An automated system to interpret echocardiography to predict adverse outcomes in patients with right ventricular dysfunction in daily hospital practice

一种解释超声心动图的自动化系统，以预测日常医院实践中右心室功能障碍患者的不良后果

• 批准号: 10326000
• 负责人: JAMES W SCHILLING
• 金额: $ 34.65万
• 依托单位: MPROBE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10326000
• 关键词: Address; Adolescent; Adopted; Adult; Algorithms; Anatomy; Automation; Big Data; Biological Markers; Businesses; Cardiac; Caring; Cessation of life; Child; Childhood; Clinical; Clinical Data; Clinical Management; Communities; Complex; Computerized Medical Record; Consumption; Data; Development; Diagnosis; Diagnostic; Disease Progression; Echocardiography; Environment; Evaluation; Functional disorder; Geometry; Health; Health care facility; Healthcare; Heart Diseases; Heart Transplantation; Heart failure; Hospitals; Improve Access; Infant; Intervention; Knowledge; Learning; Left ventricular structure; Link; Longterm Follow-up; Lung Transplantation; Machine Learning; Manuals; Medical; Modeling; Morbidity - disease rate; New York City; Operative Surgical Procedures; Outcome; Participant; Patient Care; Patient risk; Patients; Pattern; Pediatric Hospitals; Pediatric cardiology; Phase; Physiological; Plug-in; Precision Health; Probability; Process; Provider; Public Health; Right Ventricular Dysfunction; Right ventricular structure; Risk; Risk Factors; Sampling; Small Business Technology Transfer Research; System; Testing; Time; Training; Universities; Update; Validation; accurate diagnosis; adverse outcome; base; billing data; clinical decision-making; cohort; congenital heart disorder; cost; data infrastructure; deep learning; electronic structure; genomic data; health care availability; heart function; improved; individual patient; innovation; mortality; novel; novel marker; population health; prediction algorithm; pressure; prospective; risk prediction; structured data; tool; treatment effect

Project Summary Right ventricle (RV) dysfunction is a common and complex form of pediatric heart disease. It is also a common contributor to morbidity and mortality for patients with congenital heart diseases (CHD). Due to the complex geometry of the RV and its relative adaptability to changing physiologic conditions, RV dysfunction is poorly understood and difficult to characterize precisely and accurately, thus diagnosis is often delayed. The most common diagnosis tool is echocardiograms. Manual review of echocardiograms is time consuming, however. Furthermore, there might be uncovered echocardiogram patterns associated with RV dysfunctions. In adult studies, machine learning models (MLM) have been successfully implemented to assess RV functions by echocardiograms. We hypothesize that applying novel MLM to pediatric echocardiograms will allow us to improve the accuracy and reliability of assessment, as well as identify novel markers of RV dysfunction. We propose to develop an automated tool to generate a RV health score to identify RV dysfunction and predict the development and time of adverse outcomes including heart failure, heart and/or lung transplantation, and death. The automated tool will constitute an early warning system module, which will be deployed onto a big-data-based risk prediction platform developed by our small business. The study has three specific aims. First, we will extract echocardiograms and structured electronic medical records from the Stanford Children’s Hospital. Cohorts of children with normal or abnormal RV will be constructed. Second, MLM will be developed and validated to 1) predict the presence of RV dysfunction and the probability of adverse outcomes, and 2) predict the rate of progression to adverse outcomes. A deep learning-based workflow will be established to take input of pediatric echocardiogram and clinical data and generate predictions. Third, we will integrate the models developed in Aim #2 into the HBI Spotlight Solutions. The Spotlight Solutions include a healthcare surveillance platform with high-capacity data infrastructure and risk engines to offer AI solutions to care facilities participating the Healthix, the largest public health information exchange network in the US. This will prepare our algorithms for further clinical validation in other cohorts.

项目概要 右心室（RV）功能障碍是小儿心脏病的一种常见且复杂的形式。这是 也是先天性心脏病患者发病率和死亡率的常见因素 （冠心病）。由于房车的复杂几何形状及其对变化的相对适应性 生理条件下，右心室功能障碍知之甚少且难以表征 精确且准确，因此常常延误诊断。最常见的诊断工具是 超声心动图。然而，手动检查超声心动图非常耗时。 此外，可能存在与 RV 相关的未发现的超声心动图模式 功能障碍。在成人研究中，机器学习模型（MLM）已成功 通过超声心动图评估 RV 功能。我们假设应用小说 MLM 到儿科超声心动图将使我们能够提高诊断的准确性和可靠性 评估，以及识别 RV 功能障碍的新标志物。我们建议开发一个 生成 RV 健康评分的自动化工具，以识别 RV 功能障碍并预测 不良后果（包括心力衰竭、心脏和/或肺）的发展和时间 移植、死亡。自动化工具将构成预警系统模块， 将部署到我们小公司开发的基于大数据的风险预测平台上 商业。该研究有三个具体目标。首先，我们将提取超声心动图和 斯坦福儿童医院的结构化电子病历。儿童队列 将建造具有正常或异常的RV。其次，将开发和验证传销 1) 预测右心室功能障碍的存在以及不良结果的可能性，以及 2) 预测不良结果的进展率。基于深度学习的工作流程将是 建立用于输入儿科超声心动图和临床数据并生成预测。 第三，我们将把目标 #2 中开发的模型集成到 HBI Spotlight 解决方案中。这 Spotlight 解决方案包括具有大容量数据的医疗保健监控平台 基础设施和风险引擎，为参与 Healthix 的护理机构提供人工智能解决方案， 美国最大的公共卫生信息交换网络。这将为我们准备 在其他队列中进一步进行临床验证的算法。