Early detection, containment, and management of COVID-19 in dialysis facilities using multi-modal data sources

摘要 随着年龄的增长和多种并发症的发生，透析患者发生严重并发症的风险很高，甚至死亡。 来自新冠肺炎。少数族裔，特别是黑人和西班牙裔，有很大的不成比例的代表性。 超过85%的血液透析患者每周前往三次透析机构接受维持生命 他们接受治疗，无法就地避难。迫切需要对新冠肺炎的传播路径进行表征 在透析患者和诊所中，识别潜在的冠状病毒携带者，并制定程序遏制传播。 通过定期的医疗接触，随着时间的推移，已经为每个患者收集了大量数据。这些 数据还没有被充分利用，用于透析诊所的新冠肺炎预测和控制。在这项提案中，我们寻求 利用人口、临床、治疗、实验室、社会经济、血清学、代谢学、可穿戴和 机器集成传感器和新冠肺炎监控数据，以开发数学和统计模型 并在大量的透析诊所实施。数学和统计建模使用 多个数据资源将帮助我们了解新冠肺炎是如何在透析设施中传播的，确定潜在的 在新冠肺炎患者出现症状之前，识别潜在的无症状新冠肺炎患者。我们会 开发新的数学和统计模型，充分利用高维多模式数据 可供我们和其他透析提供者使用。我们利用血液透析诊所的内在优势 实施并验证所提出的预测模型。我们坚信，这种跨学科的努力将 改善患者和工作人员的安全，同时为高危人群提供高质量的个性化护理。

Abstract With older age and multiple comorbidities, dialysis patients are at high risk for serious complications, even death, from COVID-19. There is a large disproportionate representation of minorities, especially Blacks and Hispanics. Over 85% of hemodialysis patients travel three times a week to dialysis facilities to receive life-sustaining treatments and cannot shelter in place. There is a critical need to characterize COVID-19 transmission pathways in dialysis patients and clinics, identify potential coronavirus carriers, and develop procedures to curb the spread. With regular medical encounters, a large amount of data has been collected for each patient over time. These data have not been fully utilized for COVID-19 prediction and control in dialysis clinics. In this proposal, we seek to leverage demographic, clinical, treatment, laboratory, socioeconomic, serological, metabolomic, wearable and machine-integrated sensors, and COVID-19 surveillance data to develop mathematical and statistical models and implement them in a large number of dialysis clinics. The mathematical and statistical modeling using multiple data resources will help us understand how COVID-19 spread in dialysis facilities, identify potential COVID-19 patients before symptoms appear, and identify potential asymptomatic COVID-19 patients. We will develop novel mathematical and statistical models that fully utilize the high dimensional multimodal data available to us and other dialysis providers. We capitalize on the intrinsic advantages of hemodialysis clinics to implement and validate the proposed prediction models. We firmly believe that this cross-disciplinary effort will improve patients’ and staff’s safety while delivering high-quality, individualized care to a high-risk population.