADHF. While detecting B-lines can be as easy as looking at two lung zones to make a clinical decision of ADHF, counting B-lines requires both skill and training in B-line identification, and in aggregating B-line counts over 8+ lung zones for accuracy. For a busy ED physician this is prohibitive given constraints on time, training, and cognitive load. To ease this problem, ED physicians need tools that can automatically count and aggregate the B-lines to quantify the severity of the congestion. Without this automation, it is entirely possible that either suboptimal or even no treatment will be initiated for ADHF patients in the ED leading to increased hospital length of stay, further perpetuating the ED boarding. The creation of tools for automatic quantification has the potential to enable workflows with reassessment to meet the changing patient care needs. Our long-term goals are to develop computational tools that mitigate the operator-dependence endemic to ultrasound image acquisition and interpretation. The objective of this Trailblazer R21 application is to develop and validate computational methods for quantifying pulmonary congestion from bedside lung ultrasound in the ED, which will be achieved by (1) developing and evaluating explainable tools for automated quantification of pulmonary congestion using retrospective lung ultrasound data and (2) validating the performance of the trained models in a workflow demonstrated by a prospective observational study in which patients presenting to the ED with ADHF will be assessed with lung ultrasound both pre-and post-therapeutic intervention, and findings typically used to measure pulmonary congestion on inpatient services will be recorded for both time points.

自从Covid-19-19大流行病开始以来，“登机”患者的做法被送往医院 急诊科（ED）达到了前所未有的水平。对于重症患者，包括患者 随着患者在急诊室花费数小时，急性心力衰竭（ADHF），ED登机的结果恶化 等待转移到适当的住院病房进行专业护理。鉴于未升高 ED登机，ED停留时间以及随后的专业评估和管理时间，开发了新的 在这些持久的ED停留期间，可以快速重新评估ADHF患者的技术至关重要 改善护理和患者的结果。在急诊科的典型工作流程中，医生表演 床边肺超声检查一次，在初始患者出现时，并使用'b-的存在或不存在 图像中的线条是肺部充血的生物标志物。经常在二进制中由ED医生评估 方式，B线的存在与临床检查和血液检查一起用于急性ADHF。 虽然检测B线可能就像看两个肺部区域以做出ADHF的临床决定一样容易 B线需要在B线识别方面的技能和培训，并且在8个以上的B线计数中需要 区域的准确性。对于忙碌的ED物理学，这是禁止的时间，培训和认知的限制 加载。为了缓解这个问题，ED医生需要可以自动计算和汇总B线的工具 量化拥塞的严重性。没有这种自动化，次优或 即使没有为ED中的ADHF患者开始治疗，导致住院时间增加，进一步 永久登机。创建自动量化工具有可能启用 重新评估工作流程以满足不断变化的患者护理需求。我们的长期目标是发展 减轻操作员依赖性内在对超声图像采集的计算工具和 解释。此开拓者R21应用程序的目的是开发和验证计算方法 用于量化ED中床边肺超声的肺部充血，这将通过（1）实现 开发和评估可解释的工具，用于使用肺部充血的自动数量 回顾性肺超声数据和（2）在工作流程中验证训练有素的模型的性能 通过一项前瞻性观察性研究证明，在该研究中，向ADHF介绍ED的患者将是 用肺部超声检查术前后干预措施，以及通常用于测量的发现 两个时间点都将记录住院服务的肺部充血。