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%的冠状病毒病2019(新冠肺炎)患者只经历了轻微的疾病，但 据报道，脆弱人群的死亡率为6.4%-13.4%，包括老年人和 患有多种合并症的患者。药物治疗主要用于中度癌症患者。 到严重的疾病。药物治疗的最佳处方在很大程度上依赖于基于以下因素的准确风险分层 患者预后。由于已知新冠肺炎通常会导致临床迅速恶化，因此至关重要的是 有一个预测工具可以很好地预测疾病的进展和不良的临床结果，所以 可以及时启动药物治疗或其他干预措施。另外，在新冠肺炎期间 在大流行期间，许多医疗机构需要在资源有限的情况下超出正常能力，例如 机械呼吸机、治疗剂和重症监护病房(ICU)床位的可用性。可靠的预言家 工具对于有关医疗处置的最佳决策至关重要(例如，家庭监护与入院)以及 资源分配(例如，ICU床位和机械呼吸机)。虽然有看似丰富的数据，但 对于新冠肺炎患者的预后预测，仍存在两个主要的知识差距。首先，所有的 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，这些模型的临床适用性有限，因为其中许多 生物标志物在一个疗程中重复多次，临床医生需要知道这些 动态变化可以为医疗决策提供信息。第二，虽然药物使用和启动时间是 疾病严重程度的高度信息量，在以前的模型中没有用于预后预测。我们的目标是 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 来自麻省总医院Brigham，这是马萨诸塞州的一个大型护理服务网络，包括2个三级医院和11个 二级医院和30个门诊中心。我们已经建立了基本的基础设施，目前 每周接收数据更新。该数据库目前有14,000例新冠肺炎确诊病例， 以每周500-1000例确诊病例的速度扩大，使我们能够建立具有丰富信息的预测模型 数据输入和执行预期验证的能力。我们将开发一种动态预测工具，包括 基线特征、时变因素及其动态变化、用药及其时机 预测关键的临床结果。2020年3月至8月的数据将用于模型推导和 2020年9月至12月的数据将用于前瞻性验证。除了预测因素外， 在文献报道中，我们将通过筛选丰富的EHR数据来寻找新的预测因素 TreeScan，一种新的、经过验证的统计工具，被美国食品和药物管理局(FDA)采用 疫苗和药品安全监测。我们将评估年龄改变对危险因素的影响。这会有帮助的 研究人员了解老年人对新冠肺炎的脆弱性。

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