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

基本信息

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
项目类别：
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

项目摘要

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COVID-19大流行爆发以来，医院收治的患者“寄宿”的做法急诊科（ED）对于重症患者（包括患有以下疾病的患者）的治疗达到了前所未有的水平。急性失代偿性心力衰竭 (ADHF)、急诊室寄宿会导致结果恶化，因为患者要在急诊室呆上几个小时鉴于人数有增无减，等待转移到适当的住院病房接受专门护理。急诊室寄宿、急诊室住院时间以及随后接受专家评估和管理的时间，制定新的在这些长期急诊室住院期间能够快速重新评估 ADHF 患者的技术对于在急诊科的典型工作流程中，医生执行的任务是改善护理和患者治疗效果。床边肺部超声检查一次，在患者初次就诊时进行，并使用是否存在“B-” 图像中的线条作为肺充血的生物标志物，通常由急诊医生以二元法进行评估。以这种方式，B 线的存在与临床检查和血液检查结合使用来诊断急性 ADHF。虽然检测 B 线可以像查看两个肺部区域一样简单，以做出 ADHF 的临床决策，但 B 线需要 B 线识别以及汇总超过 8 个肺的 B 线计数的技能和培训对于忙碌的急诊医生来说，鉴于时间、培训和认知的限制，这是令人望而却步的。为了缓解这个问题，急诊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，则完全有可能出现次优或拥塞的情况。即使急诊室不会对 ADHF 患者进行任何治疗，也会导致住院时间延长，进一步自动量化工具的创建有可能使 ED 登机永久化。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。计算工具可减轻超声图像采集对操作员的依赖， Trailblazer R21 应用程序的目标是开发和验证计算方法。用于通过急诊科床旁肺部超声量化肺充血，这将通过以下方式实现：(1) 开发和评估可解释的工具，用于自动量化肺充血回顾性肺部超声数据以及 (2) 在工作流程中验证训练模型的性能通过一项前瞻性观察研究，在该研究中，患有 ADHF 的 ED 患者将被在治疗干预前和治疗后通过肺部超声进行评估，以及通常用于测量的结果将记录两个时间点住院服务中的肺充血情况。