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

开发动态预后和风险分层模型，为患有 2019 年冠状病毒病的老年人的处方决策提供信息

• 批准号: 10189838
• 负责人: JOSHUA K LIN
• 金额: $ 52.47万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10189838
• 关键词: Admission activity; Adopted; Age; Beds; Biological Markers; Biological Models; COVID-19; COVID-19 pandemic; COVID-19 patient; Cessation of life; Characteristics; Clinical; Code; Continuity of Patient Care; Data; Databases; Derivation procedure; Deterioration; Diagnosis; Diagnostic; Dimensions; Disease; Disease Progression; Drug Prescriptions; Early Intervention; Elderly; Electronic Health Record; Health care facility; Home; Hospitalization; Hospitals; Infrastructure; Inpatients; Intensive Care Units; Intervention; Knowledge; Literature; Massachusetts; Measures; Mechanical Ventilators; Mechanical ventilation; Medical; Modeling; Modification; Monitor; Outcome; Patients; Pharmaceutical Preparations; Pharmacological Treatment; Pharmacotherapy; Procedures; Prognosis; Prognostic Factor; Reporting; Research Personnel; Resource Allocation; Resources; Respiratory Failure; Risk Factors; Scanning; Severity of illness; Supportive care; System; Therapeutic; Therapeutic Agents; Time; United States Food and Drug Administration; Update; Validation; Vulnerable Populations; adverse outcome; age effect; age group; base; care delivery; clinical application; clinically relevant; comorbidity; data mining; experience; flexibility; high risk; medication safety; mortality; novel; older patient; predictive modeling; profiles in patients; prognostic; prognostic model; prognostic tool; prospective; risk stratification; screening; tool; vaccine safety

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.

项目摘要 虽然超过80％的冠状病毒疾病患者2019（Covid-19）仅出现轻度疾病，但 据报道，易受伤害人群的死亡率为6.4-13.4％，包括老年人和 患有多种合并症的患者。药理治疗主要用于中度患者 发生严重疾病。药物治疗的最佳处方在很大程度上取决于基于准确的风险分层 患者预后。由于众所周知，Covid-19通常会引起临床快速恶化，因此至关重要 对疾病进展和不良临床结果具有预测的预后工具，因此 可以及时开始药理治疗或其他干预措施。另外，在19号期间 大流行，许多医疗机构需要以有限的资源（例如 机械呼吸机，治疗剂和重症监护室（ICU）床的可用性。可靠的预后 工具对于有关医疗处置的最佳决定至关重要（例如，家庭监控与入学）和 资源分配（例如，ICU床和机械呼吸机）。虽然似乎有很多数据 对于19. Covid-19患者的预后预测，存在两个主要的知识差距。首先，所有 现有的预测模型仅考虑在医院入院时衡量的因素而不纳入动态 随着时间的推移，生物标志物的变化。因此，模型的临床适用性有限，因为其中许多 在治疗课程中多次重复生物标志物，临床医生需要知道如何 动态变化可以为医疗决定提供信息。第二，使用药物使用和启动时间是 在疾病严重程度方面有很高的信息，它们在先前模型中没有用于预后预测。我们的目标 建立基于近实时电子健康记录（EHR）数据的前瞻性预后建模系统 来自马萨诸塞州的大型护理递送网络大众杨百翰，其中包括2个三级和11 二级医院和> 30个门诊中心。我们已经建立了基本基础架构，目前 接收每周的数据更新。该数据库目前有> 14,000个确认的Covid案例，是 以每周确认的500-1000个确认案例的速度扩展，使我们能够以富富构建预测模型 数据输入和执行前瞻性验证的能力。我们将开发一种动态的预后工具，以结合 基线特征，具有动态变化的时变因素，使用药物的使用以及其时间的时间 预测关键的临床结果。从2020年3月至8月产生的数据将用于模型推导和 从2020年9月到12月的数据将用于预期验证。除了预测变量 在文献中报道，我们将通过使用丰富的EHR数据筛选来搜索新的预测因子 Treescan是美国食品药品监督管理局（FDA）采用的小说，经过验证的统计工具 疫苗和药物安全监视。我们将评估对风险因素的年龄效应修改。这将有所帮助 研究人员了解老年人对Covid-19的脆弱性。

项目成果

Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.

用于识别快速恶化高风险的 COVID-19 患者的动态预后模型的前瞻性验证。

• DOI: 10.1002/pds.5580
• 发表时间: 2023
• 期刊: Pharmacoepidemiology and drug safety
• 影响因子: 2.6
• 作者: Lin,KueiyuJoshua;D'Andrea,Elvira;Desai,RishiJ;Gagne,JoshuaJ;Liu,Jun;Wang,ShirleyV
• 通讯作者: Wang,ShirleyV

Prescribing Trends of Oral Anticoagulants in US Patients With Cirrhosis and Nonvalvular Atrial Fibrillation.

• DOI: 10.1161/jaha.122.026863
• 发表时间: 2023-02-07
• 期刊: JOURNAL OF THE AMERICAN HEART ASSOCIATION
• 影响因子: 5.4
• 作者: Simon, Tracey G.;Schneeweiss, Sebastian;Singer, Daniel E.;Sreedhara, Sushama Kattinakere;Lin, Kueiyu Joshua
• 通讯作者: Lin, Kueiyu Joshua

Gastrointestinal prophylaxis for COVID-19: an illustration of severe bias arising from inappropriate comparators in observational studies.

• DOI: 10.1016/j.jclinepi.2022.07.009
• 发表时间: 2022-11
• 期刊: JOURNAL OF CLINICAL EPIDEMIOLOGY
• 影响因子: 7.2
• 作者: Lin, Kueiyu Joshua;Feldman, William B.;Wang, Shirley V.;Umarje, Siddhi Pramod;D'Andrea, Elvira;Tesfaye, Helen;Zabotka, Luke E.;Liu, Jun;Desai, Rishi J.
• 通讯作者: Desai, Rishi J.

Antipsychotic Medication Use Among Older Adults Following Infection-Related Hospitalization.

• DOI: 10.1001/jamanetworkopen.2023.0063
• 发表时间: 2023-02-01
• 期刊: JAMA NETWORK OPEN
• 影响因子: 13.8
• 作者: Zhang, Yichi;Wilkins, James M.;Bessette, Lily Gui;York, Cassandra;Wong, Vincent;Lin, Kueiyu Joshua
• 通讯作者: Lin, Kueiyu Joshua