RAPID: Prediction of Cardiac Dysfunction in COVID-19 Patients Using Machine Learning

RAPID：使用机器学习预测 COVID-19 患者的心脏功能障碍

• 批准号: 2029603
• 负责人: Natalia Trayanova
• 金额: $ 19.56万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029603&HistoricalAwards=false
• 关键词: RAPID; Prediction; Cardiac; Dysfunction; COVID

Recent reports demonstrate the critical influence of COVID-19 on the cardiovascular system, with up to 20% of COVID-19 patients suffering acute cardiac injury. Approaches to identify COVID-19 patients at risk for cardiac dysfunction have not yet been developed, and no alerting clinical parameters are available to address the impending decline of cardiac function and mortality. The goal of this project is to develop a machine learning approach to identify COVID-19 patients at risk for cardiac dysfunction and sudden cardiac death. Utilizing such an approach will provide early warning and enable the delivery of early goal-directed therapy, reducing mortality and optimizing allocation of resources. The machine learning classifier is to be distributed to any interested healthcare institution, to augment their ability to successfully treat patients. This project also provides fundamental new scientific knowledge: how COVID-19-related cardiac injury could result in cardiac dysfunction and sudden cardiac death. Such knowledge is of paramount importance in the fight against COVID-19 and the post-disease adverse effects on human health. Features that will serve as input into the machine learning classifier will be extracted from both time series (ECG, cardiac-specific laboratory values, continuously-obtained vital signs) and imaging data (CT, echocardiography). Data will be collected from patients admitted to Johns Hopkins Hospital and Johns Hopkins Health System; other hospitals in the Chesapeake area; and potetially hospitals in NYC, with a confirmed diagnosis of COVID-19 based on nucleic acid or polymerase chain reaction testing. We will develop a time-varying risk score that will determine the posterior probability of hemodynamically-significant cardiac disease outcome within 24 hours of certain time points. For new patients, the model will be used to perform a baseline prediction which will be updated in a Bayesian fashion each time new data becomes available.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

最近的报告显示，COVID-19对心血管系统的影响至关重要，高达20%的COVID-19患者患有急性心脏损伤。目前尚未开发出识别有心功能障碍风险的COVID-19患者的方法，也没有可用于应对即将到来的心脏功能下降和死亡率的预警临床参数。该项目的目标是开发一种机器学习方法，以识别有心功能障碍和心源性猝死风险的COVID-19患者。利用这种方法将提供早期预警，并能够提供早期目标导向治疗，降低死亡率和优化资源分配。机器学习分类器将分发给任何感兴趣的医疗机构，以增强他们成功治疗患者的能力。该项目还提供了基本的新科学知识：COVID-19相关的心脏损伤如何导致心脏功能障碍和心源性猝死。有关知识对抗击COVID-19及疫后对人类健康的不利影响至关重要。将从时间序列（ECG、心脏特定实验室值、连续获得的生命体征）和成像数据（CT、超声心动图）中提取将作为机器学习分类器输入的特征。数据将收集自约翰霍普金斯医院和约翰霍普金斯卫生系统、切萨皮克地区的其他医院以及纽约市的潜在医院，根据核酸或聚合酶链反应检测确诊为COVID-19的患者。我们将制定一个随时间变化的风险评分，以确定某些时间点24小时内血液动力学显著性心脏病结局的后验概率。对于新患者，该模型将用于执行基线预测，每次获得新数据时，该预测将以贝叶斯方式更新。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。