Incidence, Outcomes, and Predictors of Sepsis in Hospitalized Patients with COVID-19

COVID-19 住院患者脓毒症的发生率、结果和预测因素

基本信息

批准号：
10556324
负责人：
Claire Shappell
金额：
$ 8.56万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-15 至 2023-08-14
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10556324
关键词：
2019-nCoV Accounting Acute Respiratory Distress Syndrome Admission activity Adult Age Anti-Inflammatory Agents Award Bacteremia Bacterial Infections Big Data COVID-19 COVID-19 patient COVID-19 risk Caring Centers for Disease Control and Prevention (U.S.)Cessation of life Clinical Clinical Data Cohort Studies Complex Computer software Consensus Critical Care Critical Illness Data Data Analyses Data Science Databases Development Diagnosis Discipline of Nursing Disease Electronic Health Record Epidemiologic Monitoring Epidemiology Event Foundations Functional disorder Funding Goals Health Healthcare Systems Hospitalization Hospitals Human Hypertension Immune Immune response Incidence Infection Infectious Agent Influenza Intensive Care International Laboratories Life Lung Mediating Mentors Mentorship Methods Mining Modeling Modernization Multiple Organ Failure Obesity Organ Outcome Pathway interactions Patient Triage Patients Physicians Physiological Pneumonia Population trends Prevention Prevention strategy Prognosis Public Health Public Policy Race Recommendation Reporting Research Resource Allocation Resources Risk Factors Risk Management SARS-CoV-2 infection Scientist Sepsis Septic Shock Severity of illness Societies Statistical Data Interpretation Structure Test Result Time Tissues Training United States Viral Virus Work base clinical diagnosis comorbidity coronavirus disease data acquisition demographics evidence base experience health care service organization healthcare-associated infections improved influenza infection innovation insight model development mortality national surveillance novel novel virus pandemic disease pathogen patient stratification patient subsets patient-clinician communication predictive modeling respiratory virus risk stratification severe COVID-19 skills

项目摘要

SARS-CoV-2 causes a wide spectrum of illness ranging from asymptomatic infection to multiorgan failure and death. Patients infected with SARS-CoV-2 who develop organ dysfunction technically meet the Third International Consensus Definitions of Sepsis and Septic Shock, which define sepsis as a dysregulated host response to infection leading to life-threatening infection. Clinicians rarely frame severe COVID-19 as sepsis, however, despite the fact that doing so could help rapidly convey the seriousness of the condition and invoke existing hospital structures and management pathways to optimize care for this very ill subset of patients. At the same time, severe COVID-19 likely has unique features that differentiate it from routine bacterial sepsis as well as from other respiratory viruses. The overall objective of this proposal is to characterize the incidence and outcomes for COVID-19- associated sepsis, its risk factors, and how these compare to sepsis from influenza or bacterial infections. Our proposed project will leverage objective definitions of sepsis and organ dysfunction based on CDC’s Adult Sepsis Event criteria, a large database with detailed electronic health record data from five hospitals, and a research group with extensive experience using big data to conduct innovative and impactful studies on epidemiology, diagnosis, prevention, and treatment of sepsis, pneumonia, and other healthcare-associated infections. In Aim 1 of our proposal, we will utilize state-of-the-art methods in sepsis surveillance to provide rigorous estimates of sepsis incidence in COVID-19 and enable evidence-based comparisons between outcomes and risk factors for sepsis due to COVID-19 versus influenza or bacterial infections. In Aim 2, we will incorporate patient demographics, comorbidities, vital signs, and laboratory test results available at the time of presentation in order to predict the development of sepsis in patients with COVID-19, allowing improved risk stratification of patients at the time of presentation and more efficient hospital resource allocation. The proposed work will have important public health impact by informing evidence-based comparisons of COVID-19, influenza, and bacterial sepsis to guide public policy. Characterizing the distinctive features of sepsis in COVID-19 could also help inform better risk stratification and management strategies and generate hypotheses into the pathophysiology of severe COVID-19. The award will also enable the candidate, Dr. Claire Shappell, to gain essential skills in data acquisition and analysis, predictive modeling, use of statistical analysis software, and grantsmanship, enabling her to achieve her long term objective of becoming an independently-funded physician-scientist with expertise in mining the increasingly abundant clinical and physiologic data available via electronic health records (EHRs) in order to characterize population trends and identify novel opportunities for care improvement in critical illness.

SARS-CoV-2 引起多种疾病，从无症状感染到多器官感染感染 SARS-CoV-2 的患者出现器官功能障碍，从技术上讲符合标准。脓毒症和脓毒性休克的第三次国际共识定义，将脓毒症定义为一种失调的疾病宿主对感染的反应导致危及生命的感染，临床医生很少将严重的 COVID-19 视为严重的感染。然而，脓毒症，尽管这样做可以帮助迅速传达病情的严重性，并且调用现有的医院结构和管理途径来优化对这个重病患者的护理与此同时，重症 COVID-19 可能具有与常规患者不同的独特特征。细菌性败血症以及其他呼吸道病毒引起的败血症。该提案的总体目标是描述 COVID-19 的发病率和结果相关脓毒症、其危险因素，以及这些因素与流感或细菌感染引起的脓毒症的比较。拟议的项目将利用基于 CDC 成人的脓毒症和器官功能障碍的客观定义脓毒症事件标准，一个包含来自五家医院的详细电子健康记录数据的大型数据库，以及研究小组拥有丰富的经验，利用大数据进行创新和有影响力的研究脓毒症、肺炎和其他医疗相关疾病的流行病学、诊断、预防和治疗在我们建议的目标 1 中，我们将利用最先进的脓毒症监测方法来提供感染。严格估计 COVID-19 中的脓毒症发病率，并能够进行基于证据的比较在目标 2 中，我们将讨论 COVID-19 与流感或细菌感染引起的脓毒症的结果和危险因素。纳入患者人口统计数据、合并症、生命体征和当时可用的实验室测试结果旨在预测 COVID-19 患者败血症的发展，从而降低风险在就诊时对患者进行分层，并更有效地分配医院资源。拟议的工作将通过提供基于证据的比较来产生重要的公共卫生影响 COVID-19、流感和细菌性败血症的特征来指导公共政策。 COVID-19 中的脓毒症还可以帮助提供更好的风险分层和管理策略，并产生重症 COVID-19 病理生理学的假设。该奖项还将使候选人 Claire Shappell 博士获得数据采集方面的基本技能和分析、预测建模、统计分析软件的使用以及资助，使她能够实现她的长期目标，成为一名独立资助的具有专业知识的医师科学家通过电子健康记录 (EHR) 挖掘日益丰富的临床和生理数据为了描述人口趋势并确定改善危重疾病护理的新机会。