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

项目摘要 败血症在婴儿中的死亡率高于其他儿科年龄组，与严重的长期- 30-50%的幸存者患有长期发病率，并长期加重医疗资源负担 住院和复杂的干预措施。快速识别脓毒症并及时启动 抗生素治疗对改善婴儿结局至关重要。然而，当前诊断的局限性 方法包括婴儿的异质性，微妙的临床表现和有限的准确性， 实验室测试因此，迫切需要改善脓毒症早期检测的策略 以改善婴儿的预后。我们的目标是通过培养一个婴儿， 脓毒症早期识别系统，将患者数据与预测模型输出相结合， 为临床医生和护士提供及时、准确和相关的信息。我们的假设是 具有临床数据显示的预测模型可提高情境意识， 识别和管理。我们将利用前期工作的坚实基础， 我们的新生儿败血症登记处的建模和现有数据，以产生新的方法来识别婴儿 新生儿败血症的风险最大我们已经组建了一个多学科的调查小组， 数据科学、临床信息学、病理学和脓毒症/传染病研究等学科 提供专家共识建议。在拟议工作结束时，我们将有 开发了支持将临床数据和机器学习输出整合到 适合临床工作流程的决策支持工具。我们期待这样的系统， 具有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。这项工作将为未来的临床试验奠定基础，以评估其 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 需要改善他们的健康和安全。

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.