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

• 批准号: 10610089
• 负责人: Claire Shappell
• 金额: $ 0.25万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 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/10610089
• 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

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可引起广泛的疾病，从无症状感染到多器官感染

项目成果

Use of Electronic Clinical Data to Track Incidence and Mortality for SARS-CoV-2-Associated Sepsis.

• DOI: 10.1001/jamanetworkopen.2023.35728
• 发表时间: 2023-09-05
• 期刊: JAMA NETWORK OPEN
• 影响因子: 13.8
• 作者: Shappell, Claire N.;Klompas, Michael;Chan, Christina;Chen, Tom;Kanjilal, Sanjat;Mckenna, Caroline;Rhee, Chanu
• 通讯作者: Rhee, Chanu

Prevalence, Clinical Characteristics, and Outcomes of Sepsis Caused by Severe Acute Respiratory Syndrome Coronavirus 2 Versus Other Pathogens in Hospitalized Patients With COVID-19.

• DOI: 10.1097/cce.0000000000000703
• 发表时间: 2022-05
• 期刊: Critical care explorations
• 作者: Shappell, Claire N.;Klompas, Michael;Kanjilal, Sanjat;Chan, Christina;Rhee, Chanu
• 通讯作者: Rhee, Chanu