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Situation Awareness to Improve Infant Sepsis Recognition in the Presence of Clinical Uncertainty

在存在临床不确定性的情况下提高对婴儿脓毒症的识别能力

基本信息

批准号：
10296851
负责人：
Robert W Grundmeier
金额：
$ 38.82万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
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：
2021-08-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10296851
关键词：
Address Antibiotics Awareness Benchmarking Caring Childhood Clinical Clinical Data Clinical Informatics Clinical Trials Complex Comprehension Consensus Critical Illness Data Data Display Data Science Decision Making Decision Support Systems Development Devices Diagnosis Diagnostic Discipline Early Diagnosis Effectiveness Electronic Health Record Engineering Ensure Environment Evaluation Evaluation Methodology Foundations Future Global Awareness Goals Health Healthcare Hospitalization Hour Human Infant Infant Mortality Infectious Diseases Research Information Systems Intervention Knowledge Laboratories Machine Learning Measures Medical Medical center Methods Modeling Monitor Morbidity - disease rate Neonatal Intensive Care Units Neonatal Mortality Neonatology Nurses Outcome Output Patients Pediatric Hospitals Philadelphia Physicians Population Positioning Attribute Prevention Process Recommendation Registries Research Personnel Resources Risk Safety Sepsis Site Survivors System Techniques Testing Time Training and Education Treatment outcome Uncertainty United States National Library of Medicine Work age group antimicrobial base clinical care clinical implementation comorbidity data modeling education research effectiveness measure experience experimental study health information technology high risk high risk infant improved improved outcome infant infection infant morbidity infant outcome innovation mortality multidisciplinary neonatal sepsis novel prediction algorithm predictive modeling prototype rapid diagnosis risk prediction risk prediction model simulation support tools usability user centered design web services

项目摘要

PROJECT SUMMARY Sepsis has higher mortality in infants than other pediatric age groups, is associated with severe long- term morbidities in 30-50% of survivors, and burdens healthcare resources with prolonged hospitalization and complex interventions. Rapid identification of sepsis and timely initiation of antimicrobial therapy are critical to improve infant outcomes. However, limitations to current diagnostic approaches include the heterogeneous, subtle clinical presentation of infants and limited accuracy of laboratory tests. There is therefore an urgent need for strategies to improve the early detection of sepsis in infants to improve outcomes. Our objective is to improve sepsis recognition by developing an infant sepsis early recognition system that combines patient data with predictive model outputs to deliver timely, precise and relevant information to clinicians and nurses. Our hypothesis is that the integration of a predictive model with clinical data displays that improve situation awareness will improve timely sepsis recognition and management. We will utilize the strong foundation of our preliminary work in predictive modeling and existing data from our neonatal sepsis registry to produce novel methods to identify infants at greatest risk for neonatal sepsis. We have assembled a multi-disciplinary team of investigators from the disciplines of data science, clinical informatics, neonatology, and sepsis/infectious disease research to provide expert consensus recommendations. At the conclusion of the proposed work, we will have developed methods that support the integration of clinical data and machine learning outputs into decision support tools suited to clinical workflows. We anticipate such systems that pair advanced prediction methods with user-centered design processes will have broad applicability to many conditions and populations. This work will form the foundation for a future clinical trial to evaluate its ability to identify infants at highest risk of sepsis and provide clinicians and nurses with the decision support needed to improve their health and safety.

项目总结