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2019冠状病毒病大流行以来， 急诊科（艾德）达到了前所未有的水平。对于重症患者，包括 急性失代偿性心力衰竭（ADHF），艾德寄宿会影响患者的结局，因为患者在艾德中花费数小时 等待被转移到适当的住院病房接受专门护理。鉴于美国经济持续增长， 艾德寄宿，艾德住院时间，以及随后的专家评估和管理时间，开发新的 在这些长期艾德住院期间，能够快速重新评估ADHF患者的技术至关重要， 改善护理和患者的预后。在急诊科的典型工作流程中，医生执行 在患者初次就诊时进行一次床旁肺部超声检查，并使用是否存在“B- 图像中的“线”作为肺充血的生物标志物。通常由艾德医生以二进制方式进行评估 以这种方式，B线的存在与临床检查和血液检查结合使用，以确定急性ADHF。 虽然检测B线可以像查看两个肺区一样容易，以做出ADHF的临床决策，但计数 B线需要在B线识别和在8+肺内聚集B线计数方面的技能和培训 区域的准确性。对于一个忙碌的艾德医生来说，考虑到时间、培训和认知方面的限制，这是禁止的。 即可.为了缓解这个问题，艾德医生需要能够自动计数和汇总B线的工具， 量化拥塞的严重程度。如果没有这种自动化，完全可能出现次优或 甚至在艾德不对ADHF患者进行治疗，导致住院时间延长， 让艾德的寄宿制度永久化创建自动量化工具有可能使 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 计算工具，其减轻了超声图像采集特有的操作员依赖性， 解释。本开拓者R21应用程序的目标是开发和验证计算方法 用于在艾德中通过床旁肺部超声量化肺充血，将通过（1） 开发和评估可解释的工具，用于使用 回顾性肺超声数据，以及（2）在工作流程中验证训练模型的性能 一项前瞻性观察性研究证实了这一点，在该研究中，因ADHF就诊于艾德的患者将 在治疗干预前后用肺部超声进行评估，结果通常用于测量 将在两个时间点记录住院服务的肺充血。