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

• 批准号: 10641794
• 负责人: Robert W Grundmeier
• 金额: $ 36.91万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10641794
• 关键词: Address; Antibiotics; Awareness; Bacteremia; 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; Discipline; Early Diagnosis; Education; 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; Research Personnel; Resources; Risk; Safety; Sepsis; Site; Survivors; System; Techniques; Testing; Training; Uncertainty; United States National Library of Medicine; Work; age group; antimicrobial; clinical care; clinical implementation; comorbidity; data modeling; diagnostic strategy; effectiveness measure; experience; experimental study; health information technology; high risk; high risk infant; improved; improved outcome; infant infection; infant morbidity/mortality; infant outcome; innovation; mortality; multidisciplinary; neonatal sepsis; novel; predictive modeling; prototype; rapid diagnosis; risk prediction model; simulation; support tools; treatment and outcome; usability; user centered design; web services

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.

项目摘要 脓毒症的婴儿的死亡率比其他小儿年龄组高，与严重的长期有关 30-50％的幸存者的期限病毒和延长的医疗保健资源负担负担 住院和复杂的干预措施。快速鉴定败血症和及时开始 抗菌治疗对于改善婴儿预后至关重要。但是，当前诊断的局限性 方法包括婴儿的异质，微妙的临床表现和有限的精度 实验室测试。因此，迫切需要策略来改善败血症的早期检测 在婴儿中改善预后。我们的目标是通过发展婴儿来改善败血症的识别 败血症的早期识别系统将患者数据与预测模型输出相结合以交付 及时向临床医生和护士的及时，准确和相关的信息。我们的假设是集成 具有临床数据显示的预测模型，以提高状况意识，将改善及时的败血症 认可和管理。我们将在预测中利用我们的初步工作的强大基础 来自新生儿败血症注册表的建模和现有数据，以产生新方法来识别婴儿 新生儿败血症的风险最大。我们已经组建了一个来自 数据科学，临床信息学，新生儿学和败血症/传染病研究的学科 提供专家共识建议。在拟议的工作结束时，我们将拥有 开发的方法支持临床数据和机器学习输出的整合到 决策支持工具适合临床工作流程。我们预计这样的系统将配对 以用户为中心的设计过程的预测方法将在许多情况下具有广泛的适用性 和人口。这项工作将构成未来临床试验的基础，以评估其能力 确定败血症风险最高的婴儿，并为临床医生和护士提供决策支持 需要改善其健康和安全。