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.

项目总结

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

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

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.