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.

项目概要 虽然超过 80% 的 2019 年冠状病毒病 (COVID-19) 患者仅出现轻微症状，但 据报道，包括老年人和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患者 至严重疾病。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 COVID-19 通常会导致临床迅速恶化，因此至关重要 有一个可以很好预测疾病进展和不良临床结果的预后工具，因此 可以及时开始药物治疗或其他干预措施。此外，在新冠肺炎 (COVID-19) 疫情期间 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运行，例如 机械呼吸机、治疗药物和重症监护病房 (ICU) 床位可用性。可靠的预测 该工具对于医疗处置（例如家庭监护与入院）的最佳决策至关重要，并且 资源分配（例如 ICU 床位和机械呼吸机）。虽然数据看似丰富 对于 COVID-19 患者的预后预测，仍然存在两大知识差距。首先，所有的 现有的预测模型仅考虑入院时测量的因素，而没有考虑动态因素 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，因为其中许多模型 生物标志物在治疗过程中会重复多次，临床医生需要知道这些生物标志物是如何产生的。 动态变化可以为医疗决策提供信息。其次，虽然药物的使用和开始时间 它们可以提供有关疾病严重程度的大量信息，但在之前的模型中并未用于预后预测。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 Mass General Brigham，马萨诸塞州的一个大型护理服务网络，包括 2 个高等教育机构和 11 个 二级医院和超过 30 个门诊中心。我们已经建立了基础设施，目前 接收每周数据更新。该数据库目前有超过 14,000 例确诊的 COVID-19 病例， 以每周 500-1000 例确诊病例的速度扩张，使我们能够建立具有丰富经验的预测模型 数据输入和执行前瞻性验证的能力。我们将开发一种动态预测工具，其中包含 基线特征、随时间变化的因素及其动态变化、药物使用及其时机 预测关键的临床结果。 2020年3月至8月积累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。除了预测因子之外 据文献报道，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，美国食品和药物管理局 (FDA) 采用的一种新颖的、经过验证的统计工具 疫苗和药品安全监测。我们将评估年龄对风险因素的影响。这会有所帮助 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