Population-level Pulmonary Embolism Outcome Prediction with Imaging and Clinical Data: A Multi-Center Study

利用影像学和临床数据预测人群水平的肺栓塞结果：一项多中心研究

• 批准号: 10298306
• 负责人: CURTIS P LANGLOTZ
• 金额: $ 48.33万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10298306
• 关键词: Acute; Algorithms; Angiography; Anticoagulant therapy; Architecture; Biological Markers; Blood Vessels; Cardiogenic Shock; Caring; Cause of Death; Chest; Clinical; Clinical Data; Code; Collaborations; Computational Technique; Computerized Medical Record; Computers; Computing Methodologies; Data; Data Analytics; Data Set; Diagnosis; Diagnostic; Diagnostic Imaging; Engineering; Environment; Evaluation; Exhibits; Fostering; Funding; Goals; Health Care Costs; Health Insurance Portability and Accountability Act; Healthcare; Heart Arrest; Hemorrhage; Hospitals; Image; Individual; Information Systems; Institution; Intelligence; Laboratories; Learning; Lung; Medical Imaging; Medicine; Methods; Modeling; Morbidity - disease rate; Multicenter Studies; National Heart, Lung, and Blood Institute; Neurologic; Outcome; Outcome Measure; Outpatients; Pathology Report; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Population; Prognosis; Pulmonary Embolism; Pulmonary Hypertension; Radiology Specialty; Recurrence; Research; Resuscitation; Risk; Risk Assessment; Scanning; Sensitivity and Specificity; Severities; Site; Source; Statistical Models; Structure; Syndrome; System; Testing; Therapeutic; Time; Training; United States; X-Ray Computed Tomography; base; breast imaging; care delivery; chronic thromboembolic pulmonary hypertension; clinical care; clinical predictors; data de-identification; data repository; deep learning; demographics; design; digital medicine; electronic structure; experience; health care quality; health data; health record; heterogenous data; high risk; hospital readmission; imaging biomarker; improved; improved outcome; indexing; learning strategy; mortality; mortality risk; outcome prediction; patient safety; patient stratification; point of care; precision medicine; predictive modeling; primary outcome; prognostic model; prospective; radiological imaging; risk stratification; secondary outcome; stem

Project Summary Pulmonary embolism (PE) is a leading cause of death in the United States. Risk stratification for acute PE treatment can reduce mortality. Risk scoring systems use clinical and laboratory electronic medical record (EMR) data. In addition, biomarkers on computed tomography imaging can identify which patients with PE are at high risk of death, independent of clinical data. Despite advances in clinical and image-driven scoring systems, improving outcomes in acute PE depends on implementation of patient-specific EMR and imaging data analytic prognostic models at the point of care. The promise of digital medicine stems in part from the hope that by digitizing health data, we can leverage computer information systems to understand and improve care. A method that can make use of these data to predict patient-specific outcomes could not only provide major benefits for patient safety and healthcare quality but also reduce healthcare costs. Unfortunately, most of this information is not yet included in predictive statistical models that clinicians use to improve care delivery. This is because traditional computational methods and techniques are insufficient at accurately analyzing such high volumes of heterogeneous data. The goal of this proposal is to develop an automated precision medicine approach to achieve point-of-care risk stratification for PE patient outcomes using a fusion deep learning strategy that can simultaneously analyze health records and imaging data. An ideal PE risk-scoring system would not only predict mortality, but also assess the risk for the many debilitating long-term consequences of acute PE. Such a system would, therefore, facilitate optimal management and would likely require intelligent use of clinical, laboratory, and imaging data together in order to provide accurate patient -specific risk scoring for multiple PE outcome measures. In order to build a robust model, we propose to apply distributed training of deep learning models across four large US healthcare institutions. By distributing the algorithm rather than the data, we avoid sharing individually identifiable patient information. If successful, this project will be the first endeavor to leverage diagnostic imaging (pixel) data in combination with structured and unstructured EMR data to predict outcomes. We have the ideal research team, experience, and methods to develop an automated risk-scoring system for acute PE patients. Using a powerful combination of clinical, laboratory, and imaging data, this system will provide patient-specific risk scoring for multiple PE outcome measures. Further, this project will foster multi- center collaborations, which will afford us the opportunity to investigate the generalizability of our approach to different populations of PE patients and to train, test, and ultimately deploy our automated predictive model in a variety of clinical environments.

项目摘要 肺栓塞（PE）是美国的主要死亡原因。急性PE的风险分层 治疗可以降低死亡率。风险评分系统使用临床和实验室电子病历 (EMR)数据此外，计算机断层扫描成像上的生物标志物可以识别哪些PE患者 死亡风险高，与临床数据无关。尽管在临床和图像驱动评分方面取得了进展， 系统，改善急性PE的结局取决于患者特异性EMR和成像的实施 数据分析预测模型在护理点。 数字医学的前景部分源于希望通过数字化健康数据，我们可以利用 计算机信息系统，以了解和改善护理。一种可以利用这些数据来 预测患者特异性结局不仅可以为患者安全和医疗保健质量提供重大益处， 还能降低医疗成本。不幸的是，这些信息中的大部分还没有包括在预测中。 临床医生用来改善医疗服务的统计模型。这是因为传统的计算 方法和技术不足以精确地分析如此大量的异构数据。 该提案的目标是开发一种自动化的精准医疗方法，以实现床旁风险 使用融合深度学习策略对PE患者结局进行分层， 健康记录和成像数据。一个理想的PE风险评分系统不仅可以预测死亡率， 评估急性PE的许多使人衰弱的长期后果的风险。这样的系统， 因此，促进最佳管理，并可能需要明智地使用临床，实验室， 结合影像学数据，为多种PE结局提供准确的患者特异性风险评分 措施为了建立一个健壮的模型，我们建议应用深度学习模型的分布式训练。 横跨美国四家大型医疗机构。通过分发算法而不是数据，我们避免了 共享可识别个人身份的患者信息。如果成功，该项目将是第一次奋进， 结合结构化和非结构化EMR数据，利用诊断成像（像素）数据预测 结果。 我们拥有理想的研究团队、经验和方法来开发自动风险评分系统， 急性PE患者。该系统结合了临床、实验室和成像数据， 为多个PE结局指标提供患者特定风险评分。此外，该项目将促进多- 中心合作，这将使我们有机会调查我们的方法的普遍性， 不同人群的PE患者，并训练，测试，并最终部署我们的自动预测模型， 各种临床环境。