Developing dynamic prognostic and risk-stratification models for informing prescribing decisions in older adults with Coronavirus Disease 2019

项目摘要 虽然超过80%的2019年冠状病毒病（COVID-19）患者只经历了轻微的疾病， 据报告，包括老年人在内的弱势群体的死亡率为6.4-13.4%， 患有多种合并症的患者。药物治疗主要用于中度 严重的疾病。药物治疗的最佳处方在很大程度上依赖于准确的风险分层， 患者预后由于已知COVID-19通常会导致临床迅速恶化，因此至关重要的是， 有一个很好的预测疾病进展和不良临床结果的预后工具， 药物治疗或其他干预可以及时启动。此外，在COVID-19期间 大流行，许多医疗机构需要在有限资源的情况下超出正常能力运行，例如 机械制冷剂、治疗剂和重症监护室（ICU）床位可用性。可靠的预测 工具对于关于医疗处置的最佳决策是必不可少的（例如，家庭监测与入院）和 资源分配（如ICU病床和机械呼吸机）。虽然有看似丰富的数据， 对于COVID-19患者的预后预测，仍然存在两个主要的知识缺口。首先，所有 现有的预测模型只考虑入院时测量的因素，而没有考虑动态 生物标志物随时间的变化。因此，这些模型具有有限的临床适用性，因为这些模型中的许多 生物标志物在治疗过程中重复多次，临床医生需要知道这些生物标志物是如何被发现的。 动态变化可以为医疗决策提供信息。其次，虽然药物使用和开始时间是 尽管它们高度提供疾病严重程度的信息，但在先前的模型中不用于预后预测。我们的目标 建立一个基于近实时电子健康记录（EHR）数据的前瞻性预后建模系统 来自马萨诸塞州布里格姆将军，这是马萨诸塞州的一个大型医疗服务网络，包括2个第三和11个 二级医院和30多个门诊中心。我们已经建立了基本的基础设施， 每周更新数据。该数据库目前有超过14，000例COVID-19确诊病例， 以每周500-1000例确诊病例的速度扩张，使我们能够建立预测模型， 数据输入和执行前瞻性验证的能力。我们将开发一个动态的预测工具， 基线特征，时变因素及其动态变化，药物使用及其时间， 预测关键临床结果。2020年3月至8月的数据将用于模型推导， 2020年9月至12月的数据将用于前瞻性验证。除了预测因素之外， 在文献中报道，我们将通过使用筛选丰富的EHR数据来寻找新的预测因子， TreeScan是美国食品和药物管理局（FDA）采用的一种新型、经验证的统计工具， 疫苗和药物安全监测。我们将评估年龄对风险因素的影响。这将有助于 研究人员了解老年人对COVID-19的脆弱性。

Project Summary While over 80% patients with Coronavirus Disease 2019 (COVID-19) experienced only mild illness, the mortality rates have been reported to be 6.4-13.4% in vulnerable populations, including older adults and patients with multiple co-morbidities. Pharmacological treatments are primarily used for patients with moderate to severe disease. Optimal prescribing of drug therapy relies heavily on accurate risk stratification based on patient prognosis. Since it is known that COVID-19 can often cause rapid clinical deterioration, it is critical to have a prognostic tool well-predictive of disease progression and adverse clinical outcomes, so the pharmacological treatments or other interventions can be initiated timely. Also, during the COVID-19 pandemic, many healthcare facilities need to operate beyond regular capacity with limited resources, such as mechanical ventilators, therapeutic agents, and intensive care unit (ICU) bed availability. A reliable prognostic tool is essential for optimal decisions regarding medical disposition (e.g., home monitoring vs. admission) and resource allocation (eg, ICU beds and mechanical ventilators). While there are seemingly abundant data in prognostic prediction for patients with COVID-19, there remain two major knowledge gaps. First, all of the existing prediction models only consider factors measured at hospital admission without incorporating dynamic changes of biomarkers over time. The models thus have limited clinical applicability since many of these biomarkers are repeated multiple times during a treatment course and clinicians need to know how these dynamic changes can inform medical decisions. Second, while medication use and the initiation timing are highly informative of disease severity, they were not used for prognostic prediction in the prior models. We aim to build a prospective prognostic modeling system based on near-real-time electronic health record (EHR) data from Mass General Brigham, a large care delivery network in Massachusetts that includes 2 tertiary and 11 secondary hospitals and >30 ambulatory centers. We have established the basic infrastructure and currently receive weekly data updates. The database currently has >14,000 confirmed cases of COVID-19 and are expanding at the rate of 500-1000 confirmed cases per week, allowing us to build prediction models with rich data input and ability to perform prospective validation. We will develop a dynamic prognostic tool incorporating baseline characteristics, time-varying factors with their dynamic changes, medication use and its timing to predict key clinical outcomes. Data accrued from March to August, 2020 will be used for model derivation and data from September to December, 2020 will be used for prospective validation. In addition to the predictors reported in the literature, we will search for novel predictors by screening through the rich EHR data using TreeScan, a novel, validated, statistical tool adopted by the US Food and Drug Administration (FDA) for vaccine and drug safety surveillance. We will assess age effect modification on risk factors. This will help researchers understand the vulnerability of older adults to COVID-19.